Histogram equalization with Bayesian estimation for noise robust speech recognition.

PubMed

Suh, Youngjoo; Kim, Hoirin

2018-02-01

The histogram equalization approach is an efficient feature normalization technique for noise robust automatic speech recognition. However, it suffers from performance degradation when some fundamental conditions are not satisfied in the test environment. To remedy these limitations of the original histogram equalization methods, class-based histogram equalization approach has been proposed. Although this approach showed substantial performance improvement under noise environments, it still suffers from performance degradation due to the overfitting problem when test data are insufficient. To address this issue, the proposed histogram equalization technique employs the Bayesian estimation method in the test cumulative distribution function estimation. It was reported in a previous study conducted on the Aurora-4 task that the proposed approach provided substantial performance gains in speech recognition systems based on the acoustic modeling of the Gaussian mixture model-hidden Markov model. In this work, the proposed approach was examined in speech recognition systems with deep neural network-hidden Markov model (DNN-HMM), the current mainstream speech recognition approach where it also showed meaningful performance improvement over the conventional maximum likelihood estimation-based method. The fusion of the proposed features with the mel-frequency cepstral coefficients provided additional performance gains in DNN-HMM systems, which otherwise suffer from performance degradation in the clean test condition.

Face averages enhance user recognition for smartphone security.

PubMed

Robertson, David J; Kramer, Robin S S; Burton, A Mike

2015-01-01

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual's 'face-average'--a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user's face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings.

Target recognition of ladar range images using even-order Zernike moments.

PubMed

Liu, Zheng-Jun; Li, Qi; Xia, Zhi-Wei; Wang, Qi

2012-11-01

Ladar range images have attracted considerable attention in automatic target recognition fields. In this paper, Zernike moments (ZMs) are applied to classify the target of the range image from an arbitrary azimuth angle. However, ZMs suffer from high computational costs. To improve the performance of target recognition based on small samples, even-order ZMs with serial-parallel backpropagation neural networks (BPNNs) are applied to recognize the target of the range image. It is found that the rotation invariance and classified performance of the even-order ZMs are both better than for odd-order moments and for moments compressed by principal component analysis. The experimental results demonstrate that combining the even-order ZMs with serial-parallel BPNNs can significantly improve the recognition rate for small samples.

Voice gender discrimination provides a measure of more than pitch-related perception in cochlear implant users.

PubMed

Li, Tianhao; Fu, Qian-Jie

2011-08-01

(1) To investigate whether voice gender discrimination (VGD) could be a useful indicator of the spectral and temporal processing abilities of individual cochlear implant (CI) users; (2) To examine the relationship between VGD and speech recognition with CI when comparable acoustic cues are used for both perception processes. VGD was measured using two talker sets with different inter-gender fundamental frequencies (F(0)), as well as different acoustic CI simulations. Vowel and consonant recognition in quiet and noise were also measured and compared with VGD performance. Eleven postlingually deaf CI users. The results showed that (1) mean VGD performance differed for different stimulus sets, (2) VGD and speech recognition performance varied among individual CI users, and (3) individual VGD performance was significantly correlated with speech recognition performance under certain conditions. VGD measured with selected stimulus sets might be useful for assessing not only pitch-related perception, but also spectral and temporal processing by individual CI users. In addition to improvements in spectral resolution and modulation detection, the improvement in higher modulation frequency discrimination might be particularly important for CI users in noisy environments.

Improvement in word recognition score with level is associated with hearing aid ownership among patients with hearing loss.

PubMed

Halpin, Chris; Rauch, Steven D

2012-01-01

Market surveys consistently show that only 22% of those with hearing loss own hearing aids. This is often ascribed to cosmetics, but is it possible that patients apply a different auditory criterion than do audiologists and manufacturers? We tabulated hearing aid ownership in a survey of 1000 consecutive patients. We separated hearing loss cases, with one cohort in which word recognition in quiet could improve with gain (vs. 40 dB HL) and another without such improvement but nonetheless with audiometric thresholds within the manufacturer's fitting ranges. Overall, we found that exactly 22% of hearing loss patients in this sample owned hearing aids; the same finding has been reported in many previous, well-accepted surveys. However, while all patients in the two cohorts experienced difficulty in noise, patients in the cohort without word recognition improvement were found to own hearing aids at a rate of 0.3%, while those patients whose word recognition could increase with level were found to own hearing aids at a rate of 50%. Results also coherently fit a logistic model where shift of the word recognition performance curve by level corresponded to the likelihood of ownership. In addition to the common attribution of low hearing aid usage to patient denial, cosmetic issues, price, or social stigma, these results provide one alternative explanation based on measurable improvement in word recognition performance. Copyright © 2011 S. Karger AG, Basel.

Effects of selective activation of M1 and M4 muscarinic receptors on object recognition memory performance in rats.

PubMed

Galloway, Claire R; Lebois, Evan P; Shagarabi, Shezza L; Hernandez, Norma A; Manns, Joseph R

2014-01-01

Acetylcholine signaling through muscarinic receptors has been shown to benefit memory performance in some conditions, but pan-muscarinic activation also frequently leads to peripheral side effects. Drug therapies that selectively target M1 or M4 muscarinic receptors could potentially improve memory while minimizing side effects mediated by the other muscarinic receptor subtypes. The ability of three recently developed drugs that selectively activate M1 or M4 receptors to improve recognition memory was tested by giving Long-Evans rats subcutaneous injections of three different doses of the M1 agonist VU0364572, the M1 positive allosteric modulator BQCA or the M4 positive allosteric modulator VU0152100 before performing an object recognition memory task. VU0364572 at 0.1 mg/kg, BQCA at 1.0 mg/kg and VU0152100 at 3.0 and 30.0 mg/kg improved the memory performance of rats that performed poorly at baseline, yet the improvements in memory performance were the most statistically robust for VU0152100 at 3.0 mg/kg. The results suggested that selective M1 and M4 receptor activation each improved memory but that the likelihood of obtaining behavioral efficacy at a given dose might vary between subjects even in healthy groups depending on baseline performance. These results also highlighted the potential of drug therapies that selectively target M1 or M4 receptors to improve memory performance in individuals with impaired memory.

Continuous multiword recognition performance of young and elderly listeners in ambient noise

NASA Astrophysics Data System (ADS)

Sato, Hiroshi

2005-09-01

Hearing threshold shift due to aging is known as a dominant factor to degrade speech recognition performance in noisy conditions. On the other hand, cognitive factors of aging-relating speech recognition performance in various speech-to-noise conditions are not well established. In this study, two kinds of speech test were performed to examine how working memory load relates to speech recognition performance. One is word recognition test with high-familiarity, four-syllable Japanese words (single-word test). In this test, each word was presented to listeners; the listeners were asked to write the word down on paper with enough time to answer. In the other test, five continuous word were presented to listeners and listeners were asked to write the word down after just five words were presented (multiword test). Both tests were done in various speech-to-noise ratios under 50-dBA Hoth spectrum noise with more than 50 young and elderly subjects. The results of two experiments suggest that (1) Hearing level is related to scores of both tests. (2) Scores of single-word test are well correlated with those of multiword test. (3) Scores of multiword test are not improved as speech-to-noise ratio improves in the condition where scores of single-word test reach their ceiling.

Intelligent form removal with character stroke preservation

NASA Astrophysics Data System (ADS)

Garris, Michael D.

1996-03-01

A new technique for intelligent form removal has been developed along with a new method for evaluating its impact on optical character recognition (OCR). All the dominant lines in the image are automatically detected using the Hough line transform and intelligently erased while simultaneously preserving overlapping character strokes by computing line width statistics and keying off of certain visual cues. This new method of form removal operates on loosely defined zones with no image deskewing. Any field in which the writer is provided a horizontal line to enter a response can be processed by this method. Several examples of processed fields are provided, including a comparison of results between the new method and a commercially available forms removal package. Even if this new form removal method did not improve character recognition accuracy, it is still a significant improvement to the technology because the requirement of a priori knowledge of the form's geometric details has been greatly reduced. This relaxes the recognition system's dependence on rigid form design, printing, and reproduction by automatically detecting and removing some of the physical structures (lines) on the form. Using the National Institute of Standards and Technology (NIST) public domain form-based handprint recognition system, the technique was tested on a large number of fields containing randomly ordered handprinted lowercase alphabets, as these letters (especially those with descenders) frequently touch and extend through the line along which they are written. Preserving character strokes improves overall lowercase recognition performance by 3%, which is a net improvement, but a single performance number like this doesn't communicate how the recognition process was really influenced. There is expected to be trade- offs with the introduction of any new technique into a complex recognition system. To understand both the improvements and the trade-offs, a new analysis was designed to compare the statistical distributions of individual confusion pairs between two systems. As OCR technology continues to improve, sophisticated analyses like this are necessary to reduce the errors remaining in complex recognition problems.

An Improved Iris Recognition Algorithm Based on Hybrid Feature and ELM

NASA Astrophysics Data System (ADS)

Wang, Juan

2018-03-01

The iris image is easily polluted by noise and uneven light. This paper proposed an improved extreme learning machine (ELM) based iris recognition algorithm with hybrid feature. 2D-Gabor filters and GLCM is employed to generate a multi-granularity hybrid feature vector. 2D-Gabor filter and GLCM feature work for capturing low-intermediate frequency and high frequency texture information, respectively. Finally, we utilize extreme learning machine for iris recognition. Experimental results reveal our proposed ELM based multi-granularity iris recognition algorithm (ELM-MGIR) has higher accuracy of 99.86%, and lower EER of 0.12% under the premise of real-time performance. The proposed ELM-MGIR algorithm outperforms other mainstream iris recognition algorithms.

Multifeature-based high-resolution palmprint recognition.

PubMed

Dai, Jifeng; Zhou, Jie

2011-05-01

Palmprint is a promising biometric feature for use in access control and forensic applications. Previous research on palmprint recognition mainly concentrates on low-resolution (about 100 ppi) palmprints. But for high-security applications (e.g., forensic usage), high-resolution palmprints (500 ppi or higher) are required from which more useful information can be extracted. In this paper, we propose a novel recognition algorithm for high-resolution palmprint. The main contributions of the proposed algorithm include the following: 1) use of multiple features, namely, minutiae, density, orientation, and principal lines, for palmprint recognition to significantly improve the matching performance of the conventional algorithm. 2) Design of a quality-based and adaptive orientation field estimation algorithm which performs better than the existing algorithm in case of regions with a large number of creases. 3) Use of a novel fusion scheme for an identification application which performs better than conventional fusion methods, e.g., weighted sum rule, SVMs, or Neyman-Pearson rule. Besides, we analyze the discriminative power of different feature combinations and find that density is very useful for palmprint recognition. Experimental results on the database containing 14,576 full palmprints show that the proposed algorithm has achieved a good performance. In the case of verification, the recognition system's False Rejection Rate (FRR) is 16 percent, which is 17 percent lower than the best existing algorithm at a False Acceptance Rate (FAR) of 10(-5), while in the identification experiment, the rank-1 live-scan partial palmprint recognition rate is improved from 82.0 to 91.7 percent.

Music Training Can Improve Music and Speech Perception in Pediatric Mandarin-Speaking Cochlear Implant Users.

PubMed

Cheng, Xiaoting; Liu, Yangwenyi; Shu, Yilai; Tao, Duo-Duo; Wang, Bing; Yuan, Yasheng; Galvin, John J; Fu, Qian-Jie; Chen, Bing

2018-01-01

Due to limited spectral resolution, cochlear implants (CIs) do not convey pitch information very well. Pitch cues are important for perception of music and tonal language; it is possible that music training may improve performance in both listening tasks. In this study, we investigated music training outcomes in terms of perception of music, lexical tones, and sentences in 22 young (4.8 to 9.3 years old), prelingually deaf Mandarin-speaking CI users. Music perception was measured using a melodic contour identification (MCI) task. Speech perception was measured for lexical tones and sentences presented in quiet. Subjects received 8 weeks of MCI training using pitch ranges not used for testing. Music and speech perception were measured at 2, 4, and 8 weeks after training was begun; follow-up measures were made 4 weeks after training was stopped. Mean baseline performance was 33.2%, 76.9%, and 45.8% correct for MCI, lexical tone recognition, and sentence recognition, respectively. After 8 weeks of MCI training, mean performance significantly improved by 22.9, 14.4, and 14.5 percentage points for MCI, lexical tone recognition, and sentence recognition, respectively ( p < .05 in all cases). Four weeks after training was stopped, there was no significant change in posttraining music and speech performance. The results suggest that music training can significantly improve pediatric Mandarin-speaking CI users' music and speech perception.

BDNF Expression in Perirhinal Cortex is Associated with Exercise-Induced Improvement in Object Recognition Memory

PubMed Central

Hopkins, Michael E.; Bucci, David J.

2010-01-01

Physical exercise induces widespread neurobiological adaptations and improves learning and memory. Most research in this field has focused on hippocampus-based spatial tasks and changes in brain-derived neurotrophic factor (BDNF) as a putative substrate underlying exercise-induced cognitive improvements. Chronic exercise can also be anxiolytic and causes adaptive changes in stress reactivity. The present study employed a perirhinal cortex-dependent object recognition task as well as the elevated plus maze to directly test for interactions between the cognitive and anxiolytic effects of exercise in male Long Evans rats. Hippocampal and perirhinal cortex tissue was collected to determine whether the relationship between BDNF and cognitive performance extends to this non-spatial and non-hippocampal-dependent task. We also examined whether the cognitive improvements persisted once the exercise regimen was terminated. Our data indicate that 4 weeks of voluntary exercise every-other-day improved object recognition memory. Importantly, BDNF expression in the perirhinal cortex of exercising rats was strongly correlated with object recognition memory. Exercise also decreased anxiety-like behavior, however there was no evidence to support a relationship between anxiety-like behavior and performance on the novel object recognition task. There was a trend for a negative relationship between anxiety-like behavior and hippocampal BDNF. Neither the cognitive improvements nor the relationship between cognitive function and perirhinal BDNF levels persisted after 2 weeks of inactivity. These are the first data demonstrating that region-specific changes in BDNF protein levels are correlated with exercise-induced improvements in non-spatial memory, mediated by structures outside the hippocampus and are consistent with the theory that, with regard to object recognition, the anxiolytic and cognitive effects of exercise may be mediated through separable mechanisms. PMID:20601027

Donepezil improves episodic memory in young individuals vulnerable to the effects of sleep deprivation.

PubMed

Chuah, Lisa Y M; Chong, Delise L; Chen, Annette K; Rekshan, William R; Tan, Jiat-Chow; Zheng, Hui; Chee, Michael W L

2009-08-01

We investigated if donepezil, a long-acting orally administered cholinesterase inhibitor, would reduce episodic memory deficits associated with 24 h of sleep deprivation. Double-blind, placebo-controlled, crossover study involving 7 laboratory visits over 2 months. Participants underwent 4 functional MRI scans; 2 sessions (donepezil or placebo) followed a normal night's sleep, and 2 sessions followed a night of sleep deprivation. The study took place in a research laboratory. 26 young, healthy volunteers with no history of any sleep, psychiatric, or neurologic disorders. 5 mg of donepezil was taken once daily for approximately 17 days. Subjects were scanned while performing a semantic judgment task and tested for word recognition outside the scanner 45 minutes later. Sleep deprivation increased the frequency of non-responses at encoding and impaired delayed recognition. No benefit of donepezil was evident when participants were well rested. When sleep deprived, individuals who showed greater performance decline improved with donepezil, whereas more resistant individuals did not benefit. Accompanying these behavioral effects, there was corresponding modulation of task-related activation in functionally relevant brain regions. Brain regions identified in relation to donepezil-induced alteration in non-response rates could be distinguished from regions relating to improved recognition memory. This suggests that donepezil can improve delayed recognition in sleep-deprived persons by improving attention as well as enhancing memory encoding. Donepezil reduced decline in recognition performance in individuals vulnerable to the effects of sleep deprivation. Additionally, our findings demonstrate the utility of combined fMRI-behavior evaluation in psychopharmacological studies.

Face recognition in age related macular degeneration: perceived disability, measured disability, and performance with a bioptic device.

PubMed

Tejeria, L; Harper, R A; Artes, P H; Dickinson, C M

2002-09-01

(1) To explore the relation between performance on tasks of familiar face recognition (FFR) and face expression difference discrimination (FED) with both perceived disability in face recognition and clinical measures of visual function in subjects with age related macular degeneration (AMD). (2) To quantify the gain in performance for face recognition tasks when subjects use a bioptic telescopic low vision device. 30 subjects with AMD (age range 66-90 years; visual acuity 0.4-1.4 logMAR) were recruited for the study. Perceived (self rated) disability in face recognition was assessed by an eight item questionnaire covering a range of issues relating to face recognition. Visual functions measured were distance visual acuity (ETDRS logMAR charts), continuous text reading acuity (MNRead charts), contrast sensitivity (Pelli-Robson chart), and colour vision (large panel D-15). In the FFR task, images of famous people had to be identified. FED was assessed by a forced choice test where subjects had to decide which one of four images showed a different facial expression. These tasks were repeated with subjects using a bioptic device. Overall perceived disability in face recognition did not correlate with performance on either task, although a specific item on difficulty recognising familiar faces did correlate with FFR (r = 0.49, p<0.05). FFR performance was most closely related to distance acuity (r = -0.69, p<0.001), while FED performance was most closely related to continuous text reading acuity (r = -0.79, p<0.001). In multiple regression, neither contrast sensitivity nor colour vision significantly increased the explained variance. When using a bioptic telescope, FFR performance improved in 86% of subjects (median gain = 49%; p<0.001), while FED performance increased in 79% of subjects (median gain = 50%; p<0.01). Distance and reading visual acuity are closely associated with measured task performance in FFR and FED. A bioptic low vision device can offer a significant improvement in performance for face recognition tasks, and may be useful in reducing the handicap associated with this disability. There is, however, little evidence for a correlation between self rated difficulty in face recognition and measured performance for either task. Further work is needed to explore the complex relation between the perception of disability and measured performance.

Face recognition in age related macular degeneration: perceived disability, measured disability, and performance with a bioptic device

PubMed Central

Tejeria, L; Harper, R A; Artes, P H; Dickinson, C M

2002-01-01

Aims: (1) To explore the relation between performance on tasks of familiar face recognition (FFR) and face expression difference discrimination (FED) with both perceived disability in face recognition and clinical measures of visual function in subjects with age related macular degeneration (AMD). (2) To quantify the gain in performance for face recognition tasks when subjects use a bioptic telescopic low vision device. Methods: 30 subjects with AMD (age range 66–90 years; visual acuity 0.4–1.4 logMAR) were recruited for the study. Perceived (self rated) disability in face recognition was assessed by an eight item questionnaire covering a range of issues relating to face recognition. Visual functions measured were distance visual acuity (ETDRS logMAR charts), continuous text reading acuity (MNRead charts), contrast sensitivity (Pelli-Robson chart), and colour vision (large panel D-15). In the FFR task, images of famous people had to be identified. FED was assessed by a forced choice test where subjects had to decide which one of four images showed a different facial expression. These tasks were repeated with subjects using a bioptic device. Results: Overall perceived disability in face recognition did not correlate with performance on either task, although a specific item on difficulty recognising familiar faces did correlate with FFR (r = 0.49, p<0.05). FFR performance was most closely related to distance acuity (r = −0.69, p<0.001), while FED performance was most closely related to continuous text reading acuity (r = −0.79, p<0.001). In multiple regression, neither contrast sensitivity nor colour vision significantly increased the explained variance. When using a bioptic telescope, FFR performance improved in 86% of subjects (median gain = 49%; p<0.001), while FED performance increased in 79% of subjects (median gain = 50%; p<0.01). Conclusion: Distance and reading visual acuity are closely associated with measured task performance in FFR and FED. A bioptic low vision device can offer a significant improvement in performance for face recognition tasks, and may be useful in reducing the handicap associated with this disability. There is, however, little evidence for a correlation between self rated difficulty in face recognition and measured performance for either task. Further work is needed to explore the complex relation between the perception of disability and measured performance. PMID:12185131

A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

Donepezil Improves Episodic Memory in Young Individuals Vulnerable to the Effects of Sleep Deprivation

PubMed Central

Chuah, Lisa Y.M.; Chong, Delise L.; Chen, Annette K.; Rekshan, William R.; Tan, Jiat-Chow; Zheng, Hui; Chee, Michael W.L.

2009-01-01

Study Objectives: We investigated if donepezil, a long-acting orally administered cholinesterase inhibitor, would reduce episodic memory deficits associated with 24 h of sleep deprivation. Design: Double-blind, placebo-controlled, crossover study involving 7 laboratory visits over 2 months. Participants underwent 4 functional MRI scans; 2 sessions (donepezil or placebo) followed a normal night's sleep, and 2 sessions followed a night of sleep deprivation. Setting: The study took place in a research laboratory. Participants: 26 young, healthy volunteers with no history of any sleep, psychiatric, or neurologic disorders. Interventions: 5 mg of donepezil was taken once daily for approximately 17 days. Measurements and Results: Subjects were scanned while performing a semantic judgment task and tested for word recognition outside the scanner 45 minutes later. Sleep deprivation increased the frequency of non-responses at encoding and impaired delayed recognition. No benefit of donepezil was evident when participants were well rested. When sleep deprived, individuals who showed greater performance decline improved with donepezil, whereas more resistant individuals did not benefit. Accompanying these behavioral effects, there was corresponding modulation of task-related activation in functionally relevant brain regions. Brain regions identified in relation to donepezil-induced alteration in non-response rates could be distinguished from regions relating to improved recognition memory. This suggests that donepezil can improve delayed recognition in sleep-deprived persons by improving attention as well as enhancing memory encoding. Conclusions: Donepezil reduced decline in recognition performance in individuals vulnerable to the effects of sleep deprivation. Additionally, our findings demonstrate the utility of combined fMRI–behavior evaluation in psychopharmacological studies. Citation: Chuah LYM; Chong DL; Chen AK; Rekshan WR; Tan JC; Zheng H; Chee MWL. Donepezil improves episodic memory in young individuals vulnerable to the effects of sleep deprivation. SLEEP 2009;32(8):999-1010. PMID:19725251

Speaker-Machine Interaction in Automatic Speech Recognition. Technical Report.

ERIC Educational Resources Information Center

Makhoul, John I.

The feasibility and limitations of speaker adaptation in improving the performance of a "fixed" (speaker-independent) automatic speech recognition system were examined. A fixed vocabulary of 55 syllables is used in the recognition system which contains 11 stops and fricatives and five tense vowels. The results of an experiment on speaker…

Face Averages Enhance User Recognition for Smartphone Security

PubMed Central

Robertson, David J.; Kramer, Robin S. S.; Burton, A. Mike

2015-01-01

Our recognition of familiar faces is excellent, and generalises across viewing conditions. However, unfamiliar face recognition is much poorer. For this reason, automatic face recognition systems might benefit from incorporating the advantages of familiarity. Here we put this to the test using the face verification system available on a popular smartphone (the Samsung Galaxy). In two experiments we tested the recognition performance of the smartphone when it was encoded with an individual’s ‘face-average’ – a representation derived from theories of human face perception. This technique significantly improved performance for both unconstrained celebrity images (Experiment 1) and for real faces (Experiment 2): users could unlock their phones more reliably when the device stored an average of the user’s face than when they stored a single image. This advantage was consistent across a wide variety of everyday viewing conditions. Furthermore, the benefit did not reduce the rejection of imposter faces. This benefit is brought about solely by consideration of suitable representations for automatic face recognition, and we argue that this is just as important as development of matching algorithms themselves. We propose that this representation could significantly improve recognition rates in everyday settings. PMID:25807251

Fear recognition impairment in early-stage Alzheimer's disease: when focusing on the eyes region improves performance.

PubMed

Hot, Pascal; Klein-Koerkamp, Yanica; Borg, Céline; Richard-Mornas, Aurélie; Zsoldos, Isabella; Paignon Adeline, Adeline; Thomas Antérion, Catherine; Baciu, Monica

2013-06-01

A decline in the ability to identify fearful expression has been frequently reported in patients with Alzheimer's disease (AD). In patients with severe destruction of the bilateral amygdala, similar difficulties have been reduced by using an explicit visual exploration strategy focusing on gaze. The current study assessed the possibility of applying a similar strategy in AD patients to improve fear recognition. It also assessed the possibility of improving fear recognition when a visual exploration strategy induced AD patients to process the eyes region. Seventeen patients with mild AD and 34 healthy subjects (17 young adults and 17 older adults) performed a classical task of emotional identification of faces expressing happiness, anger, and fear in two conditions: The face appeared progressively from the eyes region to the periphery (eyes region condition) or it appeared as a whole (global condition). Specific impairment in identifying a fearful expression was shown in AD patients compared with older adult controls during the global condition. Fear expression recognition was significantly improved in AD patients during the eyes region condition, in which they performed similarly to older adult controls. Our results suggest that using a different strategy of face exploration, starting first with processing of the eyes region, may compensate for a fear recognition deficit in AD patients. Findings suggest that a part of this deficit could be related to visuo-perceptual impairments. Additionally, these findings suggest that the decline of fearful face recognition reported in both normal aging and in AD may result from impairment of non-amygdalar processing in both groups and impairment of amygdalar-dependent processing in AD. Copyright © 2013 Elsevier Inc. All rights reserved.

Multiple template-based image matching using alpha-rooted quaternion phase correlation

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2010-04-01

In computer vision applications, image matching performed on quality-degraded imagery is difficult due to image content distortion and noise effects. State-of-the art keypoint based matchers, such as SURF and SIFT, work very well on clean imagery. However, performance can degrade significantly in the presence of high noise and clutter levels. Noise and clutter cause the formation of false features which can degrade recognition performance. To address this problem, previously we developed an extension to the classical amplitude and phase correlation forms, which provides improved robustness and tolerance to image geometric misalignments and noise. This extension, called Alpha-Rooted Phase Correlation (ARPC), combines Fourier domain-based alpha-rooting enhancement with classical phase correlation. ARPC provides tunable parameters to control the alpha-rooting enhancement. These parameter values can be optimized to tradeoff between high narrow correlation peaks, and more robust wider, but smaller peaks. Previously, we applied ARPC in the radon transform domain for logo image recognition in the presence of rotational image misalignments. In this paper, we extend ARPC to incorporate quaternion Fourier transforms, thereby creating Alpha-Rooted Quaternion Phase Correlation (ARQPC). We apply ARQPC to the logo image recognition problem. We use ARQPC to perform multiple-reference logo template matching by representing multiple same-class reference templates as quaternion-valued images. We generate recognition performance results on publicly-available logo imagery, and compare recognition results to results generated from standard approaches. We show that small deviations in reference templates of sameclass logos can lead to improved recognition performance using the joint matching inherent in ARQPC.

Using an Improved SIFT Algorithm and Fuzzy Closed-Loop Control Strategy for Object Recognition in Cluttered Scenes

PubMed Central

Nie, Haitao; Long, Kehui; Ma, Jun; Yue, Dan; Liu, Jinguo

2015-01-01

Partial occlusions, large pose variations, and extreme ambient illumination conditions generally cause the performance degradation of object recognition systems. Therefore, this paper presents a novel approach for fast and robust object recognition in cluttered scenes based on an improved scale invariant feature transform (SIFT) algorithm and a fuzzy closed-loop control method. First, a fast SIFT algorithm is proposed by classifying SIFT features into several clusters based on several attributes computed from the sub-orientation histogram (SOH), in the feature matching phase only features that share nearly the same corresponding attributes are compared. Second, a feature matching step is performed following a prioritized order based on the scale factor, which is calculated between the object image and the target object image, guaranteeing robust feature matching. Finally, a fuzzy closed-loop control strategy is applied to increase the accuracy of the object recognition and is essential for autonomous object manipulation process. Compared to the original SIFT algorithm for object recognition, the result of the proposed method shows that the number of SIFT features extracted from an object has a significant increase, and the computing speed of the object recognition processes increases by more than 40%. The experimental results confirmed that the proposed method performs effectively and accurately in cluttered scenes. PMID:25714094

Social-cognitive remediation in schizophrenia: generalization of effects of the Training of Affect Recognition (TAR).

PubMed

Wölwer, Wolfgang; Frommann, Nicole

2011-09-01

In the last decade, several social cognitive remediation programs have been developed for use in schizophrenia. Though existing evidence indicates that such programs can improve social cognition, which is essential for successful social functioning, it remains unclear whether the improvements generalize to social cognitive domains not primarily addressed by the intervention and whether the improved test performance transfers into everyday social functioning. The present study investigated whether, beyond its known effects on facial affect recognition, the Training of Affect Recognition (TAR) has effects on prosodic affect recognition, theory of mind (ToM) performance, social competence in a role-play task, and more general social and occupational functioning. Thirty-eight inpatients with a diagnosis of schizophrenia or schizoaffective disorder were randomly assigned to 6 weeks of treatment with the TAR--primarily targeted at facial affect recognition-or Cognitive Remediation Training (CRT)--primarily targeted at neurocognition. Intention-to-treat analyses found significantly larger pre-post improvements with TAR than with CRT in prosodic affect recognition, ToM, and social competence and a trend effect in global social functioning. However, the effects on ToM and social competence were no longer significant in the smaller group of patients who completed treatment according to protocol. Results suggest that TAR effects generalize to other social cognitive domains not primarily addressed. TAR may also enhance social skills and social functioning, although this has to be confirmed. Results are discussed with regard to the need to improve functional outcome in schizophrenia against the background of current evidence from other social cognitive remediation approaches.

Score Fusion and Decision Fusion for the Performance Improvement of Face Recognition

DTIC Science & Technology

2013-07-01

0.1). A Hamming distance (HD) [7] is calculated with the FP-CGF to measure the similarities among faces. The matched face has the shortest HD from...then put into a face pattern byte (FPB) pixel- by-pixel. A HD is calculated with the FPB to measure the similarities among faces, and recognition is...all query users are included in the database), the recognition performance can be measured by a verification rate (VR), the percentage of the

Instructions to mimic improve facial emotion recognition in people with sub-clinical autism traits.

PubMed

Lewis, Michael B; Dunn, Emily

2017-11-01

People tend to mimic the facial expression of others. It has been suggested that this helps provide social glue between affiliated people but it could also aid recognition of emotions through embodied cognition. The degree of facial mimicry, however, varies between individuals and is limited in people with autism spectrum conditions (ASC). The present study sought to investigate the effect of promoting facial mimicry during a facial-emotion-recognition test. In two experiments, participants without an ASC diagnosis had their autism quotient (AQ) measured. Following a baseline test, they did an emotion-recognition test again but half of the participants were asked to mimic the target face they saw prior to making their responses. Mimicry improved emotion recognition, and further analysis revealed that the largest improvement was for participants who had higher scores on the autism traits. In fact, recognition performance was best overall for people who had high AQ scores but also received the instruction to mimic. Implications for people with ASC are explored.

Voice gender discrimination provides a measure of more than pitch-related perception in cochlear implant users

PubMed Central

Li, Tianhao; Fu, Qian-Jie

2013-01-01

Objectives (1) To investigate whether voice gender discrimination (VGD) could be a useful indicator of the spectral and temporal processing abilities of individual cochlear implant (CI) users; (2) To examine the relationship between VGD and speech recognition with CI when comparable acoustic cues are used for both perception processes. Design VGD was measured using two talker sets with different inter-gender fundamental frequencies (F0), as well as different acoustic CI simulations. Vowel and consonant recognition in quiet and noise were also measured and compared with VGD performance. Study sample Eleven postlingually deaf CI users. Results The results showed that (1) mean VGD performance differed for different stimulus sets, (2) VGD and speech recognition performance varied among individual CI users, and (3) individual VGD performance was significantly correlated with speech recognition performance under certain conditions. Conclusions VGD measured with selected stimulus sets might be useful for assessing not only pitch-related perception, but also spectral and temporal processing by individual CI users. In addition to improvements in spectral resolution and modulation detection, the improvement in higher modulation frequency discrimination might be particularly important for CI users in noisy environments. PMID:21696330

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

PubMed Central

Liu, Zhong; Zhao, Changchen; Wu, Xingming; Chen, Weihai

2017-01-01

RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. PMID:28245553

Improving Negative Emotion Recognition in Young Offenders Reduces Subsequent Crime

PubMed Central

Hubble, Kelly; Bowen, Katharine L.; Moore, Simon C.; van Goozen, Stephanie H. M.

2015-01-01

Background Children with antisocial behaviour show deficits in the perception of emotional expressions in others that may contribute to the development and persistence of antisocial and aggressive behaviour. Current treatments for antisocial youngsters are limited in effectiveness. It has been argued that more attention should be devoted to interventions that target neuropsychological correlates of antisocial behaviour. This study examined the effect of emotion recognition training on criminal behaviour. Methods Emotion recognition and crime levels were studied in 50 juvenile offenders. Whilst all young offenders received their statutory interventions as the study was conducted, a subgroup of twenty-four offenders also took part in a facial affect training aimed at improving emotion recognition. Offenders in the training and control groups were matched for age, SES, IQ and lifetime crime level. All offenders were tested twice for emotion recognition performance, and recent crime data were collected after the testing had been completed. Results Before the training there were no differences between the groups in emotion recognition, with both groups displaying poor fear, sadness and anger recognition. After the training fear, sadness and anger recognition improved significantly in juvenile offenders in the training group. Although crime rates dropped in all offenders in the 6 months following emotion testing, only the group of offenders who had received the emotion training showed a significant reduction in the severity of the crimes they committed. Conclusions The study indicates that emotion recognition can be relatively easily improved in youths who engage in serious antisocial and criminal behavior. The results suggest that improved emotion recognition has the potential to reduce the severity of reoffending. PMID:26121148

Improving Negative Emotion Recognition in Young Offenders Reduces Subsequent Crime.

PubMed

Hubble, Kelly; Bowen, Katharine L; Moore, Simon C; van Goozen, Stephanie H M

2015-01-01

Children with antisocial behaviour show deficits in the perception of emotional expressions in others that may contribute to the development and persistence of antisocial and aggressive behaviour. Current treatments for antisocial youngsters are limited in effectiveness. It has been argued that more attention should be devoted to interventions that target neuropsychological correlates of antisocial behaviour. This study examined the effect of emotion recognition training on criminal behaviour. Emotion recognition and crime levels were studied in 50 juvenile offenders. Whilst all young offenders received their statutory interventions as the study was conducted, a subgroup of twenty-four offenders also took part in a facial affect training aimed at improving emotion recognition. Offenders in the training and control groups were matched for age, SES, IQ and lifetime crime level. All offenders were tested twice for emotion recognition performance, and recent crime data were collected after the testing had been completed. Before the training there were no differences between the groups in emotion recognition, with both groups displaying poor fear, sadness and anger recognition. After the training fear, sadness and anger recognition improved significantly in juvenile offenders in the training group. Although crime rates dropped in all offenders in the 6 months following emotion testing, only the group of offenders who had received the emotion training showed a significant reduction in the severity of the crimes they committed. The study indicates that emotion recognition can be relatively easily improved in youths who engage in serious antisocial and criminal behavior. The results suggest that improved emotion recognition has the potential to reduce the severity of reoffending.

Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

PubMed

Lee, Jong-Seok; Park, Cheol Hoon

2010-08-01

We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

Multi-template image matching using alpha-rooted biquaternion phase correlation with application to logo recognition

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2011-06-01

Hypercomplex approaches are seeing increased application to signal and image processing problems. The use of multicomponent hypercomplex numbers, such as quaternions, enables the simultaneous co-processing of multiple signal or image components. This joint processing capability can provide improved exploitation of the information contained in the data, thereby leading to improved performance in detection and recognition problems. In this paper, we apply hypercomplex processing techniques to the logo image recognition problem. Specifically, we develop an image matcher by generalizing classical phase correlation to the biquaternion case. We further incorporate biquaternion Fourier domain alpha-rooting enhancement to create Alpha-Rooted Biquaternion Phase Correlation (ARBPC). We present the mathematical properties which justify use of ARBPC as an image matcher. We present numerical performance results of a logo verification problem using real-world logo data, demonstrating the performance improvement obtained using the hypercomplex approach. We compare results of the hypercomplex approach to standard multi-template matching approaches.

WHOLE BODY VIBRATION IMPROVES ATTENTION AND MOTOR PERFORMANCE IN MICE DEPENDING ON THE DURATION OF THE WHOLE-BODY VIBRATION SESSION.

PubMed

Keijser, Jan N; van Heuvelen, Marieke J G; Nyakas, Csaba; Tóth, Kata; Schoemaker, Regien G; Zeinstra, Edzard; van der Zee, Eddy A

2017-01-01

Whole body vibration (WBV) is a form of physical stimulation via mechanical vibrations transmitted to a subject. It is assumed that WBV induces sensory stimulation in cortical brain regions through the activation of skin and muscle receptors responding to the vibration. The effects of WBV on muscle strength are well described. However, little is known about the impact of WBV on the brain. Recently, it was shown in humans that WBV improves attention in an acute WBV protocol. Preclinical research is needed to unravel the underlying brain mechanism. As a first step, we examined whether chronic WBV improves attention in mice. A custom made vibrating platform for mice with low intensity vibrations was used. Male CD1 mice (3 months of age) received five weeks WBV (30 Hz; 1.9 G), five days a week with sessions of five (n=12) or 30 (n=10) minutes. Control mice (pseudo-WBV; n=12 and 10 for the five and 30 minute sessions, respectively) were treated in a similar way, but did not receive the actual vibration. Object recognition tasks were used as an attention test (novel and spatial object recognition - the primary outcome measure). A Balance beam was used for motor performance, serving as a secondary outcome measure. WBV sessions of five (but not WBV sessions of 30 minutes) improved balance beam performance (mice gained 28% in time needed to cross the beam) and novel object recognition (mice paid significantly more attention to the novel object) as compared to pseudo WBV, but no change was found for spatial object performance (mice did not notice the relocation). Although 30 minutes WBV sessions were not beneficial, it did not impair either attention or motor performance. These results show that brief sessions of WBV improve, next to motor performance, attention for object recognition, but not spatial cues of the objects. The selective improvement of attention in mice opens the avenue to unravel the underlying brain mechanisms.

Hyperspectral face recognition with spatiospectral information fusion and PLS regression.

PubMed

Uzair, Muhammad; Mahmood, Arif; Mian, Ajmal

2015-03-01

Hyperspectral imaging offers new opportunities for face recognition via improved discrimination along the spectral dimension. However, it poses new challenges, including low signal-to-noise ratio, interband misalignment, and high data dimensionality. Due to these challenges, the literature on hyperspectral face recognition is not only sparse but is limited to ad hoc dimensionality reduction techniques and lacks comprehensive evaluation. We propose a hyperspectral face recognition algorithm using a spatiospectral covariance for band fusion and partial least square regression for classification. Moreover, we extend 13 existing face recognition techniques, for the first time, to perform hyperspectral face recognition.We formulate hyperspectral face recognition as an image-set classification problem and evaluate the performance of seven state-of-the-art image-set classification techniques. We also test six state-of-the-art grayscale and RGB (color) face recognition algorithms after applying fusion techniques on hyperspectral images. Comparison with the 13 extended and five existing hyperspectral face recognition techniques on three standard data sets show that the proposed algorithm outperforms all by a significant margin. Finally, we perform band selection experiments to find the most discriminative bands in the visible and near infrared response spectrum.

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

Effect of a Bluetooth-implemented hearing aid on speech recognition performance: subjective and objective measurement.

PubMed

Kim, Min-Beom; Chung, Won-Ho; Choi, Jeesun; Hong, Sung Hwa; Cho, Yang-Sun; Park, Gyuseok; Lee, Sangmin

2014-06-01

The object was to evaluate speech perception improvement through Bluetooth-implemented hearing aids in hearing-impaired adults. Thirty subjects with bilateral symmetric moderate sensorineural hearing loss participated in this study. A Bluetooth-implemented hearing aid was fitted unilaterally in all study subjects. Objective speech recognition score and subjective satisfaction were measured with a Bluetooth-implemented hearing aid to replace the acoustic connection from either a cellular phone or a loudspeaker system. In each system, participants were assigned to 4 conditions: wireless speech signal transmission into hearing aid (wireless mode) in quiet or noisy environment and conventional speech signal transmission using external microphone of hearing aid (conventional mode) in quiet or noisy environment. Also, participants completed questionnaires to investigate subjective satisfaction. Both cellular phone and loudspeaker system situation, participants showed improvements in sentence and word recognition scores with wireless mode compared to conventional mode in both quiet and noise conditions (P < .001). Participants also reported subjective improvements, including better sound quality, less noise interference, and better accuracy naturalness, when using the wireless mode (P < .001). Bluetooth-implemented hearing aids helped to improve subjective and objective speech recognition performances in quiet and noisy environments during the use of electronic audio devices.

Blood perfusion construction for infrared face recognition based on bio-heat transfer.

PubMed

Xie, Zhihua; Liu, Guodong

2014-01-01

To improve the performance of infrared face recognition for time-lapse data, a new construction of blood perfusion is proposed based on bio-heat transfer. Firstly, by quantifying the blood perfusion based on Pennes equation, the thermal information is converted into blood perfusion rate, which is stable facial biological feature of face image. Then, the separability discriminant criterion in Discrete Cosine Transform (DCT) domain is applied to extract the discriminative features of blood perfusion information. Experimental results demonstrate that the features of blood perfusion are more concentrative and discriminative for recognition than those of thermal information. The infrared face recognition based on the proposed blood perfusion is robust and can achieve better recognition performance compared with other state-of-the-art approaches.

Melodic contour identification by cochlear implant listeners.

PubMed

Galvin, John J; Fu, Qian-Jie; Nogaki, Geraldine

2007-06-01

While the cochlear implant provides many deaf patients with good speech understanding in quiet, music perception and appreciation with the cochlear implant remains a major challenge for most cochlear implant users. The present study investigated whether a closed-set melodic contour identification (MCI) task could be used to quantify cochlear implant users' ability to recognize musical melodies and whether MCI performance could be improved with moderate auditory training. The present study also compared MCI performance with familiar melody identification (FMI) performance, with and without MCI training. For the MCI task, test stimuli were melodic contours composed of 5 notes of equal duration whose frequencies corresponded to musical intervals. The interval between successive notes in each contour was varied between 1 and 5 semitones; the "root note" of the contours was also varied (A3, A4, and A5). Nine distinct musical patterns were generated for each interval and root note condition, resulting in a total of 135 musical contours. The identification of these melodic contours was measured in 11 cochlear implant users. FMI was also evaluated in the same subjects; recognition of 12 familiar melodies was tested with and without rhythm cues. MCI was also trained in 6 subjects, using custom software and melodic contours presented in a different frequency range from that used for testing. Results showed that MCI recognition performance was highly variable among cochlear implant users, ranging from 14% to 91% correct. For most subjects, MCI performance improved as the number of semitones between successive notes was increased; performance was slightly lower for the A3 root note condition. Mean FMI performance was 58% correct when rhythm cues were preserved and 29% correct when rhythm cues were removed. Statistical analyses revealed no significant correlation between MCI performance and FMI performance (with or without rhythmic cues). However, MCI performance was significantly correlated with vowel recognition performance; FMI performance was not correlated with cochlear implant subjects' phoneme recognition performance. Preliminary results also showed that the MCI training improved all subjects' MCI performance; the improved MCI performance also generalized to improved FMI performance. Preliminary data indicate that the closed-set MCI task is a viable approach toward quantifying an important component of cochlear implant users' music perception. The improvement in MCI performance and generalization to FMI performance with training suggests that MCI training may be useful for improving cochlear implant users' music perception and appreciation; such training may be necessary to properly evaluate patient performance, as acute measures may underestimate the amount of musical information transmitted by the cochlear implant device and received by cochlear implant listeners.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2004-12-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

An online handwriting recognition system for Turkish

NASA Astrophysics Data System (ADS)

Vural, Esra; Erdogan, Hakan; Oflazer, Kemal; Yanikoglu, Berrin A.

2005-01-01

Despite recent developments in Tablet PC technology, there has not been any applications for recognizing handwritings in Turkish. In this paper, we present an online handwritten text recognition system for Turkish, developed using the Tablet PC interface. However, even though the system is developed for Turkish, the addressed issues are common to online handwriting recognition systems in general. Several dynamic features are extracted from the handwriting data for each recorded point and Hidden Markov Models (HMM) are used to train letter and word models. We experimented with using various features and HMM model topologies, and report on the effects of these experiments. We started with first and second derivatives of the x and y coordinates and relative change in the pen pressure as initial features. We found that using two more additional features, that is, number of neighboring points and relative heights of each point with respect to the base-line improve the recognition rate. In addition, extracting features within strokes and using a skipping state topology improve the system performance as well. The improved system performance is 94% in recognizing handwritten words from a 1000-word lexicon.

Ensemble training to improve recognition using 2D ear

NASA Astrophysics Data System (ADS)

Middendorff, Christopher; Bowyer, Kevin W.

2009-05-01

The ear has gained popularity as a biometric feature due to the robustness of the shape over time and across emotional expression. Popular methods of ear biometrics analyze the ear as a whole, leaving these methods vulnerable to error due to occlusion. Many researchers explore ear recognition using an ensemble, but none present a method for designing the individual parts that comprise the ensemble. In this work, we introduce a method of modifying the ensemble shapes to improve performance. We determine how different properties of an ensemble training system can affect overall performance. We show that ensembles built from small parts will outperform ensembles built with larger parts, and that incorporating a large number of parts improves the performance of the ensemble.

Glucose effects on long-term memory performance: duration and domain specificity.

PubMed

Owen, Lauren; Finnegan, Yvonne; Hu, Henglong; Scholey, Andrew B; Sünram-Lea, Sandra I

2010-08-01

Previous research has suggested that long-term verbal declarative memory is particularly sensitive to enhancement by glucose loading; however, investigation of glucose effects on certain memory domains has hitherto been neglected. Therefore, domain specificity of glucose effects merits further elucidation. The aim of the present research was to provide a more comprehensive investigation of the possible effects of glucose administration on different aspects of memory by 1) contrasting the effect of glucose administration on different memory domains (implicit/explicit memory; verbal/non-verbal memory, and recognition/familiarity processes), 2) investigating whether potential effects on memory domains differ depending on the dose of glucose administered (25 g versus 60 g), 3) exploring the duration of the glucose facilitation effect (assessment of memory performance 35 min and 1 week after encoding). A double-blind between-subjects design was used to test the effects of administration of 25 and 60 g glucose on memory performance. Implicit memory was improved following administration of 60 g of glucose. Glucose supplementation failed to improve face recognition performance but significantly improved performance of word recall and recognition following administration of 60 g of glucose. However, effects were not maintained 1 week following encoding. Improved implicit memory performance following glucose administration has not been reported before. Furthermore, the current data tentatively suggest that level of processing may determine the required glucose dosage to demonstrate memory improvement and that higher dosages may be able to exert effects on memory pertaining to both hippocampal and non-hippocampal brain regions.

Modulating Memory Performance in Healthy Subjects with Transcranial Direct Current Stimulation Over the Right Dorsolateral Prefrontal Cortex.

PubMed

Smirni, Daniela; Turriziani, Patrizia; Mangano, Giuseppa Renata; Cipolotti, Lisa; Oliveri, Massimiliano

2015-01-01

The role of the Dorsolateral Prefrontal Cortex (DLPFC) in recognition memory has been well documented in lesion, neuroimaging and repetitive Transcranial Magnetic Stimulation (rTMS) studies. The aim of the present study was to investigate the effects of transcranial Direct Current Stimulation (tDCS) over the left and the right DLPFC during the delay interval of a non-verbal recognition memory task. 36 right-handed young healthy subjects participated in the study. The experimental task was an Italian version of Recognition Memory Test for unknown faces. Study included two experiments: in a first experiment, each subject underwent one session of sham tDCS and one session of left or right cathodal tDCS; in a second experiment each subject underwent one session of sham tDCS and one session of left or right anodal tDCS. Cathodal tDCS over the right DLPFC significantly improved non verbal recognition memory performance, while cathodal tDCS over the left DLPFC had no effect. Anodal tDCS of both the left and right DLPFC did not modify non verbal recognition memory performance. Complementing the majority of previous studies, reporting long term memory facilitations following left prefrontal anodal tDCS, the present findings show that cathodal tDCS of the right DLPFC can also improve recognition memory in healthy subjects.

Optogenetic Stimulation of Prefrontal Glutamatergic Neurons Enhances Recognition Memory.

PubMed

Benn, Abigail; Barker, Gareth R I; Stuart, Sarah A; Roloff, Eva V L; Teschemacher, Anja G; Warburton, E Clea; Robinson, Emma S J

2016-05-04

Finding effective cognitive enhancers is a major health challenge; however, modulating glutamatergic neurotransmission has the potential to enhance performance in recognition memory tasks. Previous studies using glutamate receptor antagonists have revealed that the medial prefrontal cortex (mPFC) plays a central role in associative recognition memory. The present study investigates short-term recognition memory using optogenetics to target glutamatergic neurons within the rodent mPFC specifically. Selective stimulation of glutamatergic neurons during the online maintenance of information enhanced associative recognition memory in normal animals. This cognitive enhancing effect was replicated by local infusions of the AMPAkine CX516, but not CX546, which differ in their effects on EPSPs. This suggests that enhancing the amplitude, but not the duration, of excitatory synaptic currents improves memory performance. Increasing glutamate release through infusions of the mGluR7 presynaptic receptor antagonist MMPIP had no effect on performance. These results provide new mechanistic information that could guide the targeting of future cognitive enhancers. Our work suggests that improved associative-recognition memory can be achieved by enhancing endogenous glutamatergic neuronal activity selectively using an optogenetic approach. We build on these observations to recapitulate this effect using drug treatments that enhance the amplitude of EPSPs; however, drugs that alter the duration of the EPSP or increase glutamate release lack efficacy. This suggests that both neural and temporal specificity are needed to achieve cognitive enhancement. Copyright © 2016 Benn et al.

A survey of context recognition in surgery.

PubMed

Pernek, Igor; Ferscha, Alois

2017-10-01

With the introduction of operating rooms of the future context awareness has gained importance in the surgical environment. This paper organizes and reviews different approaches for recognition of context in surgery. Major electronic research databases were queried to obtain relevant publications submitted between the years 2010 and 2015. Three different types of context were identified: (i) the surgical workflow context, (ii) surgeon's cognitive and (iii) technical state context. A total of 52 relevant studies were identified and grouped based on the type of context detected and sensors used. Different approaches were summarized to provide recommendations for future research. There is still room for improvement in terms of methods used and evaluations performed. Machine learning should be used more extensively to uncover hidden relationships between different properties of the surgeon's state, particularly when performing cognitive context recognition. Furthermore, validation protocols should be improved by performing more evaluations in situ and with a higher number of unique participants. The paper also provides a structured outline of recent context recognition methods to facilitate development of new generation context-aware surgical support systems.

Units of Distinction: Creating a Blueprint for Recognition of High-Performing Medical-Surgical Nursing Units.

PubMed

Jeffery, Alvin D; Mosier, Sammie; Baker, Allison; Korwek, Kimberly; Borum, Cindy; Englebright, Jane

2018-02-01

Hospital medical-surgical (M/S) nursing units are responsible for up to 28 million encounters annually, yet receive little attention from professional organizations and national initiatives targeted to improve quality and performance. We sought to develop a framework recognizing high-performing units within our large hospital system. This was a retrospective data analysis of M/S units throughout a 168-hospital system. Measures represented patient experience, employee engagement, staff scheduling, nursing-sensitive patient outcomes, professional practices, and clinical process measures. Four hundred ninety units from 129 hospitals contributed information to test the framework. A manual scoring system identified the top 5% and recognized them as a "Unit of Distinction." Secondary analyses with machine learning provided validation of the proposed framework. Similar to external recognition programs, this framework and process provide a holistic evaluation useful for meaningful recognition and lay the groundwork for benchmarking in improvement efforts.

Prediction of consonant recognition in quiet for listeners with normal and impaired hearing using an auditory model.

PubMed

Jürgens, Tim; Ewert, Stephan D; Kollmeier, Birger; Brand, Thomas

2014-03-01

Consonant recognition was assessed in normal-hearing (NH) and hearing-impaired (HI) listeners in quiet as a function of speech level using a nonsense logatome test. Average recognition scores were analyzed and compared to recognition scores of a speech recognition model. In contrast to commonly used spectral speech recognition models operating on long-term spectra, a "microscopic" model operating in the time domain was used. Variations of the model (accounting for hearing impairment) and different model parameters (reflecting cochlear compression) were tested. Using these model variations this study examined whether speech recognition performance in quiet is affected by changes in cochlear compression, namely, a linearization, which is often observed in HI listeners. Consonant recognition scores for HI listeners were poorer than for NH listeners. The model accurately predicted the speech reception thresholds of the NH and most HI listeners. A partial linearization of the cochlear compression in the auditory model, while keeping audibility constant, produced higher recognition scores and improved the prediction accuracy. However, including listener-specific information about the exact form of the cochlear compression did not improve the prediction further.

Image jitter enhances visual performance when spatial resolution is impaired.

PubMed

Watson, Lynne M; Strang, Niall C; Scobie, Fraser; Love, Gordon D; Seidel, Dirk; Manahilov, Velitchko

2012-09-06

Visibility of low-spatial frequency stimuli improves when their contrast is modulated at 5 to 10 Hz compared with stationary stimuli. Therefore, temporal modulations of visual objects could enhance the performance of low vision patients who primarily perceive images of low-spatial frequency content. We investigated the effect of retinal-image jitter on word recognition speed and facial emotion recognition in subjects with central visual impairment. Word recognition speed and accuracy of facial emotion discrimination were measured in volunteers with AMD under stationary and jittering conditions. Computer-driven and optoelectronic approaches were used to induce retinal-image jitter with duration of 100 or 166 ms and amplitude within the range of 0.5 to 2.6° visual angle. Word recognition speed was also measured for participants with simulated (Bangerter filters) visual impairment. Text jittering markedly enhanced word recognition speed for people with severe visual loss (101 ± 25%), while for those with moderate visual impairment, this effect was weaker (19 ± 9%). The ability of low vision patients to discriminate the facial emotions of jittering images improved by a factor of 2. A prototype of optoelectronic jitter goggles produced similar improvement in facial emotion discrimination. Word recognition speed in participants with simulated visual impairment was enhanced for interjitter intervals over 100 ms and reduced for shorter intervals. Results suggest that retinal-image jitter with optimal frequency and amplitude is an effective strategy for enhancing visual information processing in the absence of spatial detail. These findings will enable the development of novel tools to improve the quality of life of low vision patients.

Performance evaluation of MLP and RBF feed forward neural network for the recognition of off-line handwritten characters

NASA Astrophysics Data System (ADS)

Rishi, Rahul; Choudhary, Amit; Singh, Ravinder; Dhaka, Vijaypal Singh; Ahlawat, Savita; Rao, Mukta

2010-02-01

In this paper we propose a system for classification problem of handwritten text. The system is composed of preprocessing module, supervised learning module and recognition module on a very broad level. The preprocessing module digitizes the documents and extracts features (tangent values) for each character. The radial basis function network is used in the learning and recognition modules. The objective is to analyze and improve the performance of Multi Layer Perceptron (MLP) using RBF transfer functions over Logarithmic Sigmoid Function. The results of 35 experiments indicate that the Feed Forward MLP performs accurately and exhaustively with RBF. With the change in weight update mechanism and feature-drawn preprocessing module, the proposed system is competent with good recognition show.

Rehabilitation of face-processing skills in an adolescent with prosopagnosia: Evaluation of an online perceptual training programme.

PubMed

Bate, Sarah; Bennetts, Rachel; Mole, Joseph A; Ainge, James A; Gregory, Nicola J; Bobak, Anna K; Bussunt, Amanda

2015-01-01

In this paper we describe the case of EM, a female adolescent who acquired prosopagnosia following encephalitis at the age of eight. Initial neuropsychological and eye-movement investigations indicated that EM had profound difficulties in face perception as well as face recognition. EM underwent 14 weeks of perceptual training in an online programme that attempted to improve her ability to make fine-grained discriminations between faces. Following training, EM's face perception skills had improved, and the effect generalised to untrained faces. Eye-movement analyses also indicated that EM spent more time viewing the inner facial features post-training. Examination of EM's face recognition skills revealed an improvement in her recognition of personally-known faces when presented in a laboratory-based test, although the same gains were not noted in her everyday experiences with these faces. In addition, EM did not improve on a test assessing the recognition of newly encoded faces. One month after training, EM had maintained the improvement on the eye-tracking test, and to a lesser extent, her performance on the familiar faces test. This pattern of findings is interpreted as promising evidence that the programme can improve face perception skills, and with some adjustments, may at least partially improve face recognition skills.

Perceptual learning for speech in noise after application of binary time-frequency masks

PubMed Central

Ahmadi, Mahnaz; Gross, Vauna L.; Sinex, Donal G.

2013-01-01

Ideal time-frequency (TF) masks can reject noise and improve the recognition of speech-noise mixtures. An ideal TF mask is constructed with prior knowledge of the target speech signal. The intelligibility of a processed speech-noise mixture depends upon the threshold criterion used to define the TF mask. The study reported here assessed the effect of training on the recognition of speech in noise after processing by ideal TF masks that did not restore perfect speech intelligibility. Two groups of listeners with normal hearing listened to speech-noise mixtures processed by TF masks calculated with different threshold criteria. For each group, a threshold criterion that initially produced word recognition scores between 0.56–0.69 was chosen for training. Listeners practiced with one set of TF-masked sentences until their word recognition performance approached asymptote. Perceptual learning was quantified by comparing word-recognition scores in the first and last training sessions. Word recognition scores improved with practice for all listeners with the greatest improvement observed for the same materials used in training. PMID:23464038

Line-based logo recognition through a web-camera

NASA Astrophysics Data System (ADS)

Chen, Xiaolu; Wang, Yangsheng; Feng, Xuetao

2007-11-01

Logo recognition has gained much development in the document retrieval and shape analysis domain. As human computer interaction becomes more and more popular, the logo recognition through a web-camera is a promising technology in view of application. But for practical application, the study of logo recognition in real scene is much more difficult than the work in clear scene. To cope with the need, we make some improvements on conventional method. First, moment information is used to calculate the test image's orientation angle, which is used to normalize the test image. Second, the main structure of the test image, which is represented by lines patterns, is acquired and modified Hausdorff distance is employed to match the image and each of the existing templates. The proposed method, which is invariant to scale and rotation, gives good result and can work at real-time. The main contribution of this paper is that some improvements are introduced into the exiting recognition framework which performs much better than the original one. Besides, we have built a highly successful logo recognition system using our improved method.

Measuring Reading Performance Informally.

ERIC Educational Resources Information Center

Powell, William R.

To improve the accuracy of the informal reading inventory (IRI), a differential set of criteria is necessary for both word recognition and comprehension scores for different levels and reading conditions. In initial evaluation, word recognition scores should reflect only errors of insertions, omissions, mispronunciations, substitiutions, unkown…

Oxytocin improves emotion recognition for older males.

PubMed

Campbell, Anna; Ruffman, Ted; Murray, Janice E; Glue, Paul

2014-10-01

Older adults (≥60 years) perform worse than young adults (18-30 years) when recognizing facial expressions of emotion. The hypothesized cause of these changes might be declines in neurotransmitters that could affect information processing within the brain. In the present study, we examined the neuropeptide oxytocin that functions to increase neurotransmission. Research suggests that oxytocin benefits the emotion recognition of less socially able individuals. Men tend to have lower levels of oxytocin and older men tend to have worse emotion recognition than older women; therefore, there is reason to think that older men will be particularly likely to benefit from oxytocin. We examined this idea using a double-blind design, testing 68 older and 68 young adults randomly allocated to receive oxytocin nasal spray (20 international units) or placebo. Forty-five minutes afterward they completed an emotion recognition task assessing labeling accuracy for angry, disgusted, fearful, happy, neutral, and sad faces. Older males receiving oxytocin showed improved emotion recognition relative to those taking placebo. No differences were found for older females or young adults. We hypothesize that oxytocin facilitates emotion recognition by improving neurotransmission in the group with the worst emotion recognition. Copyright © 2014 Elsevier Inc. All rights reserved.

Food-Induced Emotional Resonance Improves Emotion Recognition.

PubMed

Pandolfi, Elisa; Sacripante, Riccardo; Cardini, Flavia

2016-01-01

The effect of food substances on emotional states has been widely investigated, showing, for example, that eating chocolate is able to reduce negative mood. Here, for the first time, we have shown that the consumption of specific food substances is not only able to induce particular emotional states, but more importantly, to facilitate recognition of corresponding emotional facial expressions in others. Participants were asked to perform an emotion recognition task before and after eating either a piece of chocolate or a small amount of fish sauce-which we expected to induce happiness or disgust, respectively. Our results showed that being in a specific emotional state improves recognition of the corresponding emotional facial expression. Indeed, eating chocolate improved recognition of happy faces, while disgusted expressions were more readily recognized after eating fish sauce. In line with the embodied account of emotion understanding, we suggest that people are better at inferring the emotional state of others when their own emotional state resonates with the observed one.

Food-Induced Emotional Resonance Improves Emotion Recognition

PubMed Central

Pandolfi, Elisa; Sacripante, Riccardo; Cardini, Flavia

2016-01-01

The effect of food substances on emotional states has been widely investigated, showing, for example, that eating chocolate is able to reduce negative mood. Here, for the first time, we have shown that the consumption of specific food substances is not only able to induce particular emotional states, but more importantly, to facilitate recognition of corresponding emotional facial expressions in others. Participants were asked to perform an emotion recognition task before and after eating either a piece of chocolate or a small amount of fish sauce—which we expected to induce happiness or disgust, respectively. Our results showed that being in a specific emotional state improves recognition of the corresponding emotional facial expression. Indeed, eating chocolate improved recognition of happy faces, while disgusted expressions were more readily recognized after eating fish sauce. In line with the embodied account of emotion understanding, we suggest that people are better at inferring the emotional state of others when their own emotional state resonates with the observed one. PMID:27973559

Computational Modeling of Emotions and Affect in Social-Cultural Interaction

DTIC Science & Technology

2013-10-02

acoustic and textual information sources. Second, a cross-lingual study was performed that shed light on how human perception and automatic recognition...speech is produced, a speaker’s pitch and intonational pattern, and word usage. Better feature representation and advanced approaches were used to...recognition performance, and improved our understanding of language/cultural impact on human perception of emotion and automatic classification. • Units

Towards online iris and periocular recognition under relaxed imaging constraints.

