Identification of Matra Region and Overlapping Characters for OCR of Printed Bengali Scripts

NASA Astrophysics Data System (ADS)

Goswami, Subhra Sundar

One of the important reasons for poor recognition rate in optical character recognition (OCR) system is the error in character segmentation. In case of Bangla scripts, the errors occur due to several reasons, which include incorrect detection of matra (headline), over-segmentation and under-segmentation. We have proposed a robust method for detecting the headline region. Existence of overlapping characters (in under-segmented parts) in scanned printed documents is a major problem in designing an effective character segmentation procedure for OCR systems. In this paper, a predictive algorithm is developed for effectively identifying overlapping characters and then selecting the cut-borders for segmentation. Our method can be successfully used in achieving high recognition result.

Safety culture perceptions of pharmacists in Malaysian hospitals and health clinics: a multicentre assessment using the Safety Attitudes Questionnaire.

PubMed

Samsuri, Srima Elina; Pei Lin, Lua; Fahrni, Mathumalar Loganathan

2015-11-26

To assess the safety attitudes of pharmacists, provide a profile of their domains of safety attitude and correlate their attitudes with self-reported rates of medication errors. A cross-sectional study utilising the Safety Attitudes Questionnaire (SAQ). 3 public hospitals and 27 health clinics. 117 pharmacists. Safety culture mean scores, variation in scores across working units and between hospitals versus health clinics, predictors of safety culture, and medication errors and their correlation. Response rate was 83.6% (117 valid questionnaires returned). Stress recognition (73.0±20.4) and working condition (54.8±17.4) received the highest and lowest mean scores, respectively. Pharmacists exhibited positive attitudes towards: stress recognition (58.1%), job satisfaction (46.2%), teamwork climate (38.5%), safety climate (33.3%), perception of management (29.9%) and working condition (15.4%). With the exception of stress recognition, those who worked in health clinics scored higher than those in hospitals (p<0.05) and higher scores (overall score as well as score for each domain except for stress recognition) correlated negatively with reported number of medication errors. Conversely, those working in hospital (versus health clinic) were 8.9 times more likely (p<0.01) to report a medication error (OR 8.9, CI 3.08 to 25.7). As stress recognition increased, the number of medication errors reported increased (p=0.023). Years of work experience (p=0.017) influenced the number of medication errors reported. For every additional year of work experience, pharmacists were 0.87 times less likely to report a medication error (OR 0.87, CI 0.78 to 0.98). A minority (20.5%) of the pharmacists working in hospitals and health clinics was in agreement with the overall SAQ questions and scales. Pharmacists in outpatient and ambulatory units and those in health clinics had better perceptions of safety culture. As perceptions improved, the number of medication errors reported decreased. Group-specific interventions that target specific domains are necessary to improve the safety culture. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

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.

The relationship between facial emotion recognition and executive functions in first-episode patients with schizophrenia and their siblings.

PubMed

Yang, Chengqing; Zhang, Tianhong; Li, Zezhi; Heeramun-Aubeeluck, Anisha; Liu, Na; Huang, Nan; Zhang, Jie; He, Leiying; Li, Hui; Tang, Yingying; Chen, Fazhan; Liu, Fei; Wang, Jijun; Lu, Zheng

2015-10-08

Although many studies have examined executive functions and facial emotion recognition in people with schizophrenia, few of them focused on the correlation between them. Furthermore, their relationship in the siblings of patients also remains unclear. The aim of the present study is to examine the correlation between executive functions and facial emotion recognition in patients with first-episode schizophrenia and their siblings. Thirty patients with first-episode schizophrenia, their twenty-six siblings, and thirty healthy controls were enrolled. They completed facial emotion recognition tasks using the Ekman Standard Faces Database, and executive functioning was measured by Wisconsin Card Sorting Test (WCST). Hierarchical regression analysis was applied to assess the correlation between executive functions and facial emotion recognition. Our study found that in siblings, the accuracy in recognizing low degree 'disgust' emotion was negatively correlated with the total correct rate in WCST (r = -0.614, p = 0.023), but was positively correlated with the total error in WCST (r = 0.623, p = 0.020); the accuracy in recognizing 'neutral' emotion was positively correlated with the total error rate in WCST (r = 0.683, p = 0.014) while negatively correlated with the total correct rate in WCST (r = -0.677, p = 0.017). People with schizophrenia showed an impairment in facial emotion recognition when identifying moderate 'happy' facial emotion, the accuracy of which was significantly correlated with the number of completed categories of WCST (R(2) = 0.432, P < .05). There were no correlations between executive functions and facial emotion recognition in the healthy control group. Our study demonstrated that facial emotion recognition impairment correlated with executive function impairment in people with schizophrenia and their unaffected siblings but not in healthy controls.

A preliminary comparison of speech recognition functionality in dental practice management systems.

PubMed

Irwin, Jeannie Y; Schleyer, Titus

2008-11-06

In this study, we examined speech recognition functionality in four leading dental practice management systems. Twenty dental students used voice to chart a simulated patient with 18 findings in each system. Results show it can take over a minute to chart one finding and that users frequently have to repeat commands. Limited functionality, poor usability and a high error rate appear to retard adoption of speech recognition in dentistry.

Constructing a safety and security system by medical applications of a fast face recognition optical parallel correlator

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Murakami, Yasuo; Kodate, Kashiko

2006-01-01

Medical errors and patient safety have always received a great deal of attention, as they can be critically life-threatening and significant matters. Hospitals and medical personnel are trying their utmost to avoid these errors. Currently in the medical field, patients' record is identified through their PIN numbers and ID cards. However, for patients who cannot speak or move, or who suffer from memory disturbances, alternative methods would be more desirable, and necessary in some cases. The authors previously proposed and fabricated a specially-designed correlator called FARCO (Fast Face Recognition Optical Correlator) based on the Vanderlugt Correlator1, which operates at the speed of 1000 faces/s 2,3,4. Combined with high-speed display devices, the four-channel processing could achieve such high operational speed as 4000 faces/s. Running trial experiments on a 1-to-N identification basis using the optical parallel correlator, we succeeded in acquiring low error rates of 1 % FMR and 2.3 % FNMR. In this paper, we propose a robust face recognition system using the FARCO for focusing on the safety and security of the medical field. We apply our face recognition system to registration of inpatients, in particular children and infants, before and after medical treatments or operations. The proposed system has recorded a higher recognition rate by multiplexing both input and database facial images from moving images. The system was also tested and evaluated for further practical use, leaving excellent results. Hence, our face recognition system could function effectively as an integral part of medical system, meeting these essential requirements of safety, security and privacy.

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.

A preliminary analysis of human factors affecting the recognition accuracy of a discrete word recognizer for C3 systems

NASA Astrophysics Data System (ADS)

Yellen, H. W.

1983-03-01

Literature pertaining to Voice Recognition abounds with information relevant to the assessment of transitory speech recognition devices. In the past, engineering requirements have dictated the path this technology followed. But, other factors do exist that influence recognition accuracy. This thesis explores the impact of Human Factors on the successful recognition of speech, principally addressing the differences or variability among users. A Threshold Technology T-600 was used for a 100 utterance vocubalary to test 44 subjects. A statistical analysis was conducted on 5 generic categories of Human Factors: Occupational, Operational, Psychological, Physiological and Personal. How the equipment is trained and the experience level of the speaker were found to be key characteristics influencing recognition accuracy. To a lesser extent computer experience, time or week, accent, vital capacity and rate of air flow, speaker cooperativeness and anxiety were found to affect overall error rates.

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

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

Gender differences in facial emotion recognition in persons with chronic schizophrenia.

PubMed

Weiss, Elisabeth M; Kohler, Christian G; Brensinger, Colleen M; Bilker, Warren B; Loughead, James; Delazer, Margarete; Nolan, Karen A

2007-03-01

The aim of the present study was to investigate possible sex differences in the recognition of facial expressions of emotion and to investigate the pattern of classification errors in schizophrenic males and females. Such an approach provides an opportunity to inspect the degree to which males and females differ in perceiving and interpreting the different emotions displayed to them and to analyze which emotions are most susceptible to recognition errors. Fifty six chronically hospitalized schizophrenic patients (38 men and 18 women) completed the Penn Emotion Recognition Test (ER40), a computerized emotion discrimination test presenting 40 color photographs of evoked happy, sad, anger, fear expressions and neutral expressions balanced for poser gender and ethnicity. We found a significant sex difference in the patterns of error rates in the Penn Emotion Recognition Test. Neutral faces were more commonly mistaken as angry in schizophrenic men, whereas schizophrenic women misinterpreted neutral faces more frequently as sad. Moreover, female faces were better recognized overall, but fear was better recognized in same gender photographs, whereas anger was better recognized in different gender photographs. The findings of the present study lend support to the notion that sex differences in aggressive behavior could be related to a cognitive style characterized by hostile attributions to neutral faces in schizophrenic men.

An Argument for the Use of Biometrics to Prevent Terrorist Access to the United States

DTIC Science & Technology

2003-12-06

that they are who they claim to be. Remote methods such as facial recognition do not rely on interaction with the individual, and can be used with or...quickly, although there is a relatively high error rate. Acsys Biometric Systems, a leader in facial recognition , reports their best system has only a...change their appearance. The facial recognition system also presents a privacy concern in the minds of many individuals. By remotely scanning without an

The Use of Error Data to Study the Development of Verbal Encoding of Pictorial Stimuli.

ERIC Educational Resources Information Center

Cramer, Phebe

If older children automatically label pictorial stimuli, then their performance should be impaired on tasks in which such labeling would increase the error rate. Children were asked to learn pairs of verbal or pictorial stimuli which, when combined, formed a different compound word (BUTTER-FLY). Subsequently, a false recognition test that included…

Reducing Error Rates for Iris Image using higher Contrast in Normalization process

NASA Astrophysics Data System (ADS)

Aminu Ghali, Abdulrahman; Jamel, Sapiee; Abubakar Pindar, Zahraddeen; Hasssan Disina, Abdulkadir; Mat Daris, Mustafa

2017-08-01

Iris recognition system is the most secured, and faster means of identification and authentication. However, iris recognition system suffers a setback from blurring, low contrast and illumination due to low quality image which compromises the accuracy of the system. The acceptance or rejection rates of verified user depend solely on the quality of the image. In many cases, iris recognition system with low image contrast could falsely accept or reject user. Therefore this paper adopts Histogram Equalization Technique to address the problem of False Rejection Rate (FRR) and False Acceptance Rate (FAR) by enhancing the contrast of the iris image. A histogram equalization technique enhances the image quality and neutralizes the low contrast of the image at normalization stage. The experimental result shows that Histogram Equalization Technique has reduced FRR and FAR compared to the existing techniques.

Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses

NASA Astrophysics Data System (ADS)

Young, A. J.; Kuiken, T. A.; Hargrove, L. J.

2014-10-01

Objective. The purpose of this study was to determine the contribution of electromyography (EMG) data, in combination with a diverse array of mechanical sensors, to locomotion mode intent recognition in transfemoral amputees using powered prostheses. Additionally, we determined the effect of adding time history information using a dynamic Bayesian network (DBN) for both the mechanical and EMG sensors. Approach. EMG signals from the residual limbs of amputees have been proposed to enhance pattern recognition-based intent recognition systems for powered lower limb prostheses, but mechanical sensors on the prosthesis—such as inertial measurement units, position and velocity sensors, and load cells—may be just as useful. EMG and mechanical sensor data were collected from 8 transfemoral amputees using a powered knee/ankle prosthesis over basic locomotion modes such as walking, slopes and stairs. An offline study was conducted to determine the benefit of different sensor sets for predicting intent. Main results. EMG information was not as accurate alone as mechanical sensor information (p < 0.05) for any classification strategy. However, EMG in combination with the mechanical sensor data did significantly reduce intent recognition errors (p < 0.05) both for transitions between locomotion modes and steady-state locomotion. The sensor time history (DBN) classifier significantly reduced error rates compared to a linear discriminant classifier for steady-state steps, without increasing the transitional error, for both EMG and mechanical sensors. Combining EMG and mechanical sensor data with sensor time history reduced the average transitional error from 18.4% to 12.2% and the average steady-state error from 3.8% to 1.0% when classifying level-ground walking, ramps, and stairs in eight transfemoral amputee subjects. Significance. These results suggest that a neural interface in combination with time history methods for locomotion mode classification can enhance intent recognition performance; this strategy should be considered for future real-time experiments.

Automated alignment system for optical wireless communication systems using image recognition.

PubMed

Brandl, Paul; Weiss, Alexander; Zimmermann, Horst

2014-07-01

In this Letter, we describe the realization of a tracked line-of-sight optical wireless communication system for indoor data distribution. We built a laser-based transmitter with adaptive focus and ray steering by a microelectromechanical systems mirror. To execute the alignment procedure, we used a CMOS image sensor at the transmitter side and developed an algorithm for image recognition to localize the receiver's position. The receiver is based on a self-developed optoelectronic integrated chip with low requirements on the receiver optics to make the system economically attractive. With this system, we were able to set up the communication link automatically without any back channel and to perform error-free (bit error rate <10⁻⁹) data transmission over a distance of 3.5 m with a data rate of 3 Gbit/s.

Biometric recognition using 3D ear shape.

PubMed

Yan, Ping; Bowyer, Kevin W

2007-08-01

Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.

Associations of hallucination proneness with free-recall intrusions and response bias in a nonclinical sample.

PubMed

Brébion, Gildas; Larøi, Frank; Van der Linden, Martial

2010-10-01

Hallucinations in patients with schizophrenia have been associated with a liberal response bias in signal detection and recognition tasks and with various types of source-memory error. We investigated the associations of hallucination proneness with free-recall intrusions and false recognitions of words in a nonclinical sample. A total of 81 healthy individuals were administered a verbal memory task involving free recall and recognition of one nonorganizable and one semantically organizable list of words. Hallucination proneness was assessed by means of a self-rating scale. Global hallucination proneness was associated with free-recall intrusions in the nonorganizable list and with a response bias reflecting tendency to make false recognitions of nontarget words in both types of list. The verbal hallucination score was associated with more intrusions and with a reduced tendency to make false recognitions of words. The associations between global hallucination proneness and two types of verbal memory error in a nonclinical sample corroborate those observed in patients with schizophrenia and suggest that common cognitive mechanisms underlie hallucinations in psychiatric and nonclinical individuals.

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.

Evaluating structural pattern recognition for handwritten math via primitive label graphs

NASA Astrophysics Data System (ADS)

Zanibbi, Richard; Mouchère, Harold; Viard-Gaudin, Christian

2013-01-01

Currently, structural pattern recognizer evaluations compare graphs of detected structure to target structures (i.e. ground truth) using recognition rates, recall and precision for object segmentation, classification and relationships. In document recognition, these target objects (e.g. symbols) are frequently comprised of multiple primitives (e.g. connected components, or strokes for online handwritten data), but current metrics do not characterize errors at the primitive level, from which object-level structure is obtained. Primitive label graphs are directed graphs defined over primitives and primitive pairs. We define new metrics obtained by Hamming distances over label graphs, which allow classification, segmentation and parsing errors to be characterized separately, or using a single measure. Recall and precision for detected objects may also be computed directly from label graphs. We illustrate the new metrics by comparing a new primitive-level evaluation to the symbol-level evaluation performed for the CROHME 2012 handwritten math recognition competition. A Python-based set of utilities for evaluating, visualizing and translating label graphs is publicly available.

Noise tolerance in optical waveguide circuits for recognition of optical 16 quadrature amplitude modulation codes

NASA Astrophysics Data System (ADS)

Inoshita, Kensuke; Hama, Yoshimitsu; Kishikawa, Hiroki; Goto, Nobuo

2016-12-01

In photonic label routers, various optical signal processing functions are required; these include optical label extraction, recognition of the label, optical switching and buffering controlled by signals based on the label information and network routing tables, and label rewriting. Among these functions, we focus on photonic label recognition. We have proposed two kinds of optical waveguide circuits to recognize 16 quadrature amplitude modulation codes, i.e., recognition from the minimum output port and from the maximum output port. The recognition function was theoretically analyzed and numerically simulated by finite-difference beam-propagation method. We discuss noise tolerance in the circuit and show numerically simulated results to evaluate bit-error-rate (BER) characteristics against optical signal-to-noise ratio (OSNR). The OSNR required to obtain a BER less than 1.0×10-3 for the symbol rate of 2.5 GBaud was 14.5 and 27.0 dB for recognition from the minimum and maximum output, respectively.

The effectiveness of the error reporting promoting program on the nursing error incidence rate in Korean operating rooms.

PubMed

Kim, Myoung-Soo; Kim, Jung-Soon; Jung, In Sook; Kim, Young Hae; Kim, Ho Jung

2007-03-01

The purpose of this study was to develop and evaluate an error reporting promoting program(ERPP) to systematically reduce the incidence rate of nursing errors in operating room. A non-equivalent control group non-synchronized design was used. Twenty-six operating room nurses who were in one university hospital in Busan participated in this study. They were stratified into four groups according to their operating room experience and were allocated to the experimental and control groups using a matching method. Mann-Whitney U Test was used to analyze the differences pre and post incidence rates of nursing errors between the two groups. The incidence rate of nursing errors decreased significantly in the experimental group compared to the pre-test score from 28.4% to 15.7%. The incidence rate by domains, it decreased significantly in the 3 domains-"compliance of aseptic technique", "management of document", "environmental management" in the experimental group while it decreased in the control group which was applied ordinary error-reporting method. Error-reporting system can make possible to hold the errors in common and to learn from them. ERPP was effective to reduce the errors of recognition-related nursing activities. For the wake of more effective error-prevention, we will be better to apply effort of risk management along the whole health care system with this program.

Toward noncooperative iris recognition: a classification approach using multiple signatures.

PubMed

Proença, Hugo; Alexandre, Luís A

2007-04-01

This paper focuses on noncooperative iris recognition, i.e., the capture of iris images at large distances, under less controlled lighting conditions, and without active participation of the subjects. This increases the probability of capturing very heterogeneous images (regarding focus, contrast, or brightness) and with several noise factors (iris obstructions and reflections). Current iris recognition systems are unable to deal with noisy data and substantially increase their error rates, especially the false rejections, in these conditions. We propose an iris classification method that divides the segmented and normalized iris image into six regions, makes an independent feature extraction and comparison for each region, and combines each of the dissimilarity values through a classification rule. Experiments show a substantial decrease, higher than 40 percent, of the false rejection rates in the recognition of noisy iris images.

Research on Signature Verification Method Based on Discrete Fréchet Distance

NASA Astrophysics Data System (ADS)

Fang, J. L.; Wu, W.

2018-05-01

This paper proposes a multi-feature signature template based on discrete Fréchet distance, which breaks through the limitation of traditional signature authentication using a single signature feature. It solves the online handwritten signature authentication signature global feature template extraction calculation workload, signature feature selection unreasonable problem. In this experiment, the false recognition rate (FAR) and false rejection rate (FRR) of the statistical signature are calculated and the average equal error rate (AEER) is calculated. The feasibility of the combined template scheme is verified by comparing the average equal error rate of the combination template and the original template.

Online adaptive neural control of a robotic lower limb prosthesis

NASA Astrophysics Data System (ADS)

Spanias, J. A.; Simon, A. M.; Finucane, S. B.; Perreault, E. J.; Hargrove, L. J.

2018-02-01

Objective. The purpose of this study was to develop and evaluate an adaptive intent recognition algorithm that continuously learns to incorporate a lower limb amputee’s neural information (acquired via electromyography (EMG)) as they ambulate with a robotic leg prosthesis. Approach. We present a powered lower limb prosthesis that was configured to acquire the user’s neural information and kinetic/kinematic information from embedded mechanical sensors, and identify and respond to the user’s intent. We conducted an experiment with eight transfemoral amputees over multiple days. EMG and mechanical sensor data were collected while subjects using a powered knee/ankle prosthesis completed various ambulation activities such as walking on level ground, stairs, and ramps. Our adaptive intent recognition algorithm automatically transitioned the prosthesis into the different locomotion modes and continuously updated the user’s model of neural data during ambulation. Main results. Our proposed algorithm accurately and consistently identified the user’s intent over multiple days, despite changing neural signals. The algorithm incorporated 96.31% [0.91%] (mean, [standard error]) of neural information across multiple experimental sessions, and outperformed non-adaptive versions of our algorithm—with a 6.66% [3.16%] relative decrease in error rate. Significance. This study demonstrates that our adaptive intent recognition algorithm enables incorporation of neural information over long periods of use, allowing assistive robotic devices to accurately respond to the user’s intent with low error rates.