PubMed

Tan, Chun-Wei; Kumar, Ajay

2013-10-01

Online iris recognition using distantly acquired images in a less imaging constrained environment requires the development of a efficient iris segmentation approach and recognition strategy that can exploit multiple features available for the potential identification. This paper presents an effective solution toward addressing such a problem. The developed iris segmentation approach exploits a random walker algorithm to efficiently estimate coarsely segmented iris images. These coarsely segmented iris images are postprocessed using a sequence of operations that can effectively improve the segmentation accuracy. The robustness of the proposed iris segmentation approach is ascertained by providing comparison with other state-of-the-art algorithms using publicly available UBIRIS.v2, FRGC, and CASIA.v4-distance databases. Our experimental results achieve improvement of 9.5%, 4.3%, and 25.7% in the average segmentation accuracy, respectively, for the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with most competing approaches. We also exploit the simultaneously extracted periocular features to achieve significant performance improvement. The joint segmentation and combination strategy suggest promising results and achieve average improvement of 132.3%, 7.45%, and 17.5% in the recognition performance, respectively, from the UBIRIS.v2, FRGC, and CASIA.v4-distance databases, as compared with the related competing approaches.

Literature review of voice recognition and generation technology for Army helicopter applications

NASA Astrophysics Data System (ADS)

Christ, K. A.

1984-08-01

This report is a literature review on the topics of voice recognition and generation. Areas covered are: manual versus vocal data input, vocabulary, stress and workload, noise, protective masks, feedback, and voice warning systems. Results of the studies presented in this report indicate that voice data entry has less of an impact on a pilot's flight performance, during low-level flying and other difficult missions, than manual data entry. However, the stress resulting from such missions may cause the pilot's voice to change, reducing the recognition accuracy of the system. The noise present in helicopter cockpits also causes the recognition accuracy to decrease. Noise-cancelling devices are being developed and improved upon to increase the recognition performance in noisy environments. Future research in the fields of voice recognition and generation should be conducted in the areas of stress and workload, vocabulary, and the types of voice generation best suited for the helicopter cockpit. Also, specific tasks should be studied to determine whether voice recognition and generation can be effectively applied.

Repetition and lag effects in movement recognition.

PubMed

Hall, C R; Buckolz, E

1982-03-01

Whether repetition and lag improve the recognition of movement patterns was investigated. Recognition memory was tested for one repetition, two-repetitions massed, and two-repetitions distributed with movement patterns at lags of 3, 5, 7, and 13. Recognition performance was examined both immediately afterwards and following a 48 hour delay. Both repetition and lag effects failed to be demonstrated, providing some support for the claim that memory is unaffected by repetition at a constant level of processing (Craik & Lockhart, 1972). There was, as expected, a significant decrease in recognition memory following the retention interval, but this appeared unrelated to repetition or lag.

Employee Retention and Performance Improvement in High-Tech Companies.

ERIC Educational Resources Information Center

Ware, B. Lynn

2001-01-01

Considers the benefits of employee retention and performance improvement in high technology, new economy companies. Discusses attracting and retaining top talent in information technology companies; targeted recruiting and hiring; employee achievement; learning and professional growth; recognition; nurturing careers; team collaboration; the TALENT…

Reconciling change blindness with long-term memory for objects.

PubMed

Wood, Katherine; Simons, Daniel J

2017-02-01

How can we reconcile remarkably precise long-term memory for thousands of images with failures to detect changes to similar images? We explored whether people can use detailed, long-term memory to improve change detection performance. Subjects studied a set of images of objects and then performed recognition and change detection tasks with those images. Recognition memory performance exceeded change detection performance, even when a single familiar object in the postchange display consistently indicated the change location. In fact, participants were no better when a familiar object predicted the change location than when the displays consisted of unfamiliar objects. When given an explicit strategy to search for a familiar object as a way to improve performance on the change detection task, they performed no better than in a 6-alternative recognition memory task. Subjects only benefited from the presence of familiar objects in the change detection task when they had more time to view the prechange array before it switched. Once the cost to using the change detection information decreased, subjects made use of it in conjunction with memory to boost performance on the familiar-item change detection task. This suggests that even useful information will go unused if it is sufficiently difficult to extract.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning.

PubMed

Lim, Kwangyong; Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).

Simulation of talking faces in the human brain improves auditory speech recognition

PubMed Central

von Kriegstein, Katharina; Dogan, Özgür; Grüter, Martina; Giraud, Anne-Lise; Kell, Christian A.; Grüter, Thomas; Kleinschmidt, Andreas; Kiebel, Stefan J.

2008-01-01

Human face-to-face communication is essentially audiovisual. Typically, people talk to us face-to-face, providing concurrent auditory and visual input. Understanding someone is easier when there is visual input, because visual cues like mouth and tongue movements provide complementary information about speech content. Here, we hypothesized that, even in the absence of visual input, the brain optimizes both auditory-only speech and speaker recognition by harvesting speaker-specific predictions and constraints from distinct visual face-processing areas. To test this hypothesis, we performed behavioral and neuroimaging experiments in two groups: subjects with a face recognition deficit (prosopagnosia) and matched controls. The results show that observing a specific person talking for 2 min improves subsequent auditory-only speech and speaker recognition for this person. In both prosopagnosics and controls, behavioral improvement in auditory-only speech recognition was based on an area typically involved in face-movement processing. Improvement in speaker recognition was only present in controls and was based on an area involved in face-identity processing. These findings challenge current unisensory models of speech processing, because they show that, in auditory-only speech, the brain exploits previously encoded audiovisual correlations to optimize communication. We suggest that this optimization is based on speaker-specific audiovisual internal models, which are used to simulate a talking face. PMID:18436648

Word recognition using a lexicon constrained by first/last character decisions

NASA Astrophysics Data System (ADS)

Zhao, Sheila X.; Srihari, Sargur N.

1995-03-01

In lexicon based recognition of machine-printed word images, the size of the lexicon can be quite extensive. The recognition performance is closely related to the size of the lexicon. Recognition performance drops quickly when lexicon size increases. Here, we present an algorithm to improve the word recognition performance by reducing the size of the given lexicon. The algorithm utilizes the information provided by the first and last characters of a word to reduce the size of the given lexicon. Given a word image and a lexicon that contains the word in the image, the first and last characters are segmented and then recognized by a character classifier. The possible candidates based on the results given by the classifier are selected, which give us the sub-lexicon. Then a word shape analysis algorithm is applied to produce the final ranking of the given lexicon. The algorithm was tested on a set of machine- printed gray-scale word images which includes a wide range of print types and qualities.

Weighted fusion of depth and inertial data to improve view invariance for real-time human action recognition

NASA Astrophysics Data System (ADS)

Chen, Chen; Hao, Huiyan; Jafari, Roozbeh; Kehtarnavaz, Nasser

2017-05-01

This paper presents an extension to our previously developed fusion framework [10] involving a depth camera and an inertial sensor in order to improve its view invariance aspect for real-time human action recognition applications. A computationally efficient view estimation based on skeleton joints is considered in order to select the most relevant depth training data when recognizing test samples. Two collaborative representation classifiers, one for depth features and one for inertial features, are appropriately weighted to generate a decision making probability. The experimental results applied to a multi-view human action dataset show that this weighted extension improves the recognition performance by about 5% over equally weighted fusion deployed in our previous fusion framework.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Santos-Villalobos, Hector J; Barstow, Del R; Karakaya, Mahmut

Iris recognition has been proven to be an accurate and reliable biometric. However, the recognition of non-ideal iris images such as off angle images is still an unsolved problem. We propose a new biometric targeted eye model and a method to reconstruct the off-axis eye to its frontal view allowing for recognition using existing methods and algorithms. This allows for existing enterprise level algorithms and approaches to be largely unmodified by using our work as a pre-processor to improve performance. In addition, we describe the `Limbus effect' and its importance for an accurate segmentation of off-axis irides. Our method usesmore » an anatomically accurate human eye model and ray-tracing techniques to compute a transformation function, which reconstructs the iris to its frontal, non-refracted state. Then, the same eye model is used to render a frontal view of the reconstructed iris. The proposed method is fully described and results from synthetic data are shown to establish an upper limit on performance improvement and establish the importance of the proposed approach over traditional linear elliptical unwrapping methods. Our results with synthetic data demonstrate the ability to perform an accurate iris recognition with an image taken as much as 70 degrees off-axis.« less

Effect of training on word-recognition performance in noise for young normal-hearing and older hearing-impaired listeners.

PubMed

Burk, Matthew H; Humes, Larry E; Amos, Nathan E; Strauser, Lauren E

2006-06-01

The objective of this study was to evaluate the effectiveness of a training program for hearing-impaired listeners to improve their speech-recognition performance within a background noise when listening to amplified speech. Both noise-masked young normal-hearing listeners, used to model the performance of elderly hearing-impaired listeners, and a group of elderly hearing-impaired listeners participated in the study. Of particular interest was whether training on an isolated word list presented by a standardized talker can generalize to everyday speech communication across novel talkers. Word-recognition performance was measured for both young normal-hearing (n = 16) and older hearing-impaired (n = 7) adults. Listeners were trained on a set of 75 monosyllabic words spoken by a single female talker over a 9- to 14-day period. Performance for the familiar (trained) talker was measured before and after training in both open-set and closed-set response conditions. Performance on the trained words of the familiar talker were then compared with those same words spoken by three novel talkers and to performance on a second set of untrained words presented by both the familiar and unfamiliar talkers. The hearing-impaired listeners returned 6 mo after their initial training to examine retention of the trained words as well as their ability to transfer any knowledge gained from word training to sentences containing both trained and untrained words. Both young normal-hearing and older hearing-impaired listeners performed significantly better on the word list in which they were trained versus a second untrained list presented by the same talker. Improvements on the untrained words were small but significant, indicating some generalization to novel words. The large increase in performance on the trained words, however, was maintained across novel talkers, pointing to the listener's greater focus on lexical memorization of the words rather than a focus on talker-specific acoustic characteristics. On return in 6 mo, listeners performed significantly better on the trained words relative to their initial baseline performance. Although the listeners performed significantly better on trained versus untrained words in isolation, once the trained words were embedded in sentences, no improvement in recognition over untrained words within the same sentences was shown. Older hearing-impaired listeners were able to significantly improve their word-recognition abilities through training with one talker and to the same degree as young normal-hearing listeners. The improved performance was maintained across talkers and across time. This might imply that training a listener using a standardized list and talker may still provide benefit when these same words are presented by novel talkers outside the clinic. However, training on isolated words was not sufficient to transfer to fluent speech for the specific sentence materials used within this study. Further investigation is needed regarding approaches to improve a hearing aid user's speech understanding in everyday communication situations.

Multimedia Security System for Security and Medical Applications

ERIC Educational Resources Information Center

Zhou, Yicong

2010-01-01

This dissertation introduces a new multimedia security system for the performance of object recognition and multimedia encryption in security and medical applications. The system embeds an enhancement and multimedia encryption process into the traditional recognition system in order to improve the efficiency and accuracy of object detection and…

Recognition of chemical entities: combining dictionary-based and grammar-based approaches.

PubMed

Akhondi, Saber A; Hettne, Kristina M; van der Horst, Eelke; van Mulligen, Erik M; Kors, Jan A

2015-01-01

The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance.

Recognition of chemical entities: combining dictionary-based and grammar-based approaches

PubMed Central

2015-01-01

Background The past decade has seen an upsurge in the number of publications in chemistry. The ever-swelling volume of available documents makes it increasingly hard to extract relevant new information from such unstructured texts. The BioCreative CHEMDNER challenge invites the development of systems for the automatic recognition of chemicals in text (CEM task) and for ranking the recognized compounds at the document level (CDI task). We investigated an ensemble approach where dictionary-based named entity recognition is used along with grammar-based recognizers to extract compounds from text. We assessed the performance of ten different commercial and publicly available lexical resources using an open source indexing system (Peregrine), in combination with three different chemical compound recognizers and a set of regular expressions to recognize chemical database identifiers. The effect of different stop-word lists, case-sensitivity matching, and use of chunking information was also investigated. We focused on lexical resources that provide chemical structure information. To rank the different compounds found in a text, we used a term confidence score based on the normalized ratio of the term frequencies in chemical and non-chemical journals. Results The use of stop-word lists greatly improved the performance of the dictionary-based recognition, but there was no additional benefit from using chunking information. A combination of ChEBI and HMDB as lexical resources, the LeadMine tool for grammar-based recognition, and the regular expressions, outperformed any of the individual systems. On the test set, the F-scores were 77.8% (recall 71.2%, precision 85.8%) for the CEM task and 77.6% (recall 71.7%, precision 84.6%) for the CDI task. Missed terms were mainly due to tokenization issues, poor recognition of formulas, and term conjunctions. Conclusions We developed an ensemble system that combines dictionary-based and grammar-based approaches for chemical named entity recognition, outperforming any of the individual systems that we considered. The system is able to provide structure information for most of the compounds that are found. Improved tokenization and better recognition of specific entity types is likely to further improve system performance. PMID:25810767

Improving the signal subtle feature extraction performance based on dual improved fractal box dimension eigenvectors

NASA Astrophysics Data System (ADS)

Chen, Xiang; Li, Jingchao; Han, Hui; Ying, Yulong

2018-05-01

Because of the limitations of the traditional fractal box-counting dimension algorithm in subtle feature extraction of radiation source signals, a dual improved generalized fractal box-counting dimension eigenvector algorithm is proposed. First, the radiation source signal was preprocessed, and a Hilbert transform was performed to obtain the instantaneous amplitude of the signal. Then, the improved fractal box-counting dimension of the signal instantaneous amplitude was extracted as the first eigenvector. At the same time, the improved fractal box-counting dimension of the signal without the Hilbert transform was extracted as the second eigenvector. Finally, the dual improved fractal box-counting dimension eigenvectors formed the multi-dimensional eigenvectors as signal subtle features, which were used for radiation source signal recognition by the grey relation algorithm. The experimental results show that, compared with the traditional fractal box-counting dimension algorithm and the single improved fractal box-counting dimension algorithm, the proposed dual improved fractal box-counting dimension algorithm can better extract the signal subtle distribution characteristics under different reconstruction phase space, and has a better recognition effect with good real-time performance.

Error Rates in Users of Automatic Face Recognition Software

PubMed Central

White, David; Dunn, James D.; Schmid, Alexandra C.; Kemp, Richard I.

2015-01-01

In recent years, wide deployment of automatic face recognition systems has been accompanied by substantial gains in algorithm performance. However, benchmarking tests designed to evaluate these systems do not account for the errors of human operators, who are often an integral part of face recognition solutions in forensic and security settings. This causes a mismatch between evaluation tests and operational accuracy. We address this by measuring user performance in a face recognition system used to screen passport applications for identity fraud. Experiment 1 measured target detection accuracy in algorithm-generated ‘candidate lists’ selected from a large database of passport images. Accuracy was notably poorer than in previous studies of unfamiliar face matching: participants made over 50% errors for adult target faces, and over 60% when matching images of children. Experiment 2 then compared performance of student participants to trained passport officers–who use the system in their daily work–and found equivalent performance in these groups. Encouragingly, a group of highly trained and experienced “facial examiners” outperformed these groups by 20 percentage points. We conclude that human performance curtails accuracy of face recognition systems–potentially reducing benchmark estimates by 50% in operational settings. Mere practise does not attenuate these limits, but superior performance of trained examiners suggests that recruitment and selection of human operators, in combination with effective training and mentorship, can improve the operational accuracy of face recognition systems. PMID:26465631

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain-computer interface

NASA Astrophysics Data System (ADS)

Chen, Yi-Feng; Atal, Kiran; Xie, Sheng-Quan; Liu, Quan

2017-08-01

Objective. Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain-computer interface (BCI) applications. Approach. Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results. We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance. The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI.

That's my teacher! Children's ability to recognize personally familiar and unfamiliar faces improves with age.

PubMed

Laurence, Sarah; Mondloch, Catherine J

2016-03-01

Most previous research on the development of face recognition has focused on recognition of highly controlled images. One of the biggest challenges of face recognition is to identify an individual across images that capture natural variability in appearance. We created a child-friendly version of Jenkins, White, Van Montford, and Burton's sorting task (Cognition, 2011, Vol. 121, pp. 313-323) to investigate children's recognition of personally familiar and unfamiliar faces. Children between 4 and 12years of age were presented with a familiar/unfamiliar teacher's house and a pile of face photographs (nine pictures each of the teacher and another identity). Each child was asked to put all the pictures of the teacher inside the house while keeping the other identity out. Children over 6years of age showed adult-like familiar face recognition. Unfamiliar face recognition improved across the entire age range, with considerable variability in children's performance. These findings suggest that children's ability to tolerate within-person variability improves with age and support a face-space framework in which faces are represented as regions, the size of which increases with age. Copyright © 2015 Elsevier Inc. All rights reserved.

Rate and onset cues can improve cochlear implant synthetic vowel recognition in noise

PubMed Central

Mc Laughlin, Myles; Reilly, Richard B.; Zeng, Fan-Gang

2013-01-01

Understanding speech-in-noise is difficult for most cochlear implant (CI) users. Speech-in-noise segregation cues are well understood for acoustic hearing but not for electric hearing. This study investigated the effects of stimulation rate and onset delay on synthetic vowel-in-noise recognition in CI subjects. In experiment I, synthetic vowels were presented at 50, 145, or 795 pulse/s and noise at the same three rates, yielding nine combinations. Recognition improved significantly if the noise had a lower rate than the vowel, suggesting that listeners can use temporal gaps in the noise to detect a synthetic vowel. This hypothesis is supported by accurate prediction of synthetic vowel recognition using a temporal integration window model. Using lower rates a similar trend was observed in normal hearing subjects. Experiment II found that for CI subjects, a vowel onset delay improved performance if the noise had a lower or higher rate than the synthetic vowel. These results show that differing rates or onset times can improve synthetic vowel-in-noise recognition, indicating a need to develop speech processing strategies that encode or emphasize these cues. PMID:23464025

Evaluating the accuracy of the Wechsler Memory Scale-Fourth Edition (WMS-IV) logical memory embedded validity index for detecting invalid test performance.

PubMed

Soble, Jason R; Bain, Kathleen M; Bailey, K Chase; Kirton, Joshua W; Marceaux, Janice C; Critchfield, Edan A; McCoy, Karin J M; O'Rourke, Justin J F

2018-01-08

Embedded performance validity tests (PVTs) allow for continuous assessment of invalid performance throughout neuropsychological test batteries. This study evaluated the utility of the Wechsler Memory Scale-Fourth Edition (WMS-IV) Logical Memory (LM) Recognition score as an embedded PVT using the Advanced Clinical Solutions (ACS) for WAIS-IV/WMS-IV Effort System. This mixed clinical sample was comprised of 97 total participants, 71 of whom were classified as valid and 26 as invalid based on three well-validated, freestanding criterion PVTs. Overall, the LM embedded PVT demonstrated poor concordance with the criterion PVTs and unacceptable psychometric properties using ACS validity base rates (42% sensitivity/79% specificity). Moreover, 15-39% of participants obtained an invalid ACS base rate despite having a normatively-intact age-corrected LM Recognition total score. Receiving operating characteristic curve analysis revealed a Recognition total score cutoff of < 61% correct improved specificity (92%) while sensitivity remained weak (31%). Thus, results indicated the LM Recognition embedded PVT is not appropriate for use from an evidence-based perspective, and that clinicians may be faced with reconciling how a normatively intact cognitive performance on the Recognition subtest could simultaneously reflect invalid performance validity.

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones

PubMed Central

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-01-01

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters. PMID:26690439

Sensor-Aware Recognition and Tracking for Wide-Area Augmented Reality on Mobile Phones.

PubMed

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-12-10

Wide-area registration in outdoor environments on mobile phones is a challenging task in mobile augmented reality fields. We present a sensor-aware large-scale outdoor augmented reality system for recognition and tracking on mobile phones. GPS and gravity information is used to improve the VLAD performance for recognition. A kind of sensor-aware VLAD algorithm, which is self-adaptive to different scale scenes, is utilized to recognize complex scenes. Considering vision-based registration algorithms are too fragile and tend to drift, data coming from inertial sensors and vision are fused together by an extended Kalman filter (EKF) to achieve considerable improvements in tracking stability and robustness. Experimental results show that our method greatly enhances the recognition rate and eliminates the tracking jitters.

Improvement in taste sensitivity following pulmonary rehabilitation in patients with chronic obstructive pulmonary disease.

PubMed

Ito, Kumiko; Kohzuki, Masahiro; Takahashi, Tamao; Ebihara, Satoru

2014-10-01

Weight loss is common in patients with chronic obstructive pulmonary disease (COPD). Anorexia, postulated to be associated with alteration in taste sensitivity, may contribute to weight loss in these patients. Pulmonary rehabilitation is known to lead to improved exercise performance in patients with COPD. However, the relationship between pulmonary rehabilitation and taste sensitivity has not been evaluated. The objective of this study was to compare taste sensitivity before and after pulmonary rehabilitation in patients with COPD. Single-group intervention trial. Twenty-two patients with COPD. The six-min walk distance (6MWD), COPD assessment test, body mass index, fat mass index, fat-free mass index and taste test were conducted before and after 4-week pulmonary rehabilitation. Taste sensitivity was evaluated using the filter-paper disc method for 4 taste stimuli. Taste stimuli were salty, sweet, sour, and bitter tastes. Taste sensitivity was evaluated before and after pulmonary rehabilitation using the taste recognition threshold. Following pulmonary rehabilitation, the 6MWD, COPD assessment test, salty recognition threshold, sweet recognition threshold and bitter recognition threshold improved significantly, whereas there were no significant improvements in body mass index, fat mass index, fat-free mass index or sour recognition threshold. Pulmonary rehabilitation may improve taste sensitivity in patients with COPD.

Measuring listening effort: driving simulator vs. simple dual-task paradigm

PubMed Central

Wu, Yu-Hsiang; Aksan, Nazan; Rizzo, Matthew; Stangl, Elizabeth; Zhang, Xuyang; Bentler, Ruth

2014-01-01

Objectives The dual-task paradigm has been widely used to measure listening effort. The primary objectives of the study were to (1) investigate the effect of hearing aid amplification and a hearing aid directional technology on listening effort measured by a complicated, more real world dual-task paradigm, and (2) compare the results obtained with this paradigm to a simpler laboratory-style dual-task paradigm. Design The listening effort of adults with hearing impairment was measured using two dual-task paradigms, wherein participants performed a speech recognition task simultaneously with either a driving task in a simulator or a visual reaction-time task in a sound-treated booth. The speech materials and road noises for the speech recognition task were recorded in a van traveling on the highway in three hearing aid conditions: unaided, aided with omni directional processing (OMNI), and aided with directional processing (DIR). The change in the driving task or the visual reaction-time task performance across the conditions quantified the change in listening effort. Results Compared to the driving-only condition, driving performance declined significantly with the addition of the speech recognition task. Although the speech recognition score was higher in the OMNI and DIR conditions than in the unaided condition, driving performance was similar across these three conditions, suggesting that listening effort was not affected by amplification and directional processing. Results from the simple dual-task paradigm showed a similar trend: hearing aid technologies improved speech recognition performance, but did not affect performance in the visual reaction-time task (i.e., reduce listening effort). The correlation between listening effort measured using the driving paradigm and the visual reaction-time task paradigm was significant. The finding showing that our older (56 to 85 years old) participants’ better speech recognition performance did not result in reduced listening effort was not consistent with literature that evaluated younger (approximately 20 years old), normal hearing adults. Because of this, a follow-up study was conducted. In the follow-up study, the visual reaction-time dual-task experiment using the same speech materials and road noises was repeated on younger adults with normal hearing. Contrary to findings with older participants, the results indicated that the directional technology significantly improved performance in both speech recognition and visual reaction-time tasks. Conclusions Adding a speech listening task to driving undermined driving performance. Hearing aid technologies significantly improved speech recognition while driving, but did not significantly reduce listening effort. Listening effort measured by dual-task experiments using a simulated real-world driving task and a conventional laboratory-style task was generally consistent. For a given listening environment, the benefit of hearing aid technologies on listening effort measured from younger adults with normal hearing may not be fully translated to older listeners with hearing impairment. PMID:25083599

Diagnosing criterion-level effects on memory: what aspects of memory are enhanced by repeated retrieval?

PubMed

Vaughn, Kalif E; Rawson, Katherine A

2011-09-01

Previous research has shown that increasing the criterion level (i.e., the number of times an item must be correctly retrieved during practice) improves subsequent memory, but which specific components of memory does increased criterion level enhance? In two experiments, we examined the extent to which the criterion level affects associative memory, target memory, and cue memory. Participants studied Lithuanian-English word pairs via cued recall with restudy until items were correctly recalled one to five times. In Experiment 1, participants took one of four recall tests and one of three recognition tests after a 2-day delay. In Experiment 2, participants took only recognition tests after a 1-week delay. In both experiments, increasing the criterion level enhanced associative memory, as indicated by enhanced performance on forward and backward cued-recall tests and on tests of associative recognition. An increased criterion level also improved target memory, as indicated by enhanced free recall and recognition of targets, and improved cue memory, as indicated by enhanced free recall and recognition of cues.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

PubMed Central

Hong, Yongwon; Choi, Yeongwoo; Byun, Hyeran

2017-01-01

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea). PMID:28264011

Scene recognition based on integrating active learning with dictionary learning

NASA Astrophysics Data System (ADS)

Wang, Chengxi; Yin, Xueyan; Yang, Lin; Gong, Chengrong; Zheng, Caixia; Yi, Yugen

2018-04-01

Scene recognition is a significant topic in the field of computer vision. Most of the existing scene recognition models require a large amount of labeled training samples to achieve a good performance. However, labeling image manually is a time consuming task and often unrealistic in practice. In order to gain satisfying recognition results when labeled samples are insufficient, this paper proposed a scene recognition algorithm named Integrating Active Learning and Dictionary Leaning (IALDL). IALDL adopts projective dictionary pair learning (DPL) as classifier and introduces active learning mechanism into DPL for improving its performance. When constructing sampling criterion in active learning, IALDL considers both the uncertainty and representativeness as the sampling criteria to effectively select the useful unlabeled samples from a given sample set for expanding the training dataset. Experiment results on three standard databases demonstrate the feasibility and validity of the proposed IALDL.

Kazakh Traditional Dance Gesture Recognition

NASA Astrophysics Data System (ADS)

Nussipbekov, A. K.; Amirgaliyev, E. N.; Hahn, Minsoo

2014-04-01

Full body gesture recognition is an important and interdisciplinary research field which is widely used in many application spheres including dance gesture recognition. The rapid growth of technology in recent years brought a lot of contribution in this domain. However it is still challenging task. In this paper we implement Kazakh traditional dance gesture recognition. We use Microsoft Kinect camera to obtain human skeleton and depth information. Then we apply tree-structured Bayesian network and Expectation Maximization algorithm with K-means clustering to calculate conditional linear Gaussians for classifying poses. And finally we use Hidden Markov Model to detect dance gestures. Our main contribution is that we extend Kinect skeleton by adding headwear as a new skeleton joint which is calculated from depth image. This novelty allows us to significantly improve the accuracy of head gesture recognition of a dancer which in turn plays considerable role in whole body gesture recognition. Experimental results show the efficiency of the proposed method and that its performance is comparable to the state-of-the-art system performances.

Learning to recognize letters in the periphery: Effects of repeated exposure, letter frequency, and letter complexity

PubMed Central

Husk, Jesse S.; Yu, Deyue

2017-01-01

Patients with central vision loss must rely on their peripheral vision for reading. Unfortunately, limitations of peripheral vision, such as crowding, pose significant challenges to letter recognition. As a result, there is a need for developing effective training methods for improving crowded letter recognition in the periphery. Several studies have shown that extensive practice with letter stimuli is beneficial to peripheral letter recognition. Here, we explore stimulus-related factors that might influence the effectiveness of peripheral letter recognition training. Specifically, we examined letter exposure (number of letter occurrences), frequency of letter use in English print, and letter complexity and evaluated their contributions to the amount of improvement observed in crowded letter recognition following training. We analyzed data collected across a range of training protocols. Using linear regression, we identified the best-fitting model and observed that all three stimulus-related factors contributed to improvement in peripheral letter recognition with letter exposure being the most important factor. As an important explanatory variable, pretest accuracy was included in the model as well to avoid estimate biases and was shown to have influence on the relationship between training improvement and letter exposure. When developing training protocols for peripheral letter recognition, it may be beneficial to not only consider the overall length of training, but also to tailor the number of stimulus occurrences for each letter according to its initial performance level, frequency, and complexity. PMID:28265651

Quality management of human resources. Providers should begin by focusing on education, performance management, and reward systems.

PubMed

Blair, C S; Fordyce, M; Barney, S M

1993-10-01

For a quality management transformation to occur, a healthcare organization must focus on education and development, performance management, and recognition and reward systems during the first years of implementation. Education and development are perhaps the most important human resource management functions when implementing quality management principles and processes because behavioral changes will be required at all organizational levels. Specific programs that support an organization's quality management effort will vary but should include the conceptual, cultural, and technical aspects of quality management. The essence of quality management is to always satisfy the customer and to continuously improve the services and products the organization offers. The approach to performance management should therefore rely on customer feedback and satisfaction. An organization committed to quality management should base its performance management approach on customer orientation, process improvement, employee involvement, decision making with data, and continuous improvement. Managers and trustees are being challenged to provide innovative recognition and reward systems that reinforce the values and behaviors consistent with quality management. Such systems must also be aligned with the behaviors and outcomes that support the philosophy, mission, and values of the Catholic healthcare ministry. The following components should be considered for a recognition and reward system: base pay, incentives, benefits, and nonmonetary rewards.

Implementation and preliminary evaluation of 'C-tone': A novel algorithm to improve lexical tone recognition in Mandarin-speaking cochlear implant users.

PubMed

Ping, Lichuan; Wang, Ningyuan; Tang, Guofang; Lu, Thomas; Yin, Li; Tu, Wenhe; Fu, Qian-Jie

2017-09-01

Because of limited spectral resolution, Mandarin-speaking cochlear implant (CI) users have difficulty perceiving fundamental frequency (F0) cues that are important to lexical tone recognition. To improve Mandarin tone recognition in CI users, we implemented and evaluated a novel real-time algorithm (C-tone) to enhance the amplitude contour, which is strongly correlated with the F0 contour. The C-tone algorithm was implemented in clinical processors and evaluated in eight users of the Nurotron NSP-60 CI system. Subjects were given 2 weeks of experience with C-tone. Recognition of Chinese tones, monosyllables, and disyllables in quiet was measured with and without the C-tone algorithm. Subjective quality ratings were also obtained for C-tone. After 2 weeks of experience with C-tone, there were small but significant improvements in recognition of lexical tones, monosyllables, and disyllables (P < 0.05 in all cases). Among lexical tones, the largest improvements were observed for Tone 3 (falling-rising) and the smallest for Tone 4 (falling). Improvements with C-tone were greater for disyllables than for monosyllables. Subjective quality ratings showed no strong preference for or against C-tone, except for perception of own voice, where C-tone was preferred. The real-time C-tone algorithm provided small but significant improvements for speech performance in quiet with no change in sound quality. Pre-processing algorithms to reduce noise and better real-time F0 extraction would improve the benefits of C-tone in complex listening environments. Chinese CI users' speech recognition in quiet can be significantly improved by modifying the amplitude contour to better resemble the F0 contour.

Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing.

PubMed

Vatsa, Mayank; Singh, Richa; Noore, Afzel

2008-08-01

This paper proposes algorithms for iris segmentation, quality enhancement, match score fusion, and indexing to improve both the accuracy and the speed of iris recognition. A curve evolution approach is proposed to effectively segment a nonideal iris image using the modified Mumford-Shah functional. Different enhancement algorithms are concurrently applied on the segmented iris image to produce multiple enhanced versions of the iris image. A support-vector-machine-based learning algorithm selects locally enhanced regions from each globally enhanced image and combines these good-quality regions to create a single high-quality iris image. Two distinct features are extracted from the high-quality iris image. The global textural feature is extracted using the 1-D log polar Gabor transform, and the local topological feature is extracted using Euler numbers. An intelligent fusion algorithm combines the textural and topological matching scores to further improve the iris recognition performance and reduce the false rejection rate, whereas an indexing algorithm enables fast and accurate iris identification. The verification and identification performance of the proposed algorithms is validated and compared with other algorithms using the CASIA Version 3, ICE 2005, and UBIRIS iris databases.

Combining approaches to on-line handwriting information retrieval

NASA Astrophysics Data System (ADS)

Peña Saldarriaga, Sebastián; Viard-Gaudin, Christian; Morin, Emmanuel

2010-01-01

In this work, we propose to combine two quite different approaches for retrieving handwritten documents. Our hypothesis is that different retrieval algorithms should retrieve different sets of documents for the same query. Therefore, significant improvements in retrieval performances can be expected. The first approach is based on information retrieval techniques carried out on the noisy texts obtained through handwriting recognition, while the second approach is recognition-free using a word spotting algorithm. Results shows that for texts having a word error rate (WER) lower than 23%, the performances obtained with the combined system are close to the performances obtained on clean digital texts. In addition, for poorly recognized texts (WER > 52%), an improvement of nearly 17% can be observed with respect to the best available baseline method.

Designing Clinical Examples To Promote Pattern Recognition: Nursing Education-Based Research and Practical Applications.

ERIC Educational Resources Information Center

Welk, Dorette Sugg

2002-01-01

Sophomore nursing students (n=162) examined scenarios depicting typical and atypical signs of heart attack. Examples were structured to include essential and nonessential symptoms, enabling pattern recognition and improved performance. The method provides a way to prepare students to anticipate and recognize life-threatening situations. (Contains…

Segment-based acoustic models for continuous speech recognition

NASA Astrophysics Data System (ADS)

Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

This research aims to develop new and more accurate stochastic models for speaker-independent continuous speech recognition, by extending previous work in segment-based modeling and by introducing a new hierarchical approach to representing intra-utterance statistical dependencies. These techniques, which are more costly than traditional approaches because of the large search space associated with higher order models, are made feasible through rescoring a set of HMM-generated N-best sentence hypotheses. We expect these different modeling techniques to result in improved recognition performance over that achieved by current systems, which handle only frame-based observations and assume that these observations are independent given an underlying state sequence. In the fourth quarter of the project, we have completed the following: (1) ported our recognition system to the Wall Street Journal task, a standard task in the ARPA community; (2) developed an initial dependency-tree model of intra-utterance observation correlation; and (3) implemented baseline language model estimation software. Our initial results on the Wall Street Journal task are quite good and represent significantly improved performance over most HMM systems reporting on the Nov. 1992 5k vocabulary test set.

Transcranial direct current stimulation of dorsolateral prefrontal cortex during encoding improves recall but not recognition memory

DOE PAGES

Leshikar, Eric D.; Leach, Ryan C.; McCurdy, Matthew P.; ...

2017-10-19

Prior work demonstrates that application of transcranial direct current stimulation (tDCS) improves memory. In this study, we investigated tDCS effects on face-name associative memory using both recall and recognition tests. Participants encoded face-name pairs under either active (1.5 mA) or sham (.1 mA) stimulation applied to the scalp adjacent to the left dorsolateral prefrontal cortex (dlPFC), an area known to support associative memory. Participants’ memory was then tested after study (day one) and then again after a 24-h delay (day two), to assess both immediate and delayed stimulation effects on memory. Results indicated that active relative to sham stimulation ledmore » to substantially improved recall (more than 50%) at both day one and day two. Recognition memory performance did not differ between stimulation groups at either time point. These results suggest that stimulation at encoding improves memory performance by enhancing memory for details that enable a rich recollective experience, but that these improvements are evident only under some testing conditions, especially those that rely on recollection. Altogether, stimulation of the dlPFC could have led to recall improvement through enhanced encoding from stimulation or from carryover effects of stimulation that influenced retrieval processes, or both.« less

Transcranial direct current stimulation of dorsolateral prefrontal cortex during encoding improves recall but not recognition memory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leshikar, Eric D.; Leach, Ryan C.; McCurdy, Matthew P.

Prior work demonstrates that application of transcranial direct current stimulation (tDCS) improves memory. In this study, we investigated tDCS effects on face-name associative memory using both recall and recognition tests. Participants encoded face-name pairs under either active (1.5 mA) or sham (.1 mA) stimulation applied to the scalp adjacent to the left dorsolateral prefrontal cortex (dlPFC), an area known to support associative memory. Participants’ memory was then tested after study (day one) and then again after a 24-h delay (day two), to assess both immediate and delayed stimulation effects on memory. Results indicated that active relative to sham stimulation ledmore » to substantially improved recall (more than 50%) at both day one and day two. Recognition memory performance did not differ between stimulation groups at either time point. These results suggest that stimulation at encoding improves memory performance by enhancing memory for details that enable a rich recollective experience, but that these improvements are evident only under some testing conditions, especially those that rely on recollection. Altogether, stimulation of the dlPFC could have led to recall improvement through enhanced encoding from stimulation or from carryover effects of stimulation that influenced retrieval processes, or both.« less

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

Hassan, Mohammad Mehedi; Huda, Shamsul; Uddin, Md Zia; Almogren, Ahmad; Alrubaian, Majed

2018-04-16

In recent years, human activity recognition from body sensor data or wearable sensor data has become a considerable research attention from academia and health industry. This research can be useful for various e-health applications such as monitoring elderly and physical impaired people at Smart home to improve their rehabilitation processes. However, it is not easy to accurately and automatically recognize physical human activity through wearable sensors due to the complexity and variety of body activities. In this paper, we address the human activity recognition problem as a classification problem using wearable body sensor data. In particular, we propose to utilize a Deep Belief Network (DBN) model for successful human activity recognition. First, we extract the important initial features from the raw body sensor data. Then, a kernel principal component analysis (KPCA) and linear discriminant analysis (LDA) are performed to further process the features and make them more robust to be useful for fast activity recognition. Finally, the DBN is trained by these features. Various experiments were performed on a real-world wearable sensor dataset to verify the effectiveness of the deep learning algorithm. The results show that the proposed DBN outperformed other algorithms and achieves satisfactory activity recognition performance.

Target recognitions in multiple-camera closed-circuit television using color constancy

NASA Astrophysics Data System (ADS)

Soori, Umair; Yuen, Peter; Han, Ji Wen; Ibrahim, Izzati; Chen, Wentao; Hong, Kan; Merfort, Christian; James, David; Richardson, Mark

2013-04-01

People tracking in crowded scenes from closed-circuit television (CCTV) footage has been a popular and challenging task in computer vision. Due to the limited spatial resolution in the CCTV footage, the color of people's dress may offer an alternative feature for their recognition and tracking. However, there are many factors, such as variable illumination conditions, viewing angles, and camera calibration, that may induce illusive modification of intrinsic color signatures of the target. Our objective is to recognize and track targets in multiple camera views using color as the detection feature, and to understand if a color constancy (CC) approach may help to reduce these color illusions due to illumination and camera artifacts and thereby improve target recognition performance. We have tested a number of CC algorithms using various color descriptors to assess the efficiency of target recognition from a real multicamera Imagery Library for Intelligent Detection Systems (i-LIDS) data set. Various classifiers have been used for target detection, and the figure of merit to assess the efficiency of target recognition is achieved through the area under the receiver operating characteristics (AUROC). We have proposed two modifications of luminance-based CC algorithms: one with a color transfer mechanism and the other using a pixel-wise sigmoid function for an adaptive dynamic range compression, a method termed enhanced luminance reflectance CC (ELRCC). We found that both algorithms improve the efficiency of target recognitions substantially better than that of the raw data without CC treatment, and in some cases the ELRCC improves target tracking by over 100% within the AUROC assessment metric. The performance of the ELRCC has been assessed over 10 selected targets from three different camera views of the i-LIDS footage, and the averaged target recognition efficiency over all these targets is found to be improved by about 54% in AUROC after the data are processed by the proposed ELRCC algorithm. This amount of improvement represents a reduction of probability of false alarm by about a factor of 5 at the probability of detection of 0.5. Our study concerns mainly the detection of colored targets; and issues for the recognition of white or gray targets will be addressed in a forthcoming study.

Internal Control, CPA Recognition and Performance Consequence: Evidence from Chinese Real Estate Enterprises

NASA Astrophysics Data System (ADS)

Zhang, Chuan; Zhang, Lili; Geng, Yi

In recent years, internal control has caught more and more attention over the whole globe. However, whether internal control could improve business efficiency also lacks the empirical supports. Based on a sample size of 146 Chinese real estate enterprises, this study analyses the CPA’s recognition degree on firm’s implementing internal control, and its performance consequence. The evidence suggests that CPAs are able to give exact evaluation on firm’s internal control implement, and the higher the internal control implemented, the better performance the enterprise will have.

New technique for real-time distortion-invariant multiobject recognition and classification

NASA Astrophysics Data System (ADS)

Hong, Rutong; Li, Xiaoshun; Hong, En; Wang, Zuyi; Wei, Hongan

2001-04-01

A real-time hybrid distortion-invariant OPR system was established to make 3D multiobject distortion-invariant automatic pattern recognition. Wavelet transform technique was used to make digital preprocessing of the input scene, to depress the noisy background and enhance the recognized object. A three-layer backpropagation artificial neural network was used in correlation signal post-processing to perform multiobject distortion-invariant recognition and classification. The C-80 and NOA real-time processing ability and the multithread programming technology were used to perform high speed parallel multitask processing and speed up the post processing rate to ROIs. The reference filter library was constructed for the distortion version of 3D object model images based on the distortion parameter tolerance measuring as rotation, azimuth and scale. The real-time optical correlation recognition testing of this OPR system demonstrates that using the preprocessing, post- processing, the nonlinear algorithm os optimum filtering, RFL construction technique and the multithread programming technology, a high possibility of recognition and recognition rate ere obtained for the real-time multiobject distortion-invariant OPR system. The recognition reliability and rate was improved greatly. These techniques are very useful to automatic target recognition.

Interface Prostheses With Classifier-Feedback-Based User Training.

PubMed

Fang, Yinfeng; Zhou, Dalin; Li, Kairu; Liu, Honghai

2017-11-01

It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.It is evident that user training significantly affects performance of pattern-recognition-based myoelectric prosthetic device control. Despite plausible classification accuracy on offline datasets, online accuracy usually suffers from the changes in physiological conditions and electrode displacement. The user ability in generating consistent electromyographic (EMG) patterns can be enhanced via proper user training strategies in order to improve online performance. This study proposes a clustering-feedback strategy that provides real-time feedback to users by means of a visualized online EMG signal input as well as the centroids of the training samples, whose dimensionality is reduced to minimal number by dimension reduction. Clustering feedback provides a criterion that guides users to adjust motion gestures and muscle contraction forces intentionally. The experiment results have demonstrated that hand motion recognition accuracy increases steadily along the progress of the clustering-feedback-based user training, while conventional classifier-feedback methods, i.e., label feedback, hardly achieve any improvement. The result concludes that the use of proper classifier feedback can accelerate the process of user training, and implies prosperous future for the amputees with limited or no experience in pattern-recognition-based prosthetic device manipulation.

New baseline correction algorithm for text-line recognition with bidirectional recurrent neural networks

NASA Astrophysics Data System (ADS)

Morillot, Olivier; Likforman-Sulem, Laurence; Grosicki, Emmanuèle

2013-04-01

Many preprocessing techniques have been proposed for isolated word recognition. However, recently, recognition systems have dealt with text blocks and their compound text lines. In this paper, we propose a new preprocessing approach to efficiently correct baseline skew and fluctuations. Our approach is based on a sliding window within which the vertical position of the baseline is estimated. Segmentation of text lines into subparts is, thus, avoided. Experiments conducted on a large publicly available database (Rimes), with a BLSTM (bidirectional long short-term memory) recurrent neural network recognition system, show that our baseline correction approach highly improves performance.

Skeleton-Based Human Action Recognition With Global Context-Aware Attention LSTM Networks

NASA Astrophysics Data System (ADS)

Liu, Jun; Wang, Gang; Duan, Ling-Yu; Abdiyeva, Kamila; Kot, Alex C.

2018-04-01

Human action recognition in 3D skeleton sequences has attracted a lot of research attention. Recently, Long Short-Term Memory (LSTM) networks have shown promising performance in this task due to their strengths in modeling the dependencies and dynamics in sequential data. As not all skeletal joints are informative for action recognition, and the irrelevant joints often bring noise which can degrade the performance, we need to pay more attention to the informative ones. However, the original LSTM network does not have explicit attention ability. In this paper, we propose a new class of LSTM network, Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action recognition. This network is capable of selectively focusing on the informative joints in each frame of each skeleton sequence by using a global context memory cell. To further improve the attention capability of our network, we also introduce a recurrent attention mechanism, with which the attention performance of the network can be enhanced progressively. Moreover, we propose a stepwise training scheme in order to train our network effectively. Our approach achieves state-of-the-art performance on five challenging benchmark datasets for skeleton based action recognition.

Deep learning and non-negative matrix factorization in recognition of mammograms

NASA Astrophysics Data System (ADS)

Swiderski, Bartosz; Kurek, Jaroslaw; Osowski, Stanislaw; Kruk, Michal; Barhoumi, Walid

2017-02-01

This paper presents novel approach to the recognition of mammograms. The analyzed mammograms represent the normal and breast cancer (benign and malignant) cases. The solution applies the deep learning technique in image recognition. To obtain increased accuracy of classification the nonnegative matrix factorization and statistical self-similarity of images are applied. The images reconstructed by using these two approaches enrich the data base and thanks to this improve of quality measures of mammogram recognition (increase of accuracy, sensitivity and specificity). The results of numerical experiments performed on large DDSM data base containing more than 10000 mammograms have confirmed good accuracy of class recognition, exceeding the best results reported in the actual publications for this data base.

Face recognition based on matching of local features on 3D dynamic range sequences

NASA Astrophysics Data System (ADS)

Echeagaray-Patrón, B. A.; Kober, Vitaly

2016-09-01

3D face recognition has attracted attention in the last decade due to improvement of technology of 3D image acquisition and its wide range of applications such as access control, surveillance, human-computer interaction and biometric identification systems. Most research on 3D face recognition has focused on analysis of 3D still data. In this work, a new method for face recognition using dynamic 3D range sequences is proposed. Experimental results are presented and discussed using 3D sequences in the presence of pose variation. The performance of the proposed method is compared with that of conventional face recognition algorithms based on descriptors.

The effect of mild acute stress during memory consolidation on emotional recognition memory.

PubMed

Corbett, Brittany; Weinberg, Lisa; Duarte, Audrey

2017-11-01

Stress during consolidation improves recognition memory performance. Generally, this memory benefit is greater for emotionally arousing stimuli than neutral stimuli. The strength of the stressor also plays a role in memory performance, with memory performance improving up to a moderate level of stress and thereafter worsening. As our daily stressors are generally minimal in strength, we chose to induce mild acute stress to determine its effect on memory performance. In the current study, we investigated if mild acute stress during consolidation improves memory performance for emotionally arousing images. To investigate this, we had participants encode highly arousing negative, minimally arousing negative, and neutral images. We induced stress using the Montreal Imaging Stress Task (MIST) in half of the participants and a control task to the other half of the participants directly after encoding (i.e. during consolidation) and tested recognition 48h later. We found no difference in memory performance between the stress and control group. We found a graded pattern among confidence, with responders in the stress group having the least amount of confidence in their hits and controls having the most. Across groups, we found highly arousing negative images were better remembered than minimally arousing negative or neutral images. Although stress did not affect memory accuracy, responders, as defined by cortisol reactivity, were less confident in their decisions. Our results suggest that the daily stressors humans experience, regardless of their emotional affect, do not have adverse effects on memory. Copyright © 2017 Elsevier Inc. All rights reserved.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Barstow, Del R; Patlolla, Dilip Reddy; Mann, Christopher J

Abstract The data captured by existing standoff biometric systems typically has lower biometric recognition performance than their close range counterparts due to imaging challenges, pose challenges, and other factors. To assist in overcoming these limitations systems typically perform in a multi-modal capacity such as Honeywell s Combined Face and Iris (CFAIRS) [21] system. While this improves the systems performance, standoff systems have yet to be proven as accurate as their close range equivalents. We will present a standoff system capable of operating up to 7 meters in range. Unlike many systems such as the CFAIRS our system captures high qualitymore » 12 MP video allowing for a multi-sample as well as multi-modal comparison. We found that for standoff systems multi-sample improved performance more than multi-modal. For a small test group of 50 subjects we were able to achieve 100% rank one recognition performance with our system.« less

Efficient local representations for three-dimensional palmprint recognition

NASA Astrophysics Data System (ADS)

Yang, Bing; Wang, Xiaohua; Yao, Jinliang; Yang, Xin; Zhu, Wenhua

2013-10-01

Palmprints have been broadly used for personal authentication because they are highly accurate and incur low cost. Most previous works have focused on two-dimensional (2-D) palmprint recognition in the past decade. Unfortunately, 2-D palmprint recognition systems lose the shape information when capturing palmprint images. Moreover, such 2-D palmprint images can be easily forged or affected by noise. Hence, three-dimensional (3-D) palmprint recognition has been regarded as a promising way to further improve the performance of palmprint recognition systems. We have developed a simple, but efficient method for 3-D palmprint recognition by using local features. We first utilize shape index representation to describe the geometry of local regions in 3-D palmprint data. Then, we extract local binary pattern and Gabor wavelet features from the shape index image. The two types of complementary features are finally fused at a score level for further improvements. The experimental results on the Hong Kong Polytechnic 3-D palmprint database, which contains 8000 samples from 400 palms, illustrate the effectiveness of the proposed method.

Emotional recognition from dynamic facial, vocal and musical expressions following traumatic brain injury.

PubMed

Drapeau, Joanie; Gosselin, Nathalie; Peretz, Isabelle; McKerral, Michelle

2017-01-01

To assess emotion recognition from dynamic facial, vocal and musical expressions in sub-groups of adults with traumatic brain injuries (TBI) of different severities and identify possible common underlying mechanisms across domains. Forty-one adults participated in this study: 10 with moderate-severe TBI, nine with complicated mild TBI, 11 with uncomplicated mild TBI and 11 healthy controls, who were administered experimental (emotional recognition, valence-arousal) and control tasks (emotional and structural discrimination) for each domain. Recognition of fearful faces was significantly impaired in moderate-severe and in complicated mild TBI sub-groups, as compared to those with uncomplicated mild TBI and controls. Effect sizes were medium-large. Participants with lower GCS scores performed more poorly when recognizing fearful dynamic facial expressions. Emotion recognition from auditory domains was preserved following TBI, irrespective of severity. All groups performed equally on control tasks, indicating no perceptual disorders. Although emotional recognition from vocal and musical expressions was preserved, no correlation was found across auditory domains. This preliminary study may contribute to improving comprehension of emotional recognition following TBI. Future studies of larger samples could usefully include measures of functional impacts of recognition deficits for fearful facial expressions. These could help refine interventions for emotional recognition following a brain injury.

Iterative cross section sequence graph for handwritten character segmentation.

PubMed

Dawoud, Amer

2007-08-01

The iterative cross section sequence graph (ICSSG) is an algorithm for handwritten character segmentation. It expands the cross section sequence graph concept by applying it iteratively at equally spaced thresholds. The iterative thresholding reduces the effect of information loss associated with image binarization. ICSSG preserves the characters' skeletal structure by preventing the interference of pixels that causes flooding of adjacent characters' segments. Improving the structural quality of the characters' skeleton facilitates better feature extraction and classification, which improves the overall performance of optical character recognition (OCR). Experimental results showed significant improvements in OCR recognition rates compared to other well-established segmentation algorithms.

Opportunities for Recognition Can Improve Learning and Performance

ERIC Educational Resources Information Center

French, Ron; Henderson, Hester L.; Lavay, Barry; Silliman-French, Lisa

2013-01-01

Physical educators need to make an effort to catch students being good and recognize them for their positive accomplishments. Unfortunately, it is usually the students who act inappropriately who receive the majority of the teachers' attention. In order to help increase learning and improve performance and behavior, the physical educator must…

Effects of exposure to facial expression variation in face learning and recognition.

PubMed

Liu, Chang Hong; Chen, Wenfeng; Ward, James

2015-11-01

Facial expression is a major source of image variation in face images. Linking numerous expressions to the same face can be a huge challenge for face learning and recognition. It remains largely unknown what level of exposure to this image variation is critical for expression-invariant face recognition. We examined this issue in a recognition memory task, where the number of facial expressions of each face being exposed during a training session was manipulated. Faces were either trained with multiple expressions or a single expression, and they were later tested in either the same or different expressions. We found that recognition performance after learning three emotional expressions had no improvement over learning a single emotional expression (Experiments 1 and 2). However, learning three emotional expressions improved recognition compared to learning a single neutral expression (Experiment 3). These findings reveal both the limitation and the benefit of multiple exposures to variations of emotional expression in achieving expression-invariant face recognition. The transfer of expression training to a new type of expression is likely to depend on a relatively extensive level of training and a certain degree of variation across the types of expressions.

Cotinine improves visual recognition memory and decreases cortical Tau phosphorylation in the Tg6799 mice.

PubMed

Grizzell, J Alex; Patel, Sagar; Barreto, George E; Echeverria, Valentina

2017-08-01

Alzheimer's disease (AD) is associated with the progressive aggregation of hyperphosphorylated forms of the microtubule associated protein Tau in the central nervous system. Cotinine, the main metabolite of nicotine, reduced working memory deficits, synaptic loss, and amyloid β peptide aggregation into oligomers and plaques as well as inhibited the cerebral Tau kinase, glycogen synthase 3β (GSK3β) in the transgenic (Tg)6799 (5XFAD) mice. In this study, the effect of cotinine on visual recognition memory and cortical Tau phosphorylation at the GSK3β sites Serine (Ser)-396/Ser-404 and phospho-CREB were investigated in the Tg6799 and non-transgenic (NT) littermate mice. Tg mice showed short-term visual recognition memory impairment in the novel object recognition test, and higher levels of Tau phosphorylation when compared to NT mice. Cotinine significantly improved visual recognition memory performance increased CREB phosphorylation and reduced cortical Tau phosphorylation. Potential mechanisms underlying theses beneficial effects are discussed. Copyright © 2017. Published by Elsevier Inc.

Face Recognition in 4- to 7-Year-Olds: Processing of Configural, Featural, and Paraphernalia Information.

ERIC Educational Resources Information Center

Freire, Alejo; Lee, Kang

2001-01-01

Tested in two studies 4- to 7-year-olds' face recognition by manipulating the faces' configural and featural information. Found that even with only a single 5-second exposure, most children could use configural and featural cues to make identity judgments. Repeated exposure and feedback improved others' performance. Even proficient memories were…

Recognition of Time-Compressed and Natural Speech with Selective Temporal Enhancements by Young and Elderly Listeners

ERIC Educational Resources Information Center

Gordon-Salant, Sandra; Fitzgibbons, Peter J.; Friedman, Sarah A.

2007-01-01

Purpose: The goal of this experiment was to determine whether selective slowing of speech segments improves recognition performance by young and elderly listeners. The hypotheses were (a) the benefits of time expansion occur for rapid speech but not for natural-rate speech, (b) selective time expansion of consonants produces greater score…

Increase in Speech Recognition Due to Linguistic Mismatch between Target and Masker Speech: Monolingual and Simultaneous Bilingual Performance

ERIC Educational Resources Information Center

Calandruccio, Lauren; Zhou, Haibo

2014-01-01

Purpose: To examine whether improved speech recognition during linguistically mismatched target-masker experiments is due to linguistic unfamiliarity of the masker speech or linguistic dissimilarity between the target and masker speech. Method: Monolingual English speakers (n = 20) and English-Greek simultaneous bilinguals (n = 20) listened to…

A Potential Spatial Working Memory Training Task to Improve Both Episodic Memory and Fluid Intelligence

PubMed Central

Rudebeck, Sarah R.; Bor, Daniel; Ormond, Angharad; O’Reilly, Jill X.; Lee, Andy C. H.

2012-01-01

One current challenge in cognitive training is to create a training regime that benefits multiple cognitive domains, including episodic memory, without relying on a large battery of tasks, which can be time-consuming and difficult to learn. By giving careful consideration to the neural correlates underlying episodic and working memory, we devised a computerized working memory training task in which neurologically healthy participants were required to monitor and detect repetitions in two streams of spatial information (spatial location and scene identity) presented simultaneously (i.e. a dual n-back paradigm). Participants’ episodic memory abilities were assessed before and after training using two object and scene recognition memory tasks incorporating memory confidence judgments. Furthermore, to determine the generalizability of the effects of training, we also assessed fluid intelligence using a matrix reasoning task. By examining the difference between pre- and post-training performance (i.e. gain scores), we found that the trainers, compared to non-trainers, exhibited a significant improvement in fluid intelligence after 20 days. Interestingly, pre-training fluid intelligence performance, but not training task improvement, was a significant predictor of post-training fluid intelligence improvement, with lower pre-training fluid intelligence associated with greater post-training gain. Crucially, trainers who improved the most on the training task also showed an improvement in recognition memory as captured by d-prime scores and estimates of recollection and familiarity memory. Training task improvement was a significant predictor of gains in recognition and familiarity memory performance, with greater training improvement leading to more marked gains. In contrast, lower pre-training recollection memory scores, and not training task improvement, led to greater recollection memory performance after training. Our findings demonstrate that practice on a single working memory task can potentially improve aspects of both episodic memory and fluid intelligence, and that an extensive training regime with multiple tasks may not be necessary. PMID:23209740

A potential spatial working memory training task to improve both episodic memory and fluid intelligence.

PubMed

Rudebeck, Sarah R; Bor, Daniel; Ormond, Angharad; O'Reilly, Jill X; Lee, Andy C H

2012-01-01

One current challenge in cognitive training is to create a training regime that benefits multiple cognitive domains, including episodic memory, without relying on a large battery of tasks, which can be time-consuming and difficult to learn. By giving careful consideration to the neural correlates underlying episodic and working memory, we devised a computerized working memory training task in which neurologically healthy participants were required to monitor and detect repetitions in two streams of spatial information (spatial location and scene identity) presented simultaneously (i.e. a dual n-back paradigm). Participants' episodic memory abilities were assessed before and after training using two object and scene recognition memory tasks incorporating memory confidence judgments. Furthermore, to determine the generalizability of the effects of training, we also assessed fluid intelligence using a matrix reasoning task. By examining the difference between pre- and post-training performance (i.e. gain scores), we found that the trainers, compared to non-trainers, exhibited a significant improvement in fluid intelligence after 20 days. Interestingly, pre-training fluid intelligence performance, but not training task improvement, was a significant predictor of post-training fluid intelligence improvement, with lower pre-training fluid intelligence associated with greater post-training gain. Crucially, trainers who improved the most on the training task also showed an improvement in recognition memory as captured by d-prime scores and estimates of recollection and familiarity memory. Training task improvement was a significant predictor of gains in recognition and familiarity memory performance, with greater training improvement leading to more marked gains. In contrast, lower pre-training recollection memory scores, and not training task improvement, led to greater recollection memory performance after training. Our findings demonstrate that practice on a single working memory task can potentially improve aspects of both episodic memory and fluid intelligence, and that an extensive training regime with multiple tasks may not be necessary.

Stress reaction process-based hierarchical recognition algorithm for continuous intrusion events in optical fiber prewarning system

NASA Astrophysics Data System (ADS)

Qu, Hongquan; Yuan, Shijiao; Wang, Yanping; Yang, Dan

2018-04-01

To improve the recognition performance of optical fiber prewarning system (OFPS), this study proposed a hierarchical recognition algorithm (HRA). Compared with traditional methods, which employ only a complex algorithm that includes multiple extracted features and complex classifiers to increase the recognition rate with a considerable decrease in recognition speed, HRA takes advantage of the continuity of intrusion events, thereby creating a staged recognition flow inspired by stress reaction. HRA is expected to achieve high-level recognition accuracy with less time consumption. First, this work analyzed the continuity of intrusion events and then presented the algorithm based on the mechanism of stress reaction. Finally, it verified the time consumption through theoretical analysis and experiments, and the recognition accuracy was obtained through experiments. Experiment results show that the processing speed of HRA is 3.3 times faster than that of a traditional complicated algorithm and has a similar recognition rate of 98%. The study is of great significance to fast intrusion event recognition in OFPS.

Voice gender and the segregation of competing talkers: Perceptual learning in cochlear implant simulations

PubMed Central

Sullivan, Jessica R.; Assmann, Peter F.; Hossain, Shaikat; Schafer, Erin C.

2017-01-01

Two experiments explored the role of differences in voice gender in the recognition of speech masked by a competing talker in cochlear implant simulations. Experiment 1 confirmed that listeners with normal hearing receive little benefit from differences in voice gender between a target and masker sentence in four- and eight-channel simulations, consistent with previous findings that cochlear implants deliver an impoverished representation of the cues for voice gender. However, gender differences led to small but significant improvements in word recognition with 16 and 32 channels. Experiment 2 assessed the benefits of perceptual training on the use of voice gender cues in an eight-channel simulation. Listeners were assigned to one of four groups: (1) word recognition training with target and masker differing in gender; (2) word recognition training with same-gender target and masker; (3) gender recognition training; or (4) control with no training. Significant improvements in word recognition were observed from pre- to post-test sessions for all three training groups compared to the control group. These improvements were maintained at the late session (one week following the last training session) for all three groups. There was an overall improvement in masked word recognition performance provided by gender mismatch following training, but the amount of benefit did not differ as a function of the type of training. The training effects observed here are consistent with a form of rapid perceptual learning that contributes to the segregation of competing voices but does not specifically enhance the benefits provided by voice gender cues. PMID:28372046

A robust probabilistic collaborative representation based classification for multimodal biometrics

NASA Astrophysics Data System (ADS)

Zhang, Jing; Liu, Huanxi; Ding, Derui; Xiao, Jianli

2018-04-01

Most of the traditional biometric recognition systems perform recognition with a single biometric indicator. These systems have suffered noisy data, interclass variations, unacceptable error rates, forged identity, and so on. Due to these inherent problems, it is not valid that many researchers attempt to enhance the performance of unimodal biometric systems with single features. Thus, multimodal biometrics is investigated to reduce some of these defects. This paper proposes a new multimodal biometric recognition approach by fused faces and fingerprints. For more recognizable features, the proposed method extracts block local binary pattern features for all modalities, and then combines them into a single framework. For better classification, it employs the robust probabilistic collaborative representation based classifier to recognize individuals. Experimental results indicate that the proposed method has improved the recognition accuracy compared to the unimodal biometrics.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Improving activity recognition using a wearable barometric pressure sensor in mobility-impaired stroke patients.

PubMed

Massé, Fabien; Gonzenbach, Roman R; Arami, Arash; Paraschiv-Ionescu, Anisoara; Luft, Andreas R; Aminian, Kamiar

2015-08-25

Stroke survivors often suffer from mobility deficits. Current clinical evaluation methods, including questionnaires and motor function tests, cannot provide an objective measure of the patients' mobility in daily life. Physical activity performance in daily-life can be assessed using unobtrusive monitoring, for example with a single sensor module fixed on the trunk. Existing approaches based on inertial sensors have limited performance, particularly in detecting transitions between different activities and postures, due to the inherent inter-patient variability of kinematic patterns. To overcome these limitations, one possibility is to use additional information from a barometric pressure (BP) sensor. Our study aims at integrating BP and inertial sensor data into an activity classifier in order to improve the activity (sitting, standing, walking, lying) recognition and the corresponding body elevation (during climbing stairs or when taking an elevator). Taking into account the trunk elevation changes during postural transitions (sit-to-stand, stand-to-sit), we devised an event-driven activity classifier based on fuzzy-logic. Data were acquired from 12 stroke patients with impaired mobility, using a trunk-worn inertial and BP sensor. Events, including walking and lying periods and potential postural transitions, were first extracted. These events were then fed into a double-stage hierarchical Fuzzy Inference System (H-FIS). The first stage processed the events to infer activities and the second stage improved activity recognition by applying behavioral constraints. Finally, the body elevation was estimated using a pattern-enhancing algorithm applied on BP. The patients were videotaped for reference. The performance of the algorithm was estimated using the Correct Classification Rate (CCR) and F-score. The BP-based classification approach was benchmarked against a previously-published fuzzy-logic classifier (FIS-IMU) and a conventional epoch-based classifier (EPOCH). The algorithm performance for posture/activity detection, in terms of CCR was 90.4 %, with 3.3 % and 5.6 % improvements against FIS-IMU and EPOCH, respectively. The proposed classifier essentially benefits from a better recognition of standing activity (70.3 % versus 61.5 % [FIS-IMU] and 42.5 % [EPOCH]) with 98.2 % CCR for body elevation estimation. The monitoring and recognition of daily activities in mobility-impaired stoke patients can be significantly improved using a trunk-fixed sensor that integrates BP, inertial sensors, and an event-based activity classifier.

Results with cochlear implantation in adults with speech recognition scores exceeding current criteria.

PubMed

Amoodi, Hosam A; Mick, Paul T; Shipp, David B; Friesen, Lendra M; Nedzelski, Julian M; Chen, Joseph M; Lin, Vincent Y W

2012-01-01

The primary purpose of this study was to evaluate a group of postlingually deafened adults, whose aided speech recognition exceeded commonly accepted candidacy criteria for implantation. The study aimed to define performance and qualitative outcomes of cochlear implants in these individuals compared with their optimally fitted hearing aid(s). Retrospective case series. Tertiary referral center. All postlingually deafened subjects (N = 27), who were unsuccessful hearing aid users implanted between 2000 and 2010 with a preimplantation Hearing in Noise Test (HINT) score of 60% or more were included. We compared patients' preoperative performance (HINT score) with hearing aids to postoperative performance with the cochlear implant after 12 months of device use. In addition, the Hearing Handicap Inventory questionnaire was used to quantify the hearing-related handicap change perceived after the implantation. The study group demonstrated significant postoperative improvement on all outcome measures; most notably, the mean HINT score improved from 68.4% (standard deviation, 8.3) to 91.9% (standard deviation, 9.7). Additionally, there was a significant improvement in hearing-related handicap perceived by all patients. The envelope of implantation candidacy criteria continues to expand as shown by this study's cohort. Patient satisfaction and speech recognition results are very encouraging in support of treating those who currently perform at a level above the conventional candidacy threshold but struggle with optimally fitted hearing aids.

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons.

PubMed

Filippoupolitis, Avgoustinos; Oliff, William; Takand, Babak; Loukas, George

2017-05-27

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation.

Uniform Local Binary Pattern Based Texture-Edge Feature for 3D Human Behavior Recognition.

PubMed

Ming, Yue; Wang, Guangchao; Fan, Chunxiao

2015-01-01

With the rapid development of 3D somatosensory technology, human behavior recognition has become an important research field. Human behavior feature analysis has evolved from traditional 2D features to 3D features. In order to improve the performance of human activity recognition, a human behavior recognition method is proposed, which is based on a hybrid texture-edge local pattern coding feature extraction and integration of RGB and depth videos information. The paper mainly focuses on background subtraction on RGB and depth video sequences of behaviors, extracting and integrating historical images of the behavior outlines, feature extraction and classification. The new method of 3D human behavior recognition has achieved the rapid and efficient recognition of behavior videos. A large number of experiments show that the proposed method has faster speed and higher recognition rate. The recognition method has good robustness for different environmental colors, lightings and other factors. Meanwhile, the feature of mixed texture-edge uniform local binary pattern can be used in most 3D behavior recognition.

Experimental study on GMM-based speaker recognition

NASA Astrophysics Data System (ADS)

Ye, Wenxing; Wu, Dapeng; Nucci, Antonio

2010-04-01

Speaker recognition plays a very important role in the field of biometric security. In order to improve the recognition performance, many pattern recognition techniques have be explored in the literature. Among these techniques, the Gaussian Mixture Model (GMM) is proved to be an effective statistic model for speaker recognition and is used in most state-of-the-art speaker recognition systems. The GMM is used to represent the 'voice print' of a speaker through modeling the spectral characteristic of speech signals of the speaker. In this paper, we implement a speaker recognition system, which consists of preprocessing, Mel-Frequency Cepstrum Coefficients (MFCCs) based feature extraction, and GMM based classification. We test our system with TIDIGITS data set (325 speakers) and our own recordings of more than 200 speakers; our system achieves 100% correct recognition rate. Moreover, we also test our system under the scenario that training samples are from one language but test samples are from a different language; our system also achieves 100% correct recognition rate, which indicates that our system is language independent.

Speech recognition in advanced rotorcraft - Using speech controls to reduce manual control overload

NASA Technical Reports Server (NTRS)

Vidulich, Michael A.; Bortolussi, Michael R.

1988-01-01

An experiment has been conducted to ascertain the usefulness of helicopter pilot speech controls and their effect on time-sharing performance, under the impetus of multiple-resource theories of attention which predict that time-sharing should be more efficient with mixed manual and speech controls than with all-manual ones. The test simulation involved an advanced, single-pilot scout/attack helicopter. Performance and subjective workload levels obtained supported the claimed utility of speech recognition-based controls; specifically, time-sharing performance was improved while preparing a data-burst transmission of information during helicopter hover.

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance

PubMed Central

Hong, Ha; Solomon, Ethan A.; DiCarlo, James J.

2015-01-01

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT (“face patches”) did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. SIGNIFICANCE STATEMENT We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. PMID:26424887

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Sub-pattern based multi-manifold discriminant analysis for face recognition

NASA Astrophysics Data System (ADS)

Dai, Jiangyan; Guo, Changlu; Zhou, Wei; Shi, Yanjiao; Cong, Lin; Yi, Yugen

2018-04-01

In this paper, we present a Sub-pattern based Multi-manifold Discriminant Analysis (SpMMDA) algorithm for face recognition. Unlike existing Multi-manifold Discriminant Analysis (MMDA) approach which is based on holistic information of face image for recognition, SpMMDA operates on sub-images partitioned from the original face image and then extracts the discriminative local feature from the sub-images separately. Moreover, the structure information of different sub-images from the same face image is considered in the proposed method with the aim of further improve the recognition performance. Extensive experiments on three standard face databases (Extended YaleB, CMU PIE and AR) demonstrate that the proposed method is effective and outperforms some other sub-pattern based face recognition methods.

The Benefits of Residual Hair Cell Function for Speech and Music Perception in Pediatric Bimodal Cochlear Implant Listeners.

PubMed

Cheng, Xiaoting; Liu, Yangwenyi; Wang, Bing; Yuan, Yasheng; Galvin, John J; Fu, Qian-Jie; Shu, Yilai; Chen, Bing

2018-01-01

The aim of this study was to investigate the benefits of residual hair cell function for speech and music perception in bimodal pediatric Mandarin-speaking cochlear implant (CI) listeners. Speech and music performance was measured in 35 Mandarin-speaking pediatric CI users for unilateral (CI-only) and bimodal listening. Mandarin speech perception was measured for vowels, consonants, lexical tones, and sentences in quiet. Music perception was measured for melodic contour identification (MCI). Combined electric and acoustic hearing significantly improved MCI and Mandarin tone recognition performance, relative to CI-only performance. For MCI, performance was significantly better with bimodal listening for all semitone spacing conditions ( p < 0.05 in all cases). For tone recognition, bimodal performance was significantly better only for tone 2 (rising; p < 0.05). There were no significant differences between CI-only and CI + HA for vowel, consonant, or sentence recognition. The results suggest that combined electric and acoustic hearing can significantly improve perception of music and Mandarin tones in pediatric Mandarin-speaking CI patients. Music and lexical tone perception depends strongly on pitch perception, and the contralateral acoustic hearing coming from residual hair cell function provided pitch cues that are generally not well preserved in electric hearing.

Cognitive aspects of haptic form recognition by blind and sighted subjects.

PubMed

Bailes, S M; Lambert, R M

1986-11-01

Studies using haptic form recognition tasks have generally concluded that the adventitiously blind perform better than the congenitally blind, implicating the importance of early visual experience in improved spatial functioning. The hypothesis was tested that the adventitiously blind have retained some ability to encode successive information obtained haptically in terms of a global visual representation, while the congenitally blind use a coding system based on successive inputs. Eighteen blind (adventitiously and congenitally) and 18 sighted (blindfolded and performing with vision) subjects were tested on their recognition of raised line patterns when the standard was presented in segments: in immediate succession, or with unfilled intersegmental delays of 5, 10, or 15 seconds. The results did not support the above hypothesis. Three main findings were obtained: normally sighted subjects were both faster and more accurate than the other groups; all groups improved in accuracy of recognition as a function of length of interstimulus interval; sighted subjects tended to report using strategies with a strong verbal component while the blind tended to rely on imagery coding. These results are explained in terms of information-processing theory consistent with dual encoding systems in working memory.

Weighted score-level feature fusion based on Dempster-Shafer evidence theory for action recognition

NASA Astrophysics Data System (ADS)

Zhang, Guoliang; Jia, Songmin; Li, Xiuzhi; Zhang, Xiangyin

2018-01-01

The majority of human action recognition methods use multifeature fusion strategy to improve the classification performance, where the contribution of different features for specific action has not been paid enough attention. We present an extendible and universal weighted score-level feature fusion method using the Dempster-Shafer (DS) evidence theory based on the pipeline of bag-of-visual-words. First, the partially distinctive samples in the training set are selected to construct the validation set. Then, local spatiotemporal features and pose features are extracted from these samples to obtain evidence information. The DS evidence theory and the proposed rule of survival of the fittest are employed to achieve evidence combination and calculate optimal weight vectors of every feature type belonging to each action class. Finally, the recognition results are deduced via the weighted summation strategy. The performance of the established recognition framework is evaluated on Penn Action dataset and a subset of the joint-annotated human metabolome database (sub-JHMDB). The experiment results demonstrate that the proposed feature fusion method can adequately exploit the complementarity among multiple features and improve upon most of the state-of-the-art algorithms on Penn Action and sub-JHMDB datasets.

Pushing typists back on the learning curve: Memory chunking improves retrieval of prior typing episodes.

PubMed

Yamaguchi, Motonori; Randle, James M; Wilson, Thomas L; Logan, Gordon D

2017-09-01

Hierarchical control of skilled performance depends on chunking of several lower-level units into a single higher-level unit. The present study examined the relationship between chunking and recognition of trained materials in the context of typewriting. In 3 experiments, participants were trained with typing nonwords and were later tested on their recognition of the trained materials. In Experiment 1, participants typed the same words or nonwords in 5 consecutive trials while performing a concurrent memory task. In Experiment 2, participants typed the materials with lags between repetitions without a concurrent memory task. In both experiments, recognition of typing materials was associated with better chunking of the materials. Experiment 3 used the remember-know procedure to test the recollection and familiarity components of recognition. Remember judgments were associated with better chunking than know judgments or nonrecognition. These results indicate that chunking is associated with explicit recollection of prior typing episodes. The relevance of the existing memory models to chunking in typewriting was considered, and it is proposed that memory chunking improves retrieval of trained typing materials by integrating contextual cues into the memory traces. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Controlled synthesis and chiral recognition of immobilized cellulose and amylose tris(cyclohexylcarbamate)s/3-(triethoxysilyl)propylcarbamates as chiral packing materials for high-performance liquid chromatography.

PubMed

Shen, Jun; Liu, Shuangyan; Li, Pengfei; Shen, Xiande; Okamoto, Yoshio

2012-07-13

The cyclohexylcarbamates of cellulose and amylose bearing a controlled amount of 3-(triethoxysilyl)propyl residue were synthesized by a one-pot process and efficiently immobilized onto silica gel through the intermolecular polycondensation of triethoxysilyl group. Their chiral recognition abilities were evaluated as chiral packing materials (CPMs) for high-performance liquid chromatography (HPLC). The immobilized CPMs exhibited comparable or higher recognition abilities than the conventional coated-type CPMs. The universal solvent compatibility of the immobilized CPMs clearly contributes to the improvement of chiral recognition for most racemates used in the present study. Interestingly, a significantly improved resolution for racemic trans-stilbene oxide (α=2.23) could be attained on the immobilized CPM using the eluent containing 30 vol.% chloroform in hexane, which cannot be used for the conventional coated-type CPMs. On the CPMs, almost no resolution of trans-stilbene oxide was attained by a typical eluent, hexane-2-propanol mixture (90/10, v/v). The novel immobilized CPM can also be used in thin-layer chromatography (TLC) due to the absence of an aromatic group. Copyright © 2012 Elsevier B.V. All rights reserved.

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

PubMed Central

2014-01-01

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system. PMID:24693243

Novel approaches to improve iris recognition system performance based on local quality evaluation and feature fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; Chen, Huiling; He, Fei; Pang, Yutong

2014-01-01

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.

Training Strategies for Mitigating the Effect of Proportional Control on Classification in Pattern Recognition Based Myoelectric Control

PubMed Central

Scheme, Erik; Englehart, Kevin

2013-01-01

The performance of pattern recognition based myoelectric control has seen significant interest in the research community for many years. Due to a recent surge in the development of dexterous prosthetic devices, determining the clinical viability of multifunction myoelectric control has become paramount. Several factors contribute to differences between offline classification accuracy and clinical usability, but the overriding theme is that the variability of the elicited patterns increases greatly during functional use. Proportional control has been shown to greatly improve the usability of conventional myoelectric control systems. Typically, a measure of the amplitude of the electromyogram (a rectified and smoothed version) is used to dictate the velocity of control of a device. The discriminatory power of myoelectric pattern classifiers, however, is also largely based on amplitude features of the electromyogram. This work presents an introductory look at the effect of contraction strength and proportional control on pattern recognition based control. These effects are investigated using typical pattern recognition data collection methods as well as a real-time position tracking test. Training with dynamically force varying contractions and appropriate gain selection is shown to significantly improve (p<0.001) the classifier’s performance and tolerance to proportional control. PMID:23894224

Container-code recognition system based on computer vision and deep neural networks

NASA Astrophysics Data System (ADS)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Emotion Recognition from EEG Signals Using Multidimensional Information in EMD Domain.

PubMed

Zhuang, Ning; Zeng, Ying; Tong, Li; Zhang, Chi; Zhang, Hanming; Yan, Bin

2017-01-01

This paper introduces a method for feature extraction and emotion recognition based on empirical mode decomposition (EMD). By using EMD, EEG signals are decomposed into Intrinsic Mode Functions (IMFs) automatically. Multidimensional information of IMF is utilized as features, the first difference of time series, the first difference of phase, and the normalized energy. The performance of the proposed method is verified on a publicly available emotional database. The results show that the three features are effective for emotion recognition. The role of each IMF is inquired and we find that high frequency component IMF1 has significant effect on different emotional states detection. The informative electrodes based on EMD strategy are analyzed. In addition, the classification accuracy of the proposed method is compared with several classical techniques, including fractal dimension (FD), sample entropy, differential entropy, and discrete wavelet transform (DWT). Experiment results on DEAP datasets demonstrate that our method can improve emotion recognition performance.

The recognition of emotional expression in prosopagnosia: decoding whole and part faces.

PubMed

Stephan, Blossom Christa Maree; Breen, Nora; Caine, Diana

2006-11-01

Prosopagnosia is currently viewed within the constraints of two competing theories of face recognition, one highlighting the analysis of features, the other focusing on configural processing of the whole face. This study investigated the role of feature analysis versus whole face configural processing in the recognition of facial expression. A prosopagnosic patient, SC made expression decisions from whole and incomplete (eyes-only and mouth-only) faces where features had been obscured. SC was impaired at recognizing some (e.g., anger, sadness, and fear), but not all (e.g., happiness) emotional expressions from the whole face. Analyses of his performance on incomplete faces indicated that his recognition of some expressions actually improved relative to his performance on the whole face condition. We argue that in SC interference from damaged configural processes seem to override an intact ability to utilize part-based or local feature cues.

The Last Meter: Blind Visual Guidance to a Target.

PubMed

Manduchi, Roberto; Coughlan, James M

2014-01-01

Smartphone apps can use object recognition software to provide information to blind or low vision users about objects in the visual environment. A crucial challenge for these users is aiming the camera properly to take a well-framed picture of the desired target object. We investigate the effects of two fundamental constraints of object recognition - frame rate and camera field of view - on a blind person's ability to use an object recognition smartphone app. The app was used by 18 blind participants to find visual targets beyond arm's reach and approach them to within 30 cm. While we expected that a faster frame rate or wider camera field of view should always improve search performance, our experimental results show that in many cases increasing the field of view does not help, and may even hurt, performance. These results have important implications for the design of object recognition systems for blind users.

Speech recognition-based and automaticity programs to help students with severe reading and spelling problems.

PubMed

Higgins, Eleanor L; Raskind, Marshall H

2004-12-01

This study was conducted to assess the effectiveness of two programs developed by the Frostig Center Research Department to improve the reading and spelling of students with learning disabilities (LD): a computer Speech Recognition-based Program (SRBP) and a computer and text-based Automaticity Program (AP). Twenty-eight LD students with reading and spelling difficulties (aged 8 to 18) received each program for 17 weeks and were compared with 16 students in a contrast group who did not receive either program. After adjusting for age and IQ, both the SRBP and AP groups showed significant differences over the contrast group in improving word recognition and reading comprehension. Neither program showed significant differences over contrasts in spelling. The SRBP also improved the performance of the target group when compared with the contrast group on phonological elision and nonword reading efficiency tasks. The AP showed significant differences in all process and reading efficiency measures.

Offline handwritten word recognition using MQDF-HMMs

NASA Astrophysics Data System (ADS)

Ramachandrula, Sitaram; Hambarde, Mangesh; Patial, Ajay; Sahoo, Dushyant; Kochar, Shaivi

2015-01-01

We propose an improved HMM formulation for offline handwriting recognition (HWR). The main contribution of this work is using modified quadratic discriminant function (MQDF) [1] within HMM framework. In an MQDF-HMM the state observation likelihood is calculated by a weighted combination of MQDF likelihoods of individual Gaussians of GMM (Gaussian Mixture Model). The quadratic discriminant function (QDF) of a multivariate Gaussian can be rewritten by avoiding the inverse of covariance matrix by using the Eigen values and Eigen vectors of it. The MQDF is derived from QDF by substituting few of badly estimated lower-most Eigen values by an appropriate constant. The estimation errors of non-dominant Eigen vectors and Eigen values of covariance matrix for which the training data is insufficient can be controlled by this approach. MQDF has been successfully shown to improve the character recognition performance [1]. The usage of MQDF in HMM improves the computation, storage and modeling power of HMM when there is limited training data. We have got encouraging results on offline handwritten character (NIST database) and word recognition in English using MQDF HMMs.

Chronic minocycline treatment improves social recognition memory in adult male Fmr1 knockout mice.

PubMed

Yau, Suk Yu; Chiu, Christine; Vetrici, Mariana; Christie, Brian R

2016-10-01

Fragile X syndrome (FXS) is caused by a mutation in the Fmr1 gene that leads to silencing of the gene and a loss of its gene product, Fragile X mental retardation protein (FMRP). Some of the key behavioral phenotypes for FXS include abnormal social anxiety and sociability. Here we show that Fmr1 knock-out (KO) mice exhibit impaired social recognition when presented with a novel mouse, and they display normal social interactions in other sociability tests. Administering minocycline to Fmr1 KO mice throughout critical stages of neural development improved social recognition memory in the novel mouse recognition task. To determine if synaptic changes in the prefrontal cortex (PFC) could have played a role in this improvement, we examined PSD-95, a member of the membrane-associated guanylate kinase family, and signaling molecules (ERK1/2, and Akt) linked to synaptic plasticity in the PFC. Our analyses indicated that while minocycline treatment can enhance behavioral performance, it does not enhance expression of PSD-95, ERK1/2 or Akt in the PFC. Copyright © 2016 Elsevier B.V. All rights reserved.

Improving Protein Fold Recognition by Deep Learning Networks.

PubMed

Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

2015-12-04

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl's benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

Biometric recognition via texture features of eye movement trajectories in a visual searching task.

PubMed

Li, Chunyong; Xue, Jiguo; Quan, Cheng; Yue, Jingwei; Zhang, Chenggang

2018-01-01

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers' temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases.

Biometric recognition via texture features of eye movement trajectories in a visual searching task

PubMed Central

Li, Chunyong; Xue, Jiguo; Quan, Cheng; Yue, Jingwei

2018-01-01

Biometric recognition technology based on eye-movement dynamics has been in development for more than ten years. Different visual tasks, feature extraction and feature recognition methods are proposed to improve the performance of eye movement biometric system. However, the correct identification and verification rates, especially in long-term experiments, as well as the effects of visual tasks and eye trackers’ temporal and spatial resolution are still the foremost considerations in eye movement biometrics. With a focus on these issues, we proposed a new visual searching task for eye movement data collection and a new class of eye movement features for biometric recognition. In order to demonstrate the improvement of this visual searching task being used in eye movement biometrics, three other eye movement feature extraction methods were also tested on our eye movement datasets. Compared with the original results, all three methods yielded better results as expected. In addition, the biometric performance of these four feature extraction methods was also compared using the equal error rate (EER) and Rank-1 identification rate (Rank-1 IR), and the texture features introduced in this paper were ultimately shown to offer some advantages with regard to long-term stability and robustness over time and spatial precision. Finally, the results of different combinations of these methods with a score-level fusion method indicated that multi-biometric methods perform better in most cases. PMID:29617383

A robust pointer segmentation in biomedical images toward building a visual ontology for biomedical article retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Simpson, Matthew; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-01-01

Pointers (arrows and symbols) are frequently used in biomedical images to highlight specific image regions of interest (ROIs) that are mentioned in figure captions and/or text discussion. Detection of pointers is the first step toward extracting relevant visual features from ROIs and combining them with textual descriptions for a multimodal (text and image) biomedical article retrieval system. Recently we developed a pointer recognition algorithm based on an edge-based pointer segmentation method, and subsequently reported improvements made on our initial approach involving the use of Active Shape Models (ASM) for pointer recognition and region growing-based method for pointer segmentation. These methods contributed to improving the recall of pointer recognition but not much to the precision. The method discussed in this article is our recent effort to improve the precision rate. Evaluation performed on two datasets and compared with other pointer segmentation methods show significantly improved precision and the highest F1 score.

Multi-modal gesture recognition using integrated model of motion, audio and video

NASA Astrophysics Data System (ADS)

Goutsu, Yusuke; Kobayashi, Takaki; Obara, Junya; Kusajima, Ikuo; Takeichi, Kazunari; Takano, Wataru; Nakamura, Yoshihiko

2015-07-01

Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely.

The review and results of different methods for facial recognition

NASA Astrophysics Data System (ADS)

Le, Yifan

2017-09-01

In recent years, facial recognition draws much attention due to its wide potential applications. As a unique technology in Biometric Identification, facial recognition represents a significant improvement since it could be operated without cooperation of people under detection. Hence, facial recognition will be taken into defense system, medical detection, human behavior understanding, etc. Several theories and methods have been established to make progress in facial recognition: (1) A novel two-stage facial landmark localization method is proposed which has more accurate facial localization effect under specific database; (2) A statistical face frontalization method is proposed which outperforms state-of-the-art methods for face landmark localization; (3) It proposes a general facial landmark detection algorithm to handle images with severe occlusion and images with large head poses; (4) There are three methods proposed on Face Alignment including shape augmented regression method, pose-indexed based multi-view method and a learning based method via regressing local binary features. The aim of this paper is to analyze previous work of different aspects in facial recognition, focusing on concrete method and performance under various databases. In addition, some improvement measures and suggestions in potential applications will be put forward.

Implementation of a professional portfolio: a tool to demonstrate professional development for advanced practice.

PubMed

Chamblee, Tracy B; Dale, Juanita Conkin; Drews, Barbie; Spahis, Joanna; Hardin, Teri

2015-01-01

The literature has a gap related to professional development for APRNs. In the United States, many health care organizations use clinical advancement programs for registered nurses, but APRNs are not often included in these programs. If APRNs are included, advancement opportunities are very limited. At CMC, implementation of a professional portfolio resulted in increased satisfaction among APPs regarding their ability to showcase professional growth and expertise, as well as the uniqueness of their advanced practice. Use of the professional portfolio led to improved recognition by APS and organizational leaders of APP performance excellence during the annual performance evaluation, as well as improved recognition among APP colleagues in terms of nominations for honors and awards.

Improving the Robustness of Real-Time Myoelectric Pattern Recognition against Arm Position Changes in Transradial Amputees

PubMed Central

Geng, Yanjuan; Wei, Yue

2017-01-01

Previous studies have showed that arm position variations would significantly degrade the classification performance of myoelectric pattern-recognition-based prosthetic control, and the cascade classifier (CC) and multiposition classifier (MPC) have been proposed to minimize such degradation in offline scenarios. However, it remains unknown whether these proposed approaches could also perform well in the clinical use of a multifunctional prosthesis control. In this study, the online effect of arm position variation on motion identification was evaluated by using a motion-test environment (MTE) developed to mimic the real-time control of myoelectric prostheses. The performance of different classifier configurations in reducing the impact of arm position variation was investigated using four real-time metrics based on dataset obtained from transradial amputees. The results of this study showed that, compared to the commonly used motion classification method, the CC and MPC configurations improved the real-time performance across seven classes of movements in five different arm positions (8.7% and 12.7% increments of motion completion rate, resp.). The results also indicated that high offline classification accuracy might not ensure good real-time performance under variable arm positions, which necessitated the investigation of the real-time control performance to gain proper insight on the clinical implementation of EMG-pattern-recognition-based controllers for limb amputees. PMID:28523276

Improving Mobile Phone Speech Recognition by Personalized Amplification: Application in People with Normal Hearing and Mild-to-Moderate Hearing Loss.

PubMed

Kam, Anna Chi Shan; Sung, John Ka Keung; Lee, Tan; Wong, Terence Ka Cheong; van Hasselt, Andrew

In this study, the authors evaluated the effect of personalized amplification on mobile phone speech recognition in people with and without hearing loss. This prospective study used double-blind, within-subjects, repeated measures, controlled trials to evaluate the effectiveness of applying personalized amplification based on the hearing level captured on the mobile device. The personalized amplification settings were created using modified one-third gain targets. The participants in this study included 100 adults of age between 20 and 78 years (60 with age-adjusted normal hearing and 40 with hearing loss). The performance of the participants with personalized amplification and standard settings was compared using both subjective and speech-perception measures. Speech recognition was measured in quiet and in noise using Cantonese disyllabic words. Subjective ratings on the quality, clarity, and comfortableness of the mobile signals were measured with an 11-point visual analog scale. Subjective preferences of the settings were also obtained by a paired-comparison procedure. The personalized amplification application provided better speech recognition via the mobile phone both in quiet and in noise for people with hearing impairment (improved 8 to 10%) and people with normal hearing (improved 1 to 4%). The improvement in speech recognition was significantly better for people with hearing impairment. When the average device output level was matched, more participants preferred to have the individualized gain than not to have it. The personalized amplification application has the potential to improve speech recognition for people with mild-to-moderate hearing loss, as well as people with normal hearing, in particular when listening in noisy environments.

A Novel Optimization Technique to Improve Gas Recognition by Electronic Noses Based on the Enhanced Krill Herd Algorithm

PubMed Central

Wang, Li; Jia, Pengfei; Huang, Tailai; Duan, Shukai; Yan, Jia; Wang, Lidan

2016-01-01

An electronic nose (E-nose) is an intelligent system that we will use in this paper to distinguish three indoor pollutant gases (benzene (C6H6), toluene (C7H8), formaldehyde (CH2O)) and carbon monoxide (CO). The algorithm is a key part of an E-nose system mainly composed of data processing and pattern recognition. In this paper, we employ support vector machine (SVM) to distinguish indoor pollutant gases and two of its parameters need to be optimized, so in order to improve the performance of SVM, in other words, to get a higher gas recognition rate, an effective enhanced krill herd algorithm (EKH) based on a novel decision weighting factor computing method is proposed to optimize the two SVM parameters. Krill herd (KH) is an effective method in practice, however, on occasion, it cannot avoid the influence of some local best solutions so it cannot always find the global optimization value. In addition its search ability relies fully on randomness, so it cannot always converge rapidly. To address these issues we propose an enhanced KH (EKH) to improve the global searching and convergence speed performance of KH. To obtain a more accurate model of the krill behavior, an updated crossover operator is added to the approach. We can guarantee the krill group are diversiform at the early stage of iterations, and have a good performance in local searching ability at the later stage of iterations. The recognition results of EKH are compared with those of other optimization algorithms (including KH, chaotic KH (CKH), quantum-behaved particle swarm optimization (QPSO), particle swarm optimization (PSO) and genetic algorithm (GA)), and we can find that EKH is better than the other considered methods. The research results verify that EKH not only significantly improves the performance of our E-nose system, but also provides a good beginning and theoretical basis for further study about other improved krill algorithms’ applications in all E-nose application areas. PMID:27529247

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improving associative memory in older adults with unitization.

PubMed

Ahmad, Fahad N; Fernandes, Myra; Hockley, William E

2015-01-01

We examined if unitization inherent preexperimentally could reduce the associative deficit in older adults. In Experiment 1, younger and older adults studied compound word (CW; e.g., store keeper) and noncompound word (NCW; e.g., needle birth) pairs. We found a reduction in the age-related associative deficit such that older but not younger adults showed a discrimination advantage for CW relative to NCW pairs on a yes-no associative recognition test. These results suggest that CW compared to NCW word pairs provide schematic support that older adults can use to improve their memory. In Experiment 2, reducing study time in younger adults decreased associative recognition performance, but did not produce a discrimination advantage for CW pairs. In Experiment 3, both older and younger adults showed a discrimination advantage for CW pairs on a two-alternative forced-choice recognition test, which encourages greater use of familiarity. These results suggest that test format influenced young adults' use of familiarity during associative recognition of unitized pairs, and that older adults rely more on familiarity than recollection for associative recognition. Unitization of preexperimental associations, as in CW pairs, can alleviate age-related associative deficits.

Levels-of-processing effect on internal source monitoring in schizophrenia

PubMed Central

RAGLAND, J. DANIEL; McCARTHY, ERIN; BILKER, WARREN B.; RENSINGER, COLLEEN M. B; VALDEZ, JEFFREY; KOHLER, CHRISTIAN; GUR, RAQUEL E.; GUR, RUBEN C.

2015-01-01

Background Recognition can be normalized in schizophrenia by providing patients with semantic organizational strategies through a levels-of-processing (LOP) framework. However, patients may rely primarily on familiarity effects, making recognition less sensitive than source monitoring to the strength of the episodic memory trace. The current study investigates whether providing semantic organizational strategies can also normalize patients’ internal source-monitoring performance. Method Sixteen clinically stable medicated patients with schizophrenia and 15 demographically matched healthy controls were asked to identify the source of remembered words following an LOP-encoding paradigm in which they alternated between processing words on a ‘shallow’ perceptual versus a ‘deep’ semantic level. A multinomial analysis provided orthogonal measures of item recognition and source discrimination, and bootstrapping generated variance to allow for parametric analyses. LOP and group effects were tested by contrasting recognition and source-monitoring parameters for words that had been encoded during deep versus shallow processing conditions. Results As in a previous study there were no group differences in LOP effects on recognition performance, with patients and controls benefiting equally from deep versus shallow processing. Although there were no group differences in internal source monitoring, only controls had significantly better performance for words processed during the deep encoding condition. Patient performance did not correlate with clinical symptoms or medication dose. Conclusions Providing a deep processing semantic encoding strategy significantly improved patients’ recognition performance only. The lack of a significant LOP effect on internal source monitoring in patients may reffect subtle problems in the relational binding of semantic information that are independent of strategic memory processes. PMID:16608558

Levels-of-processing effect on internal source monitoring in schizophrenia.

PubMed

Ragland, J Daniel; McCarthy, Erin; Bilker, Warren B; Brensinger, Colleen M; Valdez, Jeffrey; Kohler, Christian; Gur, Raquel E; Gur, Ruben C

2006-05-01

Recognition can be normalized in schizophrenia by providing patients with semantic organizational strategies through a levels-of-processing (LOP) framework. However, patients may rely primarily on familiarity effects, making recognition less sensitive than source monitoring to the strength of the episodic memory trace. The current study investigates whether providing semantic organizational strategies can also normalize patients' internal source-monitoring performance. Sixteen clinically stable medicated patients with schizophrenia and 15 demographically matched healthy controls were asked to identify the source of remembered words following an LOP-encoding paradigm in which they alternated between processing words on a 'shallow' perceptual versus a 'deep' semantic level. A multinomial analysis provided orthogonal measures of item recognition and source discrimination, and bootstrapping generated variance to allow for parametric analyses. LOP and group effects were tested by contrasting recognition and source-monitoring parameters for words that had been encoded during deep versus shallow processing conditions. As in a previous study there were no group differences in LOP effects on recognition performance, with patients and controls benefiting equally from deep versus shallow processing. Although there were no group differences in internal source monitoring, only controls had significantly better performance for words processed during the deep encoding condition. Patient performance did not correlate with clinical symptoms or medication dose. Providing a deep processing semantic encoding strategy significantly improved patients' recognition performance only. The lack of a significant LOP effect on internal source monitoring in patients may reflect subtle problems in the relational binding of semantic information that are independent of strategic memory processes.

Strategies to Improve Activity Recognition Based on Skeletal Tracking: Applying Restrictions Regarding Body Parts and Similarity Boundaries †

PubMed Central

Gutiérrez-López-Franca, Carlos; Hervás, Ramón; Johnson, Esperanza

2018-01-01

This paper aims to improve activity recognition systems based on skeletal tracking through the study of two different strategies (and its combination): (a) specialized body parts analysis and (b) stricter restrictions for the most easily detectable activities. The study was performed using the Extended Body-Angles Algorithm, which is able to analyze activities using only a single key sample. This system allows to select, for each considered activity, which are its relevant joints, which makes it possible to monitor the body of the user selecting only a subset of the same. But this feature of the system has both advantages and disadvantages. As a consequence, in the past we had some difficulties with the recognition of activities that only have a small subset of the joints of the body as relevant. The goal of this work, therefore, is to analyze the effect produced by the application of several strategies on the results of an activity recognition system based on skeletal tracking joint oriented devices. Strategies that we applied with the purpose of improve the recognition rates of the activities with a small subset of relevant joints. Through the results of this work, we aim to give the scientific community some first indications about which considered strategy is better. PMID:29789478

Pose-Invariant Face Recognition via RGB-D Images.

PubMed

Sang, Gaoli; Li, Jing; Zhao, Qijun

2016-01-01

Three-dimensional (3D) face models can intrinsically handle large pose face recognition problem. In this paper, we propose a novel pose-invariant face recognition method via RGB-D images. By employing depth, our method is able to handle self-occlusion and deformation, both of which are challenging problems in two-dimensional (2D) face recognition. Texture images in the gallery can be rendered to the same view as the probe via depth. Meanwhile, depth is also used for similarity measure via frontalization and symmetric filling. Finally, both texture and depth contribute to the final identity estimation. Experiments on Bosphorus, CurtinFaces, Eurecom, and Kiwi databases demonstrate that the additional depth information has improved the performance of face recognition with large pose variations and under even more challenging conditions.

Hybrid Speaker Recognition Using Universal Acoustic Model

NASA Astrophysics Data System (ADS)

Nishimura, Jun; Kuroda, Tadahiro

We propose a novel speaker recognition approach using a speaker-independent universal acoustic model (UAM) for sensornet applications. In sensornet applications such as “Business Microscope”, interactions among knowledge workers in an organization can be visualized by sensing face-to-face communication using wearable sensor nodes. In conventional studies, speakers are detected by comparing energy of input speech signals among the nodes. However, there are often synchronization errors among the nodes which degrade the speaker recognition performance. By focusing on property of the speaker's acoustic channel, UAM can provide robustness against the synchronization error. The overall speaker recognition accuracy is improved by combining UAM with the energy-based approach. For 0.1s speech inputs and 4 subjects, speaker recognition accuracy of 94% is achieved at the synchronization error less than 100ms.

Character recognition from trajectory by recurrent spiking neural networks.

PubMed

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

Enhancing emotion recognition in children with autism spectrum conditions: an intervention using animated vehicles with real emotional faces.

PubMed

Golan, Ofer; Ashwin, Emma; Granader, Yael; McClintock, Suzy; Day, Kate; Leggett, Victoria; Baron-Cohen, Simon

2010-03-01

This study evaluated The Transporters, an animated series designed to enhance emotion comprehension in children with autism spectrum conditions (ASC). n = 20 children with ASC (aged 4-7) watched The Transporters everyday for 4 weeks. Participants were tested before and after intervention on emotional vocabulary and emotion recognition at three levels of generalization. Two matched control groups of children (ASC group, n = 18 and typically developing group, n = 18) were also assessed twice without any intervention. The intervention group improved significantly more than the clinical control group on all task levels, performing comparably to typical controls at Time 2. We conclude that using The Transporters significantly improves emotion recognition in children with ASC. Future research should evaluate the series' effectiveness with lower-functioning individuals.

Speech emotion recognition methods: A literature review

NASA Astrophysics Data System (ADS)

Basharirad, Babak; Moradhaseli, Mohammadreza

2017-10-01

Recently, attention of the emotional speech signals research has been boosted in human machine interfaces due to availability of high computation capability. There are many systems proposed in the literature to identify the emotional state through speech. Selection of suitable feature sets, design of a proper classifications methods and prepare an appropriate dataset are the main key issues of speech emotion recognition systems. This paper critically analyzed the current available approaches of speech emotion recognition methods based on the three evaluating parameters (feature set, classification of features, accurately usage). In addition, this paper also evaluates the performance and limitations of available methods. Furthermore, it highlights the current promising direction for improvement of speech emotion recognition systems.

Signed reward prediction errors drive declarative learning

PubMed Central

Naert, Lien; Janssens, Clio; Talsma, Durk; Van Opstal, Filip; Verguts, Tom

2018-01-01

Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning–a quintessentially human form of learning–remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; “better-than-expected” signals) during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli. PMID:29293493

Signed reward prediction errors drive declarative learning.

PubMed

De Loof, Esther; Ergo, Kate; Naert, Lien; Janssens, Clio; Talsma, Durk; Van Opstal, Filip; Verguts, Tom

2018-01-01

Reward prediction errors (RPEs) are thought to drive learning. This has been established in procedural learning (e.g., classical and operant conditioning). However, empirical evidence on whether RPEs drive declarative learning-a quintessentially human form of learning-remains surprisingly absent. We therefore coupled RPEs to the acquisition of Dutch-Swahili word pairs in a declarative learning paradigm. Signed RPEs (SRPEs; "better-than-expected" signals) during declarative learning improved recognition in a follow-up test, with increasingly positive RPEs leading to better recognition. In addition, classic declarative memory mechanisms such as time-on-task failed to explain recognition performance. The beneficial effect of SRPEs on recognition was subsequently affirmed in a replication study with visual stimuli.

Event Recognition Based on Deep Learning in Chinese Texts

PubMed Central

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%. PMID:27501231

Cross-modal face recognition using multi-matcher face scores

NASA Astrophysics Data System (ADS)

Zheng, Yufeng; Blasch, Erik

2015-05-01

The performance of face recognition can be improved using information fusion of multimodal images and/or multiple algorithms. When multimodal face images are available, cross-modal recognition is meaningful for security and surveillance applications. For example, a probe face is a thermal image (especially at nighttime), while only visible face images are available in the gallery database. Matching a thermal probe face onto the visible gallery faces requires crossmodal matching approaches. A few such studies were implemented in facial feature space with medium recognition performance. In this paper, we propose a cross-modal recognition approach, where multimodal faces are cross-matched in feature space and the recognition performance is enhanced with stereo fusion at image, feature and/or score level. In the proposed scenario, there are two cameras for stereo imaging, two face imagers (visible and thermal images) in each camera, and three recognition algorithms (circular Gaussian filter, face pattern byte, linear discriminant analysis). A score vector is formed with three cross-matched face scores from the aforementioned three algorithms. A classifier (e.g., k-nearest neighbor, support vector machine, binomial logical regression [BLR]) is trained then tested with the score vectors by using 10-fold cross validations. The proposed approach was validated with a multispectral stereo face dataset from 105 subjects. Our experiments show very promising results: ACR (accuracy rate) = 97.84%, FAR (false accept rate) = 0.84% when cross-matching the fused thermal faces onto the fused visible faces by using three face scores and the BLR classifier.

Event Recognition Based on Deep Learning in Chinese Texts.

PubMed

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

Centre-based restricted nearest feature plane with angle classifier for face recognition

NASA Astrophysics Data System (ADS)

Tang, Linlin; Lu, Huifen; Zhao, Liang; Li, Zuohua

2017-10-01

An improved classifier based on the nearest feature plane (NFP), called the centre-based restricted nearest feature plane with the angle (RNFPA) classifier, is proposed for the face recognition problems here. The famous NFP uses the geometrical information of samples to increase the number of training samples, but it increases the computation complexity and it also has an inaccuracy problem coursed by the extended feature plane. To solve the above problems, RNFPA exploits a centre-based feature plane and utilizes a threshold of angle to restrict extended feature space. By choosing the appropriate angle threshold, RNFPA can improve the performance and decrease computation complexity. Experiments in the AT&T face database, AR face database and FERET face database are used to evaluate the proposed classifier. Compared with the original NFP classifier, the nearest feature line (NFL) classifier, the nearest neighbour (NN) classifier and some other improved NFP classifiers, the proposed one achieves competitive performance.

Anatomically constrained neural network models for the categorization of facial expression

NASA Astrophysics Data System (ADS)

McMenamin, Brenton W.; Assadi, Amir H.

2004-12-01

The ability to recognize facial expression in humans is performed with the amygdala which uses parallel processing streams to identify the expressions quickly and accurately. Additionally, it is possible that a feedback mechanism may play a role in this process as well. Implementing a model with similar parallel structure and feedback mechanisms could be used to improve current facial recognition algorithms for which varied expressions are a source for error. An anatomically constrained artificial neural-network model was created that uses this parallel processing architecture and feedback to categorize facial expressions. The presence of a feedback mechanism was not found to significantly improve performance for models with parallel architecture. However the use of parallel processing streams significantly improved accuracy over a similar network that did not have parallel architecture. Further investigation is necessary to determine the benefits of using parallel streams and feedback mechanisms in more advanced object recognition tasks.

Anatomically constrained neural network models for the categorization of facial expression

NASA Astrophysics Data System (ADS)

McMenamin, Brenton W.; Assadi, Amir H.

2005-01-01

The ability to recognize facial expression in humans is performed with the amygdala which uses parallel processing streams to identify the expressions quickly and accurately. Additionally, it is possible that a feedback mechanism may play a role in this process as well. Implementing a model with similar parallel structure and feedback mechanisms could be used to improve current facial recognition algorithms for which varied expressions are a source for error. An anatomically constrained artificial neural-network model was created that uses this parallel processing architecture and feedback to categorize facial expressions. The presence of a feedback mechanism was not found to significantly improve performance for models with parallel architecture. However the use of parallel processing streams significantly improved accuracy over a similar network that did not have parallel architecture. Further investigation is necessary to determine the benefits of using parallel streams and feedback mechanisms in more advanced object recognition tasks.

Hierarchical Context Modeling for Video Event Recognition.

PubMed

Wang, Xiaoyang; Ji, Qiang

2016-10-11

Current video event recognition research remains largely target-centered. For real-world surveillance videos, targetcentered event recognition faces great challenges due to large intra-class target variation, limited image resolution, and poor detection and tracking results. To mitigate these challenges, we introduced a context-augmented video event recognition approach. Specifically, we explicitly capture different types of contexts from three levels including image level, semantic level, and prior level. At the image level, we introduce two types of contextual features including the appearance context features and interaction context features to capture the appearance of context objects and their interactions with the target objects. At the semantic level, we propose a deep model based on deep Boltzmann machine to learn event object representations and their interactions. At the prior level, we utilize two types of prior-level contexts including scene priming and dynamic cueing. Finally, we introduce a hierarchical context model that systematically integrates the contextual information at different levels. Through the hierarchical context model, contexts at different levels jointly contribute to the event recognition. We evaluate the hierarchical context model for event recognition on benchmark surveillance video datasets. Results show that incorporating contexts in each level can improve event recognition performance, and jointly integrating three levels of contexts through our hierarchical model achieves the best performance.

A New Font, Specifically Designed for Peripheral Vision, Improves Peripheral Letter and Word Recognition, but Not Eye-Mediated Reading Performance

PubMed Central

Bernard, Jean-Baptiste; Aguilar, Carlos; Castet, Eric

2016-01-01

Reading speed is dramatically reduced when readers cannot use their central vision. This is because low visual acuity and crowding negatively impact letter recognition in the periphery. In this study, we designed a new font (referred to as the Eido font) in order to reduce inter-letter similarity and consequently to increase peripheral letter recognition performance. We tested this font by running five experiments that compared the Eido font with the standard Courier font. Letter spacing and x-height were identical for the two monospaced fonts. Six normally-sighted subjects used exclusively their peripheral vision to run two aloud reading tasks (with eye movements), a letter recognition task (without eye movements), a word recognition task (without eye movements) and a lexical decision task. Results show that reading speed was not significantly different between the Eido and the Courier font when subjects had to read single sentences with a round simulated gaze-contingent central scotoma (10° diameter). In contrast, Eido significantly decreased perceptual errors in peripheral crowded letter recognition (-30% errors on average for letters briefly presented at 6° eccentricity) and in peripheral word recognition (-32% errors on average for words briefly presented at 6° eccentricity). PMID:27074013

A New Font, Specifically Designed for Peripheral Vision, Improves Peripheral Letter and Word Recognition, but Not Eye-Mediated Reading Performance.

PubMed

Bernard, Jean-Baptiste; Aguilar, Carlos; Castet, Eric

2016-01-01

Reading speed is dramatically reduced when readers cannot use their central vision. This is because low visual acuity and crowding negatively impact letter recognition in the periphery. In this study, we designed a new font (referred to as the Eido font) in order to reduce inter-letter similarity and consequently to increase peripheral letter recognition performance. We tested this font by running five experiments that compared the Eido font with the standard Courier font. Letter spacing and x-height were identical for the two monospaced fonts. Six normally-sighted subjects used exclusively their peripheral vision to run two aloud reading tasks (with eye movements), a letter recognition task (without eye movements), a word recognition task (without eye movements) and a lexical decision task. Results show that reading speed was not significantly different between the Eido and the Courier font when subjects had to read single sentences with a round simulated gaze-contingent central scotoma (10° diameter). In contrast, Eido significantly decreased perceptual errors in peripheral crowded letter recognition (-30% errors on average for letters briefly presented at 6° eccentricity) and in peripheral word recognition (-32% errors on average for words briefly presented at 6° eccentricity).

Cost-sensitive learning for emotion robust speaker recognition.

PubMed

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

PubMed Central

Li, Dongdong; Yang, Yingchun

2014-01-01

In the field of information security, voice is one of the most important parts in biometrics. Especially, with the development of voice communication through the Internet or telephone system, huge voice data resources are accessed. In speaker recognition, voiceprint can be applied as the unique password for the user to prove his/her identity. However, speech with various emotions can cause an unacceptably high error rate and aggravate the performance of speaker recognition system. This paper deals with this problem by introducing a cost-sensitive learning technology to reweight the probability of test affective utterances in the pitch envelop level, which can enhance the robustness in emotion-dependent speaker recognition effectively. Based on that technology, a new architecture of recognition system as well as its components is proposed in this paper. The experiment conducted on the Mandarin Affective Speech Corpus shows that an improvement of 8% identification rate over the traditional speaker recognition is achieved. PMID:24999492

Females scan more than males: a potential mechanism for sex differences in recognition memory.

PubMed

Heisz, Jennifer J; Pottruff, Molly M; Shore, David I

2013-07-01

Recognition-memory tests reveal individual differences in episodic memory; however, by themselves, these tests provide little information regarding the stage (or stages) in memory processing at which differences are manifested. We used eye-tracking technology, together with a recognition paradigm, to achieve a more detailed analysis of visual processing during encoding and retrieval. Although this approach may be useful for assessing differences in memory across many different populations, we focused on sex differences in face memory. Females outperformed males on recognition-memory tests, and this advantage was directly related to females' scanning behavior at encoding. Moreover, additional exposures to the faces reduced sex differences in face recognition, which suggests that males may be able to improve their recognition memory by extracting more information at encoding through increased scanning. A strategy of increased scanning at encoding may prove to be a simple way to enhance memory performance in other populations with memory impairment.

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar

PubMed Central

Shin, Young Hoon; Seo, Jiwon

2016-01-01

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker’s vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing. PMID:27801867

Towards Contactless Silent Speech Recognition Based on Detection of Active and Visible Articulators Using IR-UWB Radar.

PubMed

Shin, Young Hoon; Seo, Jiwon

2016-10-29

People with hearing or speaking disabilities are deprived of the benefits of conventional speech recognition technology because it is based on acoustic signals. Recent research has focused on silent speech recognition systems that are based on the motions of a speaker's vocal tract and articulators. Because most silent speech recognition systems use contact sensors that are very inconvenient to users or optical systems that are susceptible to environmental interference, a contactless and robust solution is hence required. Toward this objective, this paper presents a series of signal processing algorithms for a contactless silent speech recognition system using an impulse radio ultra-wide band (IR-UWB) radar. The IR-UWB radar is used to remotely and wirelessly detect motions of the lips and jaw. In order to extract the necessary features of lip and jaw motions from the received radar signals, we propose a feature extraction algorithm. The proposed algorithm noticeably improved speech recognition performance compared to the existing algorithm during our word recognition test with five speakers. We also propose a speech activity detection algorithm to automatically select speech segments from continuous input signals. Thus, speech recognition processing is performed only when speech segments are detected. Our testbed consists of commercial off-the-shelf radar products, and the proposed algorithms are readily applicable without designing specialized radar hardware for silent speech processing.

Vehicle license plate recognition in dense fog based on improved atmospheric scattering model

NASA Astrophysics Data System (ADS)

Tang, Chunming; Lin, Jun; Chen, Chunkai; Dong, Yancheng

2018-04-01

An effective method based on improved atmospheric scattering model is proposed in this paper to handle the problem of the vehicle license plate location and recognition in dense fog. Dense fog detection is performed firstly by the top-hat transformation and the vertical edge detection, and the moving vehicle image is separated from the traffic video image. After the vehicle image is decomposed into two layers: structure and texture layers, the glow layer is separated from the structure layer to get the background layer. Followed by performing the mean-pooling and the bicubic interpolation algorithm, the atmospheric light map of the background layer can be predicted, meanwhile the transmission of the background layer is estimated through the grayed glow layer, whose gray value is altered by linear mapping. Then, according to the improved atmospheric scattering model, the final restored image can be obtained by fusing the restored background layer and the optimized texture layer. License plate location is performed secondly by a series of morphological operations, connected domain analysis and various validations. Characters extraction is achieved according to the projection. Finally, an offline trained pattern classifier of hybrid discriminative restricted boltzmann machines (HDRBM) is applied to recognize the characters. Experimental results on thorough data sets are reported to demonstrate that the proposed method can achieve high recognition accuracy and works robustly in the dense fog traffic environment during 24h or one day.

Continuous Human Action Recognition Using Depth-MHI-HOG and a Spotter Model

PubMed Central

Eum, Hyukmin; Yoon, Changyong; Lee, Heejin; Park, Mignon

2015-01-01

In this paper, we propose a new method for spotting and recognizing continuous human actions using a vision sensor. The method is comprised of depth-MHI-HOG (DMH), action modeling, action spotting, and recognition. First, to effectively separate the foreground from background, we propose a method called DMH. It includes a standard structure for segmenting images and extracting features by using depth information, MHI, and HOG. Second, action modeling is performed to model various actions using extracted features. The modeling of actions is performed by creating sequences of actions through k-means clustering; these sequences constitute HMM input. Third, a method of action spotting is proposed to filter meaningless actions from continuous actions and to identify precise start and end points of actions. By employing the spotter model, the proposed method improves action recognition performance. Finally, the proposed method recognizes actions based on start and end points. We evaluate recognition performance by employing the proposed method to obtain and compare probabilities by applying input sequences in action models and the spotter model. Through various experiments, we demonstrate that the proposed method is efficient for recognizing continuous human actions in real environments. PMID:25742172

Facial expression recognition based on improved local ternary pattern and stacked auto-encoder

NASA Astrophysics Data System (ADS)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to enhance the robustness of facial expression recognition, we propose a method of facial expression recognition based on improved Local Ternary Pattern (LTP) combined with Stacked Auto-Encoder (SAE). This method uses the improved LTP extraction feature, and then uses the improved depth belief network as the detector and classifier to extract the LTP feature. The combination of LTP and improved deep belief network is realized in facial expression recognition. The recognition rate on CK+ databases has improved significantly.

Location-Enhanced Activity Recognition in Indoor Environments Using Off the Shelf Smart Watch Technology and BLE Beacons

PubMed Central

Filippoupolitis, Avgoustinos; Oliff, William; Takand, Babak; Loukas, George

2017-01-01

Activity recognition in indoor spaces benefits context awareness and improves the efficiency of applications related to personalised health monitoring, building energy management, security and safety. The majority of activity recognition frameworks, however, employ a network of specialised building sensors or a network of body-worn sensors. As this approach suffers with respect to practicality, we propose the use of commercial off-the-shelf devices. In this work, we design and evaluate an activity recognition system composed of a smart watch, which is enhanced with location information coming from Bluetooth Low Energy (BLE) beacons. We evaluate the performance of this approach for a variety of activities performed in an indoor laboratory environment, using four supervised machine learning algorithms. Our experimental results indicate that our location-enhanced activity recognition system is able to reach a classification accuracy ranging from 92% to 100%, while without location information classification accuracy it can drop to as low as 50% in some cases, depending on the window size chosen for data segmentation. PMID:28555022

Simple Learned Weighted Sums of Inferior Temporal Neuronal Firing Rates Accurately Predict Human Core Object Recognition Performance.

PubMed

Majaj, Najib J; Hong, Ha; Solomon, Ethan A; DiCarlo, James J

2015-09-30

To go beyond qualitative models of the biological substrate of object recognition, we ask: can a single ventral stream neuronal linking hypothesis quantitatively account for core object recognition performance over a broad range of tasks? We measured human performance in 64 object recognition tests using thousands of challenging images that explore shape similarity and identity preserving object variation. We then used multielectrode arrays to measure neuronal population responses to those same images in visual areas V4 and inferior temporal (IT) cortex of monkeys and simulated V1 population responses. We tested leading candidate linking hypotheses and control hypotheses, each postulating how ventral stream neuronal responses underlie object recognition behavior. Specifically, for each hypothesis, we computed the predicted performance on the 64 tests and compared it with the measured pattern of human performance. All tested hypotheses based on low- and mid-level visually evoked activity (pixels, V1, and V4) were very poor predictors of the human behavioral pattern. However, simple learned weighted sums of distributed average IT firing rates exactly predicted the behavioral pattern. More elaborate linking hypotheses relying on IT trial-by-trial correlational structure, finer IT temporal codes, or ones that strictly respect the known spatial substructures of IT ("face patches") did not improve predictive power. Although these results do not reject those more elaborate hypotheses, they suggest a simple, sufficient quantitative model: each object recognition task is learned from the spatially distributed mean firing rates (100 ms) of ∼60,000 IT neurons and is executed as a simple weighted sum of those firing rates. Significance statement: We sought to go beyond qualitative models of visual object recognition and determine whether a single neuronal linking hypothesis can quantitatively account for core object recognition behavior. To achieve this, we designed a database of images for evaluating object recognition performance. We used multielectrode arrays to characterize hundreds of neurons in the visual ventral stream of nonhuman primates and measured the object recognition performance of >100 human observers. Remarkably, we found that simple learned weighted sums of firing rates of neurons in monkey inferior temporal (IT) cortex accurately predicted human performance. Although previous work led us to expect that IT would outperform V4, we were surprised by the quantitative precision with which simple IT-based linking hypotheses accounted for human behavior. Copyright © 2015 the authors 0270-6474/15/3513402-17$15.00/0.

rTMS on left prefrontal cortex contributes to memories for positive emotional cues: a comparison between pictures and words.

PubMed

Balconi, M; Cobelli, C

2015-02-26

The present research explored the cortical correlates of emotional memories in response to words and pictures. Subjects' performance (Accuracy Index, AI; response times, RTs; RTs/AI) was considered when a repetitive Transcranial Magnetic Stimulation (rTMS) was applied on the left dorsolateral prefrontal cortex (LDLPFC). Specifically, the role of LDLPFC was tested by performing a memory task, in which old (previously encoded targets) and new (previously not encoded distractors) emotional pictures/words had to be recognized. Valence (positive vs. negative) and arousing power (high vs. low) of stimuli were also modulated. Moreover, subjective evaluation of emotional stimuli in terms of valence/arousal was explored. We found significant performance improving (higher AI, reduced RTs, improved general performance) in response to rTMS. This "better recognition effect" was only related to specific emotional features, that is positive high arousal pictures or words. Moreover no significant differences were found between stimulus categories. A direct relationship was also observed between subjective evaluation of emotional cues and memory performance when rTMS was applied to LDLPFC. Supported by valence and approach model of emotions, we supposed that a left lateralized prefrontal system may induce a better recognition of positive high arousal words, and that evaluation of emotional cue is related to prefrontal activation, affecting the recognition memories of emotions. Copyright © 2014 IBRO. Published by Elsevier Ltd. All rights reserved.

Melodic Contour Identification and Music Perception by Cochlear Implant Users

PubMed Central

Galvin, John J.; Fu, Qian-Jie; Shannon, Robert V.

2013-01-01

Research and outcomes with cochlear implants (CIs) have revealed a dichotomy in the cues necessary for speech and music recognition. CI devices typically transmit 16–22 spectral channels, each modulated slowly in time. This coarse representation provides enough information to support speech understanding in quiet and rhythmic perception in music, but not enough to support speech understanding in noise or melody recognition. Melody recognition requires some capacity for complex pitch perception, which in turn depends strongly on access to spectral fine structure cues. Thus, temporal envelope cues are adequate for speech perception under optimal listening conditions, while spectral fine structure cues are needed for music perception. In this paper, we present recent experiments that directly measure CI users’ melodic pitch perception using a melodic contour identification (MCI) task. While normal-hearing (NH) listeners’ performance was consistently high across experiments, MCI performance was highly variable across CI users. CI users’ MCI performance was significantly affected by instrument timbre, as well as by the presence of a competing instrument. In general, CI users had great difficulty extracting melodic pitch from complex stimuli. However, musically-experienced CI users often performed as well as NH listeners, and MCI training in less experienced subjects greatly improved performance. With fixed constraints on spectral resolution, such as it occurs with hearing loss or an auditory prosthesis, training and experience can provide a considerable improvements in music perception and appreciation. PMID:19673835

Learning in shifts of transient attention improves recognition of parts of ambiguous figure-ground displays.

PubMed

Kristjánsson, Arni

2009-04-24

Previously demonstrated learning effects in shifts of transient attention have only been shown to result in beneficial effects upon secondary discrimination tasks and affect landing points of express saccades. Can such learning result in more direct effects upon perception than previously demonstrated? Observers performed a cued Vernier acuity discrimination task where the cue was one of a set of ambiguous figure-ground displays (with a black and white part). The critical measure was whether, if a target appeared consistently within a part of a cue of a certain brightness, this would result in learning effects and whether such learning would then affect recognition of the cue parts. Critically the target always appeared within the same part of each individual cue. Some cues were used in early parts of streaks of repetition of cue-part brightness, and others in latter parts of such streaks. All the observers showed learning in shifts of transient attention, with improved performance the more often the target appeared within the part of the cue of the same brightness. Subsequently the observers judged whether cue-parts had been parts of the cues used on the preceding discrimination task. Recognition of the figure parts, where the target had consistently appeared, improved strongly with increased length of streaks of repetition of cue-part brightness. Learning in shifts of transient attention leads not only to faster attention shifts but to direct effects upon perception, in this case recognition of parts of figure-ground ambiguous cues.

Apelin-13 exerts antidepressant-like and recognition memory improving activities in stressed rats.