Radiological reporting that combine continuous speech recognition with error correction by transcriptionists.

PubMed

Ichikawa, Tamaki; Kitanosono, Takashi; Koizumi, Jun; Ogushi, Yoichi; Tanaka, Osamu; Endo, Jun; Hashimoto, Takeshi; Kawada, Shuichi; Saito, Midori; Kobayashi, Makiko; Imai, Yutaka

2007-12-20

We evaluated the usefulness of radiological reporting that combines continuous speech recognition (CSR) and error correction by transcriptionists. Four transcriptionists (two with more than 10 years' and two with less than 3 months' transcription experience) listened to the same 100 dictation files and created radiological reports using conventional transcription and a method that combined CSR with manual error correction by the transcriptionists. We compared the 2 groups using the 2 methods for accuracy and report creation time and evaluated the transcriptionists' inter-personal dependence on accuracy rate and report creation time. We used a CSR system that did not require the training of the system to recognize the user's voice. We observed no significant difference in accuracy between the 2 groups and 2 methods that we tested, though transcriptionists with greater experience transcribed faster than those with less experience using conventional transcription. Using the combined method, error correction speed was not significantly different between two groups of transcriptionists with different levels of experience. Combining CSR and manual error correction by transcriptionists enabled convenient and accurate radiological reporting.

Effect of speech-intrinsic variations on human and automatic recognition of spoken phonemes.

PubMed

Meyer, Bernd T; Brand, Thomas; Kollmeier, Birger

2011-01-01

The aim of this study is to quantify the gap between the recognition performance of human listeners and an automatic speech recognition (ASR) system with special focus on intrinsic variations of speech, such as speaking rate and effort, altered pitch, and the presence of dialect and accent. Second, it is investigated if the most common ASR features contain all information required to recognize speech in noisy environments by using resynthesized ASR features in listening experiments. For the phoneme recognition task, the ASR system achieved the human performance level only when the signal-to-noise ratio (SNR) was increased by 15 dB, which is an estimate for the human-machine gap in terms of the SNR. The major part of this gap is attributed to the feature extraction stage, since human listeners achieve comparable recognition scores when the SNR difference between unaltered and resynthesized utterances is 10 dB. Intrinsic variabilities result in strong increases of error rates, both in human speech recognition (HSR) and ASR (with a relative increase of up to 120%). An analysis of phoneme duration and recognition rates indicates that human listeners are better able to identify temporal cues than the machine at low SNRs, which suggests incorporating information about the temporal dynamics of speech into ASR systems.

Speaker Recognition Using Real vs. Synthetic Parallel Data for DNN Channel Compensation

DTIC Science & Technology

2016-09-08

Speaker Recognition Using Real vs Synthetic Parallel Data for DNN Channel Compensation Fred Richardson, Michael Brandstein, Jennifer Melot, and...DNNs trained with real Mixer 2 multichannel data perform only slightly better than DNNs trained with synthetic multichannel data for microphone SR on...Mixer 6. Large re- ductions in pooled error rates of 50% EER and 30% min DCF are achieved using DNNs trained on real Mixer 2 data. Nearly the same

Supporting Dictation Speech Recognition Error Correction: The Impact of External Information

ERIC Educational Resources Information Center

Shi, Yongmei; Zhou, Lina

2011-01-01

Although speech recognition technology has made remarkable progress, its wide adoption is still restricted by notable effort made and frustration experienced by users while correcting speech recognition errors. One of the promising ways to improve error correction is by providing user support. Although support mechanisms have been proposed for…

Does viotin activate violin more than viocin? On the use of visual cues during visual-word recognition.

PubMed

Perea, Manuel; Panadero, Victoria

2014-01-01

The vast majority of neural and computational models of visual-word recognition assume that lexical access is achieved via the activation of abstract letter identities. Thus, a word's overall shape should play no role in this process. In the present lexical decision experiment, we compared word-like pseudowords like viotín (same shape as its base word: violín) vs. viocín (different shape) in mature (college-aged skilled readers), immature (normally reading children), and immature/impaired (young readers with developmental dyslexia) word-recognition systems. Results revealed similar response times (and error rates) to consistent-shape and inconsistent-shape pseudowords for both adult skilled readers and normally reading children - this is consistent with current models of visual-word recognition. In contrast, young readers with developmental dyslexia made significantly more errors to viotín-like pseudowords than to viocín-like pseudowords. Thus, unlike normally reading children, young readers with developmental dyslexia are sensitive to a word's visual cues, presumably because of poor letter representations.

Implementation of a high-speed face recognition system that uses an optical parallel correlator.

PubMed

Watanabe, Eriko; Kodate, Kashiko

2005-02-10

We implement a fully automatic fast face recognition system by using a 1000 frame/s optical parallel correlator designed and assembled by us. The operational speed for the 1:N (i.e., matching one image against N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 s, including the preprocessing and postprocessing times. The binary real-only matched filter is devised for the sake of face recognition, and the system is optimized by the false-rejection rate (FRR) and the false-acceptance rate (FAR), according to 300 samples selected by the biometrics guideline. From trial 1:N identification experiments with the optical parallel correlator, we acquired low error rates of 2.6% FRR and 1.3% FAR. Facial images of people wearing thin glasses or heavy makeup that rendered identification difficult were identified with this system.

A statistical investigation into the stability of iris recognition in diverse population sets

NASA Astrophysics Data System (ADS)

Howard, John J.; Etter, Delores M.

2014-05-01

Iris recognition is increasingly being deployed on population wide scales for important applications such as border security, social service administration, criminal identification and general population management. The error rates for this incredibly accurate form of biometric identification are established using well known, laboratory quality datasets. However, it is has long been acknowledged in biometric theory that not all individuals have the same likelihood of being correctly serviced by a biometric system. Typically, techniques for identifying clients that are likely to experience a false non-match or a false match error are carried out on a per-subject basis. This research makes the novel hypothesis that certain ethnical denominations are more or less likely to experience a biometric error. Through established statistical techniques, we demonstrate this hypothesis to be true and document the notable effect that the ethnicity of the client has on iris similarity scores. Understanding the expected impact of ethnical diversity on iris recognition accuracy is crucial to the future success of this technology as it is deployed in areas where the target population consists of clientele from a range of geographic backgrounds, such as border crossings and immigration check points.

Testing the Recognition and Perception of Errors in Context

ERIC Educational Resources Information Center

Brandenburg, Laura C.

2015-01-01

This study tests the recognition of errors in context and whether the presence of errors affects the reader's perception of the writer's ethos. In an experimental, posttest only design, participants were randomly assigned a memo to read in an online survey: one version with errors and one version without. Of the six intentional errors in version…

Effects of expected-value information and display format on recognition of aircraft subsystem abnormalities

NASA Technical Reports Server (NTRS)

Palmer, Michael T.; Abbott, Kathy H.

1994-01-01

This study identifies improved methods to present system parameter information for detecting abnormal conditions and to identify system status. Two workstation experiments were conducted. The first experiment determined if including expected-value-range information in traditional parameter display formats affected subject performance. The second experiment determined if using a nontraditional parameter display format, which presented relative deviation from expected value, was better than traditional formats with expected-value ranges included. The inclusion of expected-value-range information onto traditional parameter formats was found to have essentially no effect. However, subjective results indicated support for including this information. The nontraditional column deviation parameter display format resulted in significantly fewer errors compared with traditional formats with expected-value-ranges included. In addition, error rates for the column deviation parameter display format remained stable as the scenario complexity increased, whereas error rates for the traditional parameter display formats with expected-value ranges increased. Subjective results also indicated that the subjects preferred this new format and thought that their performance was better with it. The column deviation parameter display format is recommended for display applications that require rapid recognition of out-of-tolerance conditions, especially for a large number of parameters.

Pedestrian dead reckoning employing simultaneous activity recognition cues

NASA Astrophysics Data System (ADS)

Altun, Kerem; Barshan, Billur

2012-02-01

We consider the human localization problem using body-worn inertial/magnetic sensor units. Inertial sensors are characterized by a drift error caused by the integration of their rate output to obtain position information. Because of this drift, the position and orientation data obtained from inertial sensors are reliable over only short periods of time. Therefore, position updates from externally referenced sensors are essential. However, if the map of the environment is known, the activity context of the user can provide information about his position. In particular, the switches in the activity context correspond to discrete locations on the map. By performing localization simultaneously with activity recognition, we detect the activity context switches and use the corresponding position information as position updates in a localization filter. The localization filter also involves a smoother that combines the two estimates obtained by running the zero-velocity update algorithm both forward and backward in time. We performed experiments with eight subjects in indoor and outdoor environments involving walking, turning and standing activities. Using a spatial error criterion, we show that the position errors can be decreased by about 85% on the average. We also present the results of two 3D experiments performed in realistic indoor environments and demonstrate that it is possible to achieve over 90% error reduction in position by performing localization simultaneously with activity recognition.

A star recognition method based on the Adaptive Ant Colony algorithm for star sensors.

PubMed

Quan, Wei; Fang, Jiancheng

2010-01-01

A new star recognition method based on the Adaptive Ant Colony (AAC) algorithm has been developed to increase the star recognition speed and success rate for star sensors. This method draws circles, with the center of each one being a bright star point and the radius being a special angular distance, and uses the parallel processing ability of the AAC algorithm to calculate the angular distance of any pair of star points in the circle. The angular distance of two star points in the circle is solved as the path of the AAC algorithm, and the path optimization feature of the AAC is employed to search for the optimal (shortest) path in the circle. This optimal path is used to recognize the stellar map and enhance the recognition success rate and speed. The experimental results show that when the position error is about 50″, the identification success rate of this method is 98% while the Delaunay identification method is only 94%. The identification time of this method is up to 50 ms.