PubMed

Li, E; Deng, Haifeng; Wang, Bo; Fu, Wan; You, Yong; Tian, Shaowen

2016-03-01

Apelin is the endogenous ligand for the G-protein-coupled receptor (APJ). The localization of APJ in limbic structures suggests a potential role for apelin in emotional processes. However, the role of apelin in the regulation of stress-induced responses such as depression and memory impairment is largely unknown. In the present study, we evaluated the role of apelin-13 in the regulation of stress-induced depression and memory impairment in rats. We report that repeated intracerebroventricular injections of apelin-13 reversed behavioral despair (immobility) in the forced swim (FS) test, a model widely used for the selection of new antidepressant agents. Apelin-13 also reversed behavioral deficits (escape failure) in the learned helplessness test. The magnitude of the antiimmobility and anti-escape failure effects of apelin-13 was comparable to that of imipramine, a classic antidepressant used as a positive control. Rats exposed to FS stress showed memory performance impairment in the novel object recognition test, and this impairment was improved by apelin-13 treatment. Apelin-13 did not affect recognition memory performance in non-stressed rats. Furthermore, the pretreatment of LY294002 (PI3K inhibitors) or PD98059 (ERK1/2 inhibitor) blocked apelin-13-mediated activities in FS-stressed rats. These findings suggest that apelin-13 exerts antidepressant-like and recognition memory improving activities through activating PI3K and ERK1/2 signaling pathways in stressed rats. Copyright © 2016 Elsevier B.V. and ECNP. All rights reserved.

REM sleep and emotional face memory in typically-developing children and children with autism.

PubMed

Tessier, Sophie; Lambert, Andréane; Scherzer, Peter; Jemel, Boutheina; Godbout, Roger

2015-09-01

Relationship between REM sleep and memory was assessed in 13 neurotypical and 13 children with Autistic Spectrum Disorder (ASD). A neutral/positive/negative face recognition task was administered the evening before (learning and immediate recognition) and the morning after (delayed recognition) sleep. The number of rapid eye movements (REMs), beta and theta EEG activity over the visual areas were measured during REM sleep. Compared to neurotypical children, children with ASD showed more theta activity and longer reaction time (RT) for correct responses in delayed recognition of neutral faces. Both groups showed a positive correlation between sleep and performance but different patterns emerged: in neurotypical children, accuracy for recalling neutral faces and overall RT improvement overnight was correlated with EEG activity and REMs; in children with ASD, overnight RT improvement for positive and negative faces correlated with theta and beta activity, respectively. These results suggest that neurotypical and children with ASD use different sleep-related brain networks to process faces. Copyright © 2015 Elsevier B.V. All rights reserved.

Maximum mutual information estimation of a simplified hidden MRF for offline handwritten Chinese character recognition

NASA Astrophysics Data System (ADS)

Xiong, Yan; Reichenbach, Stephen E.

1999-01-01

Understanding of hand-written Chinese characters is at such a primitive stage that models include some assumptions about hand-written Chinese characters that are simply false. So Maximum Likelihood Estimation (MLE) may not be an optimal method for hand-written Chinese characters recognition. This concern motivates the research effort to consider alternative criteria. Maximum Mutual Information Estimation (MMIE) is an alternative method for parameter estimation that does not derive its rationale from presumed model correctness, but instead examines the pattern-modeling problem in automatic recognition system from an information- theoretic point of view. The objective of MMIE is to find a set of parameters in such that the resultant model allows the system to derive from the observed data as much information as possible about the class. We consider MMIE for recognition of hand-written Chinese characters using on a simplified hidden Markov Random Field. MMIE provides improved performance improvement over MLE in this application.

An improved architecture for video rate image transformations

NASA Technical Reports Server (NTRS)

Fisher, Timothy E.; Juday, Richard D.

1989-01-01

Geometric image transformations are of interest to pattern recognition algorithms for their use in simplifying some aspects of the pattern recognition process. Examples include reducing sensitivity to rotation, scale, and perspective of the object being recognized. The NASA Programmable Remapper can perform a wide variety of geometric transforms at full video rate. An architecture is proposed that extends its abilities and alleviates many of the first version's shortcomings. The need for the improvements are discussed in the context of the initial Programmable Remapper and the benefits and limitations it has delivered. The implementation and capabilities of the proposed architecture are discussed.

Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1992-01-01

Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

Central administration of angiotensin IV rapidly enhances novel object recognition among mice.

PubMed

Paris, Jason J; Eans, Shainnel O; Mizrachi, Elisa; Reilley, Kate J; Ganno, Michelle L; McLaughlin, Jay P

2013-07-01

Angiotensin IV (Val(1)-Tyr(2)-Ile(3)-His(4)-Pro(5)-Phe(6)) has demonstrated potential cognitive-enhancing effects. The present investigation assessed and characterized: (1) dose-dependency of angiotensin IV's cognitive enhancement in a C57BL/6J mouse model of novel object recognition, (2) the time-course for these effects, (3) the identity of residues in the hexapeptide important to these effects and (4) the necessity of actions at angiotensin IV receptors for procognitive activity. Assessment of C57BL/6J mice in a novel object recognition task demonstrated that prior administration of angiotensin IV (0.1, 1.0, or 10.0, but not 0.01 nmol, i.c.v.) significantly enhanced novel object recognition in a dose-dependent manner. These effects were time dependent, with improved novel object recognition observed when angiotensin IV (0.1 nmol, i.c.v.) was administered 10 or 20, but not 30 min prior to the onset of the novel object recognition testing. An alanine scan of the angiotensin IV peptide revealed that replacement of the Val(1), Ile(3), His(4), or Phe(6) residues with Ala attenuated peptide-induced improvements in novel object recognition, whereas Tyr(2) or Pro(5) replacement did not significantly affect performance. Administration of the angiotensin IV receptor antagonist, divalinal-Ang IV (20 nmol, i.c.v.), reduced (but did not abolish) novel object recognition; however, this antagonist completely blocked the procognitive effects of angiotensin IV (0.1 nmol, i.c.v.) in this task. Rotorod testing demonstrated no locomotor effects with any angiotensin IV or divalinal-Ang IV dose tested. These data demonstrate that angiotensin IV produces a rapid enhancement of associative learning and memory performance in a mouse model that was dependent on the angiotensin IV receptor. Copyright © 2013 Elsevier Ltd. All rights reserved.

Central administration of angiotensin IV rapidly enhances novel object recognition among mice

PubMed Central

Paris, Jason J.; Eans, Shainnel O.; Mizrachi, Elisa; Reilley, Kate J.; Ganno, Michelle L.; McLaughlin, Jay P.

2013-01-01

Angiotensin IV (Val1-Tyr2-Ile3-His4-Pro5-Phe6) has demonstrated potential cognitive-enhancing effects. The present investigation assessed and characterized: (1) dose-dependency of angiotensin IV's cognitive enhancement in a C57BL/6J mouse model of novel object recognition, (2) the time-course for these effects, (3) the identity of residues in the hexapeptide important to these effects and (4) the necessity of actions at angiotensin IV receptors for pro-cognitive activity. Assessment of C57BL/6J mice in a novel object recognition task demonstrated that prior administration of angiotensin IV (0.1, 1.0, or 10.0, but not 0.01, nmol, i.c.v.) significantly enhanced novel object recognition in a dose-dependent manner. These effects were time dependent, with improved novel object recognition observed when angiotensin IV (0.1 nmol, i.c.v.) was administered 10 or 20, but not 30, min prior to the onset of the novel object recognition testing. An alanine scan of the angiotensin IV peptide revealed that replacement of the Val1, Ile3, His4, or Phe6 residues with Ala attenuated peptide-induced improvements in novel object recognition, whereas Tyr2 or Pro5 replacement did not significantly affect performance. Administration of the angiotensin IV receptor antagonist, divalinal-Ang IV (20 nmol, i.c.v.), reduced (but did not abolish) novel object recognition; however, this antagonist completely blocked the pro-cognitive effects of angiotensin IV (0.1 nmol, i.c.v.) in this task. Rotorod testing demonstrated no locomotor effects for any angiotensin IV or divalinal-Ang IV dose tested. These data demonstrate that angiotensin IV produces a rapid enhancement of associative learning and memory performance in a mouse model that was dependent on the angiotensin IV receptor. PMID:23416700

Towards Smart Homes Using Low Level Sensory Data

PubMed Central

Khattak, Asad Masood; Truc, Phan Tran Ho; Hung, Le Xuan; Vinh, La The; Dang, Viet-Hung; Guan, Donghai; Pervez, Zeeshan; Han, Manhyung; Lee, Sungyoung; Lee, Young-Koo

2011-01-01

Ubiquitous Life Care (u-Life care) is receiving attention because it provides high quality and low cost care services. To provide spontaneous and robust healthcare services, knowledge of a patient’s real-time daily life activities is required. Context information with real-time daily life activities can help to provide better services and to improve healthcare delivery. The performance and accuracy of existing life care systems is not reliable, even with a limited number of services. This paper presents a Human Activity Recognition Engine (HARE) that monitors human health as well as activities using heterogeneous sensor technology and processes these activities intelligently on a Cloud platform for providing improved care at low cost. We focus on activity recognition using video-based, wearable sensor-based, and location-based activity recognition engines and then use intelligent processing to analyze the context of the activities performed. The experimental results of all the components showed good accuracy against existing techniques. The system is deployed on Cloud for Alzheimer’s disease patients (as a case study) with four activity recognition engines to identify low level activity from the raw data captured by sensors. These are then manipulated using ontology to infer higher level activities and make decisions about a patient’s activity using patient profile information and customized rules. PMID:22247682

Approximated mutual information training for speech recognition using myoelectric signals.

PubMed

Guo, Hua J; Chan, A D C

2006-01-01

A new training algorithm called the approximated maximum mutual information (AMMI) is proposed to improve the accuracy of myoelectric speech recognition using hidden Markov models (HMMs). Previous studies have demonstrated that automatic speech recognition can be performed using myoelectric signals from articulatory muscles of the face. Classification of facial myoelectric signals can be performed using HMMs that are trained using the maximum likelihood (ML) algorithm; however, this algorithm maximizes the likelihood of the observations in the training sequence, which is not directly associated with optimal classification accuracy. The AMMI training algorithm attempts to maximize the mutual information, thereby training the HMMs to optimize their parameters for discrimination. Our results show that AMMI training consistently reduces the error rates compared to these by the ML training, increasing the accuracy by approximately 3% on average.

Cross spectral, active and passive approach to face recognition for improved performance

NASA Astrophysics Data System (ADS)

Grudzien, A.; Kowalski, M.; Szustakowski, M.

2017-08-01

Biometrics is a technique for automatic recognition of a person based on physiological or behavior characteristics. Since the characteristics used are unique, biometrics can create a direct link between a person and identity, based on variety of characteristics. The human face is one of the most important biometric modalities for automatic authentication. The most popular method of face recognition which relies on processing of visual information seems to be imperfect. Thermal infrared imagery may be a promising alternative or complement to visible range imaging due to its several reasons. This paper presents an approach of combining both methods.

Performance Study of the First 2D Prototype of Vertically Integrated Pattern Recognition Associative Memory (VIPRAM)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deptuch, Gregory; Hoff, James; Jindariani, Sergo

Extremely fast pattern recognition capabilities are necessary to find and fit billions of tracks at the hardware trigger level produced every second anticipated at high luminosity LHC (HL-LHC) running conditions. Associative Memory (AM) based approaches for fast pattern recognition have been proposed as a potential solution to the tracking trigger. However, at the HL-LHC, there is much less time available and speed performance must be improved over previous systems while maintaining a comparable number of patterns. The Vertically Integrated Pattern Recognition Associative Memory (VIPRAM) Project aims to achieve the target pattern density and performance goal using 3DIC technology. The firstmore » step taken in the VIPRAM work was the development of a 2D prototype (protoVIPRAM00) in which the associative memory building blocks were designed to be compatible with the 3D integration. In this paper, we present the results from extensive performance studies of the protoVIPRAM00 chip in both realistic HL-LHC and extreme conditions. Results indicate that the chip operates at the design frequency of 100 MHz with perfect correctness in realistic conditions and conclude that the building blocks are ready for 3D stacking. We also present performance boundary characterization of the chip under extreme conditions.« less

Environmental enrichment improves novel object recognition and enhances agonistic behavior in male mice.

PubMed

Mesa-Gresa, Patricia; Pérez-Martinez, Asunción; Redolat, Rosa

2013-01-01

Environmental enrichment (EE) is an experimental paradigm in which rodents are housed in complex environments containing objects that provide stimulation, the effects of which are expected to improve the welfare of these subjects. EE has been shown to considerably improve learning and memory in rodents. However, knowledge about the effects of EE on social interaction is generally limited and rather controversial. Thus, our aim was to evaluate both novel object recognition and agonistic behavior in NMRI mice receiving EE, hypothesizing enhanced cognition and slightly enhanced agonistic interaction upon EE rearing. During a 4-week period half the mice (n = 16) were exposed to EE and the other half (n = 16) remained in a standard environment (SE). On PND 56-57, animals performed the object recognition test, in which recognition memory was measured using a discrimination index. The social interaction test consisted of an encounter between an experimental animal and a standard opponent. Results indicated that EE mice explored the new object for longer periods than SE animals (P < .05). During social encounters, EE mice devoted more time to sociability and agonistic behavior (P < .05) than their non-EE counterparts. In conclusion, EE has been shown to improve object recognition and increase agonistic behavior in adolescent/early adulthood mice. In the future we intend to extend this study on a longitudinal basis in order to assess in more depth the effect of EE and the consistency of the above-mentioned observations in NMRI mice. Copyright © 2013 Wiley Periodicals, Inc.

EPO improved neurologic outcome in rat pups late after traumatic brain injury.

PubMed

Schober, Michelle E; Requena, Daniela F; Rodesch, Christopher K

2018-05-01

In adult rats, erythropoietin improved outcomes early and late after traumatic brain injury, associated with increased levels of Brain Derived Neurotrophic Factor. Using our model of pediatric traumatic brain injury, controlled cortical impact in 17-day old rats, we previously showed that erythropoietin increased hippocampal neuronal fraction in the first two days after injury. Erythropoietin also decreased activation of caspase3, an apoptotic enzyme modulated by Brain Derived Neurotrophic Factor, and improved Novel Object Recognition testing 14 days after injury. Data on long-term effects of erythropoietin on Brain Derived Neurotrophic Factor expression, histology and cognitive function after developmental traumatic brain injury are lacking. We hypothesized that erythropoietin would increase Brain Derived Neurotrophic Factor and improve long-term object recognition in rat pups after controlled cortical impact, associated with increased neuronal fraction in the hippocampus. Rats pups received erythropoietin or vehicle at 1, 24, and 48 h and 7 days after injury or sham surgery followed by histology at 35 days, Novel Object Recognition testing at adulthood, and Brain Derived Neurotrophic Factor measurements early and late after injury. Erythropoietin improved Novel Object Recognition performance and preserved hippocampal volume, but not neuronal fraction, late after injury. Improved object recognition in erythropoietin treated rats was associated with preserved hippocampal volume late after traumatic brain injury. Erythropoietin is approved to treat various pediatric conditions. Coupled with exciting experimental and clinical studies suggesting it is beneficial after neonatal hypoxic ischemic brain injury, our preliminary findings support further study of erythropoietin use after developmental traumatic brain injury. Copyright © 2018 The Japanese Society of Child Neurology. Published by Elsevier B.V. All rights reserved.

Improved perception of music with a harmonic based algorithm for cochlear implants.

PubMed

Li, Xing; Nie, Kaibao; Imennov, Nikita S; Rubinstein, Jay T; Atlas, Les E

2013-07-01

The lack of fine structure information in conventional cochlear implant (CI) encoding strategies presumably contributes to the generally poor music perception with CIs. To improve CI users' music perception, a harmonic-single-sideband-encoder (HSSE) strategy was developed , which explicitly tracks the harmonics of a single musical source and transforms them into modulators conveying both amplitude and temporal fine structure cues to electrodes. To investigate its effectiveness, vocoder simulations of HSSE and the conventional continuous-interleaved-sampling (CIS) strategy were implemented. Using these vocoders, five normal-hearing subjects' melody and timbre recognition performance were evaluated: a significant benefit of HSSE to both melody (p < 0.002) and timbre (p < 0.026) recognition was found. Additionally, HSSE was acutely tested in eight CI subjects. On timbre recognition, a significant advantage of HSSE over the subjects' clinical strategy was demonstrated: the largest improvement was 35% and the mean 17% (p < 0.013). On melody recognition, two subjects showed 20% improvement with HSSE; however, the mean improvement of 7% across subjects was not significant (p > 0.090). To quantify the temporal cues delivered to the auditory nerve, the neural spike patterns evoked by HSSE and CIS for one melody stimulus were simulated using an auditory nerve model. Quantitative analysis demonstrated that HSSE can convey temporal pitch cues better than CIS. The results suggest that HSSE is a promising strategy to enhance music perception with CIs.

Verbal overshadowing of visual memories: some things are better left unsaid.

PubMed

Schooler, J W; Engstler-Schooler, T Y

1990-01-01

It is widely believed that verbal processing generally improves memory performance. However, in a series of six experiments, verbalizing the appearance of previously seen visual stimuli impaired subsequent recognition performance. In Experiment 1, subjects viewed a videotape including a salient individual. Later, some subjects described the individual's face. Subjects who verbalized the face performed less well on a subsequent recognition test than control subjects who did not engage in memory verbalization. The results of Experiment 2 replicated those of Experiment 1 and further clarified the effect of memory verbalization by demonstrating that visualization does not impair face recognition. In Experiments 3 and 4 we explored the hypothesis that memory verbalization impairs memory for stimuli that are difficult to put into words. In Experiment 3 memory impairment followed the verbalization of a different visual stimulus: color. In Experiment 4 marginal memory improvement followed the verbalization of a verbal stimulus: a brief spoken statement. In Experiments 5 and 6 the source of verbally induced memory impairment was explored. The results of Experiment 5 suggested that the impairment does not reflect a temporary verbal set, but rather indicates relatively long-lasting memory interference. Finally, Experiment 6 demonstrated that limiting subjects' time to make recognition decisions alleviates the impairment, suggesting that memory verbalization overshadows but does not eradicate the original visual memory. This collection of results is consistent with a recording interference hypothesis: verbalizing a visual memory may produce a verbally biased memory representation that can interfere with the application of the original visual memory.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments.

PubMed

Baldominos, Alejandro; Saez, Yago; Isasi, Pedro

2018-04-23

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

PubMed Central

2018-01-01

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures. PMID:29690587

Improving Protein Fold Recognition by Deep Learning Networks

NASA Astrophysics Data System (ADS)

Jo, Taeho; Hou, Jie; Eickholt, Jesse; Cheng, Jianlin

2015-12-01

For accurate recognition of protein folds, a deep learning network method (DN-Fold) was developed to predict if a given query-template protein pair belongs to the same structural fold. The input used stemmed from the protein sequence and structural features extracted from the protein pair. We evaluated the performance of DN-Fold along with 18 different methods on Lindahl’s benchmark dataset and on a large benchmark set extracted from SCOP 1.75 consisting of about one million protein pairs, at three different levels of fold recognition (i.e., protein family, superfamily, and fold) depending on the evolutionary distance between protein sequences. The correct recognition rate of ensembled DN-Fold for Top 1 predictions is 84.5%, 61.5%, and 33.6% and for Top 5 is 91.2%, 76.5%, and 60.7% at family, superfamily, and fold levels, respectively. We also evaluated the performance of single DN-Fold (DN-FoldS), which showed the comparable results at the level of family and superfamily, compared to ensemble DN-Fold. Finally, we extended the binary classification problem of fold recognition to real-value regression task, which also show a promising performance. DN-Fold is freely available through a web server at http://iris.rnet.missouri.edu/dnfold.

Facial expression recognition based on improved deep belief networks

NASA Astrophysics Data System (ADS)

Wu, Yao; Qiu, Weigen

2017-08-01

In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

NASA Astrophysics Data System (ADS)

Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

2014-09-01

Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

Oxytocin Reduces Face Processing Time but Leaves Recognition Accuracy and Eye-Gaze Unaffected.

PubMed

Hubble, Kelly; Daughters, Katie; Manstead, Antony S R; Rees, Aled; Thapar, Anita; van Goozen, Stephanie H M

2017-01-01

Previous studies have found that oxytocin (OXT) can improve the recognition of emotional facial expressions; it has been proposed that this effect is mediated by an increase in attention to the eye-region of faces. Nevertheless, evidence in support of this claim is inconsistent, and few studies have directly tested the effect of oxytocin on emotion recognition via altered eye-gaze Methods: In a double-blind, within-subjects, randomized control experiment, 40 healthy male participants received 24 IU intranasal OXT and placebo in two identical experimental sessions separated by a 2-week interval. Visual attention to the eye-region was assessed on both occasions while participants completed a static facial emotion recognition task using medium intensity facial expressions. Although OXT had no effect on emotion recognition accuracy, recognition performance was improved because face processing was faster across emotions under the influence of OXT. This effect was marginally significant (p<.06). Consistent with a previous study using dynamic stimuli, OXT had no effect on eye-gaze patterns when viewing static emotional faces and this was not related to recognition accuracy or face processing time. These findings suggest that OXT-induced enhanced facial emotion recognition is not necessarily mediated by an increase in attention to the eye-region of faces, as previously assumed. We discuss several methodological issues which may explain discrepant findings and suggest the effect of OXT on visual attention may differ depending on task requirements. (JINS, 2017, 23, 23-33).

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

PubMed Central

Yoon, Sang Min

2018-01-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. PMID:29614767

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.

PubMed

Cho, Heeryon; Yoon, Sang Min

2018-04-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification.

PubMed

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database.

Performance Evaluation of Multimodal Multifeature Authentication System Using KNN Classification

PubMed Central

Rajagopal, Gayathri; Palaniswamy, Ramamoorthy

2015-01-01

This research proposes a multimodal multifeature biometric system for human recognition using two traits, that is, palmprint and iris. The purpose of this research is to analyse integration of multimodal and multifeature biometric system using feature level fusion to achieve better performance. The main aim of the proposed system is to increase the recognition accuracy using feature level fusion. The features at the feature level fusion are raw biometric data which contains rich information when compared to decision and matching score level fusion. Hence information fused at the feature level is expected to obtain improved recognition accuracy. However, information fused at feature level has the problem of curse in dimensionality; here PCA (principal component analysis) is used to diminish the dimensionality of the feature sets as they are high dimensional. The proposed multimodal results were compared with other multimodal and monomodal approaches. Out of these comparisons, the multimodal multifeature palmprint iris fusion offers significant improvements in the accuracy of the suggested multimodal biometric system. The proposed algorithm is tested using created virtual multimodal database using UPOL iris database and PolyU palmprint database. PMID:26640813

An investigation into non-invasive physical activity recognition using smartphones.

PubMed

Kelly, Daniel; Caulfield, Brian

2012-01-01

Technology utilized to automatically monitor Activities of Daily Living (ADL) could be a key component in identifying deviations from normal functional profiles and providing feedback on interventions aimed at improving health. However, if activity recognition systems are to be implemented in real world scenarios such as health and wellness monitoring, the activity sensing modality must unobtrusively fit the human environment rather than forcing humans to adhere to sensor specific conditions. Modern smart phones represent a ubiquitous computing device which has already undergone mainstream adoption. In this paper, we investigate the feasibility of using a modern smartphone, with limited placement constraints, as the sensing modality for an activity recognition system. A dataset of 4 subjects performing 7 activities, using varying sensor placement conditions, is utilized to investigate this. Initial experiments show that a decision tree classifier performs activity classification with precision and recall scores of 0.75 and 0.73 respectively. More importantly, as part of this initial experiment, 3 main problems, and subsequently 3 solutions, relating to unconstrained sensor placement were identified. Using our proposed solutions, classification precision and recall scores were improved by +13% and +14.6% respectively.

Semi-automated contour recognition using DICOMautomaton

NASA Astrophysics Data System (ADS)

Clark, H.; Wu, J.; Moiseenko, V.; Lee, R.; Gill, B.; Duzenli, C.; Thomas, S.

2014-03-01

Purpose: A system has been developed which recognizes and classifies Digital Imaging and Communication in Medicine contour data with minimal human intervention. It allows researchers to overcome obstacles which tax analysis and mining systems, including inconsistent naming conventions and differences in data age or resolution. Methods: Lexicographic and geometric analysis is used for recognition. Well-known lexicographic methods implemented include Levenshtein-Damerau, bag-of-characters, Double Metaphone, Soundex, and (word and character)-N-grams. Geometrical implementations include 3D Fourier Descriptors, probability spheres, boolean overlap, simple feature comparison (e.g. eccentricity, volume) and rule-based techniques. Both analyses implement custom, domain-specific modules (e.g. emphasis differentiating left/right organ variants). Contour labels from 60 head and neck patients are used for cross-validation. Results: Mixed-lexicographical methods show an effective improvement in more than 10% of recognition attempts compared with a pure Levenshtein-Damerau approach when withholding 70% of the lexicon. Domain-specific and geometrical techniques further boost performance. Conclusions: DICOMautomaton allows users to recognize contours semi-automatically. As usage increases and the lexicon is filled with additional structures, performance improves, increasing the overall utility of the system.

Improving a HMM-based off-line handwriting recognition system using MME-PSO optimization

NASA Astrophysics Data System (ADS)

Hamdani, Mahdi; El Abed, Haikal; Hamdani, Tarek M.; Märgner, Volker; Alimi, Adel M.

2011-01-01

One of the trivial steps in the development of a classifier is the design of its architecture. This paper presents a new algorithm, Multi Models Evolvement (MME) using Particle Swarm Optimization (PSO). This algorithm is a modified version of the basic PSO, which is used to the unsupervised design of Hidden Markov Model (HMM) based architectures. For instance, the proposed algorithm is applied to an Arabic handwriting recognizer based on discrete probability HMMs. After the optimization of their architectures, HMMs are trained with the Baum- Welch algorithm. The validation of the system is based on the IfN/ENIT database. The performance of the developed approach is compared to the participating systems at the 2005 competition organized on Arabic handwriting recognition on the International Conference on Document Analysis and Recognition (ICDAR). The final system is a combination between an optimized HMM with 6 other HMMs obtained by a simple variation of the number of states. An absolute improvement of 6% of word recognition rate with about 81% is presented. This improvement is achieved comparing to the basic system (ARAB-IfN). The proposed recognizer outperforms also most of the known state-of-the-art systems.

Recognition memory: a review of the critical findings and an integrated theory for relating them.

PubMed

Malmberg, Kenneth J

2008-12-01

The development of formal models has aided theoretical progress in recognition memory research. Here, I review the findings that are critical for testing them, including behavioral and brain imaging results of single-item recognition, plurality discrimination, and associative recognition experiments under a variety of testing conditions. I also review the major approaches to measurement and process modeling of recognition. The review indicates that several extant dual-process measures of recollection are unreliable, and thus they are unsuitable as a basis for forming strong conclusions. At the process level, however, the retrieval dynamics of recognition memory and the effect of strengthening operations suggest that a recall-to-reject process plays an important role in plurality discrimination and associative recognition, but not necessarily in single-item recognition. A new theoretical framework proposes that the contribution of recollection to recognition depends on whether the retrieval of episodic details improves accuracy, and it organizes the models around the construct of efficiency. Accordingly, subjects adopt strategies that they believe will produce a desired level of accuracy in the shortest amount of time. Several models derived from this framework are shown to account the accuracy, latency, and confidence with which the various recognition tasks are performed.

Effects of non-invasive brain stimulation on associative memory

DOE PAGES

Matzen, Laura E.; Trumbo, Michael C.; Leach, Ryan C.; ...

2015-07-30

Associative memory refers to remembering the association between two items, such as a face and a name. It is a crucial part of daily life, but it is also one of the first aspects of memory performance that is impacted by aging and by Alzheimer’s disease. Evidence suggests that transcranial direct current stimulation (tDCS) can improve memory performance, but few tDCS studies have investigated its impact on associative memory. In addition, no prior study of the effects of tDCS on memory performance has systematically evaluated the impact of tDCS on different types of memory assessments, such as recognition and recallmore » tests. In this study, we measured the effects of tDCS on associative memory performance in healthy adults, using both recognition and recall tests. Participants studied face-name pairs while receiving either active (30 minutes, 2 mA) or sham (30 minutes, 0.1 mA) stimulation with the anode placed at F9 and the cathode placed on the contralateral upper arm. Participants in the active stimulation group performed significantly better on the recall test than participants in the sham group, recalling 50% more names, on average, and making fewer recall errors. However, the two groups did not differ significantly in terms of their performance on the recognition memory test. This investigation provides evidence that stimulation at the time of study improves associative memory encoding, but that this memory benefit is evident only under certain retrieval conditions.« less

Effects of non-invasive brain stimulation on associative memory.

PubMed

Matzen, Laura E; Trumbo, Michael C; Leach, Ryan C; Leshikar, Eric D

2015-10-22

Associative memory refers to remembering the association between two items, such as a face and a name. It is a crucial part of daily life, but it is also one of the first aspects of memory performance that is impacted by aging and by Alzheimer's disease. Evidence suggests that transcranial direct current stimulation (tDCS) can improve memory performance, but few tDCS studies have investigated its impact on associative memory. In addition, no prior study of the effects of tDCS on memory performance has systematically evaluated the impact of tDCS on different types of memory assessments, such as recognition and recall tests. In this study, we measured the effects of tDCS on associative memory performance in healthy adults, using both recognition and recall tests. Participants studied face-name pairs while receiving either active (30 min, 2 mA) or sham (30 min, 0.1 mA) stimulation with the anode placed at F9 and the cathode placed on the contralateral upper arm. Participants in the active stimulation group performed significantly better on the recall test than participants in the sham group, recalling 50% more names, on average, and making fewer recall errors. However, the two groups did not differ significantly in terms of their performance on the recognition memory test. This investigation provides evidence that stimulation at the time of study improves associative memory encoding, but that this memory benefit is evident only under certain retrieval conditions. Copyright © 2015 Elsevier B.V. All rights reserved.

Effects of non-invasive brain stimulation on associative memory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzen, Laura E.; Trumbo, Michael C.; Leach, Ryan C.

Associative memory refers to remembering the association between two items, such as a face and a name. It is a crucial part of daily life, but it is also one of the first aspects of memory performance that is impacted by aging and by Alzheimer’s disease. Evidence suggests that transcranial direct current stimulation (tDCS) can improve memory performance, but few tDCS studies have investigated its impact on associative memory. In addition, no prior study of the effects of tDCS on memory performance has systematically evaluated the impact of tDCS on different types of memory assessments, such as recognition and recallmore » tests. In this study, we measured the effects of tDCS on associative memory performance in healthy adults, using both recognition and recall tests. Participants studied face-name pairs while receiving either active (30 minutes, 2 mA) or sham (30 minutes, 0.1 mA) stimulation with the anode placed at F9 and the cathode placed on the contralateral upper arm. Participants in the active stimulation group performed significantly better on the recall test than participants in the sham group, recalling 50% more names, on average, and making fewer recall errors. However, the two groups did not differ significantly in terms of their performance on the recognition memory test. This investigation provides evidence that stimulation at the time of study improves associative memory encoding, but that this memory benefit is evident only under certain retrieval conditions.« less

Body-Earth Mover's Distance: A Matching-Based Approach for Sleep Posture Recognition.

PubMed

Xu, Xiaowei; Lin, Feng; Wang, Aosen; Hu, Yu; Huang, Ming-Chun; Xu, Wenyao

2016-10-01

Sleep posture is a key component in sleep quality assessment and pressure ulcer prevention. Currently, body pressure analysis has been a popular method for sleep posture recognition. In this paper, a matching-based approach, Body-Earth Mover's Distance (BEMD), for sleep posture recognition is proposed. BEMD treats pressure images as weighted 2D shapes, and combines EMD and Euclidean distance for similarity measure. Compared with existing work, sleep posture recognition is achieved with posture similarity rather than multiple features for specific postures. A pilot study is performed with 14 persons for six different postures. The experimental results show that the proposed BEMD can achieve 91.21% accuracy, which outperforms the previous method with an improvement of 8.01%.

Face recognition using 3D facial shape and color map information: comparison and combination

NASA Astrophysics Data System (ADS)

Godil, Afzal; Ressler, Sandy; Grother, Patrick

2004-08-01

In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions.

Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

PubMed Central

Li, Xin; Guo, Rui; Chen, Chao

2014-01-01

Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR) video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians), especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach. PMID:24961216

Improved Iris Recognition through Fusion of Hamming Distance and Fragile Bit Distance.

PubMed

Hollingsworth, Karen P; Bowyer, Kevin W; Flynn, Patrick J

2011-12-01

The most common iris biometric algorithm represents the texture of an iris using a binary iris code. Not all bits in an iris code are equally consistent. A bit is deemed fragile if its value changes across iris codes created from different images of the same iris. Previous research has shown that iris recognition performance can be improved by masking these fragile bits. Rather than ignoring fragile bits completely, we consider what beneficial information can be obtained from the fragile bits. We find that the locations of fragile bits tend to be consistent across different iris codes of the same eye. We present a metric, called the fragile bit distance, which quantitatively measures the coincidence of the fragile bit patterns in two iris codes. We find that score fusion of fragile bit distance and Hamming distance works better for recognition than Hamming distance alone. To our knowledge, this is the first and only work to use the coincidence of fragile bit locations to improve the accuracy of matches.

Improving human object recognition performance using video enhancement techniques

NASA Astrophysics Data System (ADS)

Whitman, Lucy S.; Lewis, Colin; Oakley, John P.

2004-12-01

Atmospheric scattering causes significant degradation in the quality of video images, particularly when imaging over long distances. The principle problem is the reduction in contrast due to scattered light. It is known that when the scattering particles are not too large compared with the imaging wavelength (i.e. Mie scattering) then high spatial resolution information may be contained within a low-contrast image. Unfortunately this information is not easily perceived by a human observer, particularly when using a standard video monitor. A secondary problem is the difficulty of achieving a sharp focus since automatic focus techniques tend to fail in such conditions. Recently several commercial colour video processing systems have become available. These systems use various techniques to improve image quality in low contrast conditions whilst retaining colour content. These systems produce improvements in subjective image quality in some situations, particularly in conditions of haze and light fog. There is also some evidence that video enhancement leads to improved ATR performance when used as a pre-processing stage. Psychological literature indicates that low contrast levels generally lead to a reduction in the performance of human observers in carrying out simple visual tasks. The aim of this paper is to present the results of an empirical study on object recognition in adverse viewing conditions. The chosen visual task was vehicle number plate recognition at long ranges (500 m and beyond). Two different commercial video enhancement systems are evaluated using the same protocol. The results show an increase in effective range with some differences between the different enhancement systems.

A comparison study between MLP and convolutional neural network models for character recognition

NASA Astrophysics Data System (ADS)

Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

2017-05-01

Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

Using Temporal Modulation Sensitivity to Select Stimulation Sites for Processor MAPs in Cochlear Implant Listeners

PubMed Central

Garadat, Soha N.; Zwolan, Teresa A.; Pfingst, Bryan E.

2013-01-01

Previous studies in our laboratory showed that temporal acuity as assessed by modulation detection thresholds (MDTs) varied across activation sites and that this site-to-site variability was subject specific. Using two 10-channel MAPs, the previous experiments showed that processor MAPs that had better across-site mean (ASM) MDTs yielded better speech recognition than MAPs with poorer ASM MDTs tested in the same subject. The current study extends our earlier work on developing more optimal fitting strategies to test the feasibility of using a site-selection approach in the clinical domain. This study examined the hypothesis that revising the clinical speech processor MAP for cochlear implant (CI) recipients by turning off selected sites that have poorer temporal acuity and reallocating frequencies to the remaining electrodes would lead to improved speech recognition. Twelve CI recipients participated in the experiments. We found that site selection procedure based on MDTs in the presence of a masker resulted in improved performance on consonant recognition and recognition of sentences in noise. In contrast, vowel recognition was poorer with the experimental MAP than with the clinical MAP, possibly due to reduced spectral resolution when sites were removed from the experimental MAP. Overall, these results suggest a promising path for improving recipient outcomes using personalized processor-fitting strategies based on a psychophysical measure of temporal acuity. PMID:23881208

Near infrared and visible face recognition based on decision fusion of LBP and DCT features

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-03-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In order to extract the discriminative complementary features between near infrared and visible images, in this paper, we proposed a novel near infrared and visible face fusion recognition algorithm based on DCT and LBP features. Firstly, the effective features in near-infrared face image are extracted by the low frequency part of DCT coefficients and the partition histograms of LBP operator. Secondly, the LBP features of visible-light face image are extracted to compensate for the lacking detail features of the near-infrared face image. Then, the LBP features of visible-light face image, the DCT and LBP features of near-infrared face image are sent to each classifier for labeling. Finally, decision level fusion strategy is used to obtain the final recognition result. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. The experiment results show that the proposed method extracts the complementary features of near-infrared and visible face images and improves the robustness of unconstrained face recognition. Especially for the circumstance of small training samples, the recognition rate of proposed method can reach 96.13%, which has improved significantly than 92.75 % of the method based on statistical feature fusion.

Performance and strategy comparisons of human listeners and logistic regression in discriminating underwater targets.

PubMed

Yang, Lixue; Chen, Kean

2015-11-01

To improve the design of underwater target recognition systems based on auditory perception, this study compared human listeners with automatic classifiers. Performances measures and strategies in three discrimination experiments, including discriminations between man-made and natural targets, between ships and submarines, and among three types of ships, were used. In the experiments, the subjects were asked to assign a score to each sound based on how confident they were about the category to which it belonged, and logistic regression, which represents linear discriminative models, also completed three similar tasks by utilizing many auditory features. The results indicated that the performances of logistic regression improved as the ratio between inter- and intra-class differences became larger, whereas the performances of the human subjects were limited by their unfamiliarity with the targets. Logistic regression performed better than the human subjects in all tasks but the discrimination between man-made and natural targets, and the strategies employed by excellent human subjects were similar to that of logistic regression. Logistic regression and several human subjects demonstrated similar performances when discriminating man-made and natural targets, but in this case, their strategies were not similar. An appropriate fusion of their strategies led to further improvement in recognition accuracy.

Text recognition and correction for automated data collection by mobile devices

NASA Astrophysics Data System (ADS)

Ozarslan, Suleyman; Eren, P. Erhan

2014-03-01

Participatory sensing is an approach which allows mobile devices such as mobile phones to be used for data collection, analysis and sharing processes by individuals. Data collection is the first and most important part of a participatory sensing system, but it is time consuming for the participants. In this paper, we discuss automatic data collection approaches for reducing the time required for collection, and increasing the amount of collected data. In this context, we explore automated text recognition on images of store receipts which are captured by mobile phone cameras, and the correction of the recognized text. Accordingly, our first goal is to evaluate the performance of the Optical Character Recognition (OCR) method with respect to data collection from store receipt images. Images captured by mobile phones exhibit some typical problems, and common image processing methods cannot handle some of them. Consequently, the second goal is to address these types of problems through our proposed Knowledge Based Correction (KBC) method used in support of the OCR, and also to evaluate the KBC method with respect to the improvement on the accurate recognition rate. Results of the experiments show that the KBC method improves the accurate data recognition rate noticeably.

Talker variability in audio-visual speech perception

PubMed Central

Heald, Shannon L. M.; Nusbaum, Howard C.

2014-01-01

A change in talker is a change in the context for the phonetic interpretation of acoustic patterns of speech. Different talkers have different mappings between acoustic patterns and phonetic categories and listeners need to adapt to these differences. Despite this complexity, listeners are adept at comprehending speech in multiple-talker contexts, albeit at a slight but measurable performance cost (e.g., slower recognition). So far, this talker variability cost has been demonstrated only in audio-only speech. Other research in single-talker contexts have shown, however, that when listeners are able to see a talker’s face, speech recognition is improved under adverse listening (e.g., noise or distortion) conditions that can increase uncertainty in the mapping between acoustic patterns and phonetic categories. Does seeing a talker’s face reduce the cost of word recognition in multiple-talker contexts? We used a speeded word-monitoring task in which listeners make quick judgments about target word recognition in single- and multiple-talker contexts. Results show faster recognition performance in single-talker conditions compared to multiple-talker conditions for both audio-only and audio-visual speech. However, recognition time in a multiple-talker context was slower in the audio-visual condition compared to audio-only condition. These results suggest that seeing a talker’s face during speech perception may slow recognition by increasing the importance of talker identification, signaling to the listener a change in talker has occurred. PMID:25076919

Talker variability in audio-visual speech perception.

PubMed

Heald, Shannon L M; Nusbaum, Howard C

2014-01-01

A change in talker is a change in the context for the phonetic interpretation of acoustic patterns of speech. Different talkers have different mappings between acoustic patterns and phonetic categories and listeners need to adapt to these differences. Despite this complexity, listeners are adept at comprehending speech in multiple-talker contexts, albeit at a slight but measurable performance cost (e.g., slower recognition). So far, this talker variability cost has been demonstrated only in audio-only speech. Other research in single-talker contexts have shown, however, that when listeners are able to see a talker's face, speech recognition is improved under adverse listening (e.g., noise or distortion) conditions that can increase uncertainty in the mapping between acoustic patterns and phonetic categories. Does seeing a talker's face reduce the cost of word recognition in multiple-talker contexts? We used a speeded word-monitoring task in which listeners make quick judgments about target word recognition in single- and multiple-talker contexts. Results show faster recognition performance in single-talker conditions compared to multiple-talker conditions for both audio-only and audio-visual speech. However, recognition time in a multiple-talker context was slower in the audio-visual condition compared to audio-only condition. These results suggest that seeing a talker's face during speech perception may slow recognition by increasing the importance of talker identification, signaling to the listener a change in talker has occurred.

Mathematical morphology-based shape feature analysis for Chinese character recognition systems

NASA Astrophysics Data System (ADS)

Pai, Tun-Wen; Shyu, Keh-Hwa; Chen, Ling-Fan; Tai, Gwo-Chin

1995-04-01

This paper proposes an efficient technique of shape feature extraction based on the application of mathematical morphology theory. A new shape complexity index for preclassification of machine printed Chinese Character Recognition (CCR) is also proposed. For characters represented in different fonts/sizes or in a low resolution environment, a more stable local feature such as shape structure is preferred for character recognition. Morphological valley extraction filters are applied to extract the protrusive strokes from four sides of an input Chinese character. The number of extracted local strokes reflects the shape complexity of each side. These shape features of characters are encoded as corresponding shape complexity indices. Based on the shape complexity index, data base is able to be classified into 16 groups prior to recognition procedures. The performance of associating with shape feature analysis reclaims several characters from misrecognized character sets and results in an average of 3.3% improvement of recognition rate from an existing recognition system. In addition to enhance the recognition performance, the extracted stroke information can be further analyzed and classified its own stroke type. Therefore, the combination of extracted strokes from each side provides a means for data base clustering based on radical or subword components. It is one of the best solutions for recognizing high complexity characters such as Chinese characters which are divided into more than 200 different categories and consist more than 13,000 characters.

"It's Always the Judge's Fault": Attention, Emotion Recognition, and Expertise in Rhythmic Gymnastics Assessment.

PubMed

van Bokhorst, Lindsey G; Knapová, Lenka; Majoranc, Kim; Szebeni, Zea K; Táborský, Adam; Tomić, Dragana; Cañadas, Elena

2016-01-01

In many sports, such as figure skating or gymnastics, the outcome of a performance does not rely exclusively on objective measurements, but on more subjective cues. Judges need high attentional capacities to process visual information and overcome fatigue. Also their emotion recognition abilities might have an effect in detecting errors and making a more accurate assessment. Moreover, the scoring given by judges could be also influenced by their level of expertise. This study aims to assess how rhythmic gymnastics judges' emotion recognition and attentional abilities influence accuracy of performance assessment. Data will be collected from rhythmic gymnastics judges and coaches at different international levels. This study will employ an online questionnaire consisting on an emotion recognition test and attentional test. Participants' task is to watch a set of videotaped rhythmic gymnastics performances and evaluate them on the artistic and execution components of performance. Their scoring will be compared with the official scores given at the competition the video was taken from to measure the accuracy of the participants' evaluations. The proposed research represents an interdisciplinary approach that integrates cognitive and sport psychology within experimental and applied contexts. The current study advances the theoretical understanding of how emotional and attentional aspects affect the evaluation of sport performance. The results will provide valuable evidence on the direction and strength of the relationship between the above-mentioned factors and the accuracy of sport performance evaluation. Importantly, practical implications might be drawn from this study. Intervention programs directed at improving the accuracy of judges could be created based on the understanding of how emotion recognition and attentional abilities are related to the accuracy of performance assessment.

“It’s Always the Judge’s Fault”: Attention, Emotion Recognition, and Expertise in Rhythmic Gymnastics Assessment

PubMed Central

van Bokhorst, Lindsey G.; Knapová, Lenka; Majoranc, Kim; Szebeni, Zea K.; Táborský, Adam; Tomić, Dragana; Cañadas, Elena

2016-01-01

In many sports, such as figure skating or gymnastics, the outcome of a performance does not rely exclusively on objective measurements, but on more subjective cues. Judges need high attentional capacities to process visual information and overcome fatigue. Also their emotion recognition abilities might have an effect in detecting errors and making a more accurate assessment. Moreover, the scoring given by judges could be also influenced by their level of expertise. This study aims to assess how rhythmic gymnastics judges’ emotion recognition and attentional abilities influence accuracy of performance assessment. Data will be collected from rhythmic gymnastics judges and coaches at different international levels. This study will employ an online questionnaire consisting on an emotion recognition test and attentional test. Participants’ task is to watch a set of videotaped rhythmic gymnastics performances and evaluate them on the artistic and execution components of performance. Their scoring will be compared with the official scores given at the competition the video was taken from to measure the accuracy of the participants’ evaluations. The proposed research represents an interdisciplinary approach that integrates cognitive and sport psychology within experimental and applied contexts. The current study advances the theoretical understanding of how emotional and attentional aspects affect the evaluation of sport performance. The results will provide valuable evidence on the direction and strength of the relationship between the above-mentioned factors and the accuracy of sport performance evaluation. Importantly, practical implications might be drawn from this study. Intervention programs directed at improving the accuracy of judges could be created based on the understanding of how emotion recognition and attentional abilities are related to the accuracy of performance assessment. PMID:27458406

Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

NASA Astrophysics Data System (ADS)

Yin, Xi; Liu, Xiaoming

2018-02-01

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

Effects of the duration of expressions on the recognition of microexpressions*

PubMed Central

Shen, Xun-bing; Wu, Qi; Fu, Xiao-lan

2012-01-01

Objective: The purpose of this study was to investigate the effects of the duration of expressions on the recognition of microexpressions, which are closely related to deception. Methods: In two experiments, participants were briefly (from 20 to 300 ms) shown one of six basic expressions and then were asked to identify the expression. Results: The results showed that the participants’ performance in recognition of microexpressions increased with the duration of the expressions, reaching a turning point at 200 ms before levelling off. The results also indicated that practice could improve the participants’ performance. Conclusions: The results of this study suggest that the proper upper limit of the duration of microexpressions might be around 1/5 of a second and confirmed that the ability to recognize microexpressions can be enhanced with practice. PMID:22374615

No childhood development of viewpoint-invariant face recognition: evidence from 8-year-olds and adults.

PubMed

Crookes, Kate; Robbins, Rachel A

2014-10-01

Performance on laboratory face tasks improves across childhood, not reaching adult levels until adolescence. Debate surrounds the source of this development, with recent reviews suggesting that underlying face processing mechanisms are mature early in childhood and that the improvement seen on experimental tasks instead results from general cognitive/perceptual development. One face processing mechanism that has been argued to develop slowly is the ability to encode faces in a view-invariant manner (i.e., allowing recognition across changes in viewpoint). However, many previous studies have not controlled for general cognitive factors. In the current study, 8-year-olds and adults performed a recognition memory task with two study-test viewpoint conditions: same view (study front view, test front view) and change view (study front view, test three-quarter view). To allow quantitative comparison between children and adults, performance in the same view condition was matched across the groups by increasing the learning set size for adults. Results showed poorer memory in the change view condition than in the same view condition for both adults and children. Importantly, there was no quantitative difference between children and adults in the size of decrement in memory performance resulting from a change in viewpoint. This finding adds to growing evidence that face processing mechanisms are mature early in childhood. Copyright © 2014 Elsevier Inc. All rights reserved.

Improved facial affect recognition in schizophrenia following an emotion intervention, but not training attention-to-facial-features or treatment-as-usual.

PubMed

Tsotsi, Stella; Kosmidis, Mary H; Bozikas, Vasilis P

2017-08-01

In schizophrenia, impaired facial affect recognition (FAR) has been associated with patients' overall social functioning. Interventions targeting attention or FAR per se have invariably yielded improved FAR performance in these patients. Here, we compared the effects of two interventions, one targeting FAR and one targeting attention-to-facial-features, with treatment-as-usual on patients' FAR performance. Thirty-nine outpatients with schizophrenia were randomly assigned to one of three groups: FAR intervention (training to recognize emotional information, conveyed by changes in facial features), attention-to-facial-features intervention (training to detect changes in facial features), and treatment-as-usual. Also, 24 healthy controls, matched for age and education, were assigned to one of the two interventions. Two FAR measurements, baseline and post-intervention, were conducted using an original experimental procedure with alternative sets of stimuli. We found improved FAR performance following the intervention targeting FAR in comparison to the other patient groups, which in fact was comparable to the pre-intervention performance of healthy controls in the corresponding intervention group. This improvement was more pronounced in recognizing fear. Our findings suggest that compared to interventions targeting attention, and treatment-as-usual, training programs targeting FAR can be more effective in improving FAR in patients with schizophrenia, particularly assisting them in perceiving threat-related information more accurately. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Developing a hybrid dictionary-based bio-entity recognition technique.

PubMed

Song, Min; Yu, Hwanjo; Han, Wook-Shin

2015-01-01

Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.

Developing a hybrid dictionary-based bio-entity recognition technique

PubMed Central

2015-01-01

Background Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall. PMID:26043907

Boosting drug named entity recognition using an aggregate classifier.

PubMed

Korkontzelos, Ioannis; Piliouras, Dimitrios; Dowsey, Andrew W; Ananiadou, Sophia

2015-10-01

Drug named entity recognition (NER) is a critical step for complex biomedical NLP tasks such as the extraction of pharmacogenomic, pharmacodynamic and pharmacokinetic parameters. Large quantities of high quality training data are almost always a prerequisite for employing supervised machine-learning techniques to achieve high classification performance. However, the human labour needed to produce and maintain such resources is a significant limitation. In this study, we improve the performance of drug NER without relying exclusively on manual annotations. We perform drug NER using either a small gold-standard corpus (120 abstracts) or no corpus at all. In our approach, we develop a voting system to combine a number of heterogeneous models, based on dictionary knowledge, gold-standard corpora and silver annotations, to enhance performance. To improve recall, we employed genetic programming to evolve 11 regular-expression patterns that capture common drug suffixes and used them as an extra means for recognition. Our approach uses a dictionary of drug names, i.e. DrugBank, a small manually annotated corpus, i.e. the pharmacokinetic corpus, and a part of the UKPMC database, as raw biomedical text. Gold-standard and silver annotated data are used to train maximum entropy and multinomial logistic regression classifiers. Aggregating drug NER methods, based on gold-standard annotations, dictionary knowledge and patterns, improved the performance on models trained on gold-standard annotations, only, achieving a maximum F-score of 95%. In addition, combining models trained on silver annotations, dictionary knowledge and patterns are shown to achieve comparable performance to models trained exclusively on gold-standard data. The main reason appears to be the morphological similarities shared among drug names. We conclude that gold-standard data are not a hard requirement for drug NER. Combining heterogeneous models build on dictionary knowledge can achieve similar or comparable classification performance with that of the best performing model trained on gold-standard annotations. Copyright © 2015 The Authors. Published by Elsevier B.V. All rights reserved.

Speaker emotion recognition: from classical classifiers to deep neural networks

NASA Astrophysics Data System (ADS)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2018-04-01

Speaker emotion recognition is considered among the most challenging tasks in recent years. In fact, automatic systems for security, medicine or education can be improved when considering the speech affective state. In this paper, a twofold approach for speech emotion classification is proposed. At the first side, a relevant set of features is adopted, and then at the second one, numerous supervised training techniques, involving classic methods as well as deep learning, are experimented. Experimental results indicate that deep architecture can improve classification performance on two affective databases, the Berlin Dataset of Emotional Speech and the SAVEE Dataset Surrey Audio-Visual Expressed Emotion.

Recognition Imaging of Acetylated Chromatin Using a DNA Aptamer

PubMed Central

Lin, Liyun; Fu, Qiang; Williams, Berea A.R.; Azzaz, Abdelhamid M.; Shogren-Knaak, Michael A.; Chaput, John C.; Lindsay, Stuart

2009-01-01

Histone acetylation plays an important role in the regulation of gene expression. A DNA aptamer generated by in vitro selection to be highly specific for histone H4 protein acetylated at lysine 16 was used as a recognition element for atomic force microscopy-based recognition imaging of synthetic nucleosomal arrays with precisely controlled acetylation. The aptamer proved to be reasonably specific at recognizing acetylated histones, with recognition efficiencies of 60% on-target and 12% off-target. Though this selectivity is much poorer than the >2000:1 equilibrium specificity of the aptamer, it is a large improvement on the performance of a ChIP-quality antibody, which is not selective at all in this application, and it should permit high-fidelity recognition with repeated imaging. The ability to image the precise location of posttranslational modifications may permit nanometer-scale investigation of their effect on chromatin structure. PMID:19751687

An Individual Finger Gesture Recognition System Based on Motion-Intent Analysis Using Mechanomyogram Signal

PubMed Central

Ding, Huijun; He, Qing; Zhou, Yongjin; Dan, Guo; Cui, Song

2017-01-01

Motion-intent-based finger gesture recognition systems are crucial for many applications such as prosthesis control, sign language recognition, wearable rehabilitation system, and human–computer interaction. In this article, a motion-intent-based finger gesture recognition system is designed to correctly identify the tapping of every finger for the first time. Two auto-event annotation algorithms are firstly applied and evaluated for detecting the finger tapping frame. Based on the truncated signals, the Wavelet packet transform (WPT) coefficients are calculated and compressed as the features, followed by a feature selection method that is able to improve the performance by optimizing the feature set. Finally, three popular classifiers including naive Bayes (NBC), K-nearest neighbor (KNN), and support vector machine (SVM) are applied and evaluated. The recognition accuracy can be achieved up to 94%. The design and the architecture of the system are presented with full system characterization results. PMID:29167655

Comparison of an Electromagnetic Middle Ear Implant and Hearing Aid Word Recognition Performance to Word Recognition Performance Obtained Under Earphones.

PubMed

Chang, C Y Joseph; Spearman, Michael; Spearman, Brian; McCraney, Anna; Glasscock, Michael E

2017-10-01

To report the results of patients with the Maxum middle ear implant (MEI) and compare word recognition scores (WRS) and speech perception gap (SP Gap) of Maxum versus optimally fit hearing aids (HA). Case series with chart review. Single, private otology clinic. Eleven ears, in nine adult patients (two women; average age 62.7 yr). Twelve consecutive ears with moderate to severe sensorineural hearing loss (SNHL) underwent implantation of the Maxum system. One patient was not included due to inadequate preoperative testing. Primary outcome measures included word recognition score (WRS) and SP Gap (maximum word understanding [PB max] - WRSaided) improvement compared with HAs. The average Maxum WRS was 64.7% (range, 28-94%), a 41.6% improvement (range, 10-66%) over HAs (p < 0.001). The average Maxum SP Gap was 6.6% (range, -8 to 24%), a 41.6% improvement (range, 10-66%) over HAs (p < 0.001). These data demonstrate that the Maxum provides superior WRS than HAs for patients with significant aided SP Gaps. There is a significant, very strong correlation between Maxum WRS and PB max (r = 0.85, p = 0.001). This implies that PB max may reasonably predict WRS outcomes with Maxum before implantation, and the SP Gap can reasonably predict the degree of additional potential benefit with Maxum. In advising patients who may be candidates for both a CI and MEI, PB max and SP Gap measurements will provide useful predictive information to help clinicians counsel patients on their choice of hearing technology. 4.

SA36. Atypical Memory Structure Related to Recollective Ability

PubMed Central

Greenland-White, Sarah; Niendam, Tara

2017-01-01

Abstract Background: People with schizophrenia have impaired recognition memory and disproportionate recollection rather than familiarity deficits. This pattern also occurs in individuals with early psychosis (EP) and those at clinical high risk (CHR; Ragland et al., 2016). Additionally, these groups show atypical relationships between different memory processes, with patients demonstrating a stronger reliance on familiarity to support recognition accuracy. However, it is unclear whether these group differences represent a compensatory “trade-off” in memory strategies, whereby patients adopt an overreliance on familiarity to compensate for impaired recollection. We examined data from the Relational and Item-Specific memory task (RiSE) in healthy control (HC), EP and CHR participants, and contrasted subgroups with and without prominent recollection impairments. Interrelations between these memory processes (accuracy, recollection, and familiarity) were examined with Structural Equation Modeling (SEM). Methods: A total of 181 individuals (57 HC, 101 EP, and 21 CHR) completed the RiSE. Measures of recognition accuracy, familiarity, and recollection were computed. We divided the patient group into those with poor recollection (overall d’ recognition accuracy < 1.5, n = 52) and those with good recollection (overall d’ recollection accuracy ≥ 1.5, n = 70). SEM was used to investigate the pattern of memory relationships between HC and patient groups as well as between patients with good versus bad recollection. Results: Recollection and familiarity were negatively correlated in the HC group (r = −.467, P < .01) and in the patient group, though more weakly (r = −.288,P < .05). Improved recollection was correlated with overall improvement in recognition accuracy for both the groups (HC r = .771, P < .01; r = .753, P < .01). Improved familiarity was associated with higher recognition accuracy in the patient group only (.361, P < .01). Moreover, patients with poor recollection showed a stronger association (Fisher’s Z = 2.58, P < .01) between familiarity performance and recognition accuracy (.718, P < .01) than patients with good recollection performance (.396, P < .01). Conclusion: Results suggest that patients may be overrelying on more intact familiarity processes to support recognition accuracy. This potential compensatory strategy is particularly marked in those patients with the worst recollection abilities. The finding that recognition accuracy remains impaired in both patient subgroups, however, reveals that this compensatory familiarity-based strategy is not fully successful. Further work is needed to understand how patients can be remediated for their consistently impaired recollection processes.

Molecular tips for scanning tunneling microscopy: intermolecular electron tunneling for single-molecule recognition and electronics.

PubMed

Nishino, Tomoaki

2014-01-01

This paper reviews the development of molecular tips for scanning tunneling microscopy (STM). Molecular tips offer many advantages: first is their ability to perform chemically selective imaging because of chemical interactions between the sample and the molecular tip, thus improving a major drawback of conventional STM. Rational design of the molecular tip allows sophisticated chemical recognition; e.g., chiral recognition and selective visualization of atomic defects in carbon nanotubes. Another advantage is that they provide a unique method to quantify electron transfer between single molecules. Understanding such electron transfer is mandatory for the realization of molecular electronics.

Online Feature Transformation Learning for Cross-Domain Object Category Recognition.

PubMed

Zhang, Xuesong; Zhuang, Yan; Wang, Wei; Pedrycz, Witold

2017-06-09

In this paper, we introduce a new research problem termed online feature transformation learning in the context of multiclass object category recognition. The learning of a feature transformation is viewed as learning a global similarity metric function in an online manner. We first consider the problem of online learning a feature transformation matrix expressed in the original feature space and propose an online passive aggressive feature transformation algorithm. Then these original features are mapped to kernel space and an online single kernel feature transformation (OSKFT) algorithm is developed to learn a nonlinear feature transformation. Based on the OSKFT and the existing Hedge algorithm, a novel online multiple kernel feature transformation algorithm is also proposed, which can further improve the performance of online feature transformation learning in large-scale application. The classifier is trained with k nearest neighbor algorithm together with the learned similarity metric function. Finally, we experimentally examined the effect of setting different parameter values in the proposed algorithms and evaluate the model performance on several multiclass object recognition data sets. The experimental results demonstrate the validity and good performance of our methods on cross-domain and multiclass object recognition application.

The benefits of mystery in nature on attention: assessing the impacts of presentation duration

PubMed Central

Szolosi, Andrew M.; Watson, Jason M.; Ruddell, Edward J.

2014-01-01

Although research has provided prodigious evidence in support of the cognitive benefits that natural settings have over urban settings, all nature is not equal. Within nature, natural settings that contain mystery are often among the most preferred nature scenes. With the prospect of acquiring new information, scenes of this type could more effectively elicit a person's sense of fascination, enabling that person to rest the more effortful forms of attention. The present study examined the direct cognitive benefits that mystery in nature has on attention. Settings of this sort presumably evoke a form of attention that is undemanding or effortless. In order to investigate that notion, participants (n = 144) completed a Recognition Memory Task (RMT) that evaluated recognition performance based on the presence of mystery and presentation duration (300 ms, 1 s, 5 s, and 10 s). Results revealed that with additional viewing time, images perceived high in mystery achieved greater improvements in recognition performance when compared to those images perceived low in mystery. Tests for mediation showed that the effect mystery had on recognition performance occurred through perceptions of fascination. Implications of these and other findings are discussed in the context of Attention Restoration Theory. PMID:25505441

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition.

PubMed

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-18

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters' influence on performance to provide insights about their optimisation.

Recognition of handwritten similar Chinese characters by self-growing probabilistic decision-based neural network.

PubMed

Fu, H C; Xu, Y Y; Chang, H Y

1999-12-01

Recognition of similar (confusion) characters is a difficult problem in optical character recognition (OCR). In this paper, we introduce a neural network solution that is capable of modeling minor differences among similar characters, and is robust to various personal handwriting styles. The Self-growing Probabilistic Decision-based Neural Network (SPDNN) is a probabilistic type neural network, which adopts a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment scheme. Based on the SPDNN model, we have constructed a three-stage recognition system. First, a coarse classifier determines a character to be input to one of the pre-defined subclasses partitioned from a large character set, such as Chinese mixed with alphanumerics. Then a character recognizer determines the input image which best matches the reference character in the subclass. Lastly, the third module is a similar character recognizer, which can further enhance the recognition accuracy among similar or confusing characters. The prototype system has demonstrated a successful application of SPDNN to similar handwritten Chinese recognition for the public database CCL/HCCR1 (5401 characters x200 samples). Regarding performance, experiments on the CCL/HCCR1 database produced 90.12% recognition accuracy with no rejection, and 94.11% accuracy with 6.7% rejection, respectively. This recognition accuracy represents about 4% improvement on the previously announced performance. As to processing speed, processing before recognition (including image preprocessing, segmentation, and feature extraction) requires about one second for an A4 size character image, and recognition consumes approximately 0.27 second per character on a Pentium-100 based personal computer, without use of any hardware accelerator or co-processor.

Recall and recognition hypermnesia for Socratic stimuli.

PubMed

Kazén, Miguel; Solís-Macías, Víctor M

2016-01-01

In two experiments, we investigate hypermnesia, net memory improvements with repeated testing of the same material after a single study trial. In the first experiment, we found hypermnesia across three trials for the recall of word solutions to Socratic stimuli (dictionary-like definitions of concepts) replicating Erdelyi, Buschke, and Finkelstein and, for the first time using these materials, for their recognition. In the second experiment, we had two "yes/no" recognition groups, a Socratic stimuli group presented with concrete and abstract verbal materials and a word-only control group. Using signal detection measures, we found hypermnesia for concrete Socratic stimuli-and stable performance for abstract stimuli across three recognition tests. The control group showed memory decrements across tests. We interpret these findings with the alternative retrieval pathways (ARP) hypothesis, contrasting it with alternative theories of hypermnesia, such as depth of processing, generation and retrieve-recognise. We conclude that recognition hypermnesia for concrete Socratic stimuli is a reliable phenomenon, which we found in two experiments involving both forced-choice and yes/no recognition procedures.

Feature extraction for face recognition via Active Shape Model (ASM) and Active Appearance Model (AAM)

NASA Astrophysics Data System (ADS)

Iqtait, M.; Mohamad, F. S.; Mamat, M.

2018-03-01

Biometric is a pattern recognition system which is used for automatic recognition of persons based on characteristics and features of an individual. Face recognition with high recognition rate is still a challenging task and usually accomplished in three phases consisting of face detection, feature extraction, and expression classification. Precise and strong location of trait point is a complicated and difficult issue in face recognition. Cootes proposed a Multi Resolution Active Shape Models (ASM) algorithm, which could extract specified shape accurately and efficiently. Furthermore, as the improvement of ASM, Active Appearance Models algorithm (AAM) is proposed to extracts both shape and texture of specified object simultaneously. In this paper we give more details about the two algorithms and give the results of experiments, testing their performance on one dataset of faces. We found that the ASM is faster and gains more accurate trait point location than the AAM, but the AAM gains a better match to the texture.

Multimodal biometric method that combines veins, prints, and shape of a finger

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Kim, Jeong Nyeo

2011-01-01

Multimodal biometrics provides high recognition accuracy and population coverage by using various biometric features. A single finger contains finger veins, fingerprints, and finger geometry features; by using multimodal biometrics, information on these multiple features can be simultaneously obtained in a short time and their fusion can outperform the use of a single feature. This paper proposes a new finger recognition method based on the score-level fusion of finger veins, fingerprints, and finger geometry features. This research is novel in the following four ways. First, the performances of the finger-vein and fingerprint recognition are improved by using a method based on a local derivative pattern. Second, the accuracy of the finger geometry recognition is greatly increased by combining a Fourier descriptor with principal component analysis. Third, a fuzzy score normalization method is introduced; its performance is better than the conventional Z-score normalization method. Fourth, finger-vein, fingerprint, and finger geometry recognitions are combined by using three support vector machines and a weighted SUM rule. Experimental results showed that the equal error rate of the proposed method was 0.254%, which was lower than those of the other methods.

Automated recognition and extraction of tabular fields for the indexing of census records

NASA Astrophysics Data System (ADS)

Clawson, Robert; Bauer, Kevin; Chidester, Glen; Pohontsch, Milan; Kennard, Douglas; Ryu, Jongha; Barrett, William

2013-01-01

We describe a system for indexing of census records in tabular documents with the goal of recognizing the content of each cell, including both headers and handwritten entries. Each document is automatically rectified, registered and scaled to a known template following which lines and fields are detected and delimited as cells in a tabular form. Whole-word or whole-phrase recognition of noisy machine-printed text is performed using a glyph library, providing greatly increased efficiency and accuracy (approaching 100%), while avoiding the problems inherent with traditional OCR approaches. Constrained handwriting recognition results for a single author reach as high as 98% and 94.5% for the Gender field and Birthplace respectively. Multi-author accuracy (currently 82%) can be improved through an increased training set. Active integration of user feedback in the system will accelerate the indexing of records while providing a tightly coupled learning mechanism for system improvement.

A neural approach for improving the measurement capability of an electronic nose

NASA Astrophysics Data System (ADS)

Chimenti, M.; DeRossi, D.; Di Francesco, F.; Domenici, C.; Pieri, G.; Pioggia, G.; Salvetti, O.

2003-06-01

Electronic noses, instruments for automatic recognition of odours, are typically composed of an array of partially selective sensors, a sampling system, a data acquisition device and a data processing system. For the purpose of evaluating the quality of olive oil, an electronic nose based on an array of conducting polymer sensors capable of discriminating olive oil aromas was developed. The selection of suitable pattern recognition techniques for a particular application can enhance the performance of electronic noses. Therefore, an advanced neural recognition algorithm for improving the measurement capability of the device was designed and implemented. This method combines multivariate statistical analysis and a hierarchical neural-network architecture based on self-organizing maps and error back-propagation. The complete system was tested using samples composed of characteristic olive oil aromatic components in refined olive oil. The results obtained have shown that this approach is effective in grouping aromas into different categories representative of their chemical structure.

Evaluating the developmental trajectory of the episodic buffer component of working memory and its relation to word recognition in children.

PubMed

Wang, Shinmin; Allen, Richard J; Lee, Jun Ren; Hsieh, Chia-En

2015-05-01

The creation of temporary bound representation of information from different sources is one of the key abilities attributed to the episodic buffer component of working memory. Whereas the role of working memory in word learning has received substantial attention, very little is known about the link between the development of word recognition skills and the ability to bind information in the episodic buffer of working memory and how it may develop with age. This study examined the performance of Grade 2 children (8 years old), Grade 3 children (9 years old), and young adults on a task designed to measure their ability to bind visual and auditory-verbal information in working memory. Children's performance on this task significantly correlated with their word recognition skills even when chronological age, memory for individual elements, and other possible reading-related factors were taken into account. In addition, clear developmental trajectories were observed, with improvements in the ability to hold temporary bound information in working memory between Grades 2 and 3, and between the child and adult groups, that were independent from memory for the individual elements. These findings suggest that the capacity to temporarily bind novel auditory-verbal information to visual form in working memory is linked to the development of word recognition in children and improves with age. Copyright © 2015 Elsevier Inc. All rights reserved.

Can soft biometric traits assist user recognition?

NASA Astrophysics Data System (ADS)

Jain, Anil K.; Dass, Sarat C.; Nandakumar, Karthik

2004-08-01

Biometrics is rapidly gaining acceptance as the technology that can meet the ever increasing need for security in critical applications. Biometric systems automatically recognize individuals based on their physiological and behavioral characteristics. Hence, the fundamental requirement of any biometric recognition system is a human trait having several desirable properties like universality, distinctiveness, permanence, collectability, acceptability, and resistance to circumvention. However, a human characteristic that possesses all these properties has not yet been identified. As a result, none of the existing biometric systems provide perfect recognition and there is a scope for improving the performance of these systems. Although characteristics like gender, ethnicity, age, height, weight and eye color are not unique and reliable, they provide some information about the user. We refer to these characteristics as "soft" biometric traits and argue that these traits can complement the identity information provided by the primary biometric identifiers like fingerprint and face. This paper presents the motivation for utilizing soft biometric information and analyzes how the soft biometric traits can be automatically extracted and incorporated in the decision making process of the primary biometric system. Preliminary experiments were conducted on a fingerprint database of 160 users by synthetically generating soft biometric traits like gender, ethnicity, and height based on known statistics. The results show that the use of additional soft biometric user information significantly improves (approximately 6%) the recognition performance of the fingerprint biometric system.

Neural correlates of improved recognition of happy faces after erythropoietin treatment in bipolar disorder.

PubMed

Miskowiak, K W; Petersen, N A; Harmer, C J; Ehrenreich, E; Kessing, L V; Vinberg, M; Macoveanu, J; Siebner, H R

2018-06-07

Bipolar disorder is associated with impairments in social cognition including the recognition of happy faces. This is accompanied by imbalanced cortico-limbic response to emotional faces. We found that EPO improved the recognition of happy faces in patients with bipolar disorder. This randomized, controlled, longitudinal fMRI study explores the neuronal underpinnings of this effect. Forty-four patients with bipolar disorder in full or partial remission were randomized to eight weekly erythropoietin (EPO; 40 000 IU) or saline (NaCl 0.9%) infusions in a double-blind, parallel-group design. Participants underwent whole-brain fMRI at 3T, mood ratings and blood tests at baseline and week 14. During fMRI, participants viewed happy and fearful faces and performed a gender discrimination task. Thirty-four patients had complete pre- and post-treatment fMRI data (EPO: N = 18, saline: N = 16). Erythropoietin vs. saline increased right superior frontal response to happy vs. fearful faces. This correlated with improved happiness recognition in the EPO group. Erythropoietin also enhanced gender discrimination accuracy for happy faces. These effects were not influenced by medication, mood, red blood cells or blood pressure. Together with previous findings, the present observation suggests that increased dorsal prefrontal attention control is a common mechanism of EPO-associated improvements across several cognitive domains. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Sound Processing Features for Speaker-Dependent and Phrase-Independent Emotion Recognition in Berlin Database

NASA Astrophysics Data System (ADS)

Anagnostopoulos, Christos Nikolaos; Vovoli, Eftichia

An emotion recognition framework based on sound processing could improve services in human-computer interaction. Various quantitative speech features obtained from sound processing of acting speech were tested, as to whether they are sufficient or not to discriminate between seven emotions. Multilayered perceptrons were trained to classify gender and emotions on the basis of a 24-input vector, which provide information about the prosody of the speaker over the entire sentence using statistics of sound features. Several experiments were performed and the results were presented analytically. Emotion recognition was successful when speakers and utterances were “known” to the classifier. However, severe misclassifications occurred during the utterance-independent framework. At least, the proposed feature vector achieved promising results for utterance-independent recognition of high- and low-arousal emotions.

Effects of curcumin on short-term spatial and recognition memory, adult neurogenesis and neuroinflammation in a streptozotocin-induced rat model of dementia of Alzheimer's type.

PubMed

Bassani, Taysa B; Turnes, Joelle M; Moura, Eric L R; Bonato, Jéssica M; Cóppola-Segovia, Valentín; Zanata, Silvio M; Oliveira, Rúbia M M W; Vital, Maria A B F

2017-09-29

Curcumin is a natural polyphenol with evidence of antioxidant, anti-inflammatory and neuroprotective properties. Recent evidence also suggests that curcumin increases cognitive performance in animal models of dementia, and this effect would be related to its capacity to enhance adult neurogenesis. The aim of this study was to test the hypothesis that curcumin treatment would be able to preserve cognition by increasing neurogenesis and decreasing neuroinflammation in the model of dementia of Alzheimer's type induced by an intracerebroventricular injection of streptozotocin (ICV-STZ) in Wistar rats. The animals were injected with ICV-STZ or vehicle and curcumin treatments (25, 50 and 100mg/kg, gavage) were performed for 30days. Four weeks after surgery, STZ-lesioned animals exhibited impairments in short-term spatial memory (Object Location Test (OLT) and Y maze) and short-term recognition memory (Object Recognition Test - ORT), decreased cell proliferation and immature neurons (Ki-67- and doublecortin-positive cells, respectively) in the subventricular zone (SVZ) and dentate gyrus (DG) of hippocampus, and increased immunoreactivity for the glial markers GFAP and Iba-1 (neuroinflammation). Curcumin treatment in the doses of 50 and 100mg/kg prevented the deficits in recognition memory in the ORT, but not in spatial memory in the OLT and Y maze. Curcumin treatment exerted only slight improvements in neuroinflammation, resulting in no improvements in hippocampal and subventricular neurogenesis. These results suggest a positive effect of curcumin in object recognition memory which was not related to hippocampal neurogenesis. Copyright © 2017 Elsevier B.V. All rights reserved.

Leveling the playing field: attention mitigates the effects of intelligence on memory.

PubMed

Markant, Julie; Amso, Dima

2014-05-01

Effective attention and memory skills are fundamental to typical development and essential for achievement during the formal education years. It is critical to identify the specific mechanisms linking efficiency of attentional selection of an item and the quality of its memory retention. The present study capitalized on the spatial cueing paradigm to examine the role of selection via suppression in modulating children and adolescents' memory encoding. By varying a single parameter, the spatial cueing task can elicit either a simple orienting mechanism (i.e., facilitation) or one that involves both target selection and simultaneous suppression of competing information (i.e., IOR). We modified this paradigm to include images of common items in target locations. Participants were not instructed to learn the items and were not told they would be completing a memory test later. Following the cueing task, we imposed a 7-min delay and then asked participants to complete a recognition memory test. Results indicated that selection via suppression promoted recognition memory among 7-17year-olds. Moreover, individual differences in the extent of suppression during encoding predicted recognition memory accuracy. When basic cueing facilitated orienting to target items during encoding, IQ was the best predictor of recognition memory performance for the attended items. In contrast, engaging suppression (i.e., IOR) during encoding counteracted individual differences in intelligence, effectively improving recognition memory performance among children with lower IQs. This work demonstrates that engaging selection via suppression during learning and encoding improves memory retention and has broad implications for developing effective educational techniques. Copyright © 2014 Elsevier B.V. All rights reserved.

Leveling the playing field: Attention mitigates the effects of intelligence on memory

PubMed Central

Markant, Julie; Amso, Dima

2014-01-01

Effective attention and memory skills are fundamental to typical development and essential for achievement during the formal education years. It is critical to identify the specific mechanisms linking efficiency of attentional selection of an item and the quality of its memory retention. The present study capitalized on the spatial cueing paradigm to examine the role of selection via suppression in modulating children and adolescents’ memory encoding. By varying a single parameter, the spatial cueing task can elicit either a simple orienting mechanism (i.e., facilitation) or one that involves both target selection and simultaneous suppression of competing information (i.e., IOR). We modified this paradigm to include images of common items in target locations. Participants were not instructed to learn the items and were not told they would be completing a memory test later. Following the cueing task, we imposed a seven-minute delay and then asked participants to complete a recognition memory test. Results indicated that selection via suppression promoted recognition memory among 7-17 year-olds. Moreover, individual differences in the extent of suppression during encoding predicted recognition memory accuracy. When basic cueing facilitated orienting to target items during encoding, IQ was the best predictor of recognition memory performance for the attended items. In contrast, engaging suppression (i.e, IOR) during encoding counteracted individual differences in intelligence, effectively improving recognition memory performance among children with lower IQs. This work demonstrates that engaging selection via suppression during learning and encoding improves memory retention and has broad implications for developing effective educational techniques. PMID:24549142

Is having similar eye movement patterns during face learning and recognition beneficial for recognition performance? Evidence from hidden Markov modeling.

PubMed

Chuk, Tim; Chan, Antoni B; Hsiao, Janet H

2017-12-01

The hidden Markov model (HMM)-based approach for eye movement analysis is able to reflect individual differences in both spatial and temporal aspects of eye movements. Here we used this approach to understand the relationship between eye movements during face learning and recognition, and its association with recognition performance. We discovered holistic (i.e., mainly looking at the face center) and analytic (i.e., specifically looking at the two eyes in addition to the face center) patterns during both learning and recognition. Although for both learning and recognition, participants who adopted analytic patterns had better recognition performance than those with holistic patterns, a significant positive correlation between the likelihood of participants' patterns being classified as analytic and their recognition performance was only observed during recognition. Significantly more participants adopted holistic patterns during learning than recognition. Interestingly, about 40% of the participants used different patterns between learning and recognition, and among them 90% switched their patterns from holistic at learning to analytic at recognition. In contrast to the scan path theory, which posits that eye movements during learning have to be recapitulated during recognition for the recognition to be successful, participants who used the same or different patterns during learning and recognition did not differ in recognition performance. The similarity between their learning and recognition eye movement patterns also did not correlate with their recognition performance. These findings suggested that perceptuomotor memory elicited by eye movement patterns during learning does not play an important role in recognition. In contrast, the retrieval of diagnostic information for recognition, such as the eyes for face recognition, is a better predictor for recognition performance. Copyright © 2017 Elsevier Ltd. All rights reserved.

Practice makes imperfect: Working memory training can harm recognition memory performance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matzen, Laura E.; Trumbo, Michael C.; Haass, Michael J.

There is a great deal of debate concerning the benefits of working memory (WM) training and whether that training can transfer to other tasks. Although a consistent finding is that WM training programs elicit a short-term near-transfer effect (i.e., improvement in WM skills), results are inconsistent when considering persistence of such improvement and far transfer effects. In this study, we compared three groups of participants: a group that received WM training, a group that received training on how to use a mental imagery memory strategy, and a control group that received no training. Although the WM training group improved onmore » the trained task, their posttraining performance on nontrained WM tasks did not differ from that of the other two groups. In addition, although the imagery training group’s performance on a recognition memory task increased after training, the WM training group’s performance on the task decreased after training. Participants’ descriptions of the strategies they used to remember the studied items indicated that WM training may lead people to adopt memory strategies that are less effective for other types of memory tasks. Our results indicate that WM training may have unintended consequences for other types of memory performance.« less

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform.

PubMed

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-12-02

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works.

Wearable Sensor-Based Human Activity Recognition Method with Multi-Features Extracted from Hilbert-Huang Transform

PubMed Central

Xu, Huile; Liu, Jinyi; Hu, Haibo; Zhang, Yi

2016-01-01

Wearable sensors-based human activity recognition introduces many useful applications and services in health care, rehabilitation training, elderly monitoring and many other areas of human interaction. Existing works in this field mainly focus on recognizing activities by using traditional features extracted from Fourier transform (FT) or wavelet transform (WT). However, these signal processing approaches are suitable for a linear signal but not for a nonlinear signal. In this paper, we investigate the characteristics of the Hilbert-Huang transform (HHT) for dealing with activity data with properties such as nonlinearity and non-stationarity. A multi-features extraction method based on HHT is then proposed to improve the effect of activity recognition. The extracted multi-features include instantaneous amplitude (IA) and instantaneous frequency (IF) by means of empirical mode decomposition (EMD), as well as instantaneous energy density (IE) and marginal spectrum (MS) derived from Hilbert spectral analysis. Experimental studies are performed to verify the proposed approach by using the PAMAP2 dataset from the University of California, Irvine for wearable sensors-based activity recognition. Moreover, the effect of combining multi-features vs. a single-feature are investigated and discussed in the scenario of a dependent subject. The experimental results show that multi-features combination can further improve the performance measures. Finally, we test the effect of multi-features combination in the scenario of an independent subject. Our experimental results show that we achieve four performance indexes: recall, precision, F-measure, and accuracy to 0.9337, 0.9417, 0.9353, and 0.9377 respectively, which are all better than the achievements of related works. PMID:27918414

Effect of Stimulation Rate on Cochlear Implant Users’ Phoneme, Word and Sentence Recognition in Quiet and in Noise

PubMed Central

Shannon, Robert V.; Cruz, Rachel J.; Galvin, John J.

2011-01-01

High stimulation rates in cochlear implants (CI) offer better temporal sampling, can induce stochastic-like firing of auditory neurons and can increase the electric dynamic range, all of which could improve CI speech performance. While commercial CI have employed increasingly high stimulation rates, no clear or consistent advantage has been shown for high rates. In this study, speech recognition was acutely measured with experimental processors in 7 CI subjects (Clarion CII users). The stimulation rate varied between (approx.) 600 and 4800 pulses per second per electrode (ppse) and the number of active electrodes varied between 4 and 16. Vowel, consonant, consonant-nucleus-consonant word and IEEE sentence recognition was acutely measured in quiet and in steady noise (+10 dB signal-to-noise ratio). Subjective quality ratings were obtained for each of the experimental processors in quiet and in noise. Except for a small difference for vowel recognition in quiet, there were no significant differences in performance among the experimental stimulation rates for any of the speech measures. There was also a small but significant increase in subjective quality rating as stimulation rates increased from 1200 to 2400 ppse in noise. Consistent with previous studies, performance significantly improved as the number of electrodes was increased from 4 to 8, but no significant difference showed between 8, 12 and 16 electrodes. Altogether, there was little-to-no advantage of high stimulation rates in quiet or in noise, at least for the present speech tests and conditions. PMID:20639631

System for face recognition under expression variations of neutral-sampled individuals using recognized expression warping and a virtual expression-face database

NASA Astrophysics Data System (ADS)

Petpairote, Chayanut; Madarasmi, Suthep; Chamnongthai, Kosin

2018-01-01

The practical identification of individuals using facial recognition techniques requires the matching of faces with specific expressions to faces from a neutral face database. A method for facial recognition under varied expressions against neutral face samples of individuals via recognition of expression warping and the use of a virtual expression-face database is proposed. In this method, facial expressions are recognized and the input expression faces are classified into facial expression groups. To aid facial recognition, the virtual expression-face database is sorted into average facial-expression shapes and by coarse- and fine-featured facial textures. Wrinkle information is also employed in classification by using a process of masking to adjust input faces to match the expression-face database. We evaluate the performance of the proposed method using the CMU multi-PIE, Cohn-Kanade, and AR expression-face databases, and we find that it provides significantly improved results in terms of face recognition accuracy compared to conventional methods and is acceptable for facial recognition under expression variation.

Is Listening in Noise Worth It? The Neurobiology of Speech Recognition in Challenging Listening Conditions.

PubMed

Eckert, Mark A; Teubner-Rhodes, Susan; Vaden, Kenneth I

2016-01-01

This review examines findings from functional neuroimaging studies of speech recognition in noise to provide a neural systems level explanation for the effort and fatigue that can be experienced during speech recognition in challenging listening conditions. Neuroimaging studies of speech recognition consistently demonstrate that challenging listening conditions engage neural systems that are used to monitor and optimize performance across a wide range of tasks. These systems appear to improve speech recognition in younger and older adults, but sustained engagement of these systems also appears to produce an experience of effort and fatigue that may affect the value of communication. When considered in the broader context of the neuroimaging and decision making literature, the speech recognition findings from functional imaging studies indicate that the expected value, or expected level of speech recognition given the difficulty of listening conditions, should be considered when measuring effort and fatigue. The authors propose that the behavioral economics or neuroeconomics of listening can provide a conceptual and experimental framework for understanding effort and fatigue that may have clinical significance.

Is Listening in Noise Worth It? The Neurobiology of Speech Recognition in Challenging Listening Conditions

PubMed Central

Eckert, Mark A.; Teubner-Rhodes, Susan; Vaden, Kenneth I.

2016-01-01

This review examines findings from functional neuroimaging studies of speech recognition in noise to provide a neural systems level explanation for the effort and fatigue that can be experienced during speech recognition in challenging listening conditions. Neuroimaging studies of speech recognition consistently demonstrate that challenging listening conditions engage neural systems that are used to monitor and optimize performance across a wide range of tasks. These systems appear to improve speech recognition in younger and older adults, but sustained engagement of these systems also appears to produce an experience of effort and fatigue that may affect the value of communication. When considered in the broader context of the neuroimaging and decision making literature, the speech recognition findings from functional imaging studies indicate that the expected value, or expected level of speech recognition given the difficulty of listening conditions, should be considered when measuring effort and fatigue. We propose that the behavioral economics and/or neuroeconomics of listening can provide a conceptual and experimental framework for understanding effort and fatigue that may have clinical significance. PMID:27355759

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

The utility of multiple synthesized views in the recognition of unfamiliar faces.

PubMed

Jones, Scott P; Dwyer, Dominic M; Lewis, Michael B

2017-05-01

The ability to recognize an unfamiliar individual on the basis of prior exposure to a photograph is notoriously poor and prone to errors, but recognition accuracy is improved when multiple photographs are available. In applied situations, when only limited real images are available (e.g., from a mugshot or CCTV image), the generation of new images might provide a technological prosthesis for otherwise fallible human recognition. We report two experiments examining the effects of providing computer-generated additional views of a target face. In Experiment 1, provision of computer-generated views supported better target face recognition than exposure to the target image alone and equivalent performance to that for exposure of multiple photograph views. Experiment 2 replicated the advantage of providing generated views, but also indicated an advantage for multiple viewings of the single target photograph. These results strengthen the claim that identifying a target face can be improved by providing multiple synthesized views based on a single target image. In addition, our results suggest that the degree of advantage provided by synthesized views may be affected by the quality of synthesized material.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion

PubMed Central

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13. Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract PMID:27504009

Image enhancement and advanced information extraction techniques for ERTS-1 data

NASA Technical Reports Server (NTRS)

Malila, W. A. (Principal Investigator); Nalepka, R. F.; Sarno, J. E.

1975-01-01

The author has identified the following significant results. It was demonstrated and concluded that: (1) the atmosphere has significant effects on ERTS MSS data which can seriously degrade recognition performance; (2) the application of selected signature extension techniques serve to reduce the deleterious effects of both the atmosphere and changing ground conditions on recognition performance; and (3) a proportion estimation algorithm for overcoming problems in acreage estimation accuracy resulting from the coarse spatial resolution of the ERTS MSS, was able to significantly improve acreage estimation accuracy over that achievable by conventional techniques, especially for high contrast targets such as lakes and ponds.

JPRS Report, Science & Technology, Europe, FRG: Official Report om Multiyear Research Funding, Trends

DTIC Science & Technology

1988-08-23

research institutions. The special recognition of performance and the chal- lenge to top performance in research, development, and innovation will...continue to be a central concern of the federal government. Top performances , the achievement of internationally recognized breakthroughs, and suc...government places on improving busi- ness framework conditions for more growth and employ- ment, for strengthening the power of performance , com

Neuroscience education of undergraduate medical students. Part II: outcome improvement.

PubMed

Resnick, D K; Ramirez, L F

2000-04-01

Because of political and economic pressures, primary care physicians are now charged with greater responsibility for the care of patients with disease processes definitively managed by neurosurgeons. The goal of this study was to establish the feasibility and efficacy of a neurosurgical curriculum designed to teach future primary care physicians about these diseases. A compact, seven-lecture curriculum was developed to teach 3rd-year medical students about degenerative spine disease, stroke, tumor- and hydrocephalus-related raised intracranial pressure, head and spine injury, and subarachnoid hemorrhage. This curriculum was given as part of a 6-week pilot course that included neurology, neurosurgery, ophthalmology, and rehabilitation medicine components. This course was administered to two groups of 18 medical students, and an examination was administered at the end of the pilot course. The same examination was administered to an additional 19 students immediately after their completion of the neurology course currently required. Students enrolled in the pilot neuroscience course performed significantly better (p<0.001) on the examination than those who had completed the standard neurology course. Striking improvements were noted in the recognition and management of head injury, hydrocephalus, and radiculopathy. Inclusion of a short neurosurgery-related curriculum in a combined neuroscience course significantly improved student performance on an examination focusing on the recognition and management of common neurosurgical disorders. Because primary care physicians are responsible for the initial recognition and management of these disorders, the knowledge gained may lead to improved patient care.

Formal implementation of a performance evaluation model for the face recognition system.

PubMed

Shin, Yong-Nyuo; Kim, Jason; Lee, Yong-Jun; Shin, Woochang; Choi, Jin-Young

2008-01-01

Due to usability features, practical applications, and its lack of intrusiveness, face recognition technology, based on information, derived from individuals' facial features, has been attracting considerable attention recently. Reported recognition rates of commercialized face recognition systems cannot be admitted as official recognition rates, as they are based on assumptions that are beneficial to the specific system and face database. Therefore, performance evaluation methods and tools are necessary to objectively measure the accuracy and performance of any face recognition system. In this paper, we propose and formalize a performance evaluation model for the biometric recognition system, implementing an evaluation tool for face recognition systems based on the proposed model. Furthermore, we performed evaluations objectively by providing guidelines for the design and implementation of a performance evaluation system, formalizing the performance test process.

Textual emotion recognition for enhancing enterprise computing

NASA Astrophysics Data System (ADS)

Quan, Changqin; Ren, Fuji

2016-05-01

The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques - textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of 'emotion state in text' is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.

Novel Blind Recognition Algorithm of Frame Synchronization Words Based on Soft-Decision in Digital Communication Systems.

PubMed

Qin, Jiangyi; Huang, Zhiping; Liu, Chunwu; Su, Shaojing; Zhou, Jing

2015-01-01

A novel blind recognition algorithm of frame synchronization words is proposed to recognize the frame synchronization words parameters in digital communication systems. In this paper, a blind recognition method of frame synchronization words based on the hard-decision is deduced in detail. And the standards of parameter recognition are given. Comparing with the blind recognition based on the hard-decision, utilizing the soft-decision can improve the accuracy of blind recognition. Therefore, combining with the characteristics of Quadrature Phase Shift Keying (QPSK) signal, an improved blind recognition algorithm based on the soft-decision is proposed. Meanwhile, the improved algorithm can be extended to other signal modulation forms. Then, the complete blind recognition steps of the hard-decision algorithm and the soft-decision algorithm are given in detail. Finally, the simulation results show that both the hard-decision algorithm and the soft-decision algorithm can recognize the parameters of frame synchronization words blindly. What's more, the improved algorithm can enhance the accuracy of blind recognition obviously.

Bearing Fault Diagnosis Based on Statistical Locally Linear Embedding

PubMed Central

Wang, Xiang; Zheng, Yuan; Zhao, Zhenzhou; Wang, Jinping

2015-01-01

Fault diagnosis is essentially a kind of pattern recognition. The measured signal samples usually distribute on nonlinear low-dimensional manifolds embedded in the high-dimensional signal space, so how to implement feature extraction, dimensionality reduction and improve recognition performance is a crucial task. In this paper a novel machinery fault diagnosis approach based on a statistical locally linear embedding (S-LLE) algorithm which is an extension of LLE by exploiting the fault class label information is proposed. The fault diagnosis approach first extracts the intrinsic manifold features from the high-dimensional feature vectors which are obtained from vibration signals that feature extraction by time-domain, frequency-domain and empirical mode decomposition (EMD), and then translates the complex mode space into a salient low-dimensional feature space by the manifold learning algorithm S-LLE, which outperforms other feature reduction methods such as PCA, LDA and LLE. Finally in the feature reduction space pattern classification and fault diagnosis by classifier are carried out easily and rapidly. Rolling bearing fault signals are used to validate the proposed fault diagnosis approach. The results indicate that the proposed approach obviously improves the classification performance of fault pattern recognition and outperforms the other traditional approaches. PMID:26153771

Speech intelligibility index predictions for young and old listeners in automobile noise: Can the index be improved by incorporating factors other than absolute threshold?

NASA Astrophysics Data System (ADS)

Saweikis, Meghan; Surprenant, Aimée M.; Davies, Patricia; Gallant, Don

2003-10-01

While young and old subjects with comparable audiograms tend to perform comparably on speech recognition tasks in quiet environments, the older subjects have more difficulty than the younger subjects with recognition tasks in degraded listening conditions. This suggests that factors other than an absolute threshold may account for some of the difficulty older listeners have on recognition tasks in noisy environments. Many metrics, including the Speech Intelligibility Index (SII), used to measure speech intelligibility, only consider an absolute threshold when accounting for age related hearing loss. Therefore these metrics tend to overestimate the performance for elderly listeners in noisy environments [Tobias et al., J. Acoust. Soc. Am. 83, 859-895 (1988)]. The present studies examine the predictive capabilities of the SII in an environment with automobile noise present. This is of interest because people's evaluation of the automobile interior sound is closely linked to their ability to carry on conversations with their fellow passengers. The four studies examine whether, for subjects with age related hearing loss, the accuracy of the SII can be improved by incorporating factors other than an absolute threshold into the model. [Work supported by Ford Motor Company.

Speech Recognition of Bimodal Cochlear Implant Recipients Using a Wireless Audio Streaming Accessory for the Telephone.

PubMed

Wolfe, Jace; Morais, Mila; Schafer, Erin

2016-02-01

The goals of the present investigation were (1) to evaluate recognition of recorded speech presented over a mobile telephone for a group of adult bimodal cochlear implant users, and (2) to measure the potential benefits of wireless hearing assistance technology (HAT) for mobile telephone speech recognition using bimodal stimulation (i.e., a cochlear implant in one ear and a hearing aid on the other ear). A three-by-two-way repeated measures design was used to evaluate mobile telephone sentence-recognition performance differences obtained in quiet and in noise with and without the wireless HAT accessory coupled to the hearing aid alone, CI sound processor alone, and in the bimodal condition. Outpatient cochlear implant clinic. Sixteen bimodal users with Nucleus 24, Freedom, CI512, or CI422 cochlear implants participated in this study. Performance was measured with and without the use of a wireless HAT for the telephone used with the hearing aid alone, CI alone, and bimodal condition. CNC word recognition in quiet and in noise with and without the use of a wireless HAT telephone accessory in the hearing aid alone, CI alone, and bimodal conditions. Results suggested that the bimodal condition gave significantly better speech recognition on the mobile telephone with the wireless HAT. A wireless HAT for the mobile telephone provides bimodal users with significant improvement in word recognition in quiet and in noise over the mobile telephone.

Figure Text Extraction in Biomedical Literature

PubMed Central

Kim, Daehyun; Yu, Hong

2011-01-01

Background Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. Methodology We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. Results/Conclusions The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search. PMID:21249186

Figure text extraction in biomedical literature.

PubMed

Kim, Daehyun; Yu, Hong

2011-01-13

Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engine (http://figuresearch.askHERMES.org) to allow bioscientists to access figures efficiently. Since text frequently appears in figures, automatically extracting such text may assist the task of mining information from figures. Little research, however, has been conducted exploring text extraction from biomedical figures. We first evaluated an off-the-shelf Optical Character Recognition (OCR) tool on its ability to extract text from figures appearing in biomedical full-text articles. We then developed a Figure Text Extraction Tool (FigTExT) to improve the performance of the OCR tool for figure text extraction through the use of three innovative components: image preprocessing, character recognition, and text correction. We first developed image preprocessing to enhance image quality and to improve text localization. Then we adapted the off-the-shelf OCR tool on the improved text localization for character recognition. Finally, we developed and evaluated a novel text correction framework by taking advantage of figure-specific lexicons. The evaluation on 382 figures (9,643 figure texts in total) randomly selected from PubMed Central full-text articles shows that FigTExT performed with 84% precision, 98% recall, and 90% F1-score for text localization and with 62.5% precision, 51.0% recall and 56.2% F1-score for figure text extraction. When limiting figure texts to those judged by domain experts to be important content, FigTExT performed with 87.3% precision, 68.8% recall, and 77% F1-score. FigTExT significantly improved the performance of the off-the-shelf OCR tool we used, which on its own performed with 36.6% precision, 19.3% recall, and 25.3% F1-score for text extraction. In addition, our results show that FigTExT can extract texts that do not appear in figure captions or other associated text, further suggesting the potential utility of FigTExT for improving figure search.

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal.

PubMed

Arjunan, Sridhar Poosapadi; Kumar, Dinesh Kant; Jayadeva J

2016-02-01

Identifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Speech recognition for bilaterally asymmetric and symmetric hearing aid microphone modes in simulated classroom environments.

PubMed

Ricketts, Todd A; Picou, Erin M

2013-09-01

This study aimed to evaluate the potential utility of asymmetrical and symmetrical directional hearing aid fittings for school-age children in simulated classroom environments. This study also aimed to evaluate speech recognition performance of children with normal hearing in the same listening environments. Two groups of school-age children 11 to 17 years of age participated in this study. Twenty participants had normal hearing, and 29 participants had sensorineural hearing loss. Participants with hearing loss were fitted with behind-the-ear hearing aids with clinically appropriate venting and were tested in 3 hearing aid configurations: bilateral omnidirectional, bilateral directional, and asymmetrical directional microphones. Speech recognition testing was completed in each microphone configuration in 3 environments: Talker-Front, Talker-Back, and Question-Answer situations. During testing, the location of the speech signal changed, but participants were always seated in a noisy, moderately reverberant classroom-like room. For all conditions, results revealed expected effects of directional microphones on speech recognition performance. When the signal of interest was in front of the listener, bilateral directional microphone was best, and when the signal of interest was behind the listener, bilateral omnidirectional microphone was best. Performance with asymmetric directional microphones was between the 2 symmetrical conditions. The magnitudes of directional benefits and decrements were not significantly correlated. In comparison with their peers with normal hearing, children with hearing loss performed similarly to their peers with normal hearing when fitted with directional microphones and the speech was from the front. In contrast, children with normal hearing still outperformed children with hearing loss if the speech originated from behind, even when the children were fitted with the optimal hearing aid microphone mode for the situation. Bilateral directional microphones can be effective in improving speech recognition performance for children in the classroom, as long as child is facing the talker of interest. Bilateral directional microphones, however, can impair performance if the signal originates from behind a listener. However, these data suggest that the magnitude of decrement is not predictable from an individual's benefit. The results re-emphasize the importance of appropriate switching between microphone modes so children can take full advantage of directional benefits without being hurt by directional decrements. An asymmetric fitting limits decrements, but does not lead to maximum speech recognition scores when compared with the optimal symmetrical fitting. Therefore, the asymmetric mode may not be the best option as a default fitting for children in a classroom environment. While directional microphones improve performance for children with hearing loss, their performance in most conditions continues to be impaired relative to their normal-hearing peers, particularly when the signals of interest originate from behind or from an unpredictable location.

Robust Speech Processing & Recognition: Speaker ID, Language ID, Speech Recognition/Keyword Spotting, Diarization/Co-Channel/Environmental Characterization, Speaker State Assessment

DTIC Science & Technology

2015-10-01

Scoring, Gaussian Backend , etc.) as shown in Fig. 39. The methods in this domain also emphasized the ability to perform data purification for both...investigation using the same infrastructure was undertaken to explore Lombard effect “flavor” detection for improved speaker ID. The study The presence of...dimension selection and compared to a common N-gram frequency based selection. 2.1.2: Exploration on NN/DBN backend : Since Deep Neural Networks (DNN) have

Using online handwriting and audio streams for mathematical expressions recognition: a bimodal approach

NASA Astrophysics Data System (ADS)

Medjkoune, Sofiane; Mouchère, Harold; Petitrenaud, Simon; Viard-Gaudin, Christian

2013-01-01

The work reported in this paper concerns the problem of mathematical expressions recognition. This task is known to be a very hard one. We propose to alleviate the difficulties by taking into account two complementary modalities. The modalities referred to are handwriting and audio ones. To combine the signals coming from both modalities, various fusion methods are explored. Performances evaluated on the HAMEX dataset show a significant improvement compared to a single modality (handwriting) based system.

Self-organized network with a supervised training and its comparison with FALVQ in artificial odor recognition system

NASA Astrophysics Data System (ADS)

Kusumoputro, Benyamin; Rostiviani, Linda; Saptawijaya, Ari

2000-07-01

Artificial odor recognition system is developed in order to mimic the human sensory test in cosmetics, parfum and beverage industries. The developed system however, lacks of ability to recognize the unknown type of odor. To improve the system's capability, a hybrid neural system with a supervised learning paradigm is developed and used as a pattern classifier. In this paper, the performance of the hybrid neural system is investigated, together with that of FALVQ neural system.

The sweet-home project: audio technology in smart homes to improve well-being and reliance.

PubMed

Vacher, Michel; Istrate, Dan; Portet, François; Joubert, Thierry; Chevalier, Thierry; Smidtas, Serge; Meillon, Brigitte; Lecouteux, Benjamin; Sehili, Mohamed; Chahuara, Pedro; Méniard, Sylvain

2011-01-01

The Sweet-Home project aims at providing audio-based interaction technology that lets the user have full control over their home environment, at detecting distress situations and at easing the social inclusion of the elderly and frail population. This paper presents an overview of the project focusing on the multimodal sound corpus acquisition and labelling and on the investigated techniques for speech and sound recognition. The user study and the recognition performances show the interest of this audio technology.

Multiscale deep features learning for land-use scene recognition

NASA Astrophysics Data System (ADS)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Cross-sensor iris recognition through kernel learning.

PubMed

Pillai, Jaishanker K; Puertas, Maria; Chellappa, Rama

2014-01-01

Due to the increasing popularity of iris biometrics, new sensors are being developed for acquiring iris images and existing ones are being continuously upgraded. Re-enrolling users every time a new sensor is deployed is expensive and time-consuming, especially in applications with a large number of enrolled users. However, recent studies show that cross-sensor matching, where the test samples are verified using data enrolled with a different sensor, often lead to reduced performance. In this paper, we propose a machine learning technique to mitigate the cross-sensor performance degradation by adapting the iris samples from one sensor to another. We first present a novel optimization framework for learning transformations on iris biometrics. We then utilize this framework for sensor adaptation, by reducing the distance between samples of the same class, and increasing it between samples of different classes, irrespective of the sensors acquiring them. Extensive evaluations on iris data from multiple sensors demonstrate that the proposed method leads to improvement in cross-sensor recognition accuracy. Furthermore, since the proposed technique requires minimal changes to the iris recognition pipeline, it can easily be incorporated into existing iris recognition systems.

Speech recognition systems on the Cell Broadband Engine

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Y; Jones, H; Vaidya, S

In this paper we describe our design, implementation, and first results of a prototype connected-phoneme-based speech recognition system on the Cell Broadband Engine{trademark} (Cell/B.E.). Automatic speech recognition decodes speech samples into plain text (other representations are possible) and must process samples at real-time rates. Fortunately, the computational tasks involved in this pipeline are highly data-parallel and can receive significant hardware acceleration from vector-streaming architectures such as the Cell/B.E. Identifying and exploiting these parallelism opportunities is challenging, but also critical to improving system performance. We observed, from our initial performance timings, that a single Cell/B.E. processor can recognize speech from thousandsmore » of simultaneous voice channels in real time--a channel density that is orders-of-magnitude greater than the capacity of existing software speech recognizers based on CPUs (central processing units). This result emphasizes the potential for Cell/B.E.-based speech recognition and will likely lead to the future development of production speech systems using Cell/B.E. clusters.« less

Face recognition increases during saccade preparation.

PubMed

Lin, Hai; Rizak, Joshua D; Ma, Yuan-ye; Yang, Shang-chuan; Chen, Lin; Hu, Xin-tian

2014-01-01

Face perception is integral to human perception system as it underlies social interactions. Saccadic eye movements are frequently made to bring interesting visual information, such as faces, onto the fovea for detailed processing. Just before eye movement onset, the processing of some basic features, such as the orientation, of an object improves at the saccade landing point. Interestingly, there is also evidence that indicates faces are processed in early visual processing stages similar to basic features. However, it is not known whether this early enhancement of processing includes face recognition. In this study, three experiments were performed to map the timing of face presentation to the beginning of the eye movement in order to evaluate pre-saccadic face recognition. Faces were found to be similarly processed as simple objects immediately prior to saccadic movements. Starting ∼ 120 ms before a saccade to a target face, independent of whether or not the face was surrounded by other faces, the face recognition gradually improved and the critical spacing of the crowding decreased as saccade onset was approaching. These results suggest that an upcoming saccade prepares the visual system for new information about faces at the saccade landing site and may reduce the background in a crowd to target the intended face. This indicates an important role of pre-saccadic eye movement signals in human face recognition.

Hyperspectral face recognition using improved inter-channel alignment based on qualitative prediction models.

PubMed

Cho, Woon; Jang, Jinbeum; Koschan, Andreas; Abidi, Mongi A; Paik, Joonki

2016-11-28

A fundamental limitation of hyperspectral imaging is the inter-band misalignment correlated with subject motion during data acquisition. One way of resolving this problem is to assess the alignment quality of hyperspectral image cubes derived from the state-of-the-art alignment methods. In this paper, we present an automatic selection framework for the optimal alignment method to improve the performance of face recognition. Specifically, we develop two qualitative prediction models based on: 1) a principal curvature map for evaluating the similarity index between sequential target bands and a reference band in the hyperspectral image cube as a full-reference metric; and 2) the cumulative probability of target colors in the HSV color space for evaluating the alignment index of a single sRGB image rendered using all of the bands of the hyperspectral image cube as a no-reference metric. We verify the efficacy of the proposed metrics on a new large-scale database, demonstrating a higher prediction accuracy in determining improved alignment compared to two full-reference and five no-reference image quality metrics. We also validate the ability of the proposed framework to improve hyperspectral face recognition.

Intranasal oxytocin improves emotion recognition for youth with autism spectrum disorders.

PubMed

Guastella, Adam J; Einfeld, Stewart L; Gray, Kylie M; Rinehart, Nicole J; Tonge, Bruce J; Lambert, Timothy J; Hickie, Ian B

2010-04-01

A diagnostic hallmark of autism spectrum disorders is a qualitative impairment in social communication and interaction. Deficits in the ability to recognize the emotions of others are believed to contribute to this. There is currently no effective treatment for these problems. In a double-blind, randomized, placebo-controlled, crossover design, we administered oxytocin nasal spray (18 or 24 IU) or a placebo to 16 male youth aged 12 to 19 who were diagnosed with Autistic or Asperger's Disorder. Participants then completed the Reading the Mind in the Eyes Task, a widely used and reliable test of emotion recognition. In comparison with placebo, oxytocin administration improved performance on the Reading the Mind in the Eyes Task. This effect was also shown when analysis was restricted to the younger participants aged 12 to 15 who received the lower dose. This study provides the first evidence that oxytocin nasal spray improves emotion recognition in young people diagnosed with autism spectrum disorders. Findings suggest the potential of earlier intervention and further evaluation of oxytocin nasal spray as a treatment to improve social communication and interaction in young people with autism spectrum disorders. Copyright 2010 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Fixations to the eyes aids in facial encoding; covertly attending to the eyes does not.

PubMed

Laidlaw, Kaitlin E W; Kingstone, Alan

2017-02-01

When looking at images of faces, people will often focus their fixations on the eyes. It has previously been demonstrated that the eyes convey important information that may improve later facial recognition. Whether this advantage requires that the eyes be fixated, or merely attended to covertly (i.e. while looking elsewhere), is unclear from previous work. While attending to the eyes covertly without fixating them may be sufficient, the act of using overt attention to fixate the eyes may improve the processing of important details used for later recognition. In the present study, participants were shown a series of faces and, in Experiment 1, asked to attend to them normally while avoiding looking at either the eyes or, as a control, the mouth (overt attentional avoidance condition); or in Experiment 2 fixate the center of the face while covertly attending to either the eyes or the mouth (covert attention condition). After the first phase, participants were asked to perform an old/new face recognition task. We demonstrate that a) when fixations to the eyes are avoided during initial viewing then subsequent face discrimination suffers, and b) covert attention to the eyes alone is insufficient to improve face discrimination performance. Together, these findings demonstrate that fixating the eyes provides an encoding advantage that is not availed by covert attention alone. Copyright © 2016 Elsevier B.V. All rights reserved.

Sustaining Reliability on Accountability Measures at The Johns Hopkins Hospital.

PubMed

Pronovost, Peter J; Holzmueller, Christine G; Callender, Tiffany; Demski, Renee; Winner, Laura; Day, Richard; Austin, J Matthew; Berenholtz, Sean M; Miller, Marlene R

2016-02-01

In 2012 Johns Hopkins Medicine leaders challenged their health system to reliably deliver best practice care linked to nationally vetted core measures and achieve The Joint Commission Top Performer on Key Quality Measures ®program recognition and the Delmarva Foundation award. Thus, the Armstrong Institute for Patient Safety and Quality implemented an initiative to ensure that ≥96% of patients received care linked to measures. Nine low-performing process measures were targeted for improvement-eight Joint Commission accountability measures and one Delmarva Foundation core measure. In the initial evaluation at The Johns Hopkins Hospital, all accountability measures for the Top Performer program reached the required ≥95% performance, gaining them recognition by The Joint Commission in 2013. Efforts were made to sustain performance of accountability measures at The Johns Hopkins Hospital. Improvements were sustained through 2014 using the following conceptual framework: declare and communicate goals, create an enabling infrastructure, engage clinicians and connect them in peer learning communities, report transparently, and create accountability systems. One part of the accountability system was for teams to create a sustainability plan, which they presented to senior leaders. To support sustained improvements, Armstrong Institute leaders added a project management office for all externally reported quality measures and concurrent reviewers to audit performance on care processes for certain measure sets. The Johns Hopkins Hospital sustained performance on all accountability measures, and now more than 96% of patients receive recommended care consistent with nationally vetted quality measures. The initiative methods enabled the transition of quality improvement from an isolated project to a way of leading an organization.

Original and Mirror Face Images and Minimum Squared Error Classification for Visible Light Face Recognition.

PubMed

Wang, Rong

2015-01-01

In real-world applications, the image of faces varies with illumination, facial expression, and poses. It seems that more training samples are able to reveal possible images of the faces. Though minimum squared error classification (MSEC) is a widely used method, its applications on face recognition usually suffer from the problem of a limited number of training samples. In this paper, we improve MSEC by using the mirror faces as virtual training samples. We obtained the mirror faces generated from original training samples and put these two kinds of samples into a new set. The face recognition experiments show that our method does obtain high accuracy performance in classification.

Integration of multispectral face recognition and multi-PTZ camera automated surveillance for security applications

NASA Astrophysics Data System (ADS)

Chen, Chung-Hao; Yao, Yi; Chang, Hong; Koschan, Andreas; Abidi, Mongi

2013-06-01

Due to increasing security concerns, a complete security system should consist of two major components, a computer-based face-recognition system and a real-time automated video surveillance system. A computerbased face-recognition system can be used in gate access control for identity authentication. In recent studies, multispectral imaging and fusion of multispectral narrow-band images in the visible spectrum have been employed and proven to enhance the recognition performance over conventional broad-band images, especially when the illumination changes. Thus, we present an automated method that specifies the optimal spectral ranges under the given illumination. Experimental results verify the consistent performance of our algorithm via the observation that an identical set of spectral band images is selected under all tested conditions. Our discovery can be practically used for a new customized sensor design associated with given illuminations for an improved face recognition performance over conventional broad-band images. In addition, once a person is authorized to enter a restricted area, we still need to continuously monitor his/her activities for the sake of security. Because pantilt-zoom (PTZ) cameras are capable of covering a panoramic area and maintaining high resolution imagery for real-time behavior understanding, researches in automated surveillance systems with multiple PTZ cameras have become increasingly important. Most existing algorithms require the prior knowledge of intrinsic parameters of the PTZ camera to infer the relative positioning and orientation among multiple PTZ cameras. To overcome this limitation, we propose a novel mapping algorithm that derives the relative positioning and orientation between two PTZ cameras based on a unified polynomial model. This reduces the dependence on the knowledge of intrinsic parameters of PTZ camera and relative positions. Experimental results demonstrate that our proposed algorithm presents substantially reduced computational complexity and improved flexibility at the cost of slightly decreased pixel accuracy as compared to Chen and Wang's method [18].

Personal FM systems for children with autism spectrum disorders (ASD) and/or attention-deficit hyperactivity disorder (ADHD): an initial investigation.

PubMed

Schafer, Erin C; Mathews, Lauren; Mehta, Smita; Hill, Margaret; Munoz, Ashley; Bishop, Rachel; Moloney, Molly

2013-01-01

The goal of this initial investigation was to examine the potential benefit of a frequency modulation (FM) system for 11 children diagnosed with autism spectrum disorders (ASD), attention-deficit hyperactivity disorder (ADHD), or both disorders through measures of speech recognition performance in noise, observed classroom behavior, and teacher-rated educational risk and listening behaviors. Use of the FM system resulted in significant average improvements in speech recognition in noise for the children with ASD and ADHD as well as large effect sizes. When compared to typically functioning peers, children with ASD and ADHD had significantly poorer average speech recognition performance in noise without the FM system but comparable average performance when the FM system was used. Similarly, classroom observations yielded a significant increase in on-task behaviors and large effect sizes when the FM system was in use during two separate trial periods. Although teacher ratings on questionnaires showed no significant improvement in the average level of educational risk of participants, they did indicate significant improvement in average listening behaviors during two trial periods with the FM system. Given the significantly better speech recognition in noise, increased on-task behaviors, and improved teacher ratings of listening behaviors with the FM system, these devices may be a viable option for children who have ASD and ADHD in the classroom. However, an individual evaluation including audiological testing and a functional evaluation in the child's primary learning environment will be necessary to determine the benefit of an FM system for a particular student. 1. The reader will be able to describe the potential benefit of FM systems for children with ASD and/or ADHD. 2. The reader will be able to identify on-task versus off-task listening behaviors in children with ASD and/or ADHD. 3. The reader will be able to explain the components of a successful pre-fit education program that may be necessary prior to fitting an FM system in children with ASD. Copyright © 2012 Elsevier Inc. All rights reserved.

Cochlear Implants Special Issue Article: Vocal Emotion Recognition by Normal-Hearing Listeners and Cochlear Implant Users

PubMed Central

Luo, Xin; Fu, Qian-Jie; Galvin, John J.

2007-01-01

The present study investigated the ability of normal-hearing listeners and cochlear implant users to recognize vocal emotions. Sentences were produced by 1 male and 1 female talker according to 5 target emotions: angry, anxious, happy, sad, and neutral. Overall amplitude differences between the stimuli were either preserved or normalized. In experiment 1, vocal emotion recognition was measured in normal-hearing and cochlear implant listeners; cochlear implant subjects were tested using their clinically assigned processors. When overall amplitude cues were preserved, normal-hearing listeners achieved near-perfect performance, whereas listeners with cochlear implant recognized less than half of the target emotions. Removing the overall amplitude cues significantly worsened mean normal-hearing and cochlear implant performance. In experiment 2, vocal emotion recognition was measured in listeners with cochlear implant as a function of the number of channels (from 1 to 8) and envelope filter cutoff frequency (50 vs 400 Hz) in experimental speech processors. In experiment 3, vocal emotion recognition was measured in normal-hearing listeners as a function of the number of channels (from 1 to 16) and envelope filter cutoff frequency (50 vs 500 Hz) in acoustic cochlear implant simulations. Results from experiments 2 and 3 showed that both cochlear implant and normal-hearing performance significantly improved as the number of channels or the envelope filter cutoff frequency was increased. The results suggest that spectral, temporal, and overall amplitude cues each contribute to vocal emotion recognition. The poorer cochlear implant performance is most likely attributable to the lack of salient pitch cues and the limited functional spectral resolution. PMID:18003871

Divergent short- and long-term effects of acute stress in object recognition memory are mediated by endogenous opioid system activation.

PubMed

Nava-Mesa, Mauricio O; Lamprea, Marisol R; Múnera, Alejandro

2013-11-01

Acute stress induces short-term object recognition memory impairment and elicits endogenous opioid system activation. The aim of this study was thus to evaluate whether opiate system activation mediates the acute stress-induced object recognition memory changes. Adult male Wistar rats were trained in an object recognition task designed to test both short- and long-term memory. Subjects were randomly assigned to receive an intraperitoneal injection of saline, 1 mg/kg naltrexone or 3 mg/kg naltrexone, four and a half hours before the sample trial. Five minutes after the injection, half the subjects were submitted to movement restraint during four hours while the other half remained in their home cages. Non-stressed subjects receiving saline (control) performed adequately during the short-term memory test, while stressed subjects receiving saline displayed impaired performance. Naltrexone prevented such deleterious effect, in spite of the fact that it had no intrinsic effect on short-term object recognition memory. Stressed subjects receiving saline and non-stressed subjects receiving naltrexone performed adequately during the long-term memory test; however, control subjects as well as stressed subjects receiving a high dose of naltrexone performed poorly. Control subjects' dissociated performance during both memory tests suggests that the short-term memory test induced a retroactive interference effect mediated through light opioid system activation; such effect was prevented either by low dose naltrexone administration or by strongly activating the opioid system through acute stress. Both short-term memory retrieval impairment and long-term memory improvement observed in stressed subjects may have been mediated through strong opioid system activation, since they were prevented by high dose naltrexone administration. Therefore, the activation of the opioid system plays a dual modulating role in object recognition memory. Copyright © 2013 Elsevier Inc. All rights reserved.

Potential Benefits of an Integrated Electric-Acoustic Sound Processor with Children: A Preliminary Report.

PubMed

Wolfe, Jace; Neumann, Sara; Schafer, Erin; Marsh, Megan; Wood, Mark; Baker, R Stanley

2017-02-01

A number of published studies have demonstrated the benefits of electric-acoustic stimulation (EAS) over conventional electric stimulation for adults with functional low-frequency acoustic hearing and severe-to-profound high-frequency hearing loss. These benefits potentially include better speech recognition in quiet and in noise, better localization, improvements in sound quality, better music appreciation and aptitude, and better pitch recognition. There is, however, a paucity of published reports describing the potential benefits and limitations of EAS for children with functional low-frequency acoustic hearing and severe-to-profound high-frequency hearing loss. The objective of this study was to explore the potential benefits of EAS for children. A repeated measures design was used to evaluate performance differences obtained with EAS stimulation versus acoustic- and electric-only stimulation. Seven users of Cochlear Nucleus Hybrid, Nucleus 24 Freedom, CI512, and CI422 implants were included in the study. Sentence recognition (assayed using the pediatric version of the AzBio sentence recognition test) was evaluated in quiet and at three fixed signal-to-noise ratios (SNR) (0, +5, and +10 dB). Functional hearing performance was also evaluated with the use of questionnaires, including the comparative version of the Speech, Spatial, and Qualities, the Listening Inventory for Education Revised, and the Children's Home Inventory for Listening Difficulties. Speech recognition in noise was typically better with EAS compared to participants' performance with acoustic- and electric-only stimulation, particularly when evaluated at the less favorable SNR. Additionally, in real-world situations, children generally preferred to use EAS compared to electric-only stimulation. Also, the participants' classroom teachers observed better hearing performance in the classroom with the use of EAS. Use of EAS provided better speech recognition in quiet and in noise when compared to performance obtained with use of acoustic- and electric-only stimulation, and children responded favorably to the use of EAS implemented in an integrated sound processor for real-world use. American Academy of Audiology

Logo recognition using alpha-rooted phase correlation in the radon transform domain

NASA Astrophysics Data System (ADS)

DelMarco, Stephen

2009-08-01

Alpha-rooted phase correlation (ARPC) is a recently-developed variant of classical phase correlation that includes a Fourier domain image enhancement operation. ARPC combines classical phase correlation with alpha-rooting to provide tunable image enhancement. The alpha-rooting parameters may be adjusted to provide a tradeoff between height and width of the ARPC main lobe. A high narrow main lobe peak provides high matching accuracy for aligned images, but reduced matching performance for misaligned logos. A lower, wider peak trades matching accuracy on aligned logos, for improved matching performance on misaligned imagery. Previously, we developed ARPC and used it in the spatial domain for logo recognition as part of an overall automated document analysis problem. However, spatial domain ARPC performance can be sensitive to logo misalignments, including rotational misalignment. In this paper we use ARPC as a match metric in the radon transform domain for logo recognition. In the radon transform domain, rotational misalignments correspond to translations in the radon transform angle parameter. These translations are captured by ARPC, thereby producing rotation-invariant logo matching. In the paper, we first present an overview of ARPC, and then describe the logo matching algorithm. We present numerical performance results demonstrating matching tolerance to rotational misalignments. We demonstrate robustness of the radon transform domain rotation estimation to noise. We present logo verification and recognition performance results using the proposed approach on a public domain logo database. We compare performance results to performance obtained using spatial domain ARPC, and state-of-the-art SURF features, for logos in salt-and-pepper noise.

Computer-based auditory phoneme discrimination training improves speech recognition in noise in experienced adult cochlear implant listeners.

PubMed

Schumann, Annette; Serman, Maja; Gefeller, Olaf; Hoppe, Ulrich

2015-03-01

Specific computer-based auditory training may be a useful completion in the rehabilitation process for cochlear implant (CI) listeners to achieve sufficient speech intelligibility. This study evaluated the effectiveness of a computerized, phoneme-discrimination training programme. The study employed a pretest-post-test design; participants were randomly assigned to the training or control group. Over a period of three weeks, the training group was instructed to train in phoneme discrimination via computer, twice a week. Sentence recognition in different noise conditions (moderate to difficult) was tested pre- and post-training, and six months after the training was completed. The control group was tested and retested within one month. Twenty-seven adult CI listeners who had been using cochlear implants for more than two years participated in the programme; 15 adults in the training group, 12 adults in the control group. Besides significant improvements for the trained phoneme-identification task, a generalized training effect was noted via significantly improved sentence recognition in moderate noise. No significant changes were noted in the difficult noise conditions. Improved performance was maintained over an extended period. Phoneme-discrimination training improves experienced CI listeners' speech perception in noise. Additional research is needed to optimize auditory training for individual benefit.

Altitude acclimatization improves submaximal cognitive performance in mice and involves an imbalance of the cholinergic system.

PubMed

Guerra-Narbona, R; Delgado-García, J M; López-Ramos, J C

2013-06-15

The aim of this work was to reveal a hypothetical improvement of cognitive abilities in animals acclimatized to altitude and performing under ground level conditions, when looking at submaximal performance, once seen that it was not possible when looking at maximal scores. We modified contrasted cognitive tasks (object recognition, operant conditioning, eight-arm radial maze, and classical conditioning of the eyeblink reflex), increasing their complexity in an attempt to find performance differences in acclimatized animals vs. untrained controls. In addition, we studied, through immunohistochemical quantification, the expression of choline acetyltransferase and acetyl cholinesterase, enzymes involved in the synthesis and degradation of acetylcholine, in the septal area, piriform and visual cortexes, and the hippocampal CA1 area of animals submitted to acute hypobaric hypoxia, or acclimatized to this simulated altitude, to find a relationship between the cholinergic system and a cognitive improvement due to altitude acclimatization. Results showed subtle improvements of the cognitive capabilities of acclimatized animals in all of the tasks when performed under ground-level conditions (although not before 24 h), in the three tasks used to test explicit memory (object recognition, operant conditioning in the Skinner box, and eight-arm radial maze) and (from the first conditioning session) in the classical conditioning task used to evaluate implicit memory. An imbalance of choline acetyltransferase/acetyl cholinesterase expression was found in acclimatized animals, mainly 24 h after the acclimatization period. In conclusion, altitude acclimatization improves cognitive capabilities, in a process parallel to an imbalance of the cholinergic system.

Effect of motion on speech recognition.