Human Action Recognition Using Wireless Wearable In-Ear Microphone

NASA Astrophysics Data System (ADS)

Nishimura, Jun; Kuroda, Tadahiro

To realize the ubiquitous eating habits monitoring, we proposed the use of sounds sensed by an in-ear placed wireless wearable microphone. A prototype of wireless wearable in-ear microphone was developed by utilizing a common Bluetooth headset. We proposed a robust chewing action recognition algorithm which consists of two recognition stages: “chew-like” signal detection and chewing sound verification stages. We also provide empirical results on other action recognition using in-ear sound including swallowing, cough, belch, and etc. The average chewing number counting error rate of 1.93% is achieved. Lastly, chewing sound mapping is proposed as a new prototypical approach to provide an additional intuitive feedback on food groups to be able to infer the eating habits in their daily life context.

The "subjective" pupil old/new effect: is the truth plain to see?

PubMed

Montefinese, Maria; Ambrosini, Ettore; Fairfield, Beth; Mammarella, Nicola

2013-07-01

Human memory is an imperfect process, prone to distortion and errors that range from minor disturbances to major errors that can have serious consequences on everyday life. In this study, we investigated false remembering of manipulatory verbs using an explicit recognition task and pupillometry. Our results replicated the "classical" pupil old/new effect as well as data in false remembering literature that show how items must be recognize as old in order for the pupil size to increase (e.g., "subjective" pupil old/new effect), even though these items do not necessarily have to be truly old. These findings support the strength-of-memory trace account that affirms that pupil dilation is related to experience rather than to the accuracy of recognition. Moreover, behavioral results showed higher rates of true and false recognitions for manipulatory verbs and a consequent larger pupil diameter, supporting the embodied view of language. Copyright © 2013 Elsevier B.V. All rights reserved.

Warmth of familiarity and chill of error: affective consequences of recognition decisions.

PubMed

Chetverikov, Andrey

2014-04-01

The present research aimed to assess the effect of recognition decision on subsequent affective evaluations of recognised and non-recognised objects. Consistent with the proposed account of post-decisional preferences, results showed that the effect of recognition on preferences depends upon objective familiarity. If stimuli are recognised, liking ratings are positively associated with exposure frequency; if stimuli are not recognised, this link is either absent (Experiment 1) or negative (Experiments 2 and 3). This interaction between familiarity and recognition exists even when recognition accuracy is at chance level and the "mere exposure" effect is absent. Finally, data obtained from repeated measurements of preferences and using manipulations of task order confirm that recognition decisions have a causal influence on preferences. The findings suggest that affective evaluation can provide fine-grained access to the efficacy of cognitive processing even in simple cognitive tasks.

Construction of language models for an handwritten mail reading system

NASA Astrophysics Data System (ADS)

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

2012-01-01

This paper presents a system for the recognition of unconstrained handwritten mails. The main part of this system is an HMM recognizer which uses trigraphs to model contextual information. This recognition system does not require any segmentation into words or characters and directly works at line level. To take into account linguistic information and enhance performance, a language model is introduced. This language model is based on bigrams and built from training document transcriptions only. Different experiments with various vocabulary sizes and language models have been conducted. Word Error Rate and Perplexity values are compared to show the interest of specific language models, fit to handwritten mail recognition task.

Context affects nestmate recognition errors in honey bees and stingless bees.

PubMed

Couvillon, Margaret J; Segers, Francisca H I D; Cooper-Bowman, Roseanne; Truslove, Gemma; Nascimento, Daniela L; Nascimento, Fabio S; Ratnieks, Francis L W

2013-08-15

Nestmate recognition studies, where a discriminator first recognises and then behaviourally discriminates (accepts/rejects) another individual, have used a variety of methodologies and contexts. This is potentially problematic because recognition errors in discrimination behaviour are predicted to be context-dependent. Here we compare the recognition decisions (accept/reject) of discriminators in two eusocial bees, Apis mellifera and Tetragonisca angustula, under different contexts. These contexts include natural guards at the hive entrance (control); natural guards held in plastic test arenas away from the hive entrance that vary either in the presence or absence of colony odour or the presence or absence of an additional nestmate discriminator; and, for the honey bee, the inside of the nest. For both honey bee and stingless bee guards, total recognition errors of behavioural discrimination made by guards (% nestmates rejected + % non-nestmates accepted) are much lower at the colony entrance (honey bee: 30.9%; stingless bee: 33.3%) than in the test arenas (honey bee: 60-86%; stingless bee: 61-81%; P<0.001 for both). Within the test arenas, the presence of colony odour specifically reduced the total recognition errors in honey bees, although this reduction still fell short of bringing error levels down to what was found at the colony entrance. Lastly, in honey bees, the data show that the in-nest collective behavioural discrimination by ca. 30 workers that contact an intruder is insufficient to achieve error-free recognition and is not as effective as the discrimination by guards at the entrance. Overall, these data demonstrate that context is a significant factor in a discriminators' ability to make appropriate recognition decisions, and should be considered when designing recognition study methodologies.

Continuous Speech Recognition for Clinicians

PubMed Central

Zafar, Atif; Overhage, J. Marc; McDonald, Clement J.

1999-01-01

The current generation of continuous speech recognition systems claims to offer high accuracy (greater than 95 percent) speech recognition at natural speech rates (150 words per minute) on low-cost (under $2000) platforms. This paper presents a state-of-the-technology summary, along with insights the authors have gained through testing one such product extensively and other products superficially. The authors have identified a number of issues that are important in managing accuracy and usability. First, for efficient recognition users must start with a dictionary containing the phonetic spellings of all words they anticipate using. The authors dictated 50 discharge summaries using one inexpensive internal medicine dictionary ($30) and found that they needed to add an additional 400 terms to get recognition rates of 98 percent. However, if they used either of two more expensive and extensive commercial medical vocabularies ($349 and $695), they did not need to add terms to get a 98 percent recognition rate. Second, users must speak clearly and continuously, distinctly pronouncing all syllables. Users must also correct errors as they occur, because accuracy improves with error correction by at least 5 percent over two weeks. Users may find it difficult to train the system to recognize certain terms, regardless of the amount of training, and appropriate substitutions must be created. For example, the authors had to substitute “twice a day” for “bid” when using the less expensive dictionary, but not when using the other two dictionaries. From trials they conducted in settings ranging from an emergency room to hospital wards and clinicians' offices, they learned that ambient noise has minimal effect. Finally, they found that a minimal “usable” hardware configuration (which keeps up with dictation) comprises a 300-MHz Pentium processor with 128 MB of RAM and a “speech quality” sound card (e.g., SoundBlaster, $99). Anything less powerful will result in the system lagging behind the speaking rate. The authors obtained 97 percent accuracy with just 30 minutes of training when using the latest edition of one of the speech recognition systems supplemented by a commercial medical dictionary. This technology has advanced considerably in recent years and is now a serious contender to replace some or all of the increasingly expensive alternative methods of dictation with human transcription. PMID:10332653

Enhanced iris recognition method based on multi-unit iris images

NASA Astrophysics Data System (ADS)

Shin, Kwang Yong; Kim, Yeong Gon; Park, Kang Ryoung

2013-04-01

For the purpose of biometric person identification, iris recognition uses the unique characteristics of the patterns of the iris; that is, the eye region between the pupil and the sclera. When obtaining an iris image, the iris's image is frequently rotated because of the user's head roll toward the left or right shoulder. As the rotation of the iris image leads to circular shifting of the iris features, the accuracy of iris recognition is degraded. To solve this problem, conventional iris recognition methods use shifting of the iris feature codes to perform the matching. However, this increases the computational complexity and level of false acceptance error. To solve these problems, we propose a novel iris recognition method based on multi-unit iris images. Our method is novel in the following five ways compared with previous methods. First, to detect both eyes, we use Adaboost and a rapid eye detector (RED) based on the iris shape feature and integral imaging. Both eyes are detected using RED in the approximate candidate region that consists of the binocular region, which is determined by the Adaboost detector. Second, we classify the detected eyes into the left and right eyes, because the iris patterns in the left and right eyes in the same person are different, and they are therefore considered as different classes. We can improve the accuracy of iris recognition using this pre-classification of the left and right eyes. Third, by measuring the angle of head roll using the two center positions of the left and right pupils, detected by two circular edge detectors, we obtain the information of the iris rotation angle. Fourth, in order to reduce the error and processing time of iris recognition, adaptive bit-shifting based on the measured iris rotation angle is used in feature matching. Fifth, the recognition accuracy is enhanced by the score fusion of the left and right irises. Experimental results on the iris open database of low-resolution images showed that the averaged equal error rate of iris recognition using the proposed method was 4.3006%, which is lower than that of other methods.

High-accuracy and robust face recognition system based on optical parallel correlator using a temporal image sequence

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ishikawa, Mami; Ohta, Maiko; Kodate, Kashiko

2005-09-01

Face recognition is used in a wide range of security systems, such as monitoring credit card use, searching for individuals with street cameras via Internet and maintaining immigration control. There are still many technical subjects under study. For instance, the number of images that can be stored is limited under the current system, and the rate of recognition must be improved to account for photo shots taken at different angles under various conditions. We implemented a fully automatic Fast Face Recognition Optical Correlator (FARCO) system by using a 1000 frame/s optical parallel correlator designed and assembled by us. Operational speed for the 1: N (i.e. matching a pair of images among N, where N refers to the number of images in the database) identification experiment (4000 face images) amounts to less than 1.5 seconds, including the pre/post processing. From trial 1: N identification experiments using FARCO, we acquired low error rates of 2.6% False Reject Rate and 1.3% False Accept Rate. By making the most of the high-speed data-processing capability of this system, much more robustness can be achieved for various recognition conditions when large-category data are registered for a single person. We propose a face recognition algorithm for the FARCO while employing a temporal image sequence of moving images. Applying this algorithm to a natural posture, a two times higher recognition rate scored compared with our conventional system. The system has high potential for future use in a variety of purposes such as search for criminal suspects by use of street and airport video cameras, registration of babies at hospitals or handling of an immeasurable number of images in a database.