PubMed

Davis, Timothy J; Grantham, D Wesley; Gifford, René H

2016-07-01

The benefit of spatial separation for talkers in a multi-talker environment is well documented. However, few studies have examined the effect of talker motion on speech recognition. In the current study, we evaluated the effects of (1) motion of the target or distracters, (2) a priori information about the target and distracter spatial configurations, and (3) target and distracter location. In total, seventeen young adults with normal hearing were tested in a large anechoic chamber in two experiments. In Experiment 1, seven stimulus conditions were tested using the Coordinate Response Measure (Bolia et al., 2000) speech corpus, in which subjects were required to report the key words in a target sentence presented simultaneously with two distracter sentences. As in previous studies, there was a significant improvement in key word identification for conditions in which the target and distracters were spatially separated as compared to the co-located conditions. In addition, 1) motion of either talker or distracter resulted in improved performance compared to stationary presentation (talker motion yielded significantly better performance than distracter motion) 2) a priori information regarding stimulus configuration was not beneficial, and 3) performance was significantly better with key words at 0° azimuth as compared to -60° (on the listener's left). Experiment 2 included two additional conditions designed to assess whether the benefit of motion observed in Experiment 1 was due to the motion itself or to the fact that the motion conditions introduced small spatial separations in the target and distracter key words. Results showed that small spatial separations (on the order of 5-8°) resulted in improved performance (relative to co-located key words) whether the sentences were moving or stationary. These results suggest that in the presence of distracting messages, motion of either target or distracters and/or small spatial separation of the key words may be beneficial for sound source segregation and thus for improved speech recognition. Copyright © 2016 Elsevier B.V. All rights reserved.

Ciproxifan, an H3 receptor antagonist, improves short-term recognition memory impaired by isoflurane anesthesia.

PubMed

Ding, Fang; Zheng, Limin; Liu, Min; Chen, Rongfa; Leung, L Stan; Luo, Tao

2016-08-01

Exposure to volatile anesthetics has been reported to cause temporary or sustained impairments in learning and memory in pre-clinical studies. The selective antagonists of the histamine H3 receptors (H3R) are considered to be a promising group of novel therapeutic agents for the treatment of cognitive disorders. The aim of this study was to evaluate the effect of H3R antagonist ciproxifan on isoflurane-induced deficits in an object recognition task. Adult C57BL/6 J mice were exposed to isoflurane (1.3 %) or vehicle gas for 2 h. The object recognition tests were carried at 24 h or 7 days after exposure to anesthesia to exploit the tendency of mice to prefer exploring novel objects in an environment when a familiar object is also present. During the training phase, two identical objects were placed in two defined sites of the chamber. During the test phase, performed 1 or 24 h after the training phase, one of the objects was replaced by a new object with a different shape. The time spent exploring each object was recorded. A robust deficit in object recognition memory occurred 1 day after exposure to isoflurane anesthesia. Isoflurane-treated mice spent significantly less time exploring a novel object at 1 h but not at 24 h after the training phase. The deficit in short-term memory was reversed by the administration of ciproxifan 30 min before behavioral training. Isoflurane exposure induces reversible deficits in object recognition memory. Ciproxifan appears to be a potential therapeutic agent for improving post-anesthesia cognitive memory performance.

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Palmprint and face score level fusion: hardware implementation of a contactless small sample biometric system

NASA Astrophysics Data System (ADS)

Poinsot, Audrey; Yang, Fan; Brost, Vincent

2011-02-01

Including multiple sources of information in personal identity recognition and verification gives the opportunity to greatly improve performance. We propose a contactless biometric system that combines two modalities: palmprint and face. Hardware implementations are proposed on the Texas Instrument Digital Signal Processor and Xilinx Field-Programmable Gate Array (FPGA) platforms. The algorithmic chain consists of a preprocessing (which includes palm extraction from hand images), Gabor feature extraction, comparison by Hamming distance, and score fusion. Fusion possibilities are discussed and tested first using a bimodal database of 130 subjects that we designed (uB database), and then two common public biometric databases (AR for face and PolyU for palmprint). High performance has been obtained for recognition and verification purpose: a recognition rate of 97.49% with AR-PolyU database and an equal error rate of 1.10% on the uB database using only two training samples per subject have been obtained. Hardware results demonstrate that preprocessing can easily be performed during the acquisition phase, and multimodal biometric recognition can be treated almost instantly (0.4 ms on FPGA). We show the feasibility of a robust and efficient multimodal hardware biometric system that offers several advantages, such as user-friendliness and flexibility.

Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition

PubMed Central

Ordóñez, Francisco Javier; Roggen, Daniel

2016-01-01

Human activity recognition (HAR) tasks have traditionally been solved using engineered features obtained by heuristic processes. Current research suggests that deep convolutional neural networks are suited to automate feature extraction from raw sensor inputs. However, human activities are made of complex sequences of motor movements, and capturing this temporal dynamics is fundamental for successful HAR. Based on the recent success of recurrent neural networks for time series domains, we propose a generic deep framework for activity recognition based on convolutional and LSTM recurrent units, which: (i) is suitable for multimodal wearable sensors; (ii) can perform sensor fusion naturally; (iii) does not require expert knowledge in designing features; and (iv) explicitly models the temporal dynamics of feature activations. We evaluate our framework on two datasets, one of which has been used in a public activity recognition challenge. Our results show that our framework outperforms competing deep non-recurrent networks on the challenge dataset by 4% on average; outperforming some of the previous reported results by up to 9%. Our results show that the framework can be applied to homogeneous sensor modalities, but can also fuse multimodal sensors to improve performance. We characterise key architectural hyperparameters’ influence on performance to provide insights about their optimisation. PMID:26797612

Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition.

PubMed

Yuan, Chunfeng; Li, Xi; Hu, Weiming; Ling, Haibin; Maybank, Stephen J

2014-02-01

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.

Contribution of hearing aids to music perception by cochlear implant users.

PubMed

Peterson, Nathaniel; Bergeson, Tonya R

2015-09-01

Modern cochlear implant (CI) encoding strategies represent the temporal envelope of sounds well but provide limited spectral information. This deficit in spectral information has been implicated as a contributing factor to difficulty with speech perception in noisy conditions, discriminating between talkers and melody recognition. One way to supplement spectral information for CI users is by fitting a hearing aid (HA) to the non-implanted ear. In this study 14 postlingually deaf adults (half with a unilateral CI and the other half with a CI and an HA (CI + HA)) were tested on measures of music perception and familiar melody recognition. CI + HA listeners performed significantly better than CI-only listeners on all pitch-based music perception tasks. The CI + HA group did not perform significantly better than the CI-only group in the two tasks that relied on duration cues. Recognition of familiar melodies was significantly enhanced for the group wearing an HA in addition to their CI. This advantage in melody recognition was increased when melodic sequences were presented with the addition of harmony. These results show that, for CI recipients with aidable hearing in the non-implanted ear, using a HA in addition to their implant improves perception of musical pitch and recognition of real-world melodies.

A longitudinal study of the bilateral benefit in children with bilateral cochlear implants.

PubMed

Asp, Filip; Mäki-Torkko, Elina; Karltorp, Eva; Harder, Henrik; Hergils, Leif; Eskilsson, Gunnar; Stenfelt, Stefan

2015-02-01

To study the development of the bilateral benefit in children using bilateral cochlear implants by measurements of speech recognition and sound localization. Bilateral and unilateral speech recognition in quiet, in multi-source noise, and horizontal sound localization was measured at three occasions during a two-year period, without controlling for age or implant experience. Longitudinal and cross-sectional analyses were performed. Results were compared to cross-sectional data from children with normal hearing. Seventy-eight children aged 5.1-11.9 years, with a mean bilateral cochlear implant experience of 3.3 years and a mean age of 7.8 years, at inclusion in the study. Thirty children with normal hearing aged 4.8-9.0 years provided normative data. For children with cochlear implants, bilateral and unilateral speech recognition in quiet was comparable whereas a bilateral benefit for speech recognition in noise and sound localization was found at all three test occasions. Absolute performance was lower than in children with normal hearing. Early bilateral implantation facilitated sound localization. A bilateral benefit for speech recognition in noise and sound localization continues to exist over time for children with bilateral cochlear implants, but no relative improvement is found after three years of bilateral cochlear implant experience.

Oxytocin Promotes Facial Emotion Recognition and Amygdala Reactivity in Adults with Asperger Syndrome

PubMed Central

Domes, Gregor; Kumbier, Ekkehardt; Heinrichs, Markus; Herpertz, Sabine C

2014-01-01

The neuropeptide oxytocin has recently been shown to enhance eye gaze and emotion recognition in healthy men. Here, we report a randomized double-blind, placebo-controlled trial that examined the neural and behavioral effects of a single dose of intranasal oxytocin on emotion recognition in individuals with Asperger syndrome (AS), a clinical condition characterized by impaired eye gaze and facial emotion recognition. Using functional magnetic resonance imaging, we examined whether oxytocin would enhance emotion recognition from facial sections of the eye vs the mouth region and modulate regional activity in brain areas associated with face perception in both adults with AS, and a neurotypical control group. Intranasal administration of the neuropeptide oxytocin improved performance in a facial emotion recognition task in individuals with AS. This was linked to increased left amygdala reactivity in response to facial stimuli and increased activity in the neural network involved in social cognition. Our data suggest that the amygdala, together with functionally associated cortical areas mediate the positive effect of oxytocin on social cognitive functioning in AS. PMID:24067301

Oxytocin promotes facial emotion recognition and amygdala reactivity in adults with asperger syndrome.

PubMed

Domes, Gregor; Kumbier, Ekkehardt; Heinrichs, Markus; Herpertz, Sabine C

2014-02-01

The neuropeptide oxytocin has recently been shown to enhance eye gaze and emotion recognition in healthy men. Here, we report a randomized double-blind, placebo-controlled trial that examined the neural and behavioral effects of a single dose of intranasal oxytocin on emotion recognition in individuals with Asperger syndrome (AS), a clinical condition characterized by impaired eye gaze and facial emotion recognition. Using functional magnetic resonance imaging, we examined whether oxytocin would enhance emotion recognition from facial sections of the eye vs the mouth region and modulate regional activity in brain areas associated with face perception in both adults with AS, and a neurotypical control group. Intranasal administration of the neuropeptide oxytocin improved performance in a facial emotion recognition task in individuals with AS. This was linked to increased left amygdala reactivity in response to facial stimuli and increased activity in the neural network involved in social cognition. Our data suggest that the amygdala, together with functionally associated cortical areas mediate the positive effect of oxytocin on social cognitive functioning in AS.

Sequential Bilateral Cochlear Implantation in a Patient with Bilateral Meniere’s Disease

PubMed Central

Holden, Laura K.; Neely, J. Gail; Gotter, Brenda D.; Mispagel, Karen M.; Firszt, Jill B.

2012-01-01

This case study describes a 45 year old female with bilateral, profound sensorineural hearing loss due to Meniere’s disease. She received her first cochlear implant in the right ear in 2008 and the second cochlear implant in the left ear in 2010. The case study examines the enhancement to speech recognition, particularly in noise, provided by bilateral cochlear implants. Speech recognition tests were administered prior to obtaining the second implant and at a number of test intervals following activation of the second device. Speech recognition in quiet and noise as well as localization abilities were assessed in several conditions to determine bilateral benefit and performance differences between ears. The results of the speech recognition testing indicated a substantial improvement in the patient’s ability to understand speech in noise and her ability to localize sound when using bilateral cochlear implants compared to using a unilateral implant or an implant and a hearing aid. In addition, the patient reported considerable improvement in her ability to communicate in daily life when using bilateral implants versus a unilateral implant. This case suggests that cochlear implantation is a viable option for patients who have lost their hearing to Meniere’s disease even when a number of medical treatments and surgical interventions have been performed to control vertigo. In the case presented, bilateral cochlear implantation was necessary for this patient to communicate successfully at home and at work. PMID:22463939

Improved word recognition for observers with age-related maculopathies using compensation filters

NASA Technical Reports Server (NTRS)

Lawton, Teri B.

1988-01-01

A method for improving word recognition for people with age-related maculopathies, which cause a loss of central vision, is discussed. It is found that the use of individualized compensation filters based on an person's normalized contrast sensitivity function can improve word recognition for people with age-related maculopathies. It is shown that 27-70 pct more magnification is needed for unfiltered words compared to filtered words. The improvement in word recognition is positively correlated with the severity of vision loss.

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

PubMed

Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

2017-12-01

Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent performance improvement, indicating robustness of our approach. Furthermore, bi-clustering results of the extracted features are compatible with fold hierarchy of proteins, implying that these features are fold-specific. Together, these results suggest that the features extracted from predicted contacts are orthogonal to alignment-related features, and the combination of them could greatly facilitate fold recognition at superfamily/fold levels and template-based prediction of protein structures. Source code of DeepFR is freely available through https://github.com/zhujianwei31415/deepfr, and a web server is available through http://protein.ict.ac.cn/deepfr. zheng@itp.ac.cn or dbu@ict.ac.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Teaching emotion recognition skills to young children with autism: a randomised controlled trial of an emotion training programme.

PubMed

Williams, Beth T; Gray, Kylie M; Tonge, Bruce J

2012-12-01

Children with autism have difficulties in emotion recognition and a number of interventions have been designed to target these problems. However, few emotion training interventions have been trialled with young children with autism and co-morbid ID. This study aimed to evaluate the efficacy of an emotion training programme for a group of young children with autism with a range of intellectual ability. Participants were 55 children with autistic disorder, aged 4-7 years (FSIQ 42-107). Children were randomly assigned to an intervention (n = 28) or control group (n = 27). Participants in the intervention group watched a DVD designed to teach emotion recognition skills to children with autism (the Transporters), whereas the control group watched a DVD of Thomas the Tank Engine. Participants were assessed on their ability to complete basic emotion recognition tasks, mindreading and theory of mind (TOM) tasks before and after the 4-week intervention period, and at 3-month follow-up. Analyses controlled for the effect of chronological age, verbal intelligence, gender and DVD viewing time on outcomes. Children in the intervention group showed improved performance in the recognition of anger compared with the control group, with few improvements maintained at 3-month follow-up. There was no generalisation of skills to TOM or social skills. The Transporters programme showed limited efficacy in teaching basic emotion recognition skills to young children with autism with a lower range of cognitive ability. Improvements were limited to the recognition of expressions of anger, with poor maintenance of these skills at follow-up. These findings provide limited support for the efficacy of the Transporters programme for young children with autism of a lower cognitive range. © 2012 The Authors. Journal of Child Psychology and Psychiatry © 2012 Association for Child and Adolescent Mental Health.

The effects of digital signal processing features on children's speech recognition and loudness perception.

PubMed

Crukley, Jeffery; Scollie, Susan D

2014-03-01

The purpose of this study was to determine the effects of hearing instruments set to Desired Sensation Level version 5 (DSL v5) hearing instrument prescription algorithm targets and equipped with directional microphones and digital noise reduction (DNR) on children's sentence recognition in noise performance and loudness perception in a classroom environment. Ten children (ages 8-17 years) with stable, congenital sensorineural hearing losses participated in the study. Participants were fitted bilaterally with behind-the-ear hearing instruments set to DSL v5 prescriptive targets. Sentence recognition in noise was evaluated using the Bamford-Kowal-Bench Speech in Noise Test (Niquette et al., 2003). Loudness perception was evaluated using a modified version of the Contour Test of Loudness Perception (Cox, Alexander, Taylor, & Gray, 1997). Children's sentence recognition in noise performance was significantly better when using directional microphones alone or in combination with DNR than when using omnidirectional microphones alone or in combination with DNR. Children's loudness ratings for sounds above 72 dB SPL were lowest when fitted with the DSL v5 Noise prescription combined with directional microphones. DNR use showed no effect on loudness ratings. Use of the DSL v5 Noise prescription with a directional microphone improved sentence recognition in noise performance and reduced loudness perception ratings for loud sounds relative to a typical clinical reference fitting with the DSL v5 Quiet prescription with no digital signal processing features enabled. Potential clinical strategies are discussed.

A Human Activity Recognition System Using Skeleton Data from RGBD Sensors.

PubMed

Cippitelli, Enea; Gasparrini, Samuele; Gambi, Ennio; Spinsante, Susanna

2016-01-01

The aim of Active and Assisted Living is to develop tools to promote the ageing in place of elderly people, and human activity recognition algorithms can help to monitor aged people in home environments. Different types of sensors can be used to address this task and the RGBD sensors, especially the ones used for gaming, are cost-effective and provide much information about the environment. This work aims to propose an activity recognition algorithm exploiting skeleton data extracted by RGBD sensors. The system is based on the extraction of key poses to compose a feature vector, and a multiclass Support Vector Machine to perform classification. Computation and association of key poses are carried out using a clustering algorithm, without the need of a learning algorithm. The proposed approach is evaluated on five publicly available datasets for activity recognition, showing promising results especially when applied for the recognition of AAL related actions. Finally, the current applicability of this solution in AAL scenarios and the future improvements needed are discussed.

Diagnosing Parkinson's Diseases Using Fuzzy Neural System

PubMed Central

Abiyev, Rahib H.; Abizade, Sanan

2016-01-01

This study presents the design of the recognition system that will discriminate between healthy people and people with Parkinson's disease. A diagnosing of Parkinson's diseases is performed using fusion of the fuzzy system and neural networks. The structure and learning algorithms of the proposed fuzzy neural system (FNS) are presented. The approach described in this paper allows enhancing the capability of the designed system and efficiently distinguishing healthy individuals. It was proved through simulation of the system that has been performed using data obtained from UCI machine learning repository. A comparative study was carried out and the simulation results demonstrated that the proposed fuzzy neural system improves the recognition rate of the designed system. PMID:26881009

The value of parsing as feature generation for gene mention recognition

PubMed Central

Smith, Larry H; Wilbur, W John

2009-01-01

We measured the extent to which information surrounding a base noun phrase reflects the presence of a gene name, and evaluated seven different parsers in their ability to provide information for that purpose. Using the GENETAG corpus as a gold standard, we performed machine learning to recognize from its context when a base noun phrase contained a gene name. Starting with the best lexical features, we assessed the gain of adding dependency or dependency-like relations from a full sentence parse. Features derived from parsers improved performance in this partial gene mention recognition task by a small but statistically significant amount. There were virtually no differences between parsers in these experiments. PMID:19345281

Investigation of the effectiveness of traffic sign training in terms of training methods and sign characteristics.

PubMed

Ng, Annie W Y; Chan, Alan H S

2011-06-01

This research investigated whether different training methods had any effect on the effectiveness of traffic sign training and whether there were any relationships between traffic sign characteristics and effectiveness of the training. Thirty-six participants were randomly assigned into 4 equal-sized groups (control, paired-associate learning, recall training, and recognition training) to study the learnability of Mainland China traffic signs. In paired-associate learning, participants studied each traffic sign along with a referent describing its meaning. In addition to being informed of the meaning of traffic signs, both recall training and recognition training provided participants with questions and feedback. For recall training, the questioning process was a recall task in which participants had to produce a meaning for a given traffic sign from memory. For recognition training, the questioning process was a recognition task that required participants to identify the most appropriate referent corresponding to a given sign. No traffic sign training was given to the control group. Each training method significantly improved comprehension of the meaning of traffic signs. Participants from recall training performed better in a posttraining test than those from paired-associate learning and recognition training, indicating that the recall training elicited a deeper level of learning. In addition, questioning and feedback had a positive influence on training effectiveness. Performance in the posttest was found to be better when the questioning process matched the test process. Regarding the traffic sign characteristics, semantic closeness had a long-lasting effect, in terms of the timescale of this experiment on traffic sign comprehension, and traffic signs were perceived as more meaningful after their intended meanings were studied. Recall training is more effective in enhancing comprehension of traffic signs than paired-associate learning and recognition training. The findings of this study provide a basis for useful recommendations for designing symbol-training programs to improve road safety for road users.

Recognition of facial expressions of emotion by adults with intellectual disability: Is there evidence for the emotion specificity hypothesis?

PubMed

Scotland, Jennifer L; McKenzie, Karen; Cossar, Jill; Murray, Aja; Michie, Amanda

2016-01-01

This study aimed to evaluate the emotion recognition abilities of adults (n=23) with an intellectual disability (ID) compared with a control group of children (n=23) without ID matched for estimated cognitive ability. The study examined the impact of: task paradigm, stimulus type and preferred processing style (global/local) on accuracy. We found that, after controlling for estimated cognitive ability, the control group performed significantly better than the individuals with ID. This provides some support for the emotion specificity hypothesis. Having a more local processing style did not significantly mediate the relation between having ID and emotion recognition, but did significantly predict emotion recognition ability after controlling for group. This suggests that processing style is related to emotion recognition independently of having ID. The availability of contextual information improved emotion recognition for people with ID when compared with line drawing stimuli, and identifying a target emotion from a choice of two was relatively easier for individuals with ID, compared with the other task paradigms. The results of the study are considered in the context of current theories of emotion recognition deficits in individuals with ID. Copyright © 2015 Elsevier Ltd. All rights reserved.

Contralateral Bimodal Stimulation: A Way to Enhance Speech Performance in Arabic-Speaking Cochlear Implant Patients.

PubMed

Abdeltawwab, Mohamed M; Khater, Ahmed; El-Anwar, Mohammad W

2016-01-01

The combination of acoustic and electric stimulation as a way to enhance speech recognition performance in cochlear implant (CI) users has generated considerable interest in the recent years. The purpose of this study was to evaluate the bimodal advantage of the FS4 speech processing strategy in combination with hearing aids (HA) as a means to improve low-frequency resolution in CI patients. Nineteen postlingual CI adults were selected to participate in this study. All patients wore implants on one side and HA on the contralateral side with residual hearing. Monosyllabic word recognition, speech in noise, and emotion and talker identification were assessed using CI with fine structure processing/FS4 and high-definition continuous interleaved sampling strategies, HA alone, and a combination of CI and HA. The bimodal stimulation showed improvement in speech performance and emotion identification for the question/statement/order tasks, which was statistically significant compared to patients with CI alone, but there were no significant statistical differences in intragender talker discrimination and emotion identification for the happy/angry/neutral tasks. The poorest performance was obtained with HA only, and it was statistically significant compared to the other modalities. The bimodal stimulation showed enhanced speech performance in CI patients, and it improves the limitations provided by electric or acoustic stimulation alone. © 2016 S. Karger AG, Basel.

Facial Recognition Training: Improving Intelligence Collection by Soldiers

DTIC Science & Technology

2008-01-01

Facial Recognition Training: Improving Intelligence Collection by Soldiers By: 2LT Michael Mitchell, MI, ALARNG “In combat, you don’t rise to...technology, but on patrol a Soldier cannot use a device as quickly as simply looking at the subject. Why is Facial Recognition Difficult? Soldiers...00-2008 to 00-00-2008 4. TITLE AND SUBTITLE Facial Recognition Training: Improving Intelligence Collection by Soldiers 5a. CONTRACT NUMBER 5b

Facial emotion recognition and sleep in mentally disordered patients: A natural experiment in a high security hospital.

PubMed

Chu, Simon; McNeill, Kimberley; Ireland, Jane L; Qurashi, Inti

2015-12-15

We investigated the relationship between a change in sleep quality and facial emotion recognition accuracy in a group of mentally-disordered inpatients at a secure forensic psychiatric unit. Patients whose sleep improved over time also showed improved facial emotion recognition while patients who showed no sleep improvement showed no change in emotion recognition. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Effects of lexical characteristics and demographic factors on mandarin chinese open-set word recognition in children with cochlear implants.

PubMed

Liu, Haihong; Liu, Sha; Wang, Suju; Liu, Chang; Kong, Ying; Zhang, Ning; Li, Shujing; Yang, Yilin; Han, Demin; Zhang, Luo

2013-01-01

The purpose of this study was to examine the open-set word recognition performance of Mandarin Chinese-speaking children who had received a multichannel cochlear implant (CI) and examine the effects of lexical characteristics and demographic factors (i.e., age at implantation and duration of implant use) on Mandarin Chinese open-set word recognition in these children. Participants were 230 prelingually deafened children with CIs. Age at implantation ranged from 0.9 to 16.0 years, with a mean of 3.9 years. The Standard-Chinese version of the Monosyllabic Lexical Neighborhood test and the Multisyllabic Lexical Neighborhood test were used to evaluate the open-set word identification abilities of the children. A two-way analysis of variance was performed to delineate the lexical effects on the open-set word identification, with word difficulty and syllable length as the two main factors. The effects of age at implantation and duration of implant use on open-set, word-recognition performance were examined using correlational/regressional models. First, the average percent-correct scores for the disyllabic "easy" list, disyllabic "hard" list, monosyllabic "easy" list, and monosyllabic "hard" list were 65.0%, 51.3%, 58.9%, and 46.2%, respectively. For both the easy and hard lists, the percentage of words correctly identified was higher for disyllabic words than for monosyllabic words, Second, the CI group scored 26.3%, 31.3%, and 18.8 % points lower than their hearing-age-matched normal-hearing peers for 4, 5, and 6 years of hearing age, respectively. The corresponding gaps between the CI group and the chronological-age-matched normal-hearing group were 47.6, 49.6, and 42.4, respectively. The individual variations in performance were much greater in the CI group than in the normal-hearing group, Third, the children exhibited steady improvements in performance as the duration of implant use increased, especially 1 to 6 years postimplantation. Last, age at implantation had significant effects on postimplantation word-recognition performance. The benefit of early implantation was particularly evident in children 5 years old or younger. First, Mandarin Chinese-speaking pediatric CI users' open-set word recognition was influenced by the lexical characteristics of the stimuli. The score was higher for easy words than for hard words and was higher for disyllabic words than for monosyllabic words, Second, Mandarin-Chinese-speaking pediatric CI users exhibited steady progress in open-set word recognition as the duration of implant use increased. However, the present study also demonstrated that, even after 6 years of CI use, there was a significant deficit in open-set, word-recognition performance in the CI children compared with their normal-hearing peers. Third, age at implantation had significant effects on open-set, word-recognition performance. Early implanted children exhibited better performance than children implanted later.

The effect of background noise on the word activation process in nonnative spoken-word recognition.

PubMed

Scharenborg, Odette; Coumans, Juul M J; van Hout, Roeland

2018-02-01

This article investigates 2 questions: (1) does the presence of background noise lead to a differential increase in the number of simultaneously activated candidate words in native and nonnative listening? And (2) do individual differences in listeners' cognitive and linguistic abilities explain the differential effect of background noise on (non-)native speech recognition? English and Dutch students participated in an English word recognition experiment, in which either a word's onset or offset was masked by noise. The native listeners outperformed the nonnative listeners in all listening conditions. Importantly, however, the effect of noise on the multiple activation process was found to be remarkably similar in native and nonnative listening. The presence of noise increased the set of candidate words considered for recognition in both native and nonnative listening. The results indicate that the observed performance differences between the English and Dutch listeners should not be primarily attributed to a differential effect of noise, but rather to the difference between native and nonnative listening. Additional analyses showed that word-initial information was found to be more important than word-final information during spoken-word recognition. When word-initial information was no longer reliably available word recognition accuracy dropped and word frequency information could no longer be used suggesting that word frequency information is strongly tied to the onset of words and the earliest moments of lexical access. Proficiency and inhibition ability were found to influence nonnative spoken-word recognition in noise, with a higher proficiency in the nonnative language and worse inhibition ability leading to improved recognition performance. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The relationship between speech recognition, behavioural listening effort, and subjective ratings.

PubMed

Picou, Erin M; Ricketts, Todd A

2018-06-01

The purpose of this study was to evaluate the reliability and validity of four subjective questions related to listening effort. A secondary purpose of this study was to evaluate the effects of hearing aid beamforming microphone arrays on word recognition and listening effort. Participants answered subjective questions immediately following testing in a dual-task paradigm with three microphone settings in a moderately reverberant laboratory environment in two noise configurations. Participants rated their: (1) mental work, (2) desire to improve the situation, (3) tiredness, and (4) desire to give up. Data were analysed using repeated measures and reliability analyses. Eighteen adults with symmetrical sensorineural hearing loss participated. Beamforming differentially affected word recognition and listening effort. Analysis revealed the same pattern of results for behavioural listening effort and subjective ratings of desire to improve the situation. Conversely, ratings of work revealed the same pattern of results as word recognition performance. Ratings of tiredness and desire to give up were unaffected by hearing aid microphone or noise configuration. Participant ratings of their desire to control the listening situation appear to reliable subjective indicators of listening effort that align with results from a behavioural measure of listening effort.

Human action recognition based on point context tensor shape descriptor

NASA Astrophysics Data System (ADS)

Li, Jianjun; Mao, Xia; Chen, Lijiang; Wang, Lan

2017-07-01

Motion trajectory recognition is one of the most important means to determine the identity of a moving object. A compact and discriminative feature representation method can improve the trajectory recognition accuracy. This paper presents an efficient framework for action recognition using a three-dimensional skeleton kinematic joint model. First, we put forward a rotation-scale-translation-invariant shape descriptor based on point context (PC) and the normal vector of hypersurface to jointly characterize local motion and shape information. Meanwhile, an algorithm for extracting the key trajectory based on the confidence coefficient is proposed to reduce the randomness and computational complexity. Second, to decrease the eigenvalue decomposition time complexity, a tensor shape descriptor (TSD) based on PC that can globally capture the spatial layout and temporal order to preserve the spatial information of each frame is proposed. Then, a multilinear projection process is achieved by tensor dynamic time warping to map the TSD to a low-dimensional tensor subspace of the same size. Experimental results show that the proposed shape descriptor is effective and feasible, and the proposed approach obtains considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.

Improving the dictionary lookup approach for disease normalization using enhanced dictionary and query expansion.

PubMed

Jonnagaddala, Jitendra; Jue, Toni Rose; Chang, Nai-Wen; Dai, Hong-Jie

2016-01-01

The rapidly increasing biomedical literature calls for the need of an automatic approach in the recognition and normalization of disease mentions in order to increase the precision and effectivity of disease based information retrieval. A variety of methods have been proposed to deal with the problem of disease named entity recognition and normalization. Among all the proposed methods, conditional random fields (CRFs) and dictionary lookup method are widely used for named entity recognition and normalization respectively. We herein developed a CRF-based model to allow automated recognition of disease mentions, and studied the effect of various techniques in improving the normalization results based on the dictionary lookup approach. The dataset from the BioCreative V CDR track was used to report the performance of the developed normalization methods and compare with other existing dictionary lookup based normalization methods. The best configuration achieved an F-measure of 0.77 for the disease normalization, which outperformed the best dictionary lookup based baseline method studied in this work by an F-measure of 0.13.Database URL: https://github.com/TCRNBioinformatics/DiseaseExtract. © The Author(s) 2016. Published by Oxford University Press.

The beneficial effect of oxytocin on avoidance-related facial emotion recognition depends on early life stress experience.

PubMed

Feeser, Melanie; Fan, Yan; Weigand, Anne; Hahn, Adam; Gärtner, Matti; Aust, Sabine; Böker, Heinz; Bajbouj, Malek; Grimm, Simone

2014-12-01

Previous studies have shown that oxytocin (OXT) enhances social cognitive processes. It has also been demonstrated that OXT does not uniformly facilitate social cognition. The effects of OXT administration strongly depend on the exposure to stressful experiences in early life. Emotional facial recognition is crucial for social cognition. However, no study has yet examined how the effects of OXT on the ability to identify emotional faces are altered by early life stress (ELS) experiences. Given the role of OXT in modulating social motivational processes, we specifically aimed to investigate its effects on the recognition of approach- and avoidance-related facial emotions. In a double-blind, between-subjects, placebo-controlled design, 82 male participants performed an emotion recognition task with faces taken from the "Karolinska Directed Emotional Faces" set. We clustered the six basic emotions along the dimensions approach (happy, surprise, anger) and avoidance (fear, sadness, disgust). ELS was assessed with the Childhood Trauma Questionnaire (CTQ). Our results showed that OXT improved the ability to recognize avoidance-related emotional faces as compared to approach-related emotional faces. Whereas the performance for avoidance-related emotions in participants with higher ELS scores was comparable in both OXT and placebo condition, OXT enhanced emotion recognition in participants with lower ELS scores. Independent of OXT administration, we observed increased emotion recognition for avoidance-related faces in participants with high ELS scores. Our findings suggest that the investigation of OXT on social recognition requires a broad approach that takes ELS experiences as well as motivational processes into account.

Improving visual memory, attention, and school function with atomoxetine in boys with attention-deficit/hyperactivity disorder.

PubMed

Shang, Chi-Yung; Gau, Susan Shur-Fen

2012-10-01

Atomoxetine is efficacious in reducing symptoms of attention- deficit/hyperactivity disorder (ADHD), but its effect on visual memory and attention needs more investigation. This study aimed to assess the effect of atomoxetine on visual memory, attention, and school function in boys with ADHD in Taiwan. This was an open-label 12 week atomoxetine treatment trial among 30 drug-naíve boys with ADHD, aged 8-16 years. Before administration of atomoxetine, the participants were assessed using psychiatric interviews, the Wechsler Intelligence Scale for Children, 3rd edition (WISC-III), the school function of the Chinese version of the Social Adjustment Inventory for Children and Adolescents (SAICA), the Conners' Continuous Performance Test (CPT), and the tasks of the Cambridge Neuropsychological Test Automated Battery (CANTAB) involving visual memory and attention: Pattern Recognition Memory, Spatial Recognition Memory, and Reaction Time, which were reassessed at weeks 4 and 12. Our results showed there was significant improvement in pattern recognition memory and spatial recognition memory as measured by the CANTAB tasks, sustained attention and response inhibition as measured by the CPT, and reaction time as measured by the CANTAB after treatment with atomoxetine for 4 weeks or 12 weeks. In addition, atomoxetine significantly enhanced school functioning in children with ADHD. Our findings suggested that atomoxetine was associated with significant improvement in visual memory, attention, and school functioning in boys with ADHD.

Oscillatory neural network for pattern recognition: trajectory based classification and supervised learning.

PubMed

Miller, Vonda H; Jansen, Ben H

2008-12-01

Computer algorithms that match human performance in recognizing written text or spoken conversation remain elusive. The reasons why the human brain far exceeds any existing recognition scheme to date in the ability to generalize and to extract invariant characteristics relevant to category matching are not clear. However, it has been postulated that the dynamic distribution of brain activity (spatiotemporal activation patterns) is the mechanism by which stimuli are encoded and matched to categories. This research focuses on supervised learning using a trajectory based distance metric for category discrimination in an oscillatory neural network model. Classification is accomplished using a trajectory based distance metric. Since the distance metric is differentiable, a supervised learning algorithm based on gradient descent is demonstrated. Classification of spatiotemporal frequency transitions and their relation to a priori assessed categories is shown along with the improved classification results after supervised training. The results indicate that this spatiotemporal representation of stimuli and the associated distance metric is useful for simple pattern recognition tasks and that supervised learning improves classification results.

Object and event recognition for stroke rehabilitation

NASA Astrophysics Data System (ADS)

Ghali, Ahmed; Cunningham, Andrew S.; Pridmore, Tony P.

2003-06-01

Stroke is a major cause of disability and health care expenditure around the world. Existing stroke rehabilitation methods can be effective but are costly and need to be improved. Even modest improvements in the effectiveness of rehabilitation techniques could produce large benefits in terms of quality of life. The work reported here is part of an ongoing effort to integrate virtual reality and machine vision technologies to produce innovative stroke rehabilitation methods. We describe a combined object recognition and event detection system that provides real time feedback to stroke patients performing everyday kitchen tasks necessary for independent living, e.g. making a cup of coffee. The image plane position of each object, including the patient"s hand, is monitored using histogram-based recognition methods. The relative positions of hand and objects are then reported to a task monitor that compares the patient"s actions against a model of the target task. A prototype system has been constructed and is currently undergoing technical and clinical evaluation.

Improved Hip-Based Individual Recognition Using Wearable Motion Recording Sensor

NASA Astrophysics Data System (ADS)

Gafurov, Davrondzhon; Bours, Patrick

In todays society the demand for reliable verification of a user identity is increasing. Although biometric technologies based on fingerprint or iris can provide accurate and reliable recognition performance, they are inconvenient for periodic or frequent re-verification. In this paper we propose a hip-based user recognition method which can be suitable for implicit and periodic re-verification of the identity. In our approach we use a wearable accelerometer sensor attached to the hip of the person, and then the measured hip motion signal is analysed for identity verification purposes. The main analyses steps consists of detecting gait cycles in the signal and matching two sets of detected gait cycles. Evaluating the approach on a hip data set consisting of 400 gait sequences (samples) from 100 subjects, we obtained equal error rate (EER) of 7.5% and identification rate at rank 1 was 81.4%. These numbers are improvements by 37.5% and 11.2% respectively of the previous study using the same data set.

Coded aperture solution for improving the performance of traffic enforcement cameras

NASA Astrophysics Data System (ADS)

Masoudifar, Mina; Pourreza, Hamid Reza

2016-10-01

A coded aperture camera is proposed for automatic license plate recognition (ALPR) systems. It captures images using a noncircular aperture. The aperture pattern is designed for the rapid acquisition of high-resolution images while preserving high spatial frequencies of defocused regions. It is obtained by minimizing an objective function, which computes the expected value of perceptual deblurring error. The imaging conditions and camera sensor specifications are also considered in the proposed function. The designed aperture improves the depth of field (DoF) and subsequently ALPR performance. The captured images can be directly analyzed by the ALPR software up to a specific depth, which is 13 m in our case, though it is 11 m for the circular aperture. Moreover, since the deblurring results of images captured by our aperture yield fewer artifacts than those captured by the circular aperture, images can be first deblurred and then analyzed by the ALPR software. In this way, the DoF and recognition rate can be improved at the same time. Our case study shows that the proposed camera can improve the DoF up to 17 m while it is limited to 11 m in the conventional aperture.

Invariant recognition drives neural representations of action sequences

PubMed Central

Poggio, Tomaso

2017-01-01

Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences. PMID:29253864

Adjunctive selective estrogen receptor modulator increases neural activity in the hippocampus and inferior frontal gyrus during emotional face recognition in schizophrenia.

PubMed

Ji, E; Weickert, C S; Lenroot, R; Kindler, J; Skilleter, A J; Vercammen, A; White, C; Gur, R E; Weickert, T W

2016-05-03

Estrogen has been implicated in the development and course of schizophrenia with most evidence suggesting a neuroprotective effect. Treatment with raloxifene, a selective estrogen receptor modulator, can reduce symptom severity, improve cognition and normalize brain activity during learning in schizophrenia. People with schizophrenia are especially impaired in the identification of negative facial emotions. The present study was designed to determine the extent to which adjunctive raloxifene treatment would alter abnormal neural activity during angry facial emotion recognition in schizophrenia. Twenty people with schizophrenia (12 men, 8 women) participated in a 13-week, randomized, double-blind, placebo-controlled, crossover trial of adjunctive raloxifene treatment (120 mg per day orally) and performed a facial emotion recognition task during functional magnetic resonance imaging after each treatment phase. Two-sample t-tests in regions of interest selected a priori were performed to assess activation differences between raloxifene and placebo conditions during the recognition of angry faces. Adjunctive raloxifene significantly increased activation in the right hippocampus and left inferior frontal gyrus compared with the placebo condition (family-wise error, P<0.05). There was no significant difference in performance accuracy or reaction time between active and placebo conditions. To the best of our knowledge, this study provides the first evidence suggesting that adjunctive raloxifene treatment changes neural activity in brain regions associated with facial emotion recognition in schizophrenia. These findings support the hypothesis that estrogen plays a modifying role in schizophrenia and shows that adjunctive raloxifene treatment may reverse abnormal neural activity during facial emotion recognition, which is relevant to impaired social functioning in men and women with schizophrenia.

The effect of an interactive delirium e-learning tool on healthcare workers' delirium recognition, knowledge and strain in caring for delirious patients: a pilot pre-test/post-test study.

PubMed

Detroyer, Elke; Dobbels, Fabienne; Debonnaire, Deborah; Irving, Kate; Teodorczuk, Andrew; Fick, Donna M; Joosten, Etienne; Milisen, Koen

2016-01-15

Studies investigating the effectiveness of delirium e-learning tools in clinical practice are scarce. The aim of this study is to determine the effect of a delirium e-learning tool on healthcare workers' delirium recognition, delirium knowledge and care strain in delirium. A pilot pre-posttest study in a convenience sample of 59 healthcare workers recruited from medical, surgical, geronto-psychiatric and rehabilitation units of a university hospital. The intervention consisted of a live information session on how to use the e-learning tool and, a 2-month self-active learning program. The tool included 11 e-modules integrating knowledge and skill development in prevention, detection and management of delirium. Case vignettes, the Delirium Knowledge Questionnaire, and the Strain of Care for Delirium Index were used to measure delirium recognition, delirium knowledge and experienced care strain in delirium respectively. Subgroup analyses were performed for healthcare workers completing 0 to 6 versus 7 to 11 modules. The delirium recognition score improved significantly (mean 3.1 ± SD 0.9 versus 2.7 ± 1.1; P = 0.04), and more healthcare workers identified hypoactive (P = 0.04) and hyperactive (P = 0.007) delirium in the posttest compared to the pretest phase. A significant difference in the change of recognition levels over time between the 0 to 6 and 7 to 11 module groups was demonstrated (P = 0.03), with an improved recognition level in the posttest phase within the 7 to 11 module group (P = 0.007). After adjustment for potential confounders, this difference in the change over time was not significant (P = 0.07) and no change in recognition levels within the 7 to 11 module group was noted (P = 0.19). The knowledge score significantly improved in the posttest compared to the pretest phase (mean 31.7 ± SD2.6 versus 28.3 ± 4.5; P < 0.001), with a significant increased level within the 7 to 11 module group (unadjusted P < 0.001/adjusted P = 0.02). Overall, no difference between posttest and pretest phases was documented for care strain (P = 0.46). The e-learning tool improved healthcare workers' delirium recognition and knowledge. The effect of the tool is related to its level of completion, but was less explicit after controlling for potential confounders and warrants further investigation. The level of strain did not improve.

A Taxonomy of 3D Occluded Objects Recognition Techniques

NASA Astrophysics Data System (ADS)

Soleimanizadeh, Shiva; Mohamad, Dzulkifli; Saba, Tanzila; Al-ghamdi, Jarallah Saleh

2016-03-01

The overall performances of object recognition techniques under different condition (e.g., occlusion, viewpoint, and illumination) have been improved significantly in recent years. New applications and hardware are shifted towards digital photography, and digital media. This faces an increase in Internet usage requiring object recognition for certain applications; particularly occulded objects. However occlusion is still an issue unhandled, interlacing the relations between extracted feature points through image, research is going on to develop efficient techniques and easy to use algorithms that would help users to source images; this need to overcome problems and issues regarding occlusion. The aim of this research is to review recognition occluded objects algorithms and figure out their pros and cons to solve the occlusion problem features, which are extracted from occluded object to distinguish objects from other co-existing objects by determining the new techniques, which could differentiate the occluded fragment and sections inside an image.

Recognition of Speech from the Television with Use of a Wireless Technology Designed for Cochlear Implants.

PubMed

Duke, Mila Morais; Wolfe, Jace; Schafer, Erin

2016-05-01

Cochlear implant (CI) recipients often experience difficulty understanding speech in noise and speech that originates from a distance. Many CI recipients also experience difficulty understanding speech originating from a television. Use of hearing assistance technology (HAT) may improve speech recognition in noise and for signals that originate from more than a few feet from the listener; however, there are no published studies evaluating the potential benefits of a wireless HAT designed to deliver audio signals from a television directly to a CI sound processor. The objective of this study was to compare speech recognition in quiet and in noise of CI recipients with the use of their CI alone and with the use of their CI and a wireless HAT (Cochlear Wireless TV Streamer). A two-way repeated measures design was used to evaluate performance differences obtained in quiet and in competing noise (65 dBA) with the CI sound processor alone and with the sound processor coupled to the Cochlear Wireless TV Streamer. Sixteen users of Cochlear Nucleus 24 Freedom, CI512, and CI422 implants were included in the study. Participants were evaluated in four conditions including use of the sound processor alone and use of the sound processor with the wireless streamer in quiet and in the presence of competing noise at 65 dBA. Speech recognition was evaluated in each condition with two full lists of Computer-Assisted Speech Perception Testing and Training Sentence-Level Test sentences presented from a light-emitting diode television. Speech recognition in noise was significantly better with use of the wireless streamer compared to participants' performance with their CI sound processor alone. There was also a nonsignificant trend toward better performance in quiet with use of the TV Streamer. Performance was significantly poorer when evaluated in noise compared to performance in quiet when the TV Streamer was not used. Use of the Cochlear Wireless TV Streamer designed to stream audio from a television directly to a CI sound processor provides better speech recognition in quiet and in noise when compared to performance obtained with use of the CI sound processor alone. American Academy of Audiology.

Enhanced facial texture illumination normalization for face recognition.

PubMed

Luo, Yong; Guan, Ye-Peng

2015-08-01

An uncontrolled lighting condition is one of the most critical challenges for practical face recognition applications. An enhanced facial texture illumination normalization method is put forward to resolve this challenge. An adaptive relighting algorithm is developed to improve the brightness uniformity of face images. Facial texture is extracted by using an illumination estimation difference algorithm. An anisotropic histogram-stretching algorithm is proposed to minimize the intraclass distance of facial skin and maximize the dynamic range of facial texture distribution. Compared with the existing methods, the proposed method can more effectively eliminate the redundant information of facial skin and illumination. Extensive experiments show that the proposed method has superior performance in normalizing illumination variation and enhancing facial texture features for illumination-insensitive face recognition.

How color enhances visual memory for natural scenes.

PubMed

Spence, Ian; Wong, Patrick; Rusan, Maria; Rastegar, Naghmeh

2006-01-01

We offer a framework for understanding how color operates to improve visual memory for images of the natural environment, and we present an extensive data set that quantifies the contribution of color in the encoding and recognition phases. Using a continuous recognition task with colored and monochrome gray-scale images of natural scenes at short exposure durations, we found that color enhances recognition memory by conferring an advantage during encoding and by strengthening the encoding-specificity effect. Furthermore, because the pattern of performance was similar at all exposure durations, and because form and color are processed in different areas of cortex, the results imply that color must be bound as an integral part of the representation at the earliest stages of processing.

Online and unsupervised face recognition for continuous video stream

NASA Astrophysics Data System (ADS)

Huo, Hongwen; Feng, Jufu

2009-10-01

We present a novel online face recognition approach for video stream in this paper. Our method includes two stages: pre-training and online training. In the pre-training phase, our method observes interactions, collects batches of input data, and attempts to estimate their distributions (Box-Cox transformation is adopted here to normalize rough estimates). In the online training phase, our method incrementally improves classifiers' knowledge of the face space and updates it continuously with incremental eigenspace analysis. The performance achieved by our method shows its great potential in video stream processing.

Rapid effects of dorsal hippocampal G-protein coupled estrogen receptor on learning in female mice.

PubMed

Lymer, Jennifer; Robinson, Alana; Winters, Boyer D; Choleris, Elena

2017-03-01

Through rapid mechanisms of action, estrogens affect learning and memory processes. It has been shown that 17β-estradiol and an Estrogen Receptor (ER) α agonist enhances performance in social recognition, object recognition, and object placement tasks when administered systemically or infused in the dorsal hippocampus. In contrast, systemic and dorsal hippocampal ERβ activation only promote spatial learning. In addition, 17β-estradiol, the ERα and the G-protein coupled estrogen receptor (GPER) agonists increase dendritic spine density in the CA1 hippocampus. Recently, we have shown that selective systemic activation of the GPER also rapidly facilitated social recognition, object recognition, and object placement learning in female mice. Whether activation the GPER specifically in the dorsal hippocampus can also rapidly improve learning and memory prior to acquisition is unknown. Here, we investigated the rapid effects of infusion of the GPER agonist, G-1 (dose: 50nM, 100nM, 200nM), in the dorsal hippocampus on social recognition, object recognition, and object placement learning tasks in home cage. These paradigms were completed within 40min, which is within the range of rapid estrogenic effects. Dorsal hippocampal administration of G-1 improved social (doses: 50nM, 200nM G-1) and object (dose: 200nM G-1) recognition with no effect on object placement. Additionally, when spatial cues were minimized by testing in a Y-apparatus, G-1 administration promoted social (doses: 100nM, 200nM G-1) and object (doses: 50nM, 100nM, 200nM G-1) recognition. Therefore, like ERα, the GPER in the hippocampus appears to be sufficient for the rapid facilitation of social and object recognition in female mice, but not for the rapid facilitation of object placement learning. Thus, the GPER in the dorsal hippocampus is involved in estrogenic mediation of learning and memory and these effects likely occur through rapid signalling mechanisms. Copyright © 2016 Elsevier Ltd. All rights reserved.

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

PubMed

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

2018-01-01

Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.

Neural speech recognition: continuous phoneme decoding using spatiotemporal representations of human cortical activity

NASA Astrophysics Data System (ADS)

Moses, David A.; Mesgarani, Nima; Leonard, Matthew K.; Chang, Edward F.

2016-10-01

Objective. The superior temporal gyrus (STG) and neighboring brain regions play a key role in human language processing. Previous studies have attempted to reconstruct speech information from brain activity in the STG, but few of them incorporate the probabilistic framework and engineering methodology used in modern speech recognition systems. In this work, we describe the initial efforts toward the design of a neural speech recognition (NSR) system that performs continuous phoneme recognition on English stimuli with arbitrary vocabulary sizes using the high gamma band power of local field potentials in the STG and neighboring cortical areas obtained via electrocorticography. Approach. The system implements a Viterbi decoder that incorporates phoneme likelihood estimates from a linear discriminant analysis model and transition probabilities from an n-gram phonemic language model. Grid searches were used in an attempt to determine optimal parameterizations of the feature vectors and Viterbi decoder. Main results. The performance of the system was significantly improved by using spatiotemporal representations of the neural activity (as opposed to purely spatial representations) and by including language modeling and Viterbi decoding in the NSR system. Significance. These results emphasize the importance of modeling the temporal dynamics of neural responses when analyzing their variations with respect to varying stimuli and demonstrate that speech recognition techniques can be successfully leveraged when decoding speech from neural signals. Guided by the results detailed in this work, further development of the NSR system could have applications in the fields of automatic speech recognition and neural prosthetics.

Recognizing Age-Separated Face Images: Humans and Machines

PubMed Central

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components - facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario. PMID:25474200

Recognizing age-separated face images: humans and machines.

PubMed

Yadav, Daksha; Singh, Richa; Vatsa, Mayank; Noore, Afzel

2014-01-01

Humans utilize facial appearance, gender, expression, aging pattern, and other ancillary information to recognize individuals. It is interesting to observe how humans perceive facial age. Analyzing these properties can help in understanding the phenomenon of facial aging and incorporating the findings can help in designing effective algorithms. Such a study has two components--facial age estimation and age-separated face recognition. Age estimation involves predicting the age of an individual given his/her facial image. On the other hand, age-separated face recognition consists of recognizing an individual given his/her age-separated images. In this research, we investigate which facial cues are utilized by humans for estimating the age of people belonging to various age groups along with analyzing the effect of one's gender, age, and ethnicity on age estimation skills. We also analyze how various facial regions such as binocular and mouth regions influence age estimation and recognition capabilities. Finally, we propose an age-invariant face recognition algorithm that incorporates the knowledge learned from these observations. Key observations of our research are: (1) the age group of newborns and toddlers is easiest to estimate, (2) gender and ethnicity do not affect the judgment of age group estimation, (3) face as a global feature, is essential to achieve good performance in age-separated face recognition, and (4) the proposed algorithm yields improved recognition performance compared to existing algorithms and also outperforms a commercial system in the young image as probe scenario.

Facial Asymmetry-Based Age Group Estimation: Role in Recognizing Age-Separated Face Images.

PubMed

Sajid, Muhammad; Taj, Imtiaz Ahmad; Bajwa, Usama Ijaz; Ratyal, Naeem Iqbal

2018-04-23

Face recognition aims to establish the identity of a person based on facial characteristics. On the other hand, age group estimation is the automatic calculation of an individual's age range based on facial features. Recognizing age-separated face images is still a challenging research problem due to complex aging processes involving different types of facial tissues, skin, fat, muscles, and bones. Certain holistic and local facial features are used to recognize age-separated face images. However, most of the existing methods recognize face images without incorporating the knowledge learned from age group estimation. In this paper, we propose an age-assisted face recognition approach to handle aging variations. Inspired by the observation that facial asymmetry is an age-dependent intrinsic facial feature, we first use asymmetric facial dimensions to estimate the age group of a given face image. Deeply learned asymmetric facial features are then extracted for face recognition using a deep convolutional neural network (dCNN). Finally, we integrate the knowledge learned from the age group estimation into the face recognition algorithm using the same dCNN. This integration results in a significant improvement in the overall performance compared to using the face recognition algorithm alone. The experimental results on two large facial aging datasets, the MORPH and FERET sets, show that the proposed age group estimation based on the face recognition approach yields superior performance compared to some existing state-of-the-art methods. © 2018 American Academy of Forensic Sciences.

Influence of oxytocin on emotion recognition from body language: A randomized placebo-controlled trial.

PubMed

Bernaerts, Sylvie; Berra, Emmely; Wenderoth, Nicole; Alaerts, Kaat

2016-10-01

The neuropeptide 'oxytocin' (OT) is known to play a pivotal role in a variety of complex social behaviors by promoting a prosocial attitude and interpersonal bonding. One mechanism by which OT is hypothesized to promote prosocial behavior is by enhancing the processing of socially relevant information from the environment. With the present study, we explored to what extent OT can alter the 'reading' of emotional body language as presented by impoverished biological motion point light displays (PLDs). To do so, a double-blind between-subjects randomized placebo-controlled trial was conducted, assessing performance on a bodily emotion recognition task in healthy adult males before and after a single-dose of intranasal OT (24 IU). Overall, a single-dose of OT administration had a significant effect of medium size on emotion recognition from body language. OT-induced improvements in emotion recognition were not differentially modulated by the emotional valence of the presented stimuli (positive versus negative) and also, the overall tendency to label an observed emotional state as 'happy' (positive) or 'angry' (negative) was not modified by the administration of OT. Albeit moderate, the present findings of OT-induced improvements in bodily emotion recognition from whole-body PLD provide further support for a link between OT and the processing of socio-communicative cues originating from the body of others. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Estrous cycle, pregnancy, and parity enhance performance of rats in object recognition or object placement tasks

PubMed Central

Paris, Jason J; Frye, Cheryl A

2008-01-01

Ovarian hormone elevations are associated with enhanced learning/memory. During behavioral estrus or pregnancy, progestins, such as progesterone (P4) and its metabolite 5α-pregnan-3α-ol-20-one (3α,5α-THP), are elevated due, in part, to corpora luteal and placental secretion. During ‘pseudopregnancy’, the induction of corpora luteal functioning results in a hormonal milieu analogous to pregnancy, which ceases after about 12 days, due to the lack of placental formation. Multiparity is also associated with enhanced learning/memory, perhaps due to prior steroid exposure during pregnancy. Given evidence that progestins and/or parity may influence cognition, we investigated how natural alterations in the progestin milieu influence cognitive performance. In Experiment 1, virgin rats (nulliparous) or rats with two prior pregnancies (multiparous) were assessed on the object placement and recognition tasks, when in high-estrogen/P4 (behavioral estrus) or low-estrogen/P4 (diestrus) phases of the estrous cycle. In Experiment 2, primiparous or multiparous rats were tested in the object placement and recognition tasks when not pregnant, pseudopregnant, or pregnant (between gestational days (GDs) 6 and 12). In Experiment 3, pregnant primiparous or multiparous rats were assessed daily in the object placement or recognition tasks. Females in natural states associated with higher endogenous progestins (behavioral estrus, pregnancy, multiparity) outperformed rats in low progestin states (diestrus, non-pregnancy, nulliparity) on the object placement and recognition tasks. In earlier pregnancy, multiparous, compared with primiparous, rats had a lower corticosterone, but higher estrogen levels, concomitant with better object placement performance. From GD 13 until post partum, primiparous rats had higher 3α,5α-THP levels and improved object placement performance compared with multiparous rats. PMID:18390689

A Whole New World of Interventions: The Performance Technologist as Integrating Generalist.

ERIC Educational Resources Information Center

Hutchison, Cathleen Smith; Stein, Faith S.

1997-01-01

The authors update an article on intervention, discussing career development, communications, feedback, financial, human development, information, instructional, labor relations, measurement and evaluation, quality improvement, resource, reward and recognition, and selection systems; documentation and standards; ergonomics and human factors;…

Test-retest reliability and task order effects of emotional cognitive tests in healthy subjects.

PubMed

Adams, Thomas; Pounder, Zoe; Preston, Sally; Hanson, Andy; Gallagher, Peter; Harmer, Catherine J; McAllister-Williams, R Hamish

2016-11-01

Little is known of the retest reliability of emotional cognitive tasks or the impact of using different tasks employing similar emotional stimuli within a battery. We investigated this in healthy subjects. We found improved overall performance in an emotional attentional blink task (EABT) with repeat testing at one hour and one week compared to baseline, but the impact of an emotional stimulus on performance was unchanged. Similarly, performance on a facial expression recognition task (FERT) was better one week after a baseline test, though the relative effect of specific emotions was unaltered. There was no effect of repeat testing on an emotional word categorising, recall and recognition task. We found no difference in performance in the FERT and EABT irrespective of task order. We concluded that it is possible to use emotional cognitive tasks in longitudinal studies and combine tasks using emotional facial stimuli in a single battery.

Sparse Feature Extraction for Pose-Tolerant Face Recognition.

PubMed

Abiantun, Ramzi; Prabhu, Utsav; Savvides, Marios

2014-10-01

Automatic face recognition performance has been steadily improving over years of research, however it remains significantly affected by a number of factors such as illumination, pose, expression, resolution and other factors that can impact matching scores. The focus of this paper is the pose problem which remains largely overlooked in most real-world applications. Specifically, we focus on one-to-one matching scenarios where a query face image of a random pose is matched against a set of gallery images. We propose a method that relies on two fundamental components: (a) A 3D modeling step to geometrically correct the viewpoint of the face. For this purpose, we extend a recent technique for efficient synthesis of 3D face models called 3D Generic Elastic Model. (b) A sparse feature extraction step using subspace modeling and ℓ1-minimization to induce pose-tolerance in coefficient space. This in return enables the synthesis of an equivalent frontal-looking face, which can be used towards recognition. We show significant performance improvements in verification rates compared to commercial matchers, and also demonstrate the resilience of the proposed method with respect to degrading input quality. We find that the proposed technique is able to match non-frontal images to other non-frontal images of varying angles.

Vision-based posture recognition using an ensemble classifier and a vote filter

NASA Astrophysics Data System (ADS)

Ji, Peng; Wu, Changcheng; Xu, Xiaonong; Song, Aiguo; Li, Huijun

2016-10-01

Posture recognition is a very important Human-Robot Interaction (HRI) way. To segment effective posture from an image, we propose an improved region grow algorithm which combining with the Single Gauss Color Model. The experiment shows that the improved region grow algorithm can get the complete and accurate posture than traditional Single Gauss Model and region grow algorithm, and it can eliminate the similar region from the background at the same time. In the posture recognition part, and in order to improve the recognition rate, we propose a CNN ensemble classifier, and in order to reduce the misjudgments during a continuous gesture control, a vote filter is proposed and applied to the sequence of recognition results. Comparing with CNN classifier, the CNN ensemble classifier we proposed can yield a 96.27% recognition rate, which is better than that of CNN classifier, and the proposed vote filter can improve the recognition result and reduce the misjudgments during the consecutive gesture switch.

A multi-view face recognition system based on cascade face detector and improved Dlib

NASA Astrophysics Data System (ADS)

Zhou, Hongjun; Chen, Pei; Shen, Wei

2018-03-01

In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

Image ratio features for facial expression recognition application.

PubMed

Song, Mingli; Tao, Dacheng; Liu, Zicheng; Li, Xuelong; Zhou, Mengchu

2010-06-01

Video-based facial expression recognition is a challenging problem in computer vision and human-computer interaction. To target this problem, texture features have been extracted and widely used, because they can capture image intensity changes raised by skin deformation. However, existing texture features encounter problems with albedo and lighting variations. To solve both problems, we propose a new texture feature called image ratio features. Compared with previously proposed texture features, e.g., high gradient component features, image ratio features are more robust to albedo and lighting variations. In addition, to further improve facial expression recognition accuracy based on image ratio features, we combine image ratio features with facial animation parameters (FAPs), which describe the geometric motions of facial feature points. The performance evaluation is based on the Carnegie Mellon University Cohn-Kanade database, our own database, and the Japanese Female Facial Expression database. Experimental results show that the proposed image ratio feature is more robust to albedo and lighting variations, and the combination of image ratio features and FAPs outperforms each feature alone. In addition, we study asymmetric facial expressions based on our own facial expression database and demonstrate the superior performance of our combined expression recognition system.

Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogden, J.

The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation maymore » decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.« less

Impact of Social Cognition on Alcohol Dependence Treatment Outcome: Poorer Facial Emotion Recognition Predicts Relapse/Dropout.

PubMed

Rupp, Claudia I; Derntl, Birgit; Osthaus, Friederike; Kemmler, Georg; Fleischhacker, W Wolfgang

2017-12-01

Despite growing evidence for neurobehavioral deficits in social cognition in alcohol use disorder (AUD), the clinical relevance remains unclear, and little is known about its impact on treatment outcome. This study prospectively investigated the impact of neurocognitive social abilities at treatment onset on treatment completion. Fifty-nine alcohol-dependent patients were assessed with measures of social cognition including 3 core components of empathy via paradigms measuring: (i) emotion recognition (the ability to recognize emotions via facial expression), (ii) emotional perspective taking, and (iii) affective responsiveness at the beginning of inpatient treatment for alcohol dependence. Subjective measures were also obtained, including estimates of task performance and a self-report measure of empathic abilities (Interpersonal Reactivity Index). According to treatment outcomes, patients were divided into a patient group with a regular treatment course (e.g., with planned discharge and without relapse during treatment) or an irregular treatment course (e.g., relapse and/or premature and unplanned termination of treatment, "dropout"). Compared with patients completing treatment in a regular fashion, patients with relapse and/or dropout of treatment had significantly poorer facial emotion recognition ability at treatment onset. Additional logistic regression analyses confirmed these results and identified poor emotion recognition performance as a significant predictor for relapse/dropout. Self-report (subjective) measures did not correspond with neurobehavioral social cognition measures, respectively objective task performance. Analyses of individual subtypes of facial emotions revealed poorer recognition particularly of disgust, anger, and no (neutral faces) emotion in patients with relapse/dropout. Social cognition in AUD is clinically relevant. Less successful treatment outcome was associated with poorer facial emotion recognition ability at the beginning of treatment. Impaired facial emotion recognition represents a neurocognitive risk factor that should be taken into account in alcohol dependence treatment. Treatments targeting the improvement of these social cognition deficits in AUD may offer a promising future approach. Copyright © 2017 by the Research Society on Alcoholism.

New Trends in Impedimetric Biosensors for the Detection of Foodborne Pathogenic Bacteria

PubMed Central

Wang, Yixian; Ye, Zunzhong; Ying, Yibin

2012-01-01

The development of a rapid, sensitive, specific method for the foodborne pathogenic bacteria detection is of great importance to ensure food safety and security. In recent years impedimetric biosensors which integrate biological recognition technology and impedance have gained widespread application in the field of bacteria detection. This paper presents an overview on the progress and application of impedimetric biosensors for detection of foodborne pathogenic bacteria, particularly the new trends in the past few years, including the new specific bio-recognition elements such as bacteriophage and lectin, the use of nanomaterials and microfluidics techniques. The applications of these new materials or techniques have provided unprecedented opportunities for the development of high-performance impedance bacteria biosensors. The significant developments of impedimetric biosensors for bacteria detection in the last five years have been reviewed according to the classification of with or without specific bio-recognition element. In addition, some microfluidics systems, which were used in the construction of impedimetric biosensors to improve analytical performance, are introduced in this review. PMID:22737018

Sonority contours in word recognition

NASA Astrophysics Data System (ADS)

McLennan, Sean

2003-04-01

Contrary to the Generativist distinction between competence and performance which asserts that speech or perception errors are due to random, nonlinguistic factors, it seems likely that errors are principled and possibly governed by some of the same constraints as language. A preliminary investigation of errors modeled after the child's ``Chain Whisper'' game (a degraded stimulus task) suggests that a significant number of recognition errors can be characterized as an improvement in syllable sonority contour towards the linguistically least-marked, voiceless-stop-plus-vowel syllable. An independent study of sonority contours showed that approximately half of the English lexicon can be uniquely identified by their contour alone. Additionally, ``sororities'' (groups of words that share a single sonority contour), surprisingly, show no correlation to familiarity or frequency in either size or membership. Together these results imply that sonority contours may be an important factor in word recognition and in defining word ``neighborhoods.'' Moreover, they suggest that linguistic markedness constraints may be more prevalent in performance-related phenomena than previously accepted.

Point spread function engineering for iris recognition system design.

PubMed

Ashok, Amit; Neifeld, Mark A

2010-04-01

Undersampling in the detector array degrades the performance of iris-recognition imaging systems. We find that an undersampling of 8 x 8 reduces the iris-recognition performance by nearly a factor of 4 (on CASIA iris database), as measured by the false rejection ratio (FRR) metric. We employ optical point spread function (PSF) engineering via a Zernike phase mask in conjunction with multiple subpixel shifted image measurements (frames) to mitigate the effect of undersampling. A task-specific optimization framework is used to engineer the optical PSF and optimize the postprocessing parameters to minimize the FRR. The optimized Zernike phase enhanced lens (ZPEL) imager design with one frame yields an improvement of nearly 33% relative to a thin observation module by bounded optics (TOMBO) imager with one frame. With four frames the optimized ZPEL imager achieves a FRR equal to that of the conventional imager without undersampling. Further, the ZPEL imager design using 16 frames yields a FRR that is actually 15% lower than that obtained with the conventional imager without undersampling.

Computerised working memory based cognitive remediation therapy does not affect Reading the Mind in the Eyes test performance or neural activity during a Facial Emotion Recognition test in psychosis.

PubMed

Mothersill, David; Dillon, Rachael; Hargreaves, April; Castorina, Marco; Furey, Emilia; Fagan, Andrew J; Meaney, James F; Fitzmaurice, Brian; Hallahan, Brian; McDonald, Colm; Wykes, Til; Corvin, Aiden; Robertson, Ian H; Donohoe, Gary

2018-05-27

Working memory based cognitive remediation therapy (CT) for psychosis has recently been associated with broad improvements in performance on untrained tasks measuring working memory, episodic memory and IQ, and changes in associated brain regions. However, it is unclear if these improvements transfer to the domain of social cognition and neural activity related to performance on social cognitive tasks. We examined performance on the Reading the Mind in the Eyes test (Eyes test) in a large sample of participants with psychosis who underwent working memory based CT (N = 43) compared to a Control Group of participants with psychosis (N = 35). In a subset of this sample, we used functional magnetic resonance imaging (fMRI) to examine changes in neural activity during a facial emotion recognition task in participants who underwent CT (N = 15) compared to a Control Group (N = 15). No significant effects of CT were observed on Eyes test performance or on neural activity during facial emotion recognition, either at p<0.05 family-wise error, or at a p<0.001 uncorrected threshold, within a priori social cognitive regions of interest. This study suggests that working memory based CT does not significantly impact an aspect of social cognition which was measured behaviourally and neurally. It provides further evidence that deficits in the ability to decode mental state from facial expressions are dissociable from working memory deficits, and suggests that future CT programs should target social cognition in addition to working memory for the purposes of further enhancing social function. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

Speech Recognition and Parent Ratings From Auditory Development Questionnaires in Children Who Are Hard of Hearing.

PubMed

McCreery, Ryan W; Walker, Elizabeth A; Spratford, Meredith; Oleson, Jacob; Bentler, Ruth; Holte, Lenore; Roush, Patricia

2015-01-01

Progress has been made in recent years in the provision of amplification and early intervention for children who are hard of hearing. However, children who use hearing aids (HAs) may have inconsistent access to their auditory environment due to limitations in speech audibility through their HAs or limited HA use. The effects of variability in children's auditory experience on parent-reported auditory skills questionnaires and on speech recognition in quiet and in noise were examined for a large group of children who were followed as part of the Outcomes of Children with Hearing Loss study. Parent ratings on auditory development questionnaires and children's speech recognition were assessed for 306 children who are hard of hearing. Children ranged in age from 12 months to 9 years. Three questionnaires involving parent ratings of auditory skill development and behavior were used, including the LittlEARS Auditory Questionnaire, Parents Evaluation of Oral/Aural Performance in Children rating scale, and an adaptation of the Speech, Spatial, and Qualities of Hearing scale. Speech recognition in quiet was assessed using the Open- and Closed-Set Test, Early Speech Perception test, Lexical Neighborhood Test, and Phonetically Balanced Kindergarten word lists. Speech recognition in noise was assessed using the Computer-Assisted Speech Perception Assessment. Children who are hard of hearing were compared with peers with normal hearing matched for age, maternal educational level, and nonverbal intelligence. The effects of aided audibility, HA use, and language ability on parent responses to auditory development questionnaires and on children's speech recognition were also examined. Children who are hard of hearing had poorer performance than peers with normal hearing on parent ratings of auditory skills and had poorer speech recognition. Significant individual variability among children who are hard of hearing was observed. Children with greater aided audibility through their HAs, more hours of HA use, and better language abilities generally had higher parent ratings of auditory skills and better speech-recognition abilities in quiet and in noise than peers with less audibility, more limited HA use, or poorer language abilities. In addition to the auditory and language factors that were predictive for speech recognition in quiet, phonological working memory was also a positive predictor for word recognition abilities in noise. Children who are hard of hearing continue to experience delays in auditory skill development and speech-recognition abilities compared with peers with normal hearing. However, significant improvements in these domains have occurred in comparison to similar data reported before the adoption of universal newborn hearing screening and early intervention programs for children who are hard of hearing. Increasing the audibility of speech has a direct positive effect on auditory skill development and speech-recognition abilities and also may enhance these skills by improving language abilities in children who are hard of hearing. Greater number of hours of HA use also had a significant positive impact on parent ratings of auditory skills and children's speech recognition.

How can we improve the recognition, reporting and resolution of medical device-related incidents in hospitals? A qualitative study of physicians and registered nurses.

PubMed

Polisena, Julie; Gagliardi, Anna; Clifford, Tammy

2015-06-06

To explore factors that influence and to identify initiatives to improve the recognition, reporting and resolution of device-related incidents. Semi-structured telephone interviews with 16 health professionals in two tertiary care hospitals were conducted. Purposive sampling was used to identify appropriate study participants. Transcribed interviews were read independently by one individual to identify, define and organize themes and verified by another reviewer. Themes related to incident recognition were the hospital staff's knowledge and professional experience, medical device performance and clinical manifestations of patients, while incident reporting was influenced by error severity, personal attitudes of clinicians, feedback received on the error reported. Physicians often discontinued using medical devices if they malfunctioned. Education and training and the implementation of registries were discussed as important initiatives to improve medical device surveillance in clinical practice. Results from the telephone interviews suggest that multiple factors that influence participation in medical device surveillance activities are consistent with results for medical errors as reported in previous studies. The study results helped to propose a conceptual framework for a medical device surveillance system in a hospital context that would enhance patient safety and health care delivery.

A Novel Multilayer Correlation Maximization Model for Improving CCA-Based Frequency Recognition in SSVEP Brain-Computer Interface.

PubMed

Jiao, Yong; Zhang, Yu; Wang, Yu; Wang, Bei; Jin, Jing; Wang, Xingyu

2018-05-01

Multiset canonical correlation analysis (MsetCCA) has been successfully applied to optimize the reference signals by extracting common features from multiple sets of electroencephalogram (EEG) for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface application. To avoid extracting the possible noise components as common features, this study proposes a sophisticated extension of MsetCCA, called multilayer correlation maximization (MCM) model for further improving SSVEP recognition accuracy. MCM combines advantages of both CCA and MsetCCA by carrying out three layers of correlation maximization processes. The first layer is to extract the stimulus frequency-related information in using CCA between EEG samples and sine-cosine reference signals. The second layer is to learn reference signals by extracting the common features with MsetCCA. The third layer is to re-optimize the reference signals set in using CCA with sine-cosine reference signals again. Experimental study is implemented to validate effectiveness of the proposed MCM model in comparison with the standard CCA and MsetCCA algorithms. Superior performance of MCM demonstrates its promising potential for the development of an improved SSVEP-based brain-computer interface.

Interpersonal value profiles and analysis of adolescent academic performance and social thinking

PubMed Central

Gázquez, José J.; Sainz, Jorge; Pérez-Fuentes, María del C.; Molero, María del M.; Soler, Francisco J.

2015-01-01

The purposes of this study were to identify interpersonal value profiles and find out whether there were any differences in academic performance and social thinking. The study sample was 885 high school students of whom 49.8% (N = 441) were boys and 50.2% (N = 444) were girls. The results show that students with low Benevolence and Conformity levels showed higher prevalence of failures and repeated the year more often. Furthermore, students with a high level of Recognition and Leadership and low Conformity and Benevolence are socially incompetent students. Intervention programs should to achieve high levels of kindness and consideration, respect for rules and generosity, and diminish the perception of recognition by others and exertion of authority. Thus, this study shows the values that must be worked on to improve students’ Academic Performance and social competence. PMID:25999891

Audiovisual semantic congruency during encoding enhances memory performance.

PubMed

Heikkilä, Jenni; Alho, Kimmo; Hyvönen, Heidi; Tiippana, Kaisa

2015-01-01

Studies of memory and learning have usually focused on a single sensory modality, although human perception is multisensory in nature. In the present study, we investigated the effects of audiovisual encoding on later unisensory recognition memory performance. The participants were to memorize auditory or visual stimuli (sounds, pictures, spoken words, or written words), each of which co-occurred with either a semantically congruent stimulus, incongruent stimulus, or a neutral (non-semantic noise) stimulus in the other modality during encoding. Subsequent memory performance was overall better when the stimulus to be memorized was initially accompanied by a semantically congruent stimulus in the other modality than when it was accompanied by a neutral stimulus. These results suggest that semantically congruent multisensory experiences enhance encoding of both nonverbal and verbal materials, resulting in an improvement in their later recognition memory.

Antidepressant Effect and Recognition Memory Improvement of Two Novel Plant Extract Combinations - Antistress I and Anti-stress II on Rats Subjected to a Model of Mild Chronic Stress.

PubMed

Kandilarov, Ilin K; Zlatanova, Hristina I; Georgieva-Kotetarova, Maria T; Kostadinova, Ivanka I; Katsarova, Mariana N; Dimitrova, Stela Z; Lukanov, Ludmil K; Sadakov, Ferit

2018-03-01

Chronic stress is one of the main factors which lead to depression - a psychiatric disorder affecting millions of people and predicted to be the second ranked cause of premature death in 2020. Depression is often associated with cognitive disturbances and memory deficit. Plant based therapy could be effective in the treatment of mild to moderate depression due to its low level of adverse reaction, its good tolerability and compliance. 72 male Wistar rats, divided in 9 groups were given orally for 8 weeks two combinations of dry plant extracts - Antistress I and Antistress II and five individual dry extracts obtained from Serratula coronata, Hypericum perforatum, Valeriana officinalis, Crataegus monogyna and Melissa officinalis. The animals were exposed to a chronic unpredictable mild stress for 8 weeks. The depression-like symptoms were evaluated with Forced swim test while the assessment of the memory deficit was performed with Novel object recognition test. Antistress II demonstrates antidepressant effect while Antistress I doesn't improve the depressive-like symptoms. The individual extracts of Hypericum perforatum and Valeriana officinalis also possess antidepressant properties. Antistress II improves the cognition as well as the individual extracts of Hypericum perforatum, Valeriana officinalis and especially Serratula coronata. Dry extract from Serratula tend to have the best effect regarding the recognition memory. The effect of Antistress I on memory deficit is negligible. Antistress II possesses antidepressant effect and improves the recognition memory while Antistress I doesn't demonstrate any of the above-described effects.

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

Developmental changes in emotion recognition from full-light and point-light displays of body movement.

PubMed

Ross, Patrick D; Polson, Louise; Grosbras, Marie-Hélène

2012-01-01

To date, research on the development of emotion recognition has been dominated by studies on facial expression interpretation; very little is known about children's ability to recognize affective meaning from body movements. In the present study, we acquired simultaneous video and motion capture recordings of two actors portraying four basic emotions (Happiness Sadness, Fear and Anger). One hundred and seven primary and secondary school children (aged 4-17) and 14 adult volunteers participated in the study. Each participant viewed the full-light and point-light video clips and was asked to make a forced-choice as to which emotion was being portrayed. As a group, children performed worse than adults for both point-light and full-light conditions. Linear regression showed that both age and lighting condition were significant predictors of performance in children. Using piecewise regression, we found that a bilinear model with a steep improvement in performance until 8.5 years of age, followed by a much slower improvement rate through late childhood and adolescence best explained the data. These findings confirm that, like for facial expression, adolescents' recognition of basic emotions from body language is not fully mature and seems to follow a non-linear development. This is in line with observations of non-linear developmental trajectories for different aspects of human stimuli processing (voices and faces), perhaps suggesting a shift from one perceptual or cognitive strategy to another during adolescence. These results have important implications to understanding the maturation of social cognition.

Deep Neural Networks for Speech Separation With Application to Robust Speech Recognition

DTIC Science & Technology

acoustic -phonetic features. The second objective is integration of spectrotemporal context for improved separation performance. Conditional random fields...will be used to encode contextual constraints. The third objective is to achieve robust ASR in the DNN framework through integrated acoustic modeling

The use of illustration to improve older adults' comprehension of health-related information: is it helpful?

PubMed

Liu, Chiung-ju; Kemper, Susan; McDowd, Joan

2009-08-01

To examine whether explanatory illustrations can improve older adults' comprehension of written health information. Six short health-related texts were selected from websites and pamphlets. Young and older adults were randomly assigned to read health-related texts alone or texts accompanied by explanatory illustrations. Eye movements were recorded while reading. Word recognition, text comprehension, and comprehension of the illustrations were assessed after reading. Older adults performed as well as or better than young adults on the word recognition and text comprehension measures. However, older adults performed less well than young adults on the illustration comprehension measures. Analysis of readers' eye movements showed that older adults spent more time reading illustration-related phrases and fixating on the illustrations than did young adults, yet had poorer comprehension of the illustrations. Older adults might not benefit from text illustrations because illustrations can be difficult to integrate with the text. Health practitioners should not assume that illustrations will increase older adults' comprehension of health information.

Learning to recognize rat social behavior: Novel dataset and cross-dataset application.

PubMed

Lorbach, Malte; Kyriakou, Elisavet I; Poppe, Ronald; van Dam, Elsbeth A; Noldus, Lucas P J J; Veltkamp, Remco C

2018-04-15

Social behavior is an important aspect of rodent models. Automated measuring tools that make use of video analysis and machine learning are an increasingly attractive alternative to manual annotation. Because machine learning-based methods need to be trained, it is important that they are validated using data from different experiment settings. To develop and validate automated measuring tools, there is a need for annotated rodent interaction datasets. Currently, the availability of such datasets is limited to two mouse datasets. We introduce the first, publicly available rat social interaction dataset, RatSI. We demonstrate the practical value of the novel dataset by using it as the training set for a rat interaction recognition method. We show that behavior variations induced by the experiment setting can lead to reduced performance, which illustrates the importance of cross-dataset validation. Consequently, we add a simple adaptation step to our method and improve the recognition performance. Most existing methods are trained and evaluated in one experimental setting, which limits the predictive power of the evaluation to that particular setting. We demonstrate that cross-dataset experiments provide more insight in the performance of classifiers. With our novel, public dataset we encourage the development and validation of automated recognition methods. We are convinced that cross-dataset validation enhances our understanding of rodent interactions and facilitates the development of more sophisticated recognition methods. Combining them with adaptation techniques may enable us to apply automated recognition methods to a variety of animals and experiment settings. Copyright © 2017 Elsevier B.V. All rights reserved.

Effects of intranasal oxytocin on pupil dilation indicate increased salience of socioaffective stimuli.

PubMed

Prehn, Kristin; Kazzer, Philipp; Lischke, Alexander; Heinrichs, Markus; Herpertz, Sabine C; Domes, Gregor

2013-06-01

To investigate the mechanisms by which oxytocin improves socioaffective processing, we measured behavioral and pupillometric data during a dynamic facial emotion recognition task. In a double-blind between-subjects design, 47 men received either 24 IU intranasal oxytocin (OXT) or a placebo (PLC). Participants in the OXT group recognized all facial expressions at lower intensity levels than did participants in the PLC group. Improved performance was accompanied by increased task-related pupil dilation, indicating an increased recruitment of attentional resources. We also found increased pupil dilation during the processing of female compared with male faces. This gender-specific stimulus effect diminished in the OXT group, in which pupil size specifically increased for male faces. Results suggest that improved emotion recognition after OXT treatment might be due to an intensified processing of stimuli that usually do not recruit much attention. Copyright © 2013 Society for Psychophysiological Research.

Differential modulatory effects of cocaine on marmoset monkey recognition memory.

PubMed

Melamed, Jonathan L; de Jesus, Fernando M; Aquino, Jéssica; Vannuchi, Clarissa R S; Duarte, Renata B M; Maior, Rafael S; Tomaz, Carlos; Barros, Marilia

2017-01-01

Acute and repeated exposure to cocaine alters the cognitive performance of humans and animals. How each administration schedule affects the same memory task has yet to be properly established in nonhuman primates. Therefore, we assessed the performance of marmoset monkeys in a spontaneous object-location (SOL) recognition memory task after acute and repeated exposure to cocaine (COC; 5mg/kg, ip). Two identical neutral stimuli were explored on the 10-min sample trial, after which preferential exploration of the displaced vs the stationary object was analyzed on the 10-min test trial. For the acute treatment, cocaine was given immediately after the sample presentation, and spatial recognition was then tested after a 24-h interval. For the repeated exposure schedule, daily cocaine injections were given on 7 consecutive days. After a 7-day drug-free period, the SOL task was carried out with a 10-min intertrial interval. When given acutely postsample, COC improved the marmosets' recognition memory, whereas it had a detrimental effect after the repeated exposure. Thus, depending on the administration schedule, COC exerted opposing effects on the marmosets' ability to recognize spatial changes. This agrees with recent studies in rodents and the recognition impairment seen in human addicts. Further studies related to the effects of cocaine's acute×prior drug history on the same cognitive domain are warranted. © 2017 Elsevier B.V. All rights reserved.

A standardization model based on image recognition for performance evaluation of an oral scanner.

PubMed

Seo, Sang-Wan; Lee, Wan-Sun; Byun, Jae-Young; Lee, Kyu-Bok

2017-12-01

Accurate information is essential in dentistry. The image information of missing teeth is used in optically based medical equipment in prosthodontic treatment. To evaluate oral scanners, the standardized model was examined from cases of image recognition errors of linear discriminant analysis (LDA), and a model that combines the variables with reference to ISO 12836:2015 was designed. The basic model was fabricated by applying 4 factors to the tooth profile (chamfer, groove, curve, and square) and the bottom surface. Photo-type and video-type scanners were used to analyze 3D images after image capture. The scans were performed several times according to the prescribed sequence to distinguish the model from the one that did not form, and the results confirmed it to be the best. In the case of the initial basic model, a 3D shape could not be obtained by scanning even if several shots were taken. Subsequently, the recognition rate of the image was improved with every variable factor, and the difference depends on the tooth profile and the pattern of the floor surface. Based on the recognition error of the LDA, the recognition rate decreases when the model has a similar pattern. Therefore, to obtain the accurate 3D data, the difference of each class needs to be provided when developing a standardized model.

Gene/protein name recognition based on support vector machine using dictionary as features.

PubMed

Mitsumori, Tomohiro; Fation, Sevrani; Murata, Masaki; Doi, Kouichi; Doi, Hirohumi

2005-01-01

Automated information extraction from biomedical literature is important because a vast amount of biomedical literature has been published. Recognition of the biomedical named entities is the first step in information extraction. We developed an automated recognition system based on the SVM algorithm and evaluated it in Task 1.A of BioCreAtIvE, a competition for automated gene/protein name recognition. In the work presented here, our recognition system uses the feature set of the word, the part-of-speech (POS), the orthography, the prefix, the suffix, and the preceding class. We call these features "internal resource features", i.e., features that can be found in the training data. Additionally, we consider the features of matching against dictionaries to be external resource features. We investigated and evaluated the effect of these features as well as the effect of tuning the parameters of the SVM algorithm. We found that the dictionary matching features contributed slightly to the improvement in the performance of the f-score. We attribute this to the possibility that the dictionary matching features might overlap with other features in the current multiple feature setting. During SVM learning, each feature alone had a marginally positive effect on system performance. This supports the fact that the SVM algorithm is robust on the high dimensionality of the feature vector space and means that feature selection is not required.

Effects of Noise on Speech Recognition and Listening Effort in Children With Normal Hearing and Children With Mild Bilateral or Unilateral Hearing Loss.

PubMed

Lewis, Dawna; Schmid, Kendra; O'Leary, Samantha; Spalding, Jody; Heinrichs-Graham, Elizabeth; High, Robin

2016-10-01

This study examined the effects of stimulus type and hearing status on speech recognition and listening effort in children with normal hearing (NH) and children with mild bilateral hearing loss (MBHL) or unilateral hearing loss (UHL). Children (5-12 years of age) with NH (Experiment 1) and children (8-12 years of age) with MBHL, UHL, or NH (Experiment 2) performed consonant identification and word and sentence recognition in background noise. Percentage correct performance and verbal response time (VRT) were assessed (onset time, total duration). In general, speech recognition improved as signal-to-noise ratio (SNR) increased both for children with NH and children with MBHL or UHL. The groups did not differ on measures of VRT. Onset times were longer for incorrect than for correct responses. For correct responses only, there was a general increase in VRT with decreasing SNR. Findings indicate poorer sentence recognition in children with NH and MBHL or UHL as SNR decreases. VRT results suggest that greater effort was expended when processing stimuli that were incorrectly identified. Increasing VRT with decreasing SNR for correct responses also supports greater effort in poorer acoustic conditions. The absence of significant hearing status differences suggests that VRT was not differentially affected by MBHL, UHL, or NH for children in this study.

Effects of anticaricaturing vs. caricaturing and their neural correlates elucidate a role of shape for face learning.

PubMed

Schulz, Claudia; Kaufmann, Jürgen M; Walther, Lydia; Schweinberger, Stefan R

2012-08-01

To assess the role of shape information for unfamiliar face learning, we investigated effects of photorealistic spatial anticaricaturing and caricaturing on later face recognition. We assessed behavioural performance and event-related brain potential (ERP) correlates of recognition, using different images of anticaricatures, veridical faces, or caricatures at learning and test. Relative to veridical faces, recognition performance improved for caricatures, with performance decrements for anticaricatures in response times. During learning, an amplitude pattern with caricatures>veridicals=anticaricatures was seen for N170, left-hemispheric ERP negativity during the P200 and N250 time segments (200-380 ms), and for a late positive component (LPC, 430-830 ms), whereas P200 and N250 responses exhibited an additional difference between veridicals and anticaricatures over the right hemisphere. During recognition, larger amplitudes for caricatures again started in the N170, whereas the P200 and the right-hemispheric N250 exhibited a more graded pattern of amplitude effects (caricatures>veridicals>anticaricatures), a result which was specific to learned but not novel faces in the N250. Together, the results (i) emphasise the role of facial shape for visual encoding in the learning of previously unfamiliar faces and (ii) provide important information about the neuronal timing of the encoding advantage enjoyed by faces with distinctive shape. Copyright © 2012 Elsevier Ltd. All rights reserved.

X-Eye: a novel wearable vision system

NASA Astrophysics Data System (ADS)

Wang, Yuan-Kai; Fan, Ching-Tang; Chen, Shao-Ang; Chen, Hou-Ye

2011-03-01

This paper proposes a smart portable device, named the X-Eye, which provides a gesture interface with a small size but a large display for the application of photo capture and management. The wearable vision system is implemented with embedded systems and can achieve real-time performance. The hardware of the system includes an asymmetric dualcore processer with an ARM core and a DSP core. The display device is a pico projector which has a small volume size but can project large screen size. A triple buffering mechanism is designed for efficient memory management. Software functions are partitioned and pipelined for effective execution in parallel. The gesture recognition is achieved first by a color classification which is based on the expectation-maximization algorithm and Gaussian mixture model (GMM). To improve the performance of the GMM, we devise a LUT (Look Up Table) technique. Fingertips are extracted and geometrical features of fingertip's shape are matched to recognize user's gesture commands finally. In order to verify the accuracy of the gesture recognition module, experiments are conducted in eight scenes with 400 test videos including the challenge of colorful background, low illumination, and flickering. The processing speed of the whole system including the gesture recognition is with the frame rate of 22.9FPS. Experimental results give 99% recognition rate. The experimental results demonstrate that this small-size large-screen wearable system has effective gesture interface with real-time performance.

Ocular biometrics by score-level fusion of disparate experts.

PubMed

Proença, Hugo

2014-12-01

The concept of periocular biometrics emerged to improve the robustness of iris recognition to degraded data. Being a relatively recent topic, most of the periocular recognition algorithms work in a holistic way and apply a feature encoding/matching strategy without considering each biological component in the periocular area. This not only augments the correlation between the components in the resulting biometric signature, but also increases the sensitivity to particular data covariates. The main novelty in this paper is to propose a periocular recognition ensemble made of two disparate components: 1) one expert analyses the iris texture and exhaustively exploits the multispectral information in visible-light data and 2) another expert parameterizes the shape of eyelids and defines a surrounding dimensionless region-of-interest, from where statistics of the eyelids, eyelashes, and skin wrinkles/furrows are encoded. Both experts work on disjoint regions of the periocular area and meet three important properties. First, they produce practically independent responses, which is behind the better performance of the ensemble when compared to the best individual recognizer. Second, they do not share particularly sensitivity to any image covariate, which accounts for augmenting the robustness against degraded data. Finally, it should be stressed that we disregard information in the periocular region that can be easily forged (e.g., shape of eyebrows), which constitutes an active anticounterfeit measure. An empirical evaluation was conducted on two public data sets (FRGC and UBIRIS.v2), and points for consistent improvements in performance of the proposed ensemble over the state-of-the-art periocular recognition algorithms.

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data

PubMed Central

Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs

2012-01-01

Smart homes for the aging population have recently started attracting the attention of the research community. The “health state” of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario. PMID:26007727

Evaluation of Three State-of-the-Art Classifiers for Recognition of Activities of Daily Living from Smart Home Ambient Data.

PubMed

Nef, Tobias; Urwyler, Prabitha; Büchler, Marcel; Tarnanas, Ioannis; Stucki, Reto; Cazzoli, Dario; Müri, René; Mosimann, Urs

2015-05-21

Smart homes for the aging population have recently started attracting the attention of the research community. The "health state" of smart homes is comprised of many different levels; starting with the physical health of citizens, it also includes longer-term health norms and outcomes, as well as the arena of positive behavior changes. One of the problems of interest is to monitor the activities of daily living (ADL) of the elderly, aiming at their protection and well-being. For this purpose, we installed passive infrared (PIR) sensors to detect motion in a specific area inside a smart apartment and used them to collect a set of ADL. In a novel approach, we describe a technology that allows the ground truth collected in one smart home to train activity recognition systems for other smart homes. We asked the users to label all instances of all ADL only once and subsequently applied data mining techniques to cluster in-home sensor firings. Each cluster would therefore represent the instances of the same activity. Once the clusters were associated to their corresponding activities, our system was able to recognize future activities. To improve the activity recognition accuracy, our system preprocessed raw sensor data by identifying overlapping activities. To evaluate the recognition performance from a 200-day dataset, we implemented three different active learning classification algorithms and compared their performance: naive Bayesian (NB), support vector machine (SVM) and random forest (RF). Based on our results, the RF classifier recognized activities with an average specificity of 96.53%, a sensitivity of 68.49%, a precision of 74.41% and an F-measure of 71.33%, outperforming both the NB and SVM classifiers. Further clustering markedly improved the results of the RF classifier. An activity recognition system based on PIR sensors in conjunction with a clustering classification approach was able to detect ADL from datasets collected from different homes. Thus, our PIR-based smart home technology could improve care and provide valuable information to better understand the functioning of our societies, as well as to inform both individual and collective action in a smart city scenario.

Recognition of Telugu characters using neural networks.

PubMed

Sukhaswami, M B; Seetharamulu, P; Pujari, A K

1995-09-01

The aim of the present work is to recognize printed and handwritten Telugu characters using artificial neural networks (ANNs). Earlier work on recognition of Telugu characters has been done using conventional pattern recognition techniques. We make an initial attempt here of using neural networks for recognition with the aim of improving upon earlier methods which do not perform effectively in the presence of noise and distortion in the characters. The Hopfield model of neural network working as an associative memory is chosen for recognition purposes initially. Due to limitation in the capacity of the Hopfield neural network, we propose a new scheme named here as the Multiple Neural Network Associative Memory (MNNAM). The limitation in storage capacity has been overcome by combining multiple neural networks which work in parallel. It is also demonstrated that the Hopfield network is suitable for recognizing noisy printed characters as well as handwritten characters written by different "hands" in a variety of styles. Detailed experiments have been carried out using several learning strategies and results are reported. It is shown here that satisfactory recognition is possible using the proposed strategy. A detailed preprocessing scheme of the Telugu characters from digitized documents is also described.

Cognitive object recognition system (CORS)

NASA Astrophysics Data System (ADS)

Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

2010-04-01

We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

Item-method directed forgetting: Effects at retrieval?

PubMed

Taylor, Tracy L; Cutmore, Laura; Pries, Lotta

2018-02-01

In an item-method directed forgetting paradigm, words are presented one at a time, each followed by an instruction to Remember or Forget; a directed forgetting effect is measured as better subsequent memory for Remember words than Forget words. The dominant view is that the directed forgetting effect arises during encoding due to selective rehearsal of Remember over Forget items. In three experiments we attempted to falsify a strong view that directed forgetting effects in recognition are due only to encoding mechanisms when an item method is used. Across 3 experiments we tested for retrieval-based processes by colour-coding the recognition test items. Black colour provided no information; green colour cued a potential Remember item; and, red colour cued a potential Forget item. Recognition cues were mixed within-blocks in Experiment 1 and between-blocks in Experiments 2 and 3; Experiment 3 added explicit feedback on the accuracy of the recognition decision. Although overall recognition improved with cuing when explicit test performance feedback was added in Experiment 3, in no case was the magnitude of the directed forgetting effect influenced by recognition cueing. Our results argue against a role for retrieval-based strategies that limit recognition of Forget items at test and posit a role for encoding intentions only. Copyright © 2017 Elsevier B.V. All rights reserved.

A Novel Approach towards Medical Entity Recognition in Chinese Clinical Text

PubMed Central

Yu, Jian

2017-01-01

Medical entity recognition, a basic task in the language processing of clinical data, has been extensively studied in analyzing admission notes in alphabetic languages such as English. However, much less work has been done on nonstructural texts that are written in Chinese, or in the setting of differentiation of Chinese drug names between traditional Chinese medicine and Western medicine. Here, we propose a novel cascade-type Chinese medication entity recognition approach that aims at integrating the sentence category classifier from a support vector machine and the conditional random field-based medication entity recognition. We hypothesized that this approach could avoid the side effects of abundant negative samples and improve the performance of the named entity recognition from admission notes written in Chinese. Therefore, we applied this approach to a test set of 324 Chinese-written admission notes with manual annotation by medical experts. Our data demonstrated that this approach had a score of 94.2% in precision, 92.8% in recall, and 93.5% in F-measure for the recognition of traditional Chinese medicine drug names and 91.2% in precision, 92.6% in recall, and 91.7% F-measure for the recognition of Western medicine drug names. The differences in F-measure were significant compared with those in the baseline systems. PMID:29065612

Effects of sibutramine alone and with alcohol on cognitive function in healthy volunteers

PubMed Central

Wesnes, K A; Garratt, C; Wickens, M; Gudgeon, A; Oliver, S

2000-01-01

Aims To investigate the effects of sibutramine in combination with alcohol in a double-blind, randomised, placebo-controlled, four-way crossover study in 20 healthy volunteers. Methods On each study day each volunteer received either: sibutramine 20 mg+0.5 g kg−1 alcohol; sibutramine 20 mg+placebo alcohol; placebo capsules+0.5 g kg−1 alcohol; or placebo capsules+placebo alcohol. Alcohol was administered 2 h following ingestion of the study capsules. During each study day, assessments of cognitive performance were made prior to dosing, and at 3, 4.5, 6 and 10 h post dosing. Blood alcohol concentration was estimated using a breath alcometer immediately prior to each cognitive performance test session. Each study day was followed by a minimum 7 day washout period. Results Alcohol was found to produce statistically significant impairments in tests of attention (maximum impairment to speed of digit vigilance=49 ms) and episodic memory (maximum impairment to speed of word recognition=74 ms). Alcohol also increased body sway (maximum increase 17.4 units) and lowered self rated alertness (maximum decrease 13.6 mm). These effects were produced by an inferred blood alcohol level of 53.2 mg dl−1.Sibutramine was not found to potentiate any of the effects of alcohol. There was a small, yet statistically significant, interaction effect observed on the sensitivity index of the picture recognition task. In this test, the combined effects of sibutramine and alcohol were smaller than the impairments produced by alcohol alone. Sibutramine, when dosed alone, was associated with improved performance on several tasks. Sibutramine improved attention (mean speed of digit vigilance improved by 21 ms), picture recognition speed (improvement at 3=81) and motor control (tracking error at 3 h reduced by 1.58 mm). Also sibutramine improved postural stability (reducing body sway at 3 h by 14.2 units). Adverse events reported were unremarkable and consistent with the known pharmacology of sibutramine and alcohol. Conclusions There was little evidence of a clinically relevant interaction of sibutramine with the impairment of cognitive function produced by alcohol in healthy volunteers. The single statistically significant interaction indicated a reduction, rather than a worsening, of alcohol-induced impairment when sibutramine is taken concomitantly. Sibutramine when administered alone is associated with improved performance on several tasks. PMID:10671904

The effect of hearing aid technologies on listening in an automobile.

PubMed

Wu, Yu-Hsiang; Stangl, Elizabeth; Bentler, Ruth A; Stanziola, Rachel W

2013-06-01

Communication while traveling in an automobile often is very difficult for hearing aid users. This is because the automobile/road noise level is usually high, and listeners/drivers often do not have access to visual cues. Since the talker of interest usually is not located in front of the listener/driver, conventional directional processing that places the directivity beam toward the listener's front may not be helpful and, in fact, could have a negative impact on speech recognition (when compared to omnidirectional processing). Recently, technologies have become available in commercial hearing aids that are designed to improve speech recognition and/or listening effort in noisy conditions where talkers are located behind or beside the listener. These technologies include (1) a directional microphone system that uses a backward-facing directivity pattern (Back-DIR processing), (2) a technology that transmits audio signals from the ear with the better signal-to-noise ratio (SNR) to the ear with the poorer SNR (Side-Transmission processing), and (3) a signal processing scheme that suppresses the noise at the ear with the poorer SNR (Side-Suppression processing). The purpose of the current study was to determine the effect of (1) conventional directional microphones and (2) newer signal processing schemes (Back-DIR, Side-Transmission, and Side-Suppression) on listener's speech recognition performance and preference for communication in a traveling automobile. A single-blinded, repeated-measures design was used. Twenty-five adults with bilateral symmetrical sensorineural hearing loss aged 44 through 84 yr participated in the study. The automobile/road noise and sentences of the Connected Speech Test (CST) were recorded through hearing aids in a standard van moving at a speed of 70 mph on a paved highway. The hearing aids were programmed to omnidirectional microphone, conventional adaptive directional microphone, and the three newer schemes. CST sentences were presented from the side and back of the hearing aids, which were placed on the ears of a manikin. The recorded stimuli were presented to listeners via earphones in a sound-treated booth to assess speech recognition performance and preference with each programmed condition. Compared to omnidirectional microphones, conventional adaptive directional processing had a detrimental effect on speech recognition when speech was presented from the back or side of the listener. Back-DIR and Side-Transmission processing improved speech recognition performance (relative to both omnidirectional and adaptive directional processing) when speech was from the back and side, respectively. The performance with Side-Suppression processing was better than with adaptive directional processing when speech was from the side. The participants' preferences for a given processing scheme were generally consistent with speech recognition results. The finding that performance with adaptive directional processing was poorer than with omnidirectional microphones demonstrates the importance of selecting the correct microphone technology for different listening situations. The results also suggest the feasibility of using hearing aid technologies to provide a better listening experience for hearing aid users in automobiles. American Academy of Audiology.

A standards-based approach to quality improvement for HIV services at Zambia Defence Force facilities: results and lessons learned.

PubMed

Kols, Adrienne; Kim, Young-Mi; Bazant, Eva; Necochea, Edgar; Banda, Joseph; Stender, Stacie

2015-07-01

The Zambia Defence Force adopted the Standards-Based Management and Recognition approach to improve the quality of the HIV-related services at its health facilities. This quality improvement intervention relies on comprehensive, detailed assessment tools to communicate and verify adherence to national standards of care, and to test and implement changes to improve performance. A quasi-experimental evaluation of the intervention was conducted at eight Zambia Defence Force primary health facilities (four facilities implemented the intervention and four did not). Data from three previous analyses are combined to assess the effect of Standards-Based Management and Recognition on three domains: facility readiness to provide services; observed provider performance during antiretroviral therapy (ART) and antenatal care consultations; and provider perceptions of the work environment. Facility readiness scores for ART improved on four of the eight standards at intervention sites, and one standard at comparison sites. Facility readiness scores for prevention of mother-to-child transmission (PMTCT) of HIV increased by 15 percentage points at intervention sites and 7 percentage points at comparison sites. Provider performance improved significantly at intervention sites for both ART services (from 58 to 84%; P < 0.01) and PMTCT services (from 58 to 73%; P = 0.003); there was no significant change at comparison sites. Providers' perceptions of the work environment generally improved at intervention sites and declined at comparison sites; differences in trends between study groups were significant for eight items. A standards-based approach to quality improvement proved effective in supporting healthcare managers and providers to deliver ART and PMTCT services in accordance with evidence-based standards in a health system suffering from staff shortages.

Workplace wellness recognition for optimizing workplace health: a presidential advisory from the American Heart Association.

PubMed

Fonarow, Gregg C; Calitz, Chris; Arena, Ross; Baase, Catherine; Isaac, Fikry W; Lloyd-Jones, Donald; Peterson, Eric D; Pronk, Nico; Sanchez, Eduardo; Terry, Paul E; Volpp, Kevin G; Antman, Elliott M

2015-05-19

The workplace is an important setting for promoting cardiovascular health and cardiovascular disease and stroke prevention in the United States. Well-designed, comprehensive workplace wellness programs have the potential to improve cardiovascular health and to reduce mortality, morbidity, and disability resulting from cardiovascular disease and stroke. Nevertheless, widespread implementation of comprehensive workplace wellness programs is lacking, and program composition and quality vary. Several organizations provide worksite wellness recognition programs; however, there is variation in recognition criteria, and they do not specifically focus on cardiovascular disease and stroke prevention. Although there is limited evidence to suggest that company performance on employer health management scorecards is associated with favorable healthcare cost trends, these data are not currently robust, and further evaluation is needed. As a recognized national leader in evidence-based guidelines, care systems, and quality programs, the American Heart Association/American Stroke Association is uniquely positioned and committed to promoting the adoption of comprehensive workplace wellness programs, as well as improving program quality and workforce health outcomes. As part of its commitment to improve the cardiovascular health of all Americans, the American Heart Association/American Stroke Association will promote science-based best practices for comprehensive workplace wellness programs and establish benchmarks for a national workplace wellness recognition program to assist employers in applying the best systems and strategies for optimal programming. The recognition program will integrate identification of a workplace culture of health and achievement of rigorous standards for cardiovascular health based on Life's Simple 7 metrics. In addition, the American Heart Association/American Stroke Association will develop resources that assist employers in meeting these rigorous standards, facilitating access to high-quality comprehensive workplace wellness programs for both employees and dependents, and fostering innovation and additional research. © 2015 American Heart Association, Inc.

Infrared and visible fusion face recognition based on NSCT domain

NASA Astrophysics Data System (ADS)

Xie, Zhihua; Zhang, Shuai; Liu, Guodong; Xiong, Jinquan

2018-01-01

Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

AB119. Computer-aided facial recognition of Chinese individuals with 22q11.2 deletion-algorithm training using NIH atlas of human malformation syndromes from diverse population

PubMed Central

Mok, Gary Tsz Kin; Chung, Brian Hon-Yin

2017-01-01

Background 22q11.2 deletion syndrome (22q11.2DS) is a common genetic disorder with an estimated frequency of 1/4,000. It is a multi-systemic disorder with high phenotypic variability. Our previous work showed substantial under-diagnosis of 22q11.2DS as 1 in 10 adult patients with conotruncal defects were found to have 22q11.2DS. The National Institute of Health (NIH) has created an atlas of human malformation syndrome from diverse populations to provide an easy tool to assist clinician in diagnosing the syndromic across various populations. In this study, we seek to determine whether training the computer-aided facial recognition technology using images from ethnicity-matched patients from the NIH Atlas can improve the detection performance of this technology. Methods Clinical photographs of 16 Chinese subjects with molecularly confirmed 22q11.2DS, from the NIH atlas and its related publication were used for training the facial recognition technology. The system automatically localizes hundreds of facial fiducial points and takes measurements. The final classification is based on these measurements, as well as an estimated probability of subjects having 22q11.2DS based on the entire facial image. Clinical photographs of 7 patients with molecularly confirmed 22q11.2DS were obtained with informed consent and used for testing the performance in recognizing facial profiles of the Chinese subjects before and after training. Results All 7 test cases were improved in ranking and scoring after the software training. In 4 cases, 22q11.2DS did not appear as one possible syndrome match before the training; however, it appeared within the first 10 syndrome matches after training. Conclusions The present pilot data shows that this technology can be trained to recognize patients with 22q11.2DS. It also highlights the need to collect clinical photographs of patients from diverse populations to be used as resources for training the software which can lead to improvement of the performance of computer-aided facial recognition technology.

Impact of a voice recognition system on report cycle time and radiologist reading time

NASA Astrophysics Data System (ADS)

Melson, David L.; Brophy, Robert; Blaine, G. James; Jost, R. Gilbert; Brink, Gary S.

1998-07-01

Because of its exciting potential to improve clinical service, as well as reduce costs, a voice recognition system for radiological dictation was recently installed at our institution. This system will be clinically successful if it dramatically reduces radiology report turnaround time without substantially affecting radiologist dictation and editing time. This report summarizes an observer study currently under way in which radiologist reporting times using the traditional transcription system and the voice recognition system are compared. Four radiologists are observed interpreting portable intensive care unit (ICU) chest examinations at a workstation in the chest reading area. Data are recorded with the radiologists using the transcription system and using the voice recognition system. The measurements distinguish between time spent performing clerical tasks and time spent actually dictating the report. Editing time and the number of corrections made are recorded. Additionally, statistics are gathered to assess the voice recognition system's impact on the report cycle time -- the time from report dictation to availability of an edited and finalized report -- and the length of reports.

Monetary incentives at retrieval promote recognition of involuntarily learned emotional information.

PubMed

Yan, Chunping; Li, Yunyun; Zhang, Qin; Cui, Lixia

2018-03-07

Previous studies have suggested that the effects of reward on memory processes are affected by certain factors, but it remains unclear whether the effects of reward at retrieval on recognition processes are influenced by emotion. The event-related potential was used to investigate the combined effect of reward and emotion on memory retrieval and its neural mechanism. The behavioral results indicated that the reward at retrieval improved recognition performance under positive and negative emotional conditions. The event-related potential results indicated that there were significant interactions between the reward and emotion in the average amplitude during recognition, and the significant reward effects from the frontal to parietal brain areas appeared at 130-800 ms for positive pictures and at 190-800 ms for negative pictures, but there were no significant reward effects of neutral pictures; the reward effect of positive items appeared relatively earlier, starting at 130 ms, and that of negative pictures began at 190 ms. These results indicate that monetary incentives at retrieval promote recognition of involuntarily learned emotional information.

Classifier dependent feature preprocessing methods

NASA Astrophysics Data System (ADS)

Rodriguez, Benjamin M., II; Peterson, Gilbert L.

2008-04-01

In mobile applications, computational complexity is an issue that limits sophisticated algorithms from being implemented on these devices. This paper provides an initial solution to applying pattern recognition systems on mobile devices by combining existing preprocessing algorithms for recognition. In pattern recognition systems, it is essential to properly apply feature preprocessing tools prior to training classification models in an attempt to reduce computational complexity and improve the overall classification accuracy. The feature preprocessing tools extended for the mobile environment are feature ranking, feature extraction, data preparation and outlier removal. Most desktop systems today are capable of processing a majority of the available classification algorithms without concern of processing while the same is not true on mobile platforms. As an application of pattern recognition for mobile devices, the recognition system targets the problem of steganalysis, determining if an image contains hidden information. The measure of performance shows that feature preprocessing increases the overall steganalysis classification accuracy by an average of 22%. The methods in this paper are tested on a workstation and a Nokia 6620 (Symbian operating system) camera phone with similar results.

A Review of Subsequence Time Series Clustering

PubMed Central

Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. PMID:25140332

A review of subsequence time series clustering.

PubMed

Zolhavarieh, Seyedjamal; Aghabozorgi, Saeed; Teh, Ying Wah

2014-01-01

Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

Deep--deeper--deepest? Encoding strategies and the recognition of human faces.

PubMed

Sporer, S L

1991-03-01

Various encoding strategies that supposedly promote deeper processing of human faces (e.g., character judgments) have led to better recognition than more shallow processing tasks (judging the width of the nose). However, does deeper processing actually lead to an improvement in recognition, or, conversely, does shallow processing lead to a deterioration in performance when compared with naturally employed encoding strategies? Three experiments systematically compared a total of 8 different encoding strategies manipulating depth of processing, amount of elaboration, and self-generation of judgmental categories. All strategies that required a scanning of the whole face were basically equivalent but no better than natural strategy controls. The consistently worst groups were the ones that rated faces along preselected physical dimensions. This can be explained by subjects' lesser task involvement as revealed by manipulation checks.

A Grassmann graph embedding framework for gait analysis

NASA Astrophysics Data System (ADS)

Connie, Tee; Goh, Michael Kah Ong; Teoh, Andrew Beng Jin

2014-12-01

Gait recognition is important in a wide range of monitoring and surveillance applications. Gait information has often been used as evidence when other biometrics is indiscernible in the surveillance footage. Building on recent advances of the subspace-based approaches, we consider the problem of gait recognition on the Grassmann manifold. We show that by embedding the manifold into reproducing kernel Hilbert space and applying the mechanics of graph embedding on such manifold, significant performance improvement can be obtained. In this work, the gait recognition problem is studied in a unified way applicable for both supervised and unsupervised configurations. Sparse representation is further incorporated in the learning mechanism to adaptively harness the local structure of the data. Experiments demonstrate that the proposed method can tolerate variations in appearance for gait identification effectively.

Products recognition on shop-racks from local scale-invariant features

NASA Astrophysics Data System (ADS)

Zawistowski, Jacek; Kurzejamski, Grzegorz; Garbat, Piotr; Naruniec, Jacek

2016-04-01

This paper presents a system designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. System uses well known binary keypoint detection algorithms for finding characteristic points in the image. One of the main idea is object recognition based on Implicit Shape Model method. Authors of the article proposed many improvements of the algorithm. Originally fiducial points are matched with a very simple function. This leads to the limitations in the number of objects parts being success- fully separated, while various methods of classification may be validated in order to achieve higher performance. Such an extension implies research on training procedure able to deal with many objects categories. Proposed solution opens a new possibilities for many algorithms demanding fast and robust multi-object recognition.

Intonation and dialog context as constraints for speech recognition.

PubMed

Taylor, P; King, S; Isard, S; Wright, H

1998-01-01

This paper describes a way of using intonation and dialog context to improve the performance of an automatic speech recognition (ASR) system. Our experiments were run on the DCIEM Maptask corpus, a corpus of spontaneous task-oriented dialog speech. This corpus has been tagged according to a dialog analysis scheme that assigns each utterance to one of 12 "move types," such as "acknowledge," "query-yes/no" or "instruct." Most ASR systems use a bigram language model to constrain the possible sequences of words that might be recognized. Here we use a separate bigram language model for each move type. We show that when the "correct" move-specific language model is used for each utterance in the test set, the word error rate of the recognizer drops. Of course when the recognizer is run on previously unseen data, it cannot know in advance what move type the speaker has just produced. To determine the move type we use an intonation model combined with a dialog model that puts constraints on possible sequences of move types, as well as the speech recognizer likelihoods for the different move-specific models. In the full recognition system, the combination of automatic move type recognition with the move specific language models reduces the overall word error rate by a small but significant amount when compared with a baseline system that does not take intonation or dialog acts into account. Interestingly, the word error improvement is restricted to "initiating" move types, where word recognition is important. In "response" move types, where the important information is conveyed by the move type itself--for example, positive versus negative response--there is no word error improvement, but recognition of the response types themselves is good. The paper discusses the intonation model, the language models, and the dialog model in detail and describes the architecture in which they are combined.

A Follow-Up Study on Music and Lexical Tone Perception in Adult Mandarin-Speaking Cochlear Implant Users.

PubMed

Gu, Xin; Liu, Bo; Liu, Ziye; Qi, Beier; Wang, Shuo; Dong, Ruijuan; Chen, Xueqing; Zhou, Qian

2017-12-01

The aim was to evaluate the development of music and lexical tone perception in Mandarin-speaking adult cochlear implant (CI) users over a period of 1 year. Prospective patient series. Tertiary hospital and research institute. Twenty five adult CI users, with ages ranging from 19 to 75 years old, participated in a year-long follow-up evaluation. There were also 40 normal hearing adult subjects who participated as a control group to provide the normal value range. Musical sounds in cochlear implants (Mu.S.I.C.) test battery was undertaken to evaluate music perception ability. Mandarin Tone Identification in Noise Test (M-TINT) was used to assess lexical tone recognition. The tests for CI users were completed at 1, 3, 6, and 12 months after the CI switch-on. Quantitative and statistical analysis of their results from music and tone perception tests. The performance of music perception and tone recognition both demonstrated an overall improvement in outcomes during the entire 1-year follow-up process. The increasing trends were obvious in the early period especially in the first 6 months after switch-on. There was a significant improvement in the melody discrimination (p < 0.01), timbre identification (p < 0.001), tone recognition in quiet (p < 0.0001), and in noise (p < 0.0001). Adult Mandarin-speaking CI users show an increasingly improved performance on music and tone perception during the 1-year follow-up. The improvement was the most prominent in the first 6 months of CI use. It is essential to strengthen the rehabilitation training within the first 6 months.

Gum chewing affects academic performance in adolescents

USDA-ARS?s Scientific Manuscript database

Chewing gum may have an impact on improved memory during specific tasks of recognition and sustained attention. Research objective was to determine the effect of gum chewing on standardized test scores and math class grades of eighth grade students. Four math classes, 108 students, were randomized i...

Association between recognition and help-seeking preferences and stigma towards people with mental illness.

PubMed

Picco, L; Abdin, E; Pang, S; Vaingankar, J A; Jeyagurunathan, A; Chong, S A; Subramaniam, M

2018-02-01

The ability to recognise a mental illness has important implications as it can aid in timely and appropriate help-seeking, and ultimately improve outcomes for people with mental illness. This study aims to explore the association between recognition and help-seeking preferences and stigmatising attitudes, for alcohol abuse, dementia, depression, obsessive-compulsive disorder (OCD) and schizophrenia, using a vignette-based approach. This was a population-based, cross-sectional survey conducted among Singapore Residents (n = 3006) aged 18-65 years. All respondents were asked what they think is wrong with the person in the vignette and who they should seek help from. Respondents were also administered the Personal and Perceived sub scales of the Depression Stigma Scale and the Social Distance Scale. Weighted frequencies and percentages were calculated for categorical variables. A series of multiple logistic and linear regression models were performed separately by vignette to generate odd ratios and 95% confidence intervals for the relationship between help-seeking preference, and recognition and beta coefficients and 95% confidence intervals for the relationship between stigma and recognition. Correct recognition was associated with less preference to seek help from family and friends for depression and schizophrenia. Recognition was also associated with increased odds of endorsing seeking help from a psychiatric hospital for dementia, depression and schizophrenia, while there was also an increased preference to seek help from a psychologist and psychiatrist for depression. Recognition was associated with less personal and perceived stigma for OCD and less personal stigma for schizophrenia, however, increased odds of social distancing for dementia. The ability to correctly recognise a mental illness was associated with less preference to seek help from informal sources, whilst increased preference to seek help from mental health professionals and services and less personal and perceived stigma. These findings re-emphasise the need to improve mental health literacy and reinforce the potential benefits recognition can have to individuals and the wider community in Singapore.

The selective positive allosteric M1 muscarinic receptor modulator PQCA attenuates learning and memory deficits in the Tg2576 Alzheimer's disease mouse model.

PubMed

Puri, Vanita; Wang, Xiaohai; Vardigan, Joshua D; Kuduk, Scott D; Uslaner, Jason M

2015-01-01

We have recently shown that the M1 muscarinic receptor positive allosteric modulator, PQCA, improves cognitive performance in rodents and non-human primates administered the muscarinic receptor antagonist scopolamine. The purpose of the present experiments was to characterize the effects of PQCA in a model more relevant to the disease pathology of Alzheimer's disease. Tg2576 transgenic mice that have elevated Aβ were tested in the novel object recognition task to characterize recognition memory as a function of age and treatment with the PQCA. The effects of PQCA were compared to the acetylcholinesterase inhibitor donepezil, the standard of care for Alzheimer's disease. In addition, the effect of co-administering PQCA and donepezil was evaluated. Aged Tg2576 mice demonstrated a deficit in recognition memory that was significantly attenuated by PQCA. The positive control donepezil also reversed the deficit. Furthermore, doses of PQCA and donepezil that were inactive on their own were found to improve recognition memory when given together. These studies suggest that M1 muscarinic receptor positive allosteric modulation can ameliorate memory deficits in disease relevant models of Alzheimer's disease. These data, combined with our previous findings demonstrating PQCA improves scopolamine-induced cognitive deficits in both rodents and non-human primates, suggest that M1 positive allosteric modulators have therapeutic potential for the treatment of Alzheimer's disease. Copyright © 2015 Elsevier B.V. All rights reserved.

A novel speech processing algorithm based on harmonicity cues in cochlear implant

NASA Astrophysics Data System (ADS)

Wang, Jian; Chen, Yousheng; Zhang, Zongping; Chen, Yan; Zhang, Weifeng

2017-08-01

This paper proposed a novel speech processing algorithm in cochlear implant, which used harmonicity cues to enhance tonal information in Mandarin Chinese speech recognition. The input speech was filtered by a 4-channel band-pass filter bank. The frequency ranges for the four bands were: 300-621, 621-1285, 1285-2657, and 2657-5499 Hz. In each pass band, temporal envelope and periodicity cues (TEPCs) below 400 Hz were extracted by full wave rectification and low-pass filtering. The TEPCs were modulated by a sinusoidal carrier, the frequency of which was fundamental frequency (F0) and its harmonics most close to the center frequency of each band. Signals from each band were combined together to obtain an output speech. Mandarin tone, word, and sentence recognition in quiet listening conditions were tested for the extensively used continuous interleaved sampling (CIS) strategy and the novel F0-harmonic algorithm. Results found that the F0-harmonic algorithm performed consistently better than CIS strategy in Mandarin tone, word, and sentence recognition. In addition, sentence recognition rate was higher than word recognition rate, as a result of contextual information in the sentence. Moreover, tone 3 and 4 performed better than tone 1 and tone 2, due to the easily identified features of the former. In conclusion, the F0-harmonic algorithm could enhance tonal information in cochlear implant speech processing due to the use of harmonicity cues, thereby improving Mandarin tone, word, and sentence recognition. Further study will focus on the test of the F0-harmonic algorithm in noisy listening conditions.

Visual body recognition in a prosopagnosic patient.

PubMed

Moro, V; Pernigo, S; Avesani, R; Bulgarelli, C; Urgesi, C; Candidi, M; Aglioti, S M

2012-01-01

Conspicuous deficits in face recognition characterize prosopagnosia. Information on whether agnosic deficits may extend to non-facial body parts is lacking. Here we report the neuropsychological description of FM, a patient affected by a complete deficit in face recognition in the presence of mild clinical signs of visual object agnosia. His deficit involves both overt and covert recognition of faces (i.e. recognition of familiar faces, but also categorization of faces for gender or age) as well as the visual mental imagery of faces. By means of a series of matching-to-sample tasks we investigated: (i) a possible association between prosopagnosia and disorders in visual body perception; (ii) the effect of the emotional content of stimuli on the visual discrimination of faces, bodies and objects; (iii) the existence of a dissociation between identity recognition and the emotional discrimination of faces and bodies. Our results document, for the first time, the co-occurrence of body agnosia, i.e. the visual inability to discriminate body forms and body actions, and prosopagnosia. Moreover, the results show better performance in the discrimination of emotional face and body expressions with respect to body identity and neutral actions. Since FM's lesions involve bilateral fusiform areas, it is unlikely that the amygdala-temporal projections explain the relative sparing of emotion discrimination performance. Indeed, the emotional content of the stimuli did not improve the discrimination of their identity. The results hint at the existence of two segregated brain networks involved in identity and emotional discrimination that are at least partially shared by face and body processing. Copyright © 2011 Elsevier Ltd. All rights reserved.

Age-specific effects of voluntary exercise on memory and the older brain.

PubMed

Siette, Joyce; Westbrook, R Frederick; Cotman, Carl; Sidhu, Kuldip; Zhu, Wanlin; Sachdev, Perminder; Valenzuela, Michael J

2013-03-01

Physical exercise in early adulthood and mid-life improves cognitive function and enhances brain plasticity, but the effects of commencing exercise in late adulthood are not well-understood. We investigated the effects of voluntary exercise in the restoration of place recognition memory in aged rats and examined hippocampal changes of synaptic density and neurogenesis. We found a highly selective age-related deficit in place recognition memory that is stable across retest sessions and correlates strongly with loss of hippocampal synapses. Additionally, 12 weeks of voluntary running at 20 months of age removed the deficit in the hippocampally dependent place recognition memory. Voluntary running restored presynaptic density in the dentate gyrus and CA3 hippocampal subregions in aged rats to levels beyond those observed in younger animals, in which exercise had no functional or synaptic effects. By contrast, hippocampal neurogenesis, a possible memory-related mechanism, increased in both young and aged rats after physical exercise but was not linked with performance in the place recognition task. We used graph-based network analysis based on synaptic covariance patterns to characterize efficient intrahippocampal connectivity. This analysis revealed that voluntary running completely reverses the profound degradation of hippocampal network efficiency that accompanies sedentary aging. Furthermore, at an individual animal level, both overall hippocampal presynaptic density and subregional connectivity independently contribute to prediction of successful place recognition memory performance. Our findings emphasize the unique synaptic effects of exercise on the aged brain and their specific relevance to a hippocampally based memory system for place recognition. Copyright © 2013 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Sign Language Recognition System using Neural Network for Digital Hardware Implementation

NASA Astrophysics Data System (ADS)

Vargas, Lorena P.; Barba, Leiner; Torres, C. O.; Mattos, L.

2011-01-01

This work presents an image pattern recognition system using neural network for the identification of sign language to deaf people. The system has several stored image that show the specific symbol in this kind of language, which is employed to teach a multilayer neural network using a back propagation algorithm. Initially, the images are processed to adapt them and to improve the performance of discriminating of the network, including in this process of filtering, reduction and elimination noise algorithms as well as edge detection. The system is evaluated using the signs without including movement in their representation.

Long-lasting effects of prenatal dietary choline availability on object recognition memory ability in adult rats.