Major depressive disorder skews the recognition of emotional prosody.

PubMed

Péron, Julie; El Tamer, Sarah; Grandjean, Didier; Leray, Emmanuelle; Travers, David; Drapier, Dominique; Vérin, Marc; Millet, Bruno

2011-06-01

Major depressive disorder (MDD) is associated with abnormalities in the recognition of emotional stimuli. MDD patients ascribe more negative emotion but also less positive emotion to facial expressions, suggesting blunted responsiveness to positive emotional stimuli. To ascertain whether these emotional biases are modality-specific, we examined the effects of MDD on the recognition of emotions from voices using a paradigm designed to capture subtle effects of biases. Twenty-one MDD patients and 21 healthy controls (HC) underwent clinical and neuropsychological assessments, followed by a paradigm featuring pseudowords spoken by actors in five types of emotional prosody, rated on continuous scales. Overall, MDD patients performed more poorly than HC, displaying significantly impaired recognition of fear, happiness and sadness. Compared with HC, they rated fear significantly more highly when listening to anger stimuli. They also displayed a bias toward surprise, rating it far higher when they heard sad or fearful utterances. Furthermore, for happiness stimuli, MDD patients gave higher ratings for negative emotions (fear and sadness). A multiple regression model on recognition of emotional prosody in MDD patients showed that the best fit was achieved using the executive functioning (categorical fluency, number of errors in the MCST, and TMT B-A) and the total score of the Montgomery-Asberg Depression Rating Scale. Impaired recognition of emotions would appear not to be specific to the visual modality but to be present also when emotions are expressed vocally, this impairment being related to depression severity and dysexecutive syndrome. MDD seems to skew the recognition of emotional prosody toward negative emotional stimuli and the blunting of positive emotion appears not to be restricted to the visual modality. Copyright © 2011 Elsevier Inc. All rights reserved.

Face Recognition for Access Control Systems Combining Image-Difference Features Based on a Probabilistic Model

NASA Astrophysics Data System (ADS)

Miwa, Shotaro; Kage, Hiroshi; Hirai, Takashi; Sumi, Kazuhiko

We propose a probabilistic face recognition algorithm for Access Control System(ACS)s. Comparing with existing ACSs using low cost IC-cards, face recognition has advantages in usability and security that it doesn't require people to hold cards over scanners and doesn't accept imposters with authorized cards. Therefore face recognition attracts more interests in security markets than IC-cards. But in security markets where low cost ACSs exist, price competition is important, and there is a limitation on the quality of available cameras and image control. Therefore ACSs using face recognition are required to handle much lower quality images, such as defocused and poor gain-controlled images than high security systems, such as immigration control. To tackle with such image quality problems we developed a face recognition algorithm based on a probabilistic model which combines a variety of image-difference features trained by Real AdaBoost with their prior probability distributions. It enables to evaluate and utilize only reliable features among trained ones during each authentication, and achieve high recognition performance rates. The field evaluation using a pseudo Access Control System installed in our office shows that the proposed system achieves a constant high recognition performance rate independent on face image qualities, that is about four times lower EER (Equal Error Rate) under a variety of image conditions than one without any prior probability distributions. On the other hand using image difference features without any prior probabilities are sensitive to image qualities. We also evaluated PCA, and it has worse, but constant performance rates because of its general optimization on overall data. Comparing with PCA, Real AdaBoost without any prior distribution performs twice better under good image conditions, but degrades to a performance as good as PCA under poor image conditions.

Permutation coding technique for image recognition systems.

PubMed

Kussul, Ernst M; Baidyk, Tatiana N; Wunsch, Donald C; Makeyev, Oleksandr; Martín, Anabel

2006-11-01

A feature extractor and neural classifier for image recognition systems are proposed. The proposed feature extractor is based on the concept of random local descriptors (RLDs). It is followed by the encoder that is based on the permutation coding technique that allows to take into account not only detected features but also the position of each feature on the image and to make the recognition process invariant to small displacements. The combination of RLDs and permutation coding permits us to obtain a sufficiently general description of the image to be recognized. The code generated by the encoder is used as an input data for the neural classifier. Different types of images were used to test the proposed image recognition system. It was tested in the handwritten digit recognition problem, the face recognition problem, and the microobject shape recognition problem. The results of testing are very promising. The error rate for the Modified National Institute of Standards and Technology (MNIST) database is 0.44% and for the Olivetti Research Laboratory (ORL) database it is 0.1%.

Investigation of misfiled cases in the PACS environment and a solution to prevent filing errors for chest radiographs.

PubMed

Morishita, Junji; Watanabe, Hideyuki; Katsuragawa, Shigehiko; Oda, Nobuhiro; Sukenobu, Yoshiharu; Okazaki, Hiroko; Nakata, Hajime; Doi, Kunio

2005-01-01

The aim of the study was to survey misfiled cases in a picture archiving and communication system environment at two hospitals and to demonstrate the potential usefulness of an automated patient recognition method for posteroanterior chest radiographs based on a template-matching technique designed to prevent filing errors. We surveyed misfiled cases obtained from different modalities in one hospital for 25 months, and misfiled cases of chest radiographs in another hospital for 17 months. For investigating the usefulness of an automated patient recognition and identification method for chest radiographs, a prospective study has been completed in clinical settings at the latter hospital. The total numbers of misfiled cases for different modalities in one hospital and for chest radiographs in another hospital were 327 and 22, respectively. The misfiled cases in the two hospitals were mainly the result of human errors (eg, incorrect manual entries of patient information, incorrect usage of identification cards in which an identification card for the previous patient was used for the next patient's image acquisition). The prospective study indicated the usefulness of the computerized method for discovering misfiled cases with a high performance (ie, an 86.4% correct warning rate for different patients and 1.5% incorrect warning rate for the same patients). We confirmed the occurrence of misfiled cases in the two hospitals. The automated patient recognition and identification method for chest radiographs would be useful in preventing wrong images from being stored in the picture archiving and communication system environment.

Design of a digital voice data compression technique for orbiter voice channels

NASA Technical Reports Server (NTRS)

1975-01-01

Candidate techniques were investigated for digital voice compression to a transmission rate of 8 kbps. Good voice quality, speaker recognition, and robustness in the presence of error bursts were considered. The technique of delayed-decision adaptive predictive coding is described and compared with conventional adaptive predictive coding. Results include a set of experimental simulations recorded on analog tape. The two FM broadcast segments produced show the delayed-decision technique to be virtually undegraded or minimally degraded at .001 and .01 Viterbi decoder bit error rates. Preliminary estimates of the hardware complexity of this technique indicate potential for implementation in space shuttle orbiters.

Factors that influence the recognition, reporting and resolution of incidents related to medical devices and other healthcare technologies: a systematic review.

PubMed

Polisena, Julie; Gagliardi, Anna; Urbach, David; Clifford, Tammy; Fiander, Michelle

2015-03-29

Medical devices have improved the treatment of many medical conditions. Despite their benefit, the use of devices can lead to unintended incidents, potentially resulting in unnecessary harm, injury or complications to the patient, a complaint, loss or damage. Devices are used in hospitals on a routine basis. Research to date, however, has been primarily limited to describing incidents rates, so the optimal design of a hospital-based surveillance system remains unclear. Our research objectives were twofold: i) to explore factors that influence device-related incident recognition, reporting and resolution and ii) to investigate interventions or strategies to improve the recognition, reporting and resolution of medical device-related incidents. We searched the bibliographic databases: MEDLINE, Embase, the Cochrane Central Register of Controlled Trials and PsycINFO database. Grey literature (literature that is not commercially available) was searched for studies on factors that influence incident recognition, reporting and resolution published and interventions or strategies for their improvement from 2003 to 2014. Although we focused on medical devices, other health technologies were eligible for inclusion. Thirty studies were included in our systematic review, but most studies were concentrated on other health technologies. The study findings indicate that fear of punishment, uncertainty of what should be reported and how incident reports will be used and time constraints to incident reporting are common barriers to incident recognition and reporting. Relevant studies on the resolution of medical errors were not found. Strategies to improve error reporting include the use of an electronic error reporting system, increased training and feedback to frontline clinicians about the reported error. The available evidence on factors influencing medical device-related incident recognition, reporting and resolution by healthcare professionals can inform data collection and analysis in future studies. Since evidence gaps on medical device-related incidents exist, telephone interviews with frontline clinicians will be conducted to solicit information about their experiences with medical devices and suggested strategies for device surveillance improvement in a hospital context. Further research also should investigate the impact of human, system, organizational and education factors on the development and implementation of local medical device surveillance systems.

Position estimation and driving of an autonomous vehicle by monocular vision

NASA Astrophysics Data System (ADS)

Hanan, Jay C.; Kayathi, Pavan; Hughlett, Casey L.

2007-04-01

Automatic adaptive tracking in real-time for target recognition provided autonomous control of a scale model electric truck. The two-wheel drive truck was modified as an autonomous rover test-bed for vision based guidance and navigation. Methods were implemented to monitor tracking error and ensure a safe, accurate arrival at the intended science target. Some methods are situation independent relying only on the confidence error of the target recognition algorithm. Other methods take advantage of the scenario of combined motion and tracking to filter out anomalies. In either case, only a single calibrated camera was needed for position estimation. Results from real-time autonomous driving tests on the JPL simulated Mars yard are presented. Recognition error was often situation dependent. For the rover case, the background was in motion and may be characterized to provide visual cues on rover travel such as rate, pitch, roll, and distance to objects of interest or hazards. Objects in the scene may be used as landmarks, or waypoints, for such estimations. As objects are approached, their scale increases and their orientation may change. In addition, particularly on rough terrain, these orientation and scale changes may be unpredictable. Feature extraction combined with the neural network algorithm was successful in providing visual odometry in the simulated Mars environment.