PubMed

Moreno, Hayarelis C; de Brugada, Isabel; Carias, Diamela; Gallo, Milagros

2013-11-01

Choline is an essential nutrient required for early development. Previous studies have shown that prenatal choline availability influences adult memory abilities depending on the medial temporal lobe integrity. The relevance of prenatal choline availability on object recognition memory was assessed in adult Wistar rats. Three groups of pregnant Wistar rats were fed from E12 to E18 with choline-deficient (0 g/kg choline chloride), standard (1.1 g/kg choline chloride), or choline-supplemented (5 g/kg choline chloride) diets. The offspring was cross-fostered to rat dams fed a standard diet during pregnancy and tested at the age of 3 months in an object recognition memory task applying retention tests 24 and 48 hours after acquisition. Although no significant differences have been found in the performance of the three groups during the first retention test, the supplemented group exhibited improved memory compared with both the standard and the deficient group in the second retention test, 48 hours after acquisition. In addition, at the second retention test the deficient group did not differ from chance. Taken together, the results support the notion of a long-lasting beneficial effect of prenatal choline supplementation on object recognition memory which is evident when the rats reach adulthood. The results are discussed in terms of their relevance for improving the understanding of the cholinergic involvement in object recognition memory and the implications of the importance of maternal diet for lifelong cognitive abilities.

Job stress, recognition, job performance and intention to stay at work among Jordanian hospital nurses.

PubMed

AbuAlRub, Raeda Fawzi; Al-Zaru, Ibtisam Moawiah

2008-04-01

To investigate: (1) relationships between job stress, recognition of nurses' performance, job performance and intention to stay among hospital nurses; and (2) the buffering effect of recognition of staff performance on the 'stress-intention to stay at work' relationship. Workplace stress tremendously affects today's workforce. Recognition of nurses' performance needs further investigation to determine if it enhances the level of intention to stay at work and if it can buffer the negative effects of stress on nurses' intention to stay at work. The sample of the present study was a convenience one. It consisted of 206 Jordanian staff nurses who completed a structured questionnaire. The findings of the study indicated a direct and a buffering effect of recognition of nurses' performance on job stress and the level of intention to stay at work. The results of the study indicated the importance of recognition for outstanding performance as well as achievements. Implications for nursing management The results of this study support the need to focus on the implementation of recognition strategies in the workplace to reduce job stress and enhance retention.

Image based book cover recognition and retrieval

NASA Astrophysics Data System (ADS)

Sukhadan, Kalyani; Vijayarajan, V.; Krishnamoorthi, A.; Bessie Amali, D. Geraldine

2017-11-01

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

Training and cockpit design to promote expert performance

NASA Technical Reports Server (NTRS)

Chappell, Sheryl L.

1991-01-01

The behavior of expert pilots in familiar situations is explored and the implications for better training programs and cockpit designs are stated. Experts in familiar operational situations performing highly practiced tasks are said to recognize and respond to complex situations using pattern recognition or intuition. For some tasks this class of behaviors is desirable; performance can be improved by reducing cognitive load and increasing speed and accuracy. Part-task training, training for monitoring and techniques for the transfer of knowledge can facilitate the development of these skills. Methods for promoting pattern recognition through pilot-aircraft interface design include the use of spatial presentations of information and providing triggering events. In some instances, the familiar, well-practiced behavior is not appropriate and it is desirable to prevent the response. When prevention is necessary, barriers can be constructed in the interface to remind the pilot of the inappropriateness of the response.

Active transcutaneous bone conduction implant: audiological results in paediatric patients with bilateral microtia associated with external auditory canal atresia.

PubMed

Bravo-Torres, Sofía; Der-Mussa, Carolina; Fuentes-López, Eduardo

2018-01-01

To describe, in terms of functional gain and word recognition, the audiological results of patients under 18 years of age implanted with the active bone conduction implant, Bonebridge™. Retrospective case studies conducted by reviewing the medical records of patients receiving implants between 2014 and 2016 in the public health sector in Chile. All patients implanted with the Bonebridge were included (N = 15). Individuals who had bilateral conductive hearing loss, secondary to external ear malformations, were considered as candidates. The average hearing threshold one month after switch on was 25.2 dB (95%CI 23.5-26.9). Hearing thresholds between 0.5 and 4 kHz were better when compared with bone conduction hearing aids. Best performance was observed at 4 kHz, where improvements to hearing were observed throughout the adaptation process. There was evidence of a significant increase in the recognition of monosyllables. The Bonebridge implant showed improvements to hearing thresholds and word recognition in paediatric patients with congenital conductive hearing loss.

Can emotion recognition be taught to children with autism spectrum conditions?

PubMed Central

Baron-Cohen, Simon; Golan, Ofer; Ashwin, Emma

2009-01-01

Children with autism spectrum conditions (ASC) have major difficulties in recognizing and responding to emotional and mental states in others' facial expressions. Such difficulties in empathy underlie their social-communication difficulties that form a core of the diagnosis. In this paper we ask whether aspects of empathy can be taught to young children with ASC. We review a study that evaluated The Transporters, an animated series designed to enhance emotion comprehension in children with ASC. Children with ASC (4–7 years old) watched The Transporters every day for four weeks. Participants were tested before and after intervention on emotional vocabulary and emotion recognition at three levels of generalization. The intervention group improved significantly more than a clinical control group on all task levels, performing comparably to typical controls at time 2. The discussion centres on how vehicles as mechanical systems may be one key reason why The Transporters caused the improved understanding and recognition of emotions in children with ASC. The implications for the design of autism-friendly interventions are also explored. PMID:19884151

Adaptive error correction codes for face identification

NASA Astrophysics Data System (ADS)

Hussein, Wafaa R.; Sellahewa, Harin; Jassim, Sabah A.

2012-06-01

Face recognition in uncontrolled environments is greatly affected by fuzziness of face feature vectors as a result of extreme variation in recording conditions (e.g. illumination, poses or expressions) in different sessions. Many techniques have been developed to deal with these variations, resulting in improved performances. This paper aims to model template fuzziness as errors and investigate the use of error detection/correction techniques for face recognition in uncontrolled environments. Error correction codes (ECC) have recently been used for biometric key generation but not on biometric templates. We have investigated error patterns in binary face feature vectors extracted from different image windows of differing sizes and for different recording conditions. By estimating statistical parameters for the intra-class and inter-class distributions of Hamming distances in each window, we encode with appropriate ECC's. The proposed approached is tested for binarised wavelet templates using two face databases: Extended Yale-B and Yale. We shall demonstrate that using different combinations of BCH-based ECC's for different blocks and different recording conditions leads to in different accuracy rates, and that using ECC's results in significantly improved recognition results.

Particle Swarm Optimization Based Feature Enhancement and Feature Selection for Improved Emotion Recognition in Speech and Glottal Signals

PubMed Central

Muthusamy, Hariharan; Polat, Kemal; Yaacob, Sazali

2015-01-01

In the recent years, many research works have been published using speech related features for speech emotion recognition, however, recent studies show that there is a strong correlation between emotional states and glottal features. In this work, Mel-frequency cepstralcoefficients (MFCCs), linear predictive cepstral coefficients (LPCCs), perceptual linear predictive (PLP) features, gammatone filter outputs, timbral texture features, stationary wavelet transform based timbral texture features and relative wavelet packet energy and entropy features were extracted from the emotional speech (ES) signals and its glottal waveforms(GW). Particle swarm optimization based clustering (PSOC) and wrapper based particle swarm optimization (WPSO) were proposed to enhance the discerning ability of the features and to select the discriminating features respectively. Three different emotional speech databases were utilized to gauge the proposed method. Extreme learning machine (ELM) was employed to classify the different types of emotions. Different experiments were conducted and the results show that the proposed method significantly improves the speech emotion recognition performance compared to previous works published in the literature. PMID:25799141

Affect-Based Adaptation of an Applied Video Game for Educational Purposes

ERIC Educational Resources Information Center

Bontchev, Boyan; Vassileva, Dessislava

2017-01-01

Purpose: This paper aims to clarify how affect-based adaptation can improve implicit recognition of playing style of individuals during game sessions. This study presents the "Rush for Gold" game using dynamic difficulty adjustment of tasks based on both player performance and affectation inferred through electrodermal activity and…

Optimized face recognition algorithm using radial basis function neural networks and its practical applications.

PubMed

Yoo, Sung-Hoon; Oh, Sung-Kwun; Pedrycz, Witold

2015-09-01

In this study, we propose a hybrid method of face recognition by using face region information extracted from the detected face region. In the preprocessing part, we develop a hybrid approach based on the Active Shape Model (ASM) and the Principal Component Analysis (PCA) algorithm. At this step, we use a CCD (Charge Coupled Device) camera to acquire a facial image by using AdaBoost and then Histogram Equalization (HE) is employed to improve the quality of the image. ASM extracts the face contour and image shape to produce a personal profile. Then we use a PCA method to reduce dimensionality of face images. In the recognition part, we consider the improved Radial Basis Function Neural Networks (RBF NNs) to identify a unique pattern associated with each person. The proposed RBF NN architecture consists of three functional modules realizing the condition phase, the conclusion phase, and the inference phase completed with the help of fuzzy rules coming in the standard 'if-then' format. In the formation of the condition part of the fuzzy rules, the input space is partitioned with the use of Fuzzy C-Means (FCM) clustering. In the conclusion part of the fuzzy rules, the connections (weights) of the RBF NNs are represented by four kinds of polynomials such as constant, linear, quadratic, and reduced quadratic. The values of the coefficients are determined by running a gradient descent method. The output of the RBF NNs model is obtained by running a fuzzy inference method. The essential design parameters of the network (including learning rate, momentum coefficient and fuzzification coefficient used by the FCM) are optimized by means of Differential Evolution (DE). The proposed P-RBF NNs (Polynomial based RBF NNs) are applied to facial recognition and its performance is quantified from the viewpoint of the output performance and recognition rate. Copyright © 2015 Elsevier Ltd. All rights reserved.

The 3Rs of Productivity Improvement: Responsibility, Recognition, Reward.

ERIC Educational Resources Information Center

Training, 1979

1979-01-01

Describes the American Production Center and a productivity improvement system in which people become part of the productivity solution when given responsibility, recognition, and reward for productivity improvement. (LRA)

Unsupervised frequency-recognition method of SSVEPs using a filter bank implementation of binary subband CCA.

PubMed

Rabiul Islam, Md; Khademul Islam Molla, Md; Nakanishi, Masaki; Tanaka, Toshihisa

2017-04-01

Recently developed effective methods for detection commands of steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) that need calibration for visual stimuli, which cause more time and fatigue prior to the use, as the number of commands increases. This paper develops a novel unsupervised method based on canonical correlation analysis (CCA) for accurate detection of stimulus frequency. A novel unsupervised technique termed as binary subband CCA (BsCCA) is implemented in a multiband approach to enhance the frequency recognition performance of SSVEP. In BsCCA, two subbands are used and a CCA-based correlation coefficient is computed for the individual subbands. In addition, a reduced set of artificial reference signals is used to calculate CCA for the second subband. The analyzing SSVEP is decomposed into multiple subband and the BsCCA is implemented for each one. Then, the overall recognition score is determined by a weighted sum of the canonical correlation coefficients obtained from each band. A 12-class SSVEP dataset (frequency range: 9.25-14.75 Hz with an interval of 0.5 Hz) for ten healthy subjects are used to evaluate the performance of the proposed method. The results suggest that BsCCA significantly improves the performance of SSVEP-based BCI compared to the state-of-the-art methods. The proposed method is an unsupervised approach with averaged information transfer rate (ITR) of 77.04 bits min -1 across 10 subjects. The maximum individual ITR is 107.55 bits min -1 for 12-class SSVEP dataset, whereas, the ITR of 69.29 and 69.44 bits min -1 are achieved with CCA and NCCA respectively. The statistical test shows that the proposed unsupervised method significantly improves the performance of the SSVEP-based BCI. It can be usable in real world applications.

LeadMine: a grammar and dictionary driven approach to entity recognition.

PubMed

Lowe, Daniel M; Sayle, Roger A

2015-01-01

Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution.

LeadMine: a grammar and dictionary driven approach to entity recognition

PubMed Central

2015-01-01

Background Chemical entity recognition has traditionally been performed by machine learning approaches. Here we describe an approach using grammars and dictionaries. This approach has the advantage that the entities found can be directly related to a given grammar or dictionary, which allows the type of an entity to be known and, if an entity is misannotated, indicates which resource should be corrected. As recognition is driven by what is expected, if spelling errors occur, they can be corrected. Correcting such errors is highly useful when attempting to lookup an entity in a database or, in the case of chemical names, converting them to structures. Results Our system uses a mixture of expertly curated grammars and dictionaries, as well as dictionaries automatically derived from public resources. We show that the heuristics developed to filter our dictionary of trivial chemical names (from PubChem) yields a better performing dictionary than the previously published Jochem dictionary. Our final system performs post-processing steps to modify the boundaries of entities and to detect abbreviations. These steps are shown to significantly improve performance (2.6% and 4.0% F1-score respectively). Our complete system, with incremental post-BioCreative workshop improvements, achieves 89.9% precision and 85.4% recall (87.6% F1-score) on the CHEMDNER test set. Conclusions Grammar and dictionary approaches can produce results at least as good as the current state of the art in machine learning approaches. While machine learning approaches are commonly thought of as "black box" systems, our approach directly links the output entities to the input dictionaries and grammars. Our approach also allows correction of errors in detected entities, which can assist with entity resolution. PMID:25810776

A deep convolutional neural network approach to single-particle recognition in cryo-electron microscopy.

PubMed

Zhu, Yanan; Ouyang, Qi; Mao, Youdong

2017-07-21

Single-particle cryo-electron microscopy (cryo-EM) has become a mainstream tool for the structural determination of biological macromolecular complexes. However, high-resolution cryo-EM reconstruction often requires hundreds of thousands of single-particle images. Particle extraction from experimental micrographs thus can be laborious and presents a major practical bottleneck in cryo-EM structural determination. Existing computational methods for particle picking often use low-resolution templates for particle matching, making them susceptible to reference-dependent bias. It is critical to develop a highly efficient template-free method for the automatic recognition of particle images from cryo-EM micrographs. We developed a deep learning-based algorithmic framework, DeepEM, for single-particle recognition from noisy cryo-EM micrographs, enabling automated particle picking, selection and verification in an integrated fashion. The kernel of DeepEM is built upon a convolutional neural network (CNN) composed of eight layers, which can be recursively trained to be highly "knowledgeable". Our approach exhibits an improved performance and accuracy when tested on the standard KLH dataset. Application of DeepEM to several challenging experimental cryo-EM datasets demonstrated its ability to avoid the selection of un-wanted particles and non-particles even when true particles contain fewer features. The DeepEM methodology, derived from a deep CNN, allows automated particle extraction from raw cryo-EM micrographs in the absence of a template. It demonstrates an improved performance, objectivity and accuracy. Application of this novel method is expected to free the labor involved in single-particle verification, significantly improving the efficiency of cryo-EM data processing.

On the Use of Evolutionary Algorithms to Improve the Robustness of Continuous Speech Recognition Systems in Adverse Conditions

NASA Astrophysics Data System (ADS)

Selouani, Sid-Ahmed; O'Shaughnessy, Douglas

2003-12-01

Limiting the decrease in performance due to acoustic environment changes remains a major challenge for continuous speech recognition (CSR) systems. We propose a novel approach which combines the Karhunen-Loève transform (KLT) in the mel-frequency domain with a genetic algorithm (GA) to enhance the data representing corrupted speech. The idea consists of projecting noisy speech parameters onto the space generated by the genetically optimized principal axis issued from the KLT. The enhanced parameters increase the recognition rate for highly interfering noise environments. The proposed hybrid technique, when included in the front-end of an HTK-based CSR system, outperforms that of the conventional recognition process in severe interfering car noise environments for a wide range of signal-to-noise ratios (SNRs) varying from 16 dB to[InlineEquation not available: see fulltext.] dB. We also showed the effectiveness of the KLT-GA method in recognizing speech subject to telephone channel degradations.

Evaluation of Adaptive Noise Management Technologies for School-Age Children with Hearing Loss.

PubMed

Wolfe, Jace; Duke, Mila; Schafer, Erin; Jones, Christine; Rakita, Lori

2017-05-01

Children with hearing loss experience significant difficulty understanding speech in noisy and reverberant situations. Adaptive noise management technologies, such as fully adaptive directional microphones and digital noise reduction, have the potential to improve communication in noise for children with hearing aids. However, there are no published studies evaluating the potential benefits children receive from the use of adaptive noise management technologies in simulated real-world environments as well as in daily situations. The objective of this study was to compare speech recognition, speech intelligibility ratings (SIRs), and sound preferences of children using hearing aids equipped with and without adaptive noise management technologies. A single-group, repeated measures design was used to evaluate performance differences obtained in four simulated environments. In each simulated environment, participants were tested in a basic listening program with minimal noise management features, a manual program designed for that scene, and the hearing instruments' adaptive operating system that steered hearing instrument parameterization based on the characteristics of the environment. Twelve children with mild to moderately severe sensorineural hearing loss. Speech recognition and SIRs were evaluated in three hearing aid programs with and without noise management technologies across two different test sessions and various listening environments. Also, the participants' perceptual hearing performance in daily real-world listening situations with two of the hearing aid programs was evaluated during a four- to six-week field trial that took place between the two laboratory sessions. On average, the use of adaptive noise management technology improved sentence recognition in noise for speech presented in front of the participant but resulted in a decrement in performance for signals arriving from behind when the participant was facing forward. However, the improvement with adaptive noise management exceeded the decrement obtained when the signal arrived from behind. Most participants reported better subjective SIRs when using adaptive noise management technologies, particularly when the signal of interest arrived from in front of the listener. In addition, most participants reported a preference for the technology with an automatically switching, adaptive directional microphone and adaptive noise reduction in real-world listening situations when compared to conventional, omnidirectional microphone use with minimal noise reduction processing. Use of the adaptive noise management technologies evaluated in this study improves school-age children's speech recognition in noise for signals arriving from the front. Although a small decrement in speech recognition in noise was observed for signals arriving from behind the listener, most participants reported a preference for use of noise management technology both when the signal arrived from in front and from behind the child. The results of this study suggest that adaptive noise management technologies should be considered for use with school-age children when listening in academic and social situations. American Academy of Audiology

Development of a battery of functional tests for low vision.

PubMed

Dougherty, Bradley E; Martin, Scott R; Kelly, Corey B; Jones, Lisa A; Raasch, Thomas W; Bullimore, Mark A

2009-08-01

We describe the development and evaluation of a battery of tests of functional visual performance of everyday tasks intended to be suitable for assessment of low vision patients. The functional test battery comprises-Reading rate: reading aloud 20 unrelated words for each of four print sizes (8, 4, 2, & 1 M); Telephone book: finding a name and reading the telephone number; Medicine bottle label: reading the name and dosing; Utility bill: reading the due date and amount due; Cooking instructions: reading cooking time on a food package; Coin sorting: making a specified amount from coins placed on a table; Playing card recognition: identifying denomination and suit; and Face recognition: identifying expressions of printed, life-size faces at 1 and 3 m. All tests were timed except face and playing card recognition. Fourteen normally sighted and 24 low vision subjects were assessed with the functional test battery. Visual acuity, contrast sensitivity, and quality of life (National Eye Institute Visual Function Questionnaire 25 [NEI-VFQ 25]) were measured and the functional tests repeated. Subsequently, 23 low vision patients participated in a pilot randomized clinical trial with half receiving low vision rehabilitation and half a delayed intervention. The functional tests were administered at enrollment and 3 months later. Normally sighted subjects could perform all tasks but the proportion of trials performed correctly by the low vision subjects ranged from 35% for face recognition at 3 m, to 95% for the playing card identification. On average, low vision subjects performed three times slower than the normally sighted subjects. Timed tasks with a visual search component showed poorer repeatability. In the pilot clinical trial, low vision rehabilitation produced the greatest improvement for the medicine bottle and cooking instruction tasks. Performance of patients on these functional tests has been assessed. Some appear responsive to low vision rehabilitation.

Improved Open-Microphone Speech Recognition

NASA Astrophysics Data System (ADS)

Abrash, Victor

2002-12-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken dialog manager extra flexibility to recognize the signal with no audio gaps between recognition requests, as well as to rerecognize portions of the signal, or to rerecognize speech with different grammars, acoustic models, recognizers, start times, and so on. SRI expects that this new open-mic functionality will enable NASA to develop better error-correction mechanisms for spoken dialog systems, and may also enable new interaction strategies.

Improved Open-Microphone Speech Recognition

NASA Technical Reports Server (NTRS)

Abrash, Victor

2002-01-01

Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken dialog manager extra flexibility to recognize the signal with no audio gaps between recognition requests, as well as to rerecognize portions of the signal, or to rerecognize speech with different grammars, acoustic models, recognizers, start times, and so on. SRI expects that this new open-mic functionality will enable NASA to develop better error-correction mechanisms for spoken dialog systems, and may also enable new interaction strategies.

Electromagnetic versus electrical coupling of personal frequency modulation (FM) receivers to cochlear implant sound processors.

PubMed

Schafer, Erin C; Romine, Denise; Musgrave, Elizabeth; Momin, Sadaf; Huynh, Christy

2013-01-01

Previous research has suggested that electrically coupled frequency modulation (FM) systems substantially improved speech-recognition performance in noise in individuals with cochlear implants (CIs). However, there is limited evidence to support the use of electromagnetically coupled (neck loop) FM receivers with contemporary CI sound processors containing telecoils. The primary goal of this study was to compare speech-recognition performance in noise and subjective ratings of adolescents and adults using one of three contemporary CI sound processors coupled to electromagnetically and electrically coupled FM receivers from Oticon. A repeated-measures design was used to compare speech-recognition performance in noise and subjective ratings without and with the FM systems across three test sessions (Experiment 1) and to compare performance at different FM-gain settings (Experiment 2). Descriptive statistics were used in Experiment 3 to describe output differences measured through a CI sound processor. Experiment 1 included nine adolescents or adults with unilateral or bilateral Advanced Bionics Harmony (n = 3), Cochlear Nucleus 5 (n = 3), and MED-EL OPUS 2 (n = 3) CI sound processors. In Experiment 2, seven of the original nine participants were tested. In Experiment 3, electroacoustic output was measured from a Nucleus 5 sound processor when coupled to the electromagnetically coupled Oticon Arc neck loop and electrically coupled Oticon R2. In Experiment 1, participants completed a field trial with each FM receiver and three test sessions that included speech-recognition performance in noise and a subjective rating scale. In Experiment 2, participants were tested in three receiver-gain conditions. Results in both experiments were analyzed using repeated-measures analysis of variance. Experiment 3 involved electroacoustic-test measures to determine the monitor-earphone output of the CI alone and CI coupled to the two FM receivers. The results in Experiment 1 suggested that both FM receivers provided significantly better speech-recognition performance in noise than the CI alone; however, the electromagnetically coupled receiver provided significantly better speech-recognition performance in noise and better ratings in some situations than the electrically coupled receiver when set to the same gain. In Experiment 2, the primary analysis suggested significantly better speech-recognition performance in noise for the neck-loop versus electrically coupled receiver, but a second analysis, using the best performance across gain settings for each device, revealed no significant differences between the two FM receivers. Experiment 3 revealed monitor-earphone output differences in the Nucleus 5 sound processor for the two FM receivers when set to the +8 setting used in Experiment 1 but equal output when the electrically coupled device was set to a +16 gain setting and the electromagnetically coupled device was set to the +8 gain setting. Individuals with contemporary sound processors may show more favorable speech-recognition performance in noise electromagnetically coupled FM systems (i.e., Oticon Arc), which is most likely related to the input processing and signal processing pathway within the CI sound processor for direct input versus telecoil input. Further research is warranted to replicate these findings with a larger sample size and to develop and validate a more objective approach to fitting FM systems to CI sound processors. American Academy of Audiology.

Speech recognition and parent-ratings from auditory development questionnaires in children who are hard of hearing

PubMed Central

McCreery, Ryan W.; Walker, Elizabeth A.; Spratford, Meredith; Oleson, Jacob; Bentler, Ruth; Holte, Lenore; Roush, Patricia

2015-01-01

Objectives Progress has been made in recent years in the provision of amplification and early intervention for children who are hard of hearing. However, children who use hearing aids (HA) may have inconsistent access to their auditory environment due to limitations in speech audibility through their HAs or limited HA use. The effects of variability in children’s auditory experience on parent-report auditory skills questionnaires and on speech recognition in quiet and in noise were examined for a large group of children who were followed as part of the Outcomes of Children with Hearing Loss study. Design Parent ratings on auditory development questionnaires and children’s speech recognition were assessed for 306 children who are hard of hearing. Children ranged in age from 12 months to 9 years of age. Three questionnaires involving parent ratings of auditory skill development and behavior were used, including the LittlEARS Auditory Questionnaire, Parents Evaluation of Oral/Aural Performance in Children Rating Scale, and an adaptation of the Speech, Spatial and Qualities of Hearing scale. Speech recognition in quiet was assessed using the Open and Closed set task, Early Speech Perception Test, Lexical Neighborhood Test, and Phonetically-balanced Kindergarten word lists. Speech recognition in noise was assessed using the Computer-Assisted Speech Perception Assessment. Children who are hard of hearing were compared to peers with normal hearing matched for age, maternal educational level and nonverbal intelligence. The effects of aided audibility, HA use and language ability on parent responses to auditory development questionnaires and on children’s speech recognition were also examined. Results Children who are hard of hearing had poorer performance than peers with normal hearing on parent ratings of auditory skills and had poorer speech recognition. Significant individual variability among children who are hard of hearing was observed. Children with greater aided audibility through their HAs, more hours of HA use and better language abilities generally had higher parent ratings of auditory skills and better speech recognition abilities in quiet and in noise than peers with less audibility, more limited HA use or poorer language abilities. In addition to the auditory and language factors that were predictive for speech recognition in quiet, phonological working memory was also a positive predictor for word recognition abilities in noise. Conclusions Children who are hard of hearing continue to experience delays in auditory skill development and speech recognition abilities compared to peers with normal hearing. However, significant improvements in these domains have occurred in comparison to similar data reported prior to the adoption of universal newborn hearing screening and early intervention programs for children who are hard of hearing. Increasing the audibility of speech has a direct positive effect on auditory skill development and speech recognition abilities, and may also enhance these skills by improving language abilities in children who are hard of hearing. Greater number of hours of HA use also had a significant positive impact on parent ratings of auditory skills and children’s speech recognition. PMID:26731160

Standard object recognition memory and "what" and "where" components: Improvement by post-training epinephrine in highly habituated rats.

PubMed

Jurado-Berbel, Patricia; Costa-Miserachs, David; Torras-Garcia, Meritxell; Coll-Andreu, Margalida; Portell-Cortés, Isabel

2010-02-11

The present work examined whether post-training systemic epinephrine (EPI) is able to modulate short-term (3h) and long-term (24 h and 48 h) memory of standard object recognition, as well as long-term (24 h) memory of separate "what" (object identity) and "where" (object location) components of object recognition. Although object recognition training is associated to low arousal levels, all the animals received habituation to the training box in order to further reduce emotional arousal. Post-training EPI improved long-term (24 h and 48 h), but not short-term (3 h), memory in the standard object recognition task, as well as 24 h memory for both object identity and object location. These data indicate that post-training epinephrine: (1) facilitates long-term memory for standard object recognition; (2) exerts separate facilitatory effects on "what" (object identity) and "where" (object location) components of object recognition; and (3) is capable of improving memory for a low arousing task even in highly habituated rats.

Real-Time Hand Posture Recognition Using a Range Camera

NASA Astrophysics Data System (ADS)

Lahamy, Herve

The basic goal of human computer interaction is to improve the interaction between users and computers by making computers more usable and receptive to the user's needs. Within this context, the use of hand postures in replacement of traditional devices such as keyboards, mice and joysticks is being explored by many researchers. The goal is to interpret human postures via mathematical algorithms. Hand posture recognition has gained popularity in recent years, and could become the future tool for humans to interact with computers or virtual environments. An exhaustive description of the frequently used methods available in literature for hand posture recognition is provided. It focuses on the different types of sensors and data used, the segmentation and tracking methods, the features used to represent the hand postures as well as the classifiers considered in the recognition process. Those methods are usually presented as highly robust with a recognition rate close to 100%. However, a couple of critical points necessary for a successful real-time hand posture recognition system require major improvement. Those points include the features used to represent the hand segment, the number of postures simultaneously recognizable, the invariance of the features with respect to rotation, translation and scale and also the behavior of the classifiers against non-perfect hand segments for example segments including part of the arm or missing part of the palm. A 3D time-of-flight camera named SR4000 has been chosen to develop a new methodology because of its capability to provide in real-time and at high frame rate 3D information on the scene imaged. This sensor has been described and evaluated for its capability for capturing in real-time a moving hand. A new recognition method that uses the 3D information provided by the range camera to recognize hand postures has been proposed. The different steps of this methodology including the segmentation, the tracking, the hand modeling and finally the recognition process have been described and evaluated extensively. In addition, the performance of this method has been analyzed against several existing hand posture recognition techniques found in literature. The proposed system is able to recognize with an overall recognition rate of 98% and in real-time 18 out the 33 postures of the American sign language alphabet. This recognition is translation, rotation and scale invariant.

Using computerized games to teach face recognition skills to children with autism spectrum disorder: the Let's Face It! program.

PubMed

Tanaka, James W; Wolf, Julie M; Klaiman, Cheryl; Koenig, Kathleen; Cockburn, Jeffrey; Herlihy, Lauren; Brown, Carla; Stahl, Sherin; Kaiser, Martha D; Schultz, Robert T

2010-08-01

An emerging body of evidence indicates that relative to typically developing children, children with autism are selectively impaired in their ability to recognize facial identity. A critical question is whether face recognition skills can be enhanced through a direct training intervention. In a randomized clinical trial, children diagnosed with autism spectrum disorder were pre-screened with a battery of subtests (the Let's Face It! Skills battery) examining face and object processing abilities. Participants who were significantly impaired in their face processing abilities were assigned to either a treatment or a waitlist group. Children in the treatment group (N = 42) received 20 hours of face training with the Let's Face It! (LFI!) computer-based intervention. The LFI! program is comprised of seven interactive computer games that target the specific face impairments associated with autism, including the recognition of identity across image changes in expression, viewpoint and features, analytic and holistic face processing strategies and attention to information in the eye region. Time 1 and Time 2 performance for the treatment and waitlist groups was assessed with the Let's Face It! Skills battery. The main finding was that relative to the control group (N = 37), children in the face training group demonstrated reliable improvements in their analytic recognition of mouth features and holistic recognition of a face based on its eyes features. These results indicate that a relatively short-term intervention program can produce measurable improvements in the face recognition skills of children with autism. As a treatment for face processing deficits, the Let's Face It! program has advantages of being cost-free, adaptable to the specific learning needs of the individual child and suitable for home and school applications.

Analytical concepts for health management systems of liquid rocket engines

NASA Technical Reports Server (NTRS)

Williams, Richard; Tulpule, Sharayu; Hawman, Michael

1990-01-01

Substantial improvement in health management systems performance can be realized by implementing advanced analytical methods of processing existing liquid rocket engine sensor data. In this paper, such techniques ranging from time series analysis to multisensor pattern recognition to expert systems to fault isolation models are examined and contrasted. The performance of several of these methods is evaluated using data from test firings of the Space Shuttle main engines.

Face recognition performance of individuals with Asperger syndrome on the Cambridge Face Memory Test.

PubMed

Hedley, Darren; Brewer, Neil; Young, Robyn

2011-12-01

Although face recognition deficits in individuals with Autism Spectrum Disorder (ASD), including Asperger syndrome (AS), are widely acknowledged, the empirical evidence is mixed. This in part reflects the failure to use standardized and psychometrically sound tests. We contrasted standardized face recognition scores on the Cambridge Face Memory Test (CFMT) for 34 individuals with AS with those for 42, IQ-matched non-ASD individuals, and age-standardized scores from a large Australian cohort. We also examined the influence of IQ, autistic traits, and negative affect on face recognition performance. Overall, participants with AS performed significantly worse on the CFMT than the non-ASD participants and when evaluated against standardized test norms. However, while 24% of participants with AS presented with severe face recognition impairment (>2 SDs below the mean), many individuals performed at or above the typical level for their age: 53% scored within +/- 1 SD of the mean and 9% demonstrated superior performance (>1 SD above the mean). Regression analysis provided no evidence that IQ, autistic traits, or negative affect significantly influenced face recognition: diagnostic group membership was the only significant predictor of face recognition performance. In sum, face recognition performance in ASD is on a continuum, but with average levels significantly below non-ASD levels of performance. Copyright © 2011, International Society for Autism Research, Wiley-Liss, Inc.

Emotion recognition based on physiological changes in music listening.

PubMed

Kim, Jonghwa; André, Elisabeth

2008-12-01

Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological dataset to a feature-based multiclass classification. In order to collect a physiological dataset from multiple subjects over many weeks, we used a musical induction method which spontaneously leads subjects to real emotional states, without any deliberate lab setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. Improved recognition accuracy of 95\\% and 70\\% for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.

Web Video Event Recognition by Semantic Analysis From Ubiquitous Documents.

PubMed

Yu, Litao; Yang, Yang; Huang, Zi; Wang, Peng; Song, Jingkuan; Shen, Heng Tao

2016-12-01

In recent years, the task of event recognition from videos has attracted increasing interest in multimedia area. While most of the existing research was mainly focused on exploring visual cues to handle relatively small-granular events, it is difficult to directly analyze video content without any prior knowledge. Therefore, synthesizing both the visual and semantic analysis is a natural way for video event understanding. In this paper, we study the problem of Web video event recognition, where Web videos often describe large-granular events and carry limited textual information. Key challenges include how to accurately represent event semantics from incomplete textual information and how to effectively explore the correlation between visual and textual cues for video event understanding. We propose a novel framework to perform complex event recognition from Web videos. In order to compensate the insufficient expressive power of visual cues, we construct an event knowledge base by deeply mining semantic information from ubiquitous Web documents. This event knowledge base is capable of describing each event with comprehensive semantics. By utilizing this base, the textual cues for a video can be significantly enriched. Furthermore, we introduce a two-view adaptive regression model, which explores the intrinsic correlation between the visual and textual cues of the videos to learn reliable classifiers. Extensive experiments on two real-world video data sets show the effectiveness of our proposed framework and prove that the event knowledge base indeed helps improve the performance of Web video event recognition.

Progestogens’ effects and mechanisms for object recognition memory across the lifespan

PubMed Central

Walf, Alicia A.; Koonce, Carolyn J.; Frye, Cheryl A.

2016-01-01

This review explores the effects of female reproductive hormones, estrogens and progestogens, with a focus on progesterone and allopregnanolone, on object memory. Progesterone and its metabolites, in particular allopregnanolone, exert various effects on both cognitive and non-mnemonic functions in females. The well-known object recognition task is a valuable experimental paradigm that can be used to determine the effects and mechanisms of progestogens for mnemonic effects across the lifespan, which will be discussed herein. In this task there is little test-decay when different objects are used as targets and baseline valance for objects is controlled. This allows repeated testing, within-subjects designs, and longitudinal assessments, which aid understanding of changes in hormonal milieu. Objects are not aversive or food-based, which are hormone-sensitive factors. This review focuses on published data from our laboratory, and others, using the object recognition task in rodents to assess the role and mechanisms of progestogens throughout the lifespan. Improvements in object recognition performance of rodents are often associated with higher hormone levels in the hippocampus and prefrontal cortex during natural cycles, with hormone replacement following ovariectomy in young animals, or with aging. The capacity for reversal of age- and reproductive senescence-related decline in cognitive performance, and changes in neural plasticity that may be dissociated from peripheral effects with such decline, are discussed. The focus here will be on the effects of brain-derived factors, such as the neurosteroid, allopregnanolone, and other hormones, for enhancing object recognition across the lifespan. PMID:26235328

Generating virtual training samples for sparse representation of face images and face recognition

NASA Astrophysics Data System (ADS)

Du, Yong; Wang, Yu

2016-03-01

There are many challenges in face recognition. In real-world scenes, images of the same face vary with changing illuminations, different expressions and poses, multiform ornaments, or even altered mental status. Limited available training samples cannot convey these possible changes in the training phase sufficiently, and this has become one of the restrictions to improve the face recognition accuracy. In this article, we view the multiplication of two images of the face as a virtual face image to expand the training set and devise a representation-based method to perform face recognition. The generated virtual samples really reflect some possible appearance and pose variations of the face. By multiplying a training sample with another sample from the same subject, we can strengthen the facial contour feature and greatly suppress the noise. Thus, more human essential information is retained. Also, uncertainty of the training data is simultaneously reduced with the increase of the training samples, which is beneficial for the training phase. The devised representation-based classifier uses both the original and new generated samples to perform the classification. In the classification phase, we first determine K nearest training samples for the current test sample by calculating the Euclidean distances between the test sample and training samples. Then, a linear combination of these selected training samples is used to represent the test sample, and the representation result is used to classify the test sample. The experimental results show that the proposed method outperforms some state-of-the-art face recognition methods.

An improved PSO-SVM model for online recognition defects in eddy current testing

NASA Astrophysics Data System (ADS)

Liu, Baoling; Hou, Dibo; Huang, Pingjie; Liu, Banteng; Tang, Huayi; Zhang, Wubo; Chen, Peihua; Zhang, Guangxin

2013-12-01

Accurate and rapid recognition of defects is essential for structural integrity and health monitoring of in-service device using eddy current (EC) non-destructive testing. This paper introduces a novel model-free method that includes three main modules: a signal pre-processing module, a classifier module and an optimisation module. In the signal pre-processing module, a kind of two-stage differential structure is proposed to suppress the lift-off fluctuation that could contaminate the EC signal. In the classifier module, multi-class support vector machine (SVM) based on one-against-one strategy is utilised for its good accuracy. In the optimisation module, the optimal parameters of classifier are obtained by an improved particle swarm optimisation (IPSO) algorithm. The proposed IPSO technique can improve convergence performance of the primary PSO through the following strategies: nonlinear processing of inertia weight, introductions of the black hole and simulated annealing model with extremum disturbance. The good generalisation ability of the IPSO-SVM model has been validated through adding additional specimen into the testing set. Experiments show that the proposed algorithm can achieve higher recognition accuracy and efficiency than other well-known classifiers and the superiorities are more obvious with less training set, which contributes to online application.

Improving the Performance of an Auditory Brain-Computer Interface Using Virtual Sound Sources by Shortening Stimulus Onset Asynchrony

PubMed Central

Sugi, Miho; Hagimoto, Yutaka; Nambu, Isao; Gonzalez, Alejandro; Takei, Yoshinori; Yano, Shohei; Hokari, Haruhide; Wada, Yasuhiro

2018-01-01

Recently, a brain-computer interface (BCI) using virtual sound sources has been proposed for estimating user intention via electroencephalogram (EEG) in an oddball task. However, its performance is still insufficient for practical use. In this study, we examine the impact that shortening the stimulus onset asynchrony (SOA) has on this auditory BCI. While very short SOA might improve its performance, sound perception and task performance become difficult, and event-related potentials (ERPs) may not be induced if the SOA is too short. Therefore, we carried out behavioral and EEG experiments to determine the optimal SOA. In the experiments, participants were instructed to direct attention to one of six virtual sounds (target direction). We used eight different SOA conditions: 200, 300, 400, 500, 600, 700, 800, and 1,100 ms. In the behavioral experiment, we recorded participant behavioral responses to target direction and evaluated recognition performance of the stimuli. In all SOA conditions, recognition accuracy was over 85%, indicating that participants could recognize the target stimuli correctly. Next, using a silent counting task in the EEG experiment, we found significant differences between target and non-target sound directions in all but the 200-ms SOA condition. When we calculated an identification accuracy using Fisher discriminant analysis (FDA), the SOA could be shortened by 400 ms without decreasing the identification accuracies. Thus, improvements in performance (evaluated by BCI utility) could be achieved. On average, higher BCI utilities were obtained in the 400 and 500-ms SOA conditions. Thus, auditory BCI performance can be optimized for both behavioral and neurophysiological responses by shortening the SOA. PMID:29535602

Emotion recognition in Parkinson's disease: Static and dynamic factors.

PubMed

Wasser, Cory I; Evans, Felicity; Kempnich, Clare; Glikmann-Johnston, Yifat; Andrews, Sophie C; Thyagarajan, Dominic; Stout, Julie C

2018-02-01

The authors tested the hypothesis that Parkinson's disease (PD) participants would perform better in an emotion recognition task with dynamic (video) stimuli compared to a task using only static (photograph) stimuli and compared performances on both tasks to healthy control participants. In a within-subjects study, 21 PD participants and 20 age-matched healthy controls performed both static and dynamic emotion recognition tasks. The authors used a 2-way analysis of variance (controlling for individual participant variance) to determine the effect of group (PD, control) on emotion recognition performance in static and dynamic facial recognition tasks. Groups did not significantly differ in their performances on the static and dynamic tasks; however, the trend was suggestive that PD participants performed worse than controls. PD participants may have subtle emotion recognition deficits that are not ameliorated by the addition of contextual cues, similar to those found in everyday scenarios. Consistent with previous literature, the results suggest that PD participants may have underlying emotion recognition deficits, which may impact their social functioning. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Retrospective Analysis of Clinical Performance of an Estonian Speech Recognition System for Radiology: Effects of Different Acoustic and Language Models.

PubMed

Paats, A; Alumäe, T; Meister, E; Fridolin, I

2018-04-30

The aim of this study was to analyze retrospectively the influence of different acoustic and language models in order to determine the most important effects to the clinical performance of an Estonian language-based non-commercial radiology-oriented automatic speech recognition (ASR) system. An ASR system was developed for Estonian language in radiology domain by utilizing open-source software components (Kaldi toolkit, Thrax). The ASR system was trained with the real radiology text reports and dictations collected during development phases. The final version of the ASR system was tested by 11 radiologists who dictated 219 reports in total, in spontaneous manner in a real clinical environment. The audio files collected in the final phase were used to measure the performance of different versions of the ASR system retrospectively. ASR system versions were evaluated by word error rate (WER) for each speaker and modality and by WER difference for the first and the last version of the ASR system. Total average WER for the final version throughout all material was improved from 18.4% of the first version (v1) to 5.8% of the last (v8) version which corresponds to relative improvement of 68.5%. WER improvement was strongly related to modality and radiologist. In summary, the performance of the final ASR system version was close to optimal, delivering similar results to all modalities and being independent on user, the complexity of the radiology reports, user experience, and speech characteristics.

Separable spectro-temporal Gabor filter bank features: Reducing the complexity of robust features for automatic speech recognition.

PubMed

Schädler, Marc René; Kollmeier, Birger

2015-04-01

To test if simultaneous spectral and temporal processing is required to extract robust features for automatic speech recognition (ASR), the robust spectro-temporal two-dimensional-Gabor filter bank (GBFB) front-end from Schädler, Meyer, and Kollmeier [J. Acoust. Soc. Am. 131, 4134-4151 (2012)] was de-composed into a spectral one-dimensional-Gabor filter bank and a temporal one-dimensional-Gabor filter bank. A feature set that is extracted with these separate spectral and temporal modulation filter banks was introduced, the separate Gabor filter bank (SGBFB) features, and evaluated on the CHiME (Computational Hearing in Multisource Environments) keywords-in-noise recognition task. From the perspective of robust ASR, the results showed that spectral and temporal processing can be performed independently and are not required to interact with each other. Using SGBFB features permitted the signal-to-noise ratio (SNR) to be lowered by 1.2 dB while still performing as well as the GBFB-based reference system, which corresponds to a relative improvement of the word error rate by 12.8%. Additionally, the real time factor of the spectro-temporal processing could be reduced by more than an order of magnitude. Compared to human listeners, the SNR needed to be 13 dB higher when using Mel-frequency cepstral coefficient features, 11 dB higher when using GBFB features, and 9 dB higher when using SGBFB features to achieve the same recognition performance.

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

PubMed

Spoerer, Courtney J; McClure, Patrick; Kriegeskorte, Nikolaus

2017-01-01

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance.

Recognition of familiar people with a mobile cloud architecture for Alzheimer patients.

PubMed

Fardoun, Habib M; Mashat, Abdullah A; Ramirez Castillo, Jaime

2017-02-01

This article aims to the evaluation of a prototypal assistive technology for Alzheimer's disease (AD) patients that helps them to remember personal details of familiar people they meet in their daily lives. An architecture is proposed for a personal information system powered by face recognition, where the main AD patient's interaction is performed in a smart watch device and the face recognition is carried out on the Cloud. A prototype was developed to perform some tests in a real-life scenario. The prototype showed correct results as a personal information system based on face recognition. However, usability flaws were identified in the interaction with the smart watch. Our architecture showed correct performance and we realized that it could be introduced in other fields, apart from assistive technology. However, when being targeted to patients with dementia some usability problems appeared, such as difficulties to read information in a small screen or take a proper photo. These problems should be addressed in further research. Implications for Rehabilitation This article presents a prototypal assistive technology for Alzheimer's disease (AD) patients. It targets AD patients to recognize their familiars, especially in medium-advanced stages of the disease. Analysing pictures taken by a smart watch, which the patient carries, the person in front is recognized and information about him is sent to the watch. This technology enables patients to have all the information of any close person, as a remainder, easing their daily lives, improving their self-esteem and stimulating the patient with novel technology.

Learning effect of computerized cognitive tests in older adults

PubMed Central

de Oliveira, Rafaela Sanches; Trezza, Beatriz Maria; Busse, Alexandre Leopold; Jacob-Filho, Wilson

2014-01-01

ABSTRACT Objective: To evaluate the learning effect of computerized cognitive testing in the elderly. Methods: Cross-sectional study with 20 elderly, 10 women and 10 men, with average age of 77.5 (±4.28) years. The volunteers performed two series of computerized cognitive tests in sequence and their results were compared. The applied tests were: Trail Making A and B, Spatial Recognition, Go/No Go, Memory Span, Pattern Recognition Memory and Reverse Span. Results: Based on the comparison of the results, learning effects were observed only in the Trail Making A test (p=0.019). Other tests performed presented no significant performance improvements. There was no correlation between learning effect and age (p=0.337) and education (p=0.362), as well as differences between genders (p=0.465). Conclusion: The computerized cognitive tests repeated immediately afterwards, for elderly, revealed no change in their performance, with the exception of the Trail Making test, demonstrating high clinical applicability, even in short intervals. PMID:25003917

Extended target recognition in cognitive radar networks.

PubMed

Wei, Yimin; Meng, Huadong; Liu, Yimin; Wang, Xiqin

2010-01-01

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

Multiview fusion for activity recognition using deep neural networks

NASA Astrophysics Data System (ADS)

Kavi, Rahul; Kulathumani, Vinod; Rohit, Fnu; Kecojevic, Vlad

2016-07-01

Convolutional neural networks (ConvNets) coupled with long short term memory (LSTM) networks have been recently shown to be effective for video classification as they combine the automatic feature extraction capabilities of a neural network with additional memory in the temporal domain. This paper shows how multiview fusion can be applied to such a ConvNet LSTM architecture. Two different fusion techniques are presented. The system is first evaluated in the context of a driver activity recognition system using data collected in a multicamera driving simulator. These results show significant improvement in accuracy with multiview fusion and also show that deep learning performs better than a traditional approach using spatiotemporal features even without requiring any background subtraction. The system is also validated on another publicly available multiview action recognition dataset that has 12 action classes and 8 camera views.

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

PubMed Central

Chun, Hong-Woo; Tsuruoka, Yoshimasa; Kim, Jin-Dong; Shiba, Rie; Nagata, Naoki; Hishiki, Teruyoshi; Tsujii, Jun'ichi

2006-01-01

Background Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. Methods We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Results Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. Conclusion A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques. PMID:17134477

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

PubMed

Chun, Hong-Woo; Tsuruoka, Yoshimasa; Kim, Jin-Dong; Shiba, Rie; Nagata, Naoki; Hishiki, Teruyoshi; Tsujii, Jun'ichi

2006-11-24

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body.

PubMed

Nguyen, Dat Tien; Park, Kang Ryoung

2016-07-21

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images.

Face recognition by applying wavelet subband representation and kernel associative memory.

PubMed

Zhang, Bai-Ling; Zhang, Haihong; Ge, Shuzhi Sam

2004-01-01

In this paper, we propose an efficient face recognition scheme which has two features: 1) representation of face images by two-dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalised classification method based on kernel associative memory models. Compared to PCA projections and low resolution "thumb-nail" image representations, wavelet subband coefficients can efficiently capture substantial facial features while keeping computational complexity low. As there are usually very limited samples, we constructed an associative memory (AM) model for each person and proposed to improve the performance of AM models by kernel methods. Specifically, we first applied kernel transforms to each possible training pair of faces sample and then mapped the high-dimensional feature space back to input space. Our scheme using modular autoassociative memory for face recognition is inspired by the same motivation as using autoencoders for optical character recognition (OCR), for which the advantages has been proven. By associative memory, all the prototypical faces of one particular person are used to reconstruct themselves and the reconstruction error for a probe face image is used to decide if the probe face is from the corresponding person. We carried out extensive experiments on three standard face recognition datasets, the FERET data, the XM2VTS data, and the ORL data. Detailed comparisons with earlier published results are provided and our proposed scheme offers better recognition accuracy on all of the face datasets.

Enhanced Gender Recognition System Using an Improved Histogram of Oriented Gradient (HOG) Feature from Quality Assessment of Visible Light and Thermal Images of the Human Body

PubMed Central

Nguyen, Dat Tien; Park, Kang Ryoung

2016-01-01

With higher demand from users, surveillance systems are currently being designed to provide more information about the observed scene, such as the appearance of objects, types of objects, and other information extracted from detected objects. Although the recognition of gender of an observed human can be easily performed using human perception, it remains a difficult task when using computer vision system images. In this paper, we propose a new human gender recognition method that can be applied to surveillance systems based on quality assessment of human areas in visible light and thermal camera images. Our research is novel in the following two ways: First, we utilize the combination of visible light and thermal images of the human body for a recognition task based on quality assessment. We propose a quality measurement method to assess the quality of image regions so as to remove the effects of background regions in the recognition system. Second, by combining the features extracted using the histogram of oriented gradient (HOG) method and the measured qualities of image regions, we form a new image features, called the weighted HOG (wHOG), which is used for efficient gender recognition. Experimental results show that our method produces more accurate estimation results than the state-of-the-art recognition method that uses human body images. PMID:27455264

From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems

PubMed Central

Yildiz, Izzet B.; von Kriegstein, Katharina; Kiebel, Stefan J.

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents—an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments. PMID:24068902

From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

PubMed

Yildiz, Izzet B; von Kriegstein, Katharina; Kiebel, Stefan J

2013-01-01

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.

Mechanisms and neural basis of object and pattern recognition: a study with chess experts.

PubMed

Bilalić, Merim; Langner, Robert; Erb, Michael; Grodd, Wolfgang

2010-11-01

Comparing experts with novices offers unique insights into the functioning of cognition, based on the maximization of individual differences. Here we used this expertise approach to disentangle the mechanisms and neural basis behind two processes that contribute to everyday expertise: object and pattern recognition. We compared chess experts and novices performing chess-related and -unrelated (visual) search tasks. As expected, the superiority of experts was limited to the chess-specific task, as there were no differences in a control task that used the same chess stimuli but did not require chess-specific recognition. The analysis of eye movements showed that experts immediately and exclusively focused on the relevant aspects in the chess task, whereas novices also examined irrelevant aspects. With random chess positions, when pattern knowledge could not be used to guide perception, experts nevertheless maintained an advantage. Experts' superior domain-specific parafoveal vision, a consequence of their knowledge about individual domain-specific symbols, enabled improved object recognition. Functional magnetic resonance imaging corroborated this differentiation between object and pattern recognition and showed that chess-specific object recognition was accompanied by bilateral activation of the occipitotemporal junction, whereas chess-specific pattern recognition was related to bilateral activations in the middle part of the collateral sulci. Using the expertise approach together with carefully chosen controls and multiple dependent measures, we identified object and pattern recognition as two essential cognitive processes in expert visual cognition, which may also help to explain the mechanisms of everyday perception.

Preliminary normative data on the BORB for children aged 3-8.

PubMed

Brunsdon, Ruth; Joy, Pamela; Patten, Erin; Burton, Karen

2018-05-09

The Birmingham Object Recognition Battery (BORB) is a theoretically based test battery that is used in adult cognitive neuropsychology in research and for clinical assessment. It allows a detailed analysis of underlying impairments in individuals with brain injury who have visual object recognition difficulties. The BORB's usefulness in pediatrics is supported by numerous research studies. However, there is no published normative data for children, making clinical use of the test difficult. The aim of this brief report is to publish some preliminary normative data in 70 children aged between 3 and 8 years to assist both researchers and clinicians with interpretation of test scores. Results indicate that children's performance on individual BORB subtests varies according to task demands and age. For some subtests there is improvement in performance with increasing age. However, very young children (age 3-4 years) perform at adult levels on some subtests, or alternatively on other subtests they perform at the level of chance. The current paper supports the need for pediatric data for the BORB due to large normal individual variation in performance and varying age-related performance on individual BORB subtests.

Levels-Of-Processing Effect on Word Recognition in Schizophrenia

PubMed Central

Ragland, J. Daniel; Moelter, Stephen T.; McGrath, Claire; Hill, S. Kristian; Gur, Raquel E.; Bilker, Warren B.; Siegel, Steven J.; Gur, Ruben C.

2015-01-01

Background Individuals with schizophrenia have difficulty organizing words semantically to facilitate encoding. This is commonly attributed to organizational rather than semantic processing limitations. By requiring participants to classify and encode words on either a shallow (e.g., uppercase/lowercase) or deep level (e.g., concrete/abstract), the levels-of-processing paradigm eliminates the need to generate organizational strategies. Methods This paradigm was administered to 30 patients with schizophrenia and 30 healthy comparison subjects to test whether providing a strategy would improve patient performance. Results Word classification during shallow and deep encoding was slower and less accurate in patients. Patients also responded slowly during recognition testing and maintained a more conservative response bias following deep encoding; however, both groups showed a robust levels-of-processing effect on recognition accuracy, with unimpaired patient performance following both shallow and deep encoding. Conclusions This normal levels-of-processing effect in the patient sample suggests that semantic processing is sufficiently intact for patients to benefit from organizational cues. Memory remediation efforts may therefore be most successful if they focus on teaching patients to form organizational strategies during initial encoding. PMID:14643082

Circular blurred shape model for multiclass symbol recognition.

PubMed

Escalera, Sergio; Fornés, Alicia; Pujol, Oriol; Lladós, Josep; Radeva, Petia

2011-04-01

In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.

Levels-of-processing effect on word recognition in schizophrenia.

PubMed

Ragland, J Daniel; Moelter, Stephen T; McGrath, Claire; Hill, S Kristian; Gur, Raquel E; Bilker, Warren B; Siegel, Steven J; Gur, Ruben C

2003-12-01

Individuals with schizophrenia have difficulty organizing words semantically to facilitate encoding. This is commonly attributed to organizational rather than semantic processing limitations. By requiring participants to classify and encode words on either a shallow (e.g., uppercase/lowercase) or deep level (e.g., concrete/abstract), the levels-of-processing paradigm eliminates the need to generate organizational strategies. This paradigm was administered to 30 patients with schizophrenia and 30 healthy comparison subjects to test whether providing a strategy would improve patient performance. Word classification during shallow and deep encoding was slower and less accurate in patients. Patients also responded slowly during recognition testing and maintained a more conservative response bias following deep encoding; however, both groups showed a robust levels-of-processing effect on recognition accuracy, with unimpaired patient performance following both shallow and deep encoding. This normal levels-of-processing effect in the patient sample suggests that semantic processing is sufficiently intact for patients to benefit from organizational cues. Memory remediation efforts may therefore be most successful if they focus on teaching patients to form organizational strategies during initial encoding.

Hepatitis-Associated Liver Cancer: Gaps and Opportunities to Improve Care

PubMed Central

McMahon, Brian; Block, Timothy; Cohen, Chari; Evans, Alison A.; Hosangadi, Anu; London, W. Thomas; Sherman, Morris

2016-01-01

The global burden of hepatocellular carcinoma (HCC; primary liver cancer) is increasing. HCC is often unaccompanied by clear symptomatology, causing patients to be unaware of their disease. Moreover, effective treatment for those with advanced disease is lacking. As such, effective surveillance and early detection of HCC are essential. However, current screening and surveillance guidelines are not being fully implemented. Some at-risk populations fall outside of the guidelines, and patients who are screened are often not diagnosed at an early enough stage for treatment to be effective. From March 17 to 19, 2015, the Hepatitis B Foundation sponsored a workshop to identify gaps and limitations in current approaches to the detection and treatment of HCC and to define research priorities and opportunities for advocacy. In this Commentary, we summarize areas for further research and action that were discussed throughout the workshop to improve the recognition of liver disease generally, improve the recognition of liver cancer risk, and improve the recognition that screening for HCC makes a life-saving difference. Participants agreed that primary prevention of HCC relies on prevention and treatment of viral hepatitis and other underlying etiologies. Earlier diagnosis (secondary prevention) needs to be substantially improved. Areas for attention include increasing practitioner awareness, better definition of at-risk populations, and improved performance of screening approaches (ultrasound, biomarkers for detection, risk stratification, targeted therapies). The heterogeneous nature of HCC makes it unlikely that a single therapeutic agent will be universally effective. Medical management will benefit from the development of new, targeted treatment approaches. PMID:26626106

Improving Offensive Performance through Opponent Modeling

DTIC Science & Technology

2009-01-01

of of- fensive players , we improve on the yardage gained. Introduction By accessing the play history of your opponent, it is pos- sible to glean...review a video play history of the player taking a penalty kick; iden- tifying the player’s tendency to kick to the left allowed the goalkeeper to...recognition and 3) off-line review. In online track- ing, immediate future actions of individual players (passes, feints) are predicted, whereas in online

Psychometric properties of the NEPSY-II affect recognition subtest in a preschool sample: a Rasch modeling approach.

PubMed

Yao, Shih-Ying; Bull, Rebecca; Khng, Kiat Hui; Rahim, Anisa

2018-01-01

Understanding a child's ability to decode emotion expressions is important to allow early interventions for potential difficulties in social and emotional functioning. This study applied the Rasch model to investigate the psychometric properties of the NEPSY-II Affect Recognition subtest, a U.S. normed measure for 3-16 year olds which assesses the ability to recognize facial expressions of emotion. Data were collected from 1222 children attending preschools in Singapore. We first performed the Rasch analysis with the raw item data, and examined the technical qualities and difficulty pattern of the studied items. We subsequently investigated the relation of the estimated affect recognition ability from the Rasch analysis to a teacher-reported measure of a child's behaviors, emotions, and relationships. Potential gender differences were also examined. The Rasch model fits our data well. Also, the NEPSY-II Affect Recognition subtest was found to have reasonable technical qualities, expected item difficulty pattern, and desired association with the external measure of children's behaviors, emotions, and relationships for both boys and girls. Overall, findings from this study suggest that the NEPSY-II Affect Recognition subtest is a promising measure of young children's affect recognition ability. Suggestions for future test improvement and research were discussed.

Listeners remember music they like.

PubMed

Stalinski, Stephanie M; Schellenberg, E Glenn

2013-05-01

Emotions have important and powerful effects on cognitive processes. Although it is well established that memory influences liking, we sought to document whether liking influences memory. A series of 6 experiments examined whether liking is related to recognition memory for novel music excerpts. In the general method, participants listened to a set of music excerpts and rated how much they liked each one. After a delay, they heard the same excerpts plus an equal number of novel excerpts and made recognition judgments, which were then examined in conjunction with liking ratings. Higher liking ratings were associated with improved recognition performance after a 10-min (Experiment 1) or 24-hr (Experiment 2) delay between the exposure and test phases. The findings were similar when participants made liking ratings after recognition judgments (Experiments 3 and 6), when possible confounding effects of similarity and familiarity were held constant (Experiment 4), and when a deeper level of processing was encouraged for all the excerpts (Experiment 5). Recognition did not vary as a function of liking for previously unheard excerpts (Experiment 6). The results implicate a direct association between liking and recognition. Considered jointly with previous findings, it is now clear that listeners tend to like music that they remember and to remember music that they like.

Quality based approach for adaptive face recognition

NASA Astrophysics Data System (ADS)

Abboud, Ali J.; Sellahewa, Harin; Jassim, Sabah A.

2009-05-01

Recent advances in biometric technology have pushed towards more robust and reliable systems. We aim to build systems that have low recognition errors and are less affected by variation in recording conditions. Recognition errors are often attributed to the usage of low quality biometric samples. Hence, there is a need to develop new intelligent techniques and strategies to automatically measure/quantify the quality of biometric image samples and if necessary restore image quality according to the need of the intended application. In this paper, we present no-reference image quality measures in the spatial domain that have impact on face recognition. The first is called symmetrical adaptive local quality index (SALQI) and the second is called middle halve (MH). Also, an adaptive strategy has been developed to select the best way to restore the image quality, called symmetrical adaptive histogram equalization (SAHE). The main benefits of using quality measures for adaptive strategy are: (1) avoidance of excessive unnecessary enhancement procedures that may cause undesired artifacts, and (2) reduced computational complexity which is essential for real time applications. We test the success of the proposed measures and adaptive approach for a wavelet-based face recognition system that uses the nearest neighborhood classifier. We shall demonstrate noticeable improvements in the performance of adaptive face recognition system over the corresponding non-adaptive scheme.

Experience moderates overlap between object and face recognition, suggesting a common ability

PubMed Central

Gauthier, Isabel; McGugin, Rankin W.; Richler, Jennifer J.; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E.

2014-01-01

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. PMID:24993021

Experience moderates overlap between object and face recognition, suggesting a common ability.

PubMed

Gauthier, Isabel; McGugin, Rankin W; Richler, Jennifer J; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E

2014-07-03

Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. © 2014 ARVO.

Implicit Shape Models for Object Detection in 3d Point Clouds

NASA Astrophysics Data System (ADS)

Velizhev, A.; Shapovalov, R.; Schindler, K.

2012-07-01

We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m2 of urban area in total.

Relationship between listeners' nonnative speech recognition and categorization abilities

PubMed Central

Atagi, Eriko; Bent, Tessa

2015-01-01

Enhancement of the perceptual encoding of talker characteristics (indexical information) in speech can facilitate listeners' recognition of linguistic content. The present study explored this indexical-linguistic relationship in nonnative speech processing by examining listeners' performance on two tasks: nonnative accent categorization and nonnative speech-in-noise recognition. Results indicated substantial variability across listeners in their performance on both the accent categorization and nonnative speech recognition tasks. Moreover, listeners' accent categorization performance correlated with their nonnative speech-in-noise recognition performance. These results suggest that having more robust indexical representations for nonnative accents may allow listeners to more accurately recognize the linguistic content of nonnative speech. PMID:25618098