Digital Paper Technologies for Topographical Applications

DTIC Science & Technology

2011-09-19

measures examine were training time for each method, time for entry offeatures, procedural errors, handwriting recognition errors, and user preference...time for entry of features, procedural errors, handwriting recognition errors, and user preference. For these metrics, temporal association was...checkbox, text restricted to a specific list of values, etc.) that provides constraints to the handwriting recognizer. When the user fills out the form

Electrooculography-based continuous eye-writing recognition system for efficient assistive communication systems

PubMed Central

Shinozaki, Takahiro

2018-01-01

Human-computer interface systems whose input is based on eye movements can serve as a means of communication for patients with locked-in syndrome. Eye-writing is one such system; users can input characters by moving their eyes to follow the lines of the strokes corresponding to characters. Although this input method makes it easy for patients to get started because of their familiarity with handwriting, existing eye-writing systems suffer from slow input rates because they require a pause between input characters to simplify the automatic recognition process. In this paper, we propose a continuous eye-writing recognition system that achieves a rapid input rate because it accepts characters eye-written continuously, with no pauses. For recognition purposes, the proposed system first detects eye movements using electrooculography (EOG), and then a hidden Markov model (HMM) is applied to model the EOG signals and recognize the eye-written characters. Additionally, this paper investigates an EOG adaptation that uses a deep neural network (DNN)-based HMM. Experiments with six participants showed an average input speed of 27.9 character/min using Japanese Katakana as the input target characters. A Katakana character-recognition error rate of only 5.0% was achieved using 13.8 minutes of adaptation data. PMID:29425248

Finger vein recognition based on finger crease location

NASA Astrophysics Data System (ADS)

Lu, Zhiying; Ding, Shumeng; Yin, Jing

2016-07-01

Finger vein recognition technology has significant advantages over other methods in terms of accuracy, uniqueness, and stability, and it has wide promising applications in the field of biometric recognition. We propose using finger creases to locate and extract an object region. Then we use linear fitting to overcome the problem of finger rotation in the plane. The method of modular adaptive histogram equalization (MAHE) is presented to enhance image contrast and reduce computational cost. To extract the finger vein features, we use a fusion method, which can obtain clear and distinguishable vein patterns under different conditions. We used the Hausdorff average distance algorithm to examine the recognition performance of the system. The experimental results demonstrate that MAHE can better balance the recognition accuracy and the expenditure of time compared with three other methods. Our resulting equal error rate throughout the total procedure was 3.268% in a database of 153 finger vein images.

A Highly Accurate Face Recognition System Using Filtering Correlation

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Ishikawa, Sayuri; Kodate, Kashiko

2007-09-01

The authors previously constructed a highly accurate fast face recognition optical correlator (FARCO) [E. Watanabe and K. Kodate: Opt. Rev. 12 (2005) 460], and subsequently developed an improved, super high-speed FARCO (S-FARCO), which is able to process several hundred thousand frames per second. The principal advantage of our new system is its wide applicability to any correlation scheme. Three different configurations were proposed, each depending on correlation speed. This paper describes and evaluates a software correlation filter. The face recognition function proved highly accurate, seeing that a low-resolution facial image size (64 × 64 pixels) has been successfully implemented. An operation speed of less than 10 ms was achieved using a personal computer with a central processing unit (CPU) of 3 GHz and 2 GB memory. When we applied the software correlation filter to a high-security cellular phone face recognition system, experiments on 30 female students over a period of three months yielded low error rates: 0% false acceptance rate and 2% false rejection rate. Therefore, the filtering correlation works effectively when applied to low resolution images such as web-based images or faces captured by a monitoring camera.

Speech recognition technology: an outlook for human-to-machine interaction.

PubMed

Erdel, T; Crooks, S

2000-01-01

Speech recognition, as an enabling technology in healthcare-systems computing, is a topic that has been discussed for quite some time, but is just now coming to fruition. Traditionally, speech-recognition software has been constrained by hardware, but improved processors and increased memory capacities are starting to remove some of these limitations. With these barriers removed, companies that create software for the healthcare setting have the opportunity to write more successful applications. Among the criticisms of speech-recognition applications are the high rates of error and steep training curves. However, even in the face of such negative perceptions, there remains significant opportunities for speech recognition to allow healthcare providers and, more specifically, physicians, to work more efficiently and ultimately spend more time with their patients and less time completing necessary documentation. This article will identify opportunities for inclusion of speech-recognition technology in the healthcare setting and examine major categories of speech-recognition software--continuous speech recognition, command and control, and text-to-speech. We will discuss the advantages and disadvantages of each area, the limitations of the software today, and how future trends might affect them.

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.

The establishment and external validation of NIR qualitative analysis model for waste polyester-cotton blend fabrics.

PubMed

Li, Feng; Li, Wen-Xia; Zhao, Guo-Liang; Tang, Shi-Jun; Li, Xue-Jiao; Wu, Hong-Mei

2014-10-01

A series of 354 polyester-cotton blend fabrics were studied by the near-infrared spectra (NIRS) technology, and a NIR qualitative analysis model for different spectral characteristics was established by partial least squares (PLS) method combined with qualitative identification coefficient. There were two types of spectrum for dying polyester-cotton blend fabrics: normal spectrum and slash spectrum. The slash spectrum loses its spectral characteristics, which are effected by the samples' dyes, pigments, matting agents and other chemical additives. It was in low recognition rate when the model was established by the total sample set, so the samples were divided into two types of sets: normal spectrum sample set and slash spectrum sample set, and two NIR qualitative analysis models were established respectively. After the of models were established the model's spectral region, pretreatment methods and factors were optimized based on the validation results, and the robustness and reliability of the model can be improved lately. The results showed that the model recognition rate was improved greatly when they were established respectively, the recognition rate reached up to 99% when the two models were verified by the internal validation. RC (relation coefficient of calibration) values of the normal spectrum model and slash spectrum model were 0.991 and 0.991 respectively, RP (relation coefficient of prediction) values of them were 0.983 and 0.984 respectively, SEC (standard error of calibration) values of them were 0.887 and 0.453 respectively, SEP (standard error of prediction) values of them were 1.131 and 0.573 respectively. A series of 150 bounds samples reached used to verify the normal spectrum model and slash spectrum model and the recognition rate reached up to 91.33% and 88.00% respectively. It showed that the NIR qualitative analysis model can be used for identification in the recycle site for the polyester-cotton blend fabrics.

Workshops Increase Students' Proficiency at Identifying General and APA-Style Writing Errors

ERIC Educational Resources Information Center

Jorgensen, Terrence D.; Marek, Pam

2013-01-01

To determine the effectiveness of 20- to 30-min workshops on recognition of errors in American Psychological Association-style writing, 58 introductory psychology students attended one of the three workshops (on grammar, mechanics, or references) and completed error recognition tests (pretest, initial posttest, and three follow-up tests). As a…

False recognition of facial expressions of emotion: causes and implications.

PubMed

Fernández-Dols, José-Miguel; Carrera, Pilar; Barchard, Kimberly A; Gacitua, Marta

2008-08-01

This article examines the importance of semantic processes in the recognition of emotional expressions, through a series of three studies on false recognition. The first study found a high frequency of false recognition of prototypical expressions of emotion when participants viewed slides and video clips of nonprototypical fearful and happy expressions. The second study tested whether semantic processes caused false recognition. The authors found that participants made significantly higher error rates when asked to detect expressions that corresponded to semantic labels than when asked to detect visual stimuli. Finally, given that previous research reported that false memories are less prevalent in younger children, the third study tested whether false recognition of prototypical expressions increased with age. The authors found that 67% of eight- to nine-year-old children reported nonpresent prototypical expressions of fear in a fearful context, but only 40% of 6- to 7-year-old children did so. Taken together, these three studies demonstrate the importance of semantic processes in the detection and categorization of prototypical emotional expressions.

Leveraging Automatic Speech Recognition Errors to Detect Challenging Speech Segments in TED Talks

ERIC Educational Resources Information Center

Mirzaei, Maryam Sadat; Meshgi, Kourosh; Kawahara, Tatsuya

2016-01-01

This study investigates the use of Automatic Speech Recognition (ASR) systems to epitomize second language (L2) listeners' problems in perception of TED talks. ASR-generated transcripts of videos often involve recognition errors, which may indicate difficult segments for L2 listeners. This paper aims to discover the root-causes of the ASR errors…

Word Stress in German Single-Word Reading

ERIC Educational Resources Information Center

Beyermann, Sandra; Penke, Martina

2014-01-01

This article reports a lexical-decision experiment that was conducted to investigate the impact of word stress on visual word recognition in German. Reaction-time latencies and error rates of German readers on different levels of reading proficiency (i.e., third graders and fifth graders from primary school and university students) were compared…

[The endpoint detection of cough signal in continuous speech].

PubMed

Yang, Guoqing; Mo, Hongqiang; Li, Wen; Lian, Lianfang; Zheng, Zeguang

2010-06-01

The endpoint detection of cough signal in continuous speech has been researched in order to improve the efficiency and veracity of manual recognition or computer-based automatic recognition. First, using the short time zero crossing ratio(ZCR) for identifying the suspicious coughs and getting the threshold of short time energy based on acoustic characteristics of cough. Then, the short time energy is combined with short time ZCR in order to implement the endpoint detection of cough in continuous speech. To evaluate the effect of the method, first, the virtual number of coughs in each recording was identified by two experienced doctors using the graphical user interface (GUI). Second, the recordings were analyzed by automatic endpoint detection program under Matlab7.0. Finally, the comparison between these two results showed: The error rate of undetected cough is 2.18%, and 98.13% of noise, silence and speech were removed. The way of setting short time energy threshold is robust. The endpoint detection program can remove most speech and noise, thus maintaining a lower rate of error.

Linear and Order Statistics Combiners for Pattern Classification

NASA Technical Reports Server (NTRS)

Tumer, Kagan; Ghosh, Joydeep; Lau, Sonie (Technical Monitor)

2001-01-01

Several researchers have experimentally shown that substantial improvements can be obtained in difficult pattern recognition problems by combining or integrating the outputs of multiple classifiers. This chapter provides an analytical framework to quantify the improvements in classification results due to combining. The results apply to both linear combiners and order statistics combiners. We first show that to a first order approximation, the error rate obtained over and above the Bayes error rate, is directly proportional to the variance of the actual decision boundaries around the Bayes optimum boundary. Combining classifiers in output space reduces this variance, and hence reduces the 'added' error. If N unbiased classifiers are combined by simple averaging. the added error rate can be reduced by a factor of N if the individual errors in approximating the decision boundaries are uncorrelated. Expressions are then derived for linear combiners which are biased or correlated, and the effect of output correlations on ensemble performance is quantified. For order statistics based non-linear combiners, we derive expressions that indicate how much the median, the maximum and in general the i-th order statistic can improve classifier performance. The analysis presented here facilitates the understanding of the relationships among error rates, classifier boundary distributions, and combining in output space. Experimental results on several public domain data sets are provided to illustrate the benefits of combining and to support the analytical results.

Waveguide-type optical circuits for recognition of optical 8QAM-coded label

NASA Astrophysics Data System (ADS)

Surenkhorol, Tumendemberel; Kishikawa, Hiroki; Goto, Nobuo; Gonchigsumlaa, Khishigjargal

2017-10-01

Optical signal processing is expected to be applied in network nodes. In photonic routers, label recognition is one of the important functions. We have studied different kinds of label recognition methods so far for on-off keying, binary phase-shift keying, quadrature phase-shift keying, and 16 quadrature amplitude modulation-coded labels. We propose a method based on waveguide circuits to recognize an optical eight quadrature amplitude modulation (8QAM)-coded label by simple passive optical signal processing. The recognition of the proposed method is theoretically analyzed and numerically simulated by the finite difference beam propagation method. The noise tolerance is discussed, and bit-error rate against optical signal-to-noise ratio is evaluated. The scalability of the proposed method is also discussed theoretically for two-symbol length 8QAM-coded labels.

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.

Effects of Error Correction on Word Recognition and Reading Comprehension.

ERIC Educational Resources Information Center

Jenkins, Joseph R.; And Others

1983-01-01

Two procedures for correcting oral reading errors, word supply and word drill, were examined to determine their effects on measures of word recognition and comprehension with 17 learning disabled elementary school students. (Author/SW)

Automatic Recognition of Phonemes Using a Syntactic Processor for Error Correction.

DTIC Science & Technology

1980-12-01

OF PHONEMES USING A SYNTACTIC PROCESSOR FOR ERROR CORRECTION THESIS AFIT/GE/EE/8D-45 Robert B. ’Taylor 2Lt USAF Approved for public release...distribution unlimilted. AbP AFIT/GE/EE/ 80D-45 AUTOMATIC RECOGNITION OF PHONEMES USING A SYNTACTIC PROCESSOR FOR ERROR CORRECTION THESIS Presented to the...Testing ..................... 37 Bayes Decision Rule for Minimum Error ........... 37 Bayes Decision Rule for Minimum Risk ............ 39 Mini Max Test

Slant rectification in Russian passport OCR system using fast Hough transform

NASA Astrophysics Data System (ADS)

Limonova, Elena; Bezmaternykh, Pavel; Nikolaev, Dmitry; Arlazarov, Vladimir

2017-03-01

In this paper, we introduce slant detection method based on Fast Hough Transform calculation and demonstrate its application in industrial system for Russian passports recognition. About 1.5% of this kind of documents appear to be slant or italic. This fact reduces recognition rate, because Optical Recognition Systems are normally designed to process normal fonts. Our method uses Fast Hough Transform to analyse vertical strokes of characters extracted with the help of x-derivative of a text line image. To improve the quality of detector we also introduce field grouping rules. The resulting algorithm allowed to reach high detection quality. Almost all errors of considered approach happen on passports of nonstandard fonts, while slant detector works in appropriate way.

Evaluation of a Teleform-based data collection system: a multi-center obesity research case study.

PubMed

Jenkins, Todd M; Wilson Boyce, Tawny; Akers, Rachel; Andringa, Jennifer; Liu, Yanhong; Miller, Rosemary; Powers, Carolyn; Ralph Buncher, C

2014-06-01

Utilizing electronic data capture (EDC) systems in data collection and management allows automated validation programs to preemptively identify and correct data errors. For our multi-center, prospective study we chose to use TeleForm, a paper-based data capture software that uses recognition technology to create case report forms (CRFs) with similar functionality to EDC, including custom scripts to identify entry errors. We quantified the accuracy of the optimized system through a data audit of CRFs and the study database, examining selected critical variables for all subjects in the study, as well as an audit of all variables for 25 randomly selected subjects. Overall we found 6.7 errors per 10,000 fields, with similar estimates for critical (6.9/10,000) and non-critical (6.5/10,000) variables-values that fall below the acceptable quality threshold of 50 errors per 10,000 established by the Society for Clinical Data Management. However, error rates were found to widely vary by type of data field, with the highest rate observed with open text fields. Copyright © 2014 Elsevier Ltd. All rights reserved.

Improving Acoustic Models by Watching Television

NASA Technical Reports Server (NTRS)

Witbrock, Michael J.; Hauptmann, Alexander G.

1998-01-01

Obtaining sufficient labelled training data is a persistent difficulty for speech recognition research. Although well transcribed data is expensive to produce, there is a constant stream of challenging speech data and poor transcription broadcast as closed-captioned television. We describe a reliable unsupervised method for identifying accurately transcribed sections of these broadcasts, and show how these segments can be used to train a recognition system. Starting from acoustic models trained on the Wall Street Journal database, a single iteration of our training method reduced the word error rate on an independent broadcast television news test set from 62.2% to 59.5%.

Inferential false memories of events: negative consequences protect from distortions when the events are free from further elaboration.

PubMed

Mirandola, Chiara; Toffalini, Enrico; Grassano, Massimo; Cornoldi, Cesare; Melinder, Annika

2014-01-01

The present experiment was conducted to investigate whether negative emotionally charged and arousing content of to-be-remembered scripted material would affect propensity towards memory distortions. We further investigated whether elaboration of the studied material through free recall would affect the magnitude of memory errors. In this study participants saw eight scripts. Each of the scripts included an effect of an action, the cause of which was not presented. Effects were either negatively emotional or neutral. Participants were assigned to either a yes/no recognition test group (recognition), or to a recall and yes/no recognition test group (elaboration + recognition). Results showed that participants in the recognition group produced fewer memory errors in the emotional condition. Conversely, elaboration + recognition participants had lower accuracy and produced more emotional memory errors than the other group, suggesting a mediating role of semantic elaboration on the generation of false memories. The role of emotions and semantic elaboration on the generation of false memories is discussed.

[Analysis of intrusion errors in free recall].

PubMed

Diesfeldt, H F A

2017-06-01

Extra-list intrusion errors during five trials of the eight-word list-learning task of the Amsterdam Dementia Screening Test (ADST) were investigated in 823 consecutive psychogeriatric patients (87.1% suffering from major neurocognitive disorder). Almost half of the participants (45.9%) produced one or more intrusion errors on the verbal recall test. Correct responses were lower when subjects made intrusion errors, but learning slopes did not differ between subjects who committed intrusion errors and those who did not so. Bivariate regression analyses revealed that participants who committed intrusion errors were more deficient on measures of eight-word recognition memory, delayed visual recognition and tests of executive control (the Behavioral Dyscontrol Scale and the ADST-Graphical Sequences as measures of response inhibition). Using hierarchical multiple regression, only free recall and delayed visual recognition retained an independent effect in the association with intrusion errors, such that deficient scores on tests of episodic memory were sufficient to explain the occurrence of intrusion errors. Measures of inhibitory control did not add significantly to the explanation of intrusion errors in free recall, which makes insufficient strength of memory traces rather than a primary deficit in inhibition the preferred account for intrusion errors in free recall.

A paper form processing system with an error correcting function for reading handwritten Kanji strings

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

Katsumi Marukawa; Kazuki Nakashima; Masashi Koga

1994-12-31

This paper presents a paper form processing system with an error correcting function for reading handwritten kanji strings. In the paper form processing system, names and addresses are important key data, and especially this paper takes up an error correcting method for name and address recognition. The method automatically corrects errors of the kanji OCR (Optical Character Reader) with the help of word dictionaries and other knowledge. Moreover, it allows names and addresses to be written in any style. The method consists of word matching {open_quotes}furigana{close_quotes} verification for name strings, and address approval for address strings. For word matching, kanjimore » name candidates are extracted by automaton-type word matching. In {open_quotes}furigana{close_quotes} verification, kana candidate characters recognized by the kana OCR are compared with kana`s searched from the name dictionary based on kanji name candidates, given by the word matching. The correct name is selected from the results of word matching and furigana verification. Also, the address approval efficiently searches for the right address based on a bottom-up procedure which follows hierarchical relations from a lower placename to a upper one by using the positional condition among the placenames. We ascertained that the error correcting method substantially improves the recognition rate and processing speed in experiments on 5,032 forms.« less

Optimizing pattern recognition-based control for partial-hand prosthesis application.

PubMed

Earley, Eric J; Adewuyi, Adenike A; Hargrove, Levi J

2014-01-01

Partial-hand amputees often retain good residual wrist motion, which is essential for functional activities involving use of the hand. Thus, a crucial design criterion for a myoelectric, partial-hand prosthesis control scheme is that it allows the user to retain residual wrist motion. Pattern recognition (PR) of electromyographic (EMG) signals is a well-studied method of controlling myoelectric prostheses. However, wrist motion degrades a PR system's ability to correctly predict hand-grasp patterns. We studied the effects of (1) window length and number of hand-grasps, (2) static and dynamic wrist motion, and (3) EMG muscle source on the ability of a PR-based control scheme to classify functional hand-grasp patterns. Our results show that training PR classifiers with both extrinsic and intrinsic muscle EMG yields a lower error rate than training with either group by itself (p<0.001); and that training in only variable wrist positions, with only dynamic wrist movements, or with both variable wrist positions and movements results in lower error rates than training in only the neutral wrist position (p<0.001). Finally, our results show that both an increase in window length and a decrease in the number of grasps available to the classifier significantly decrease classification error (p<0.001). These results remained consistent whether the classifier selected or maintained a hand-grasp.

Finger vein verification system based on sparse representation.

PubMed

Xin, Yang; Liu, Zhi; Zhang, Haixia; Zhang, Hong

2012-09-01

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

Recognizing the Presidents: Was Alexander Hamilton President?

PubMed

Roediger, Henry L; DeSoto, K Andrew

2016-05-01

Studies over the past 40 years have shown that Americans can recall about half the U.S. presidents. Do people know the presidents even though they are unable to access them for recall? We investigated this question using the powerful cues of a recognition test. Specifically, we tested the ability of 326 online subjects to recognize U.S. presidents when presented with their full names among various types of lures. The hit rate for presidential recognition was .88, well above the proportion produced in free recall but far from perfect. Presidents Franklin Pierce and Chester Arthur were recognized less than 60% of the time. Interestingly, four nonpresidents were falsely recognized at relatively high rates, and Alexander Hamilton was more frequently identified as president than were several actual presidents. Even on a recognition test, knowledge of American presidents is imperfect and prone to error. The false alarm data support the theory that false fame can arise from contextual familiarity. © The Author(s) 2016.

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.

Verbal Serial List Learning in Mild Cognitive Impairment: A Profile Analysis of Interference, Forgetting, and Errors

PubMed Central

Libon, David J.; Bondi, Mark W.; Price, Catherine C.; Lamar, Melissa; Eppig, Joel; Wambach, Denene M.; Nieves, Christine; Delano-Wood, Lisa; Giovannetti, Tania; Lippa, Carol; Kabasakalian, Anahid; Cosentino, Stephanie; Swenson, Rod; Penney, Dana L.

2012-01-01

Using cluster analysis Libon et al. (2010) found three verbal serial list-learning profiles involving delay memory test performance in patients with mild cognitive impairment (MCI). Amnesic MCI (aMCI) patients presented with low scores on delay free recall and recognition tests; mixed MCI (mxMCI) patients scored higher on recognition compared to delay free recall tests; and dysexecutive MCI (dMCI) patients generated relatively intact scores on both delay test conditions. The aim of the current research was to further characterize memory impairment in MCI by examining forgetting/savings, interference from a competing word list, intrusion errors/perseverations, intrusion word frequency, and recognition foils in these three statistically determined MCI groups compared to normal control (NC) participants. The aMCI patients exhibited little savings, generated more highly prototypic intrusion errors, and displayed indiscriminate responding to delayed recognition foils. The mxMCI patients exhibited higher saving scores, fewer and less prototypic intrusion errors, and selectively endorsed recognition foils from the interference list. dMCI patients also selectively endorsed recognition foils from the interference list but performed similarly compared to NC participants. These data suggest the existence of distinct memory impairments in MCI and caution against the routine use of a single memory test score to operationally define MCI. PMID:21880171

Prepopulated radiology report templates: a prospective analysis of error rate and turnaround time.

PubMed

Hawkins, C M; Hall, S; Hardin, J; Salisbury, S; Towbin, A J

2012-08-01

Current speech recognition software allows exam-specific standard reports to be prepopulated into the dictation field based on the radiology information system procedure code. While it is thought that prepopulating reports can decrease the time required to dictate a study and the overall number of errors in the final report, this hypothesis has not been studied in a clinical setting. A prospective study was performed. During the first week, radiologists dictated all studies using prepopulated standard reports. During the second week, all studies were dictated after prepopulated reports had been disabled. Final radiology reports were evaluated for 11 different types of errors. Each error within a report was classified individually. The median time required to dictate an exam was compared between the 2 weeks. There were 12,387 reports dictated during the study, of which, 1,173 randomly distributed reports were analyzed for errors. There was no difference in the number of errors per report between the 2 weeks; however, radiologists overwhelmingly preferred using a standard report both weeks. Grammatical errors were by far the most common error type, followed by missense errors and errors of omission. There was no significant difference in the median dictation time when comparing studies performed each week. The use of prepopulated reports does not alone affect the error rate or dictation time of radiology reports. While it is a useful feature for radiologists, it must be coupled with other strategies in order to decrease errors.

Spoken Language Processing in the Clarissa Procedure Browser

NASA Technical Reports Server (NTRS)

Rayner, M.; Hockey, B. A.; Renders, J.-M.; Chatzichrisafis, N.; Farrell, K.

2005-01-01

Clarissa, an experimental voice enabled procedure browser that has recently been deployed on the International Space Station, is as far as we know the first spoken dialog system in space. We describe the objectives of the Clarissa project and the system's architecture. In particular, we focus on three key problems: grammar-based speech recognition using the Regulus toolkit; methods for open mic speech recognition; and robust side-effect free dialogue management for handling undos, corrections and confirmations. We first describe the grammar-based recogniser we have build using Regulus, and report experiments where we compare it against a class N-gram recogniser trained off the same 3297 utterance dataset. We obtained a 15% relative improvement in WER and a 37% improvement in semantic error rate. The grammar-based recogniser moreover outperforms the class N-gram version for utterances of all lengths from 1 to 9 words inclusive. The central problem in building an open-mic speech recognition system is being able to distinguish between commands directed at the system, and other material (cross-talk), which should be rejected. Most spoken dialogue systems make the accept/reject decision by applying a threshold to the recognition confidence score. NASA shows how a simple and general method, based on standard approaches to document classification using Support Vector Machines, can give substantially better performance, and report experiments showing a relative reduction in the task-level error rate by about 25% compared to the baseline confidence threshold method. Finally, we describe a general side-effect free dialogue management architecture that we have implemented in Clarissa, which extends the "update semantics'' framework by including task as well as dialogue information in the information state. We show that this enables elegant treatments of several dialogue management problems, including corrections, confirmations, querying of the environment, and regression testing.

Effective Prediction of Errors by Non-native Speakers Using Decision Tree for Speech Recognition-Based CALL System

NASA Astrophysics Data System (ADS)

Wang, Hongcui; Kawahara, Tatsuya

CALL (Computer Assisted Language Learning) systems using ASR (Automatic Speech Recognition) for second language learning have received increasing interest recently. However, it still remains a challenge to achieve high speech recognition performance, including accurate detection of erroneous utterances by non-native speakers. Conventionally, possible error patterns, based on linguistic knowledge, are added to the lexicon and language model, or the ASR grammar network. However, this approach easily falls in the trade-off of coverage of errors and the increase of perplexity. To solve the problem, we propose a method based on a decision tree to learn effective prediction of errors made by non-native speakers. An experimental evaluation with a number of foreign students learning Japanese shows that the proposed method can effectively generate an ASR grammar network, given a target sentence, to achieve both better coverage of errors and smaller perplexity, resulting in significant improvement in ASR accuracy.

A biometric identification system based on eigenpalm and eigenfinger features.

PubMed

Ribaric, Slobodan; Fratric, Ivan

2005-11-01

This paper presents a multimodal biometric identification system based on the features of the human hand. We describe a new biometric approach to personal identification using eigenfinger and eigenpalm features, with fusion applied at the matching-score level. The identification process can be divided into the following phases: capturing the image; preprocessing; extracting and normalizing the palm and strip-like finger subimages; extracting the eigenpalm and eigenfinger features based on the K-L transform; matching and fusion; and, finally, a decision based on the (k, l)-NN classifier and thresholding. The system was tested on a database of 237 people (1,820 hand images). The experimental results showed the effectiveness of the system in terms of the recognition rate (100 percent), the equal error rate (EER = 0.58 percent), and the total error rate (TER = 0.72 percent).

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.

Post processing for offline Chinese handwritten character string recognition

NASA Astrophysics Data System (ADS)

Wang, YanWei; Ding, XiaoQing; Liu, ChangSong

2012-01-01

Offline Chinese handwritten character string recognition is one of the most important research fields in pattern recognition. Due to the free writing style, large variability in character shapes and different geometric characteristics, Chinese handwritten character string recognition is a challenging problem to deal with. However, among the current methods over-segmentation and merging method which integrates geometric information, character recognition information and contextual information, shows a promising result. It is found experimentally that a large part of errors are segmentation error and mainly occur around non-Chinese characters. In a Chinese character string, there are not only wide characters namely Chinese characters, but also narrow characters like digits and letters of the alphabet. The segmentation error is mainly caused by uniform geometric model imposed on all segmented candidate characters. To solve this problem, post processing is employed to improve recognition accuracy of narrow characters. On one hand, multi-geometric models are established for wide characters and narrow characters respectively. Under multi-geometric models narrow characters are not prone to be merged. On the other hand, top rank recognition results of candidate paths are integrated to boost final recognition of narrow characters. The post processing method is investigated on two datasets, in total 1405 handwritten address strings. The wide character recognition accuracy has been improved lightly and narrow character recognition accuracy has been increased up by 10.41% and 10.03% respectively. It indicates that the post processing method is effective to improve recognition accuracy of narrow characters.

The Influence of Orthographic Consistency on Reading Development: Word Recognition in English and German Children.

ERIC Educational Resources Information Center

Wimmer, Heinz; Goswami, Usha

1994-01-01

Groups of seven- to nine-year olds learning to read in English and German were given three types of reading tasks. Whereas reading time and error rates in numeral and number word reading were very similar across the two orthographies, the German children showed a big advantage in reading the nonsense words, suggesting adoption of different…

Man Versus Machine: Comparing Double Data Entry and Optical Mark Recognition for Processing CAHPS Survey Data.

PubMed

Fifolt, Matthew; Blackburn, Justin; Rhodes, David J; Gillespie, Shemeka; Bennett, Aleena; Wolff, Paul; Rucks, Andrew

Historically, double data entry (DDE) has been considered the criterion standard for minimizing data entry errors. However, previous studies considered data entry alternatives through the limited lens of data accuracy. This study supplies information regarding data accuracy, operational efficiency, and cost for DDE and Optical Mark Recognition (OMR) for processing the Consumer Assessment of Healthcare Providers and Systems 5.0 survey. To assess data accuracy, we compared error rates for DDE and OMR by dividing the number of surveys that were arbitrated by the total number of surveys processed for each method. To assess operational efficiency, we tallied the cost of data entry for DDE and OMR after survey receipt. Costs were calculated on the basis of personnel, depreciation for capital equipment, and costs of noncapital equipment. The cost savings attributed to this method were negated by the operational efficiency of OMR. There was a statistical significance between rates of arbitration between DDE and OMR; however, this statistical significance did not create a practical significance. The potential benefits of DDE in terms of data accuracy did not outweigh the operational efficiency and thereby financial savings of OMR.

Learning curve of speech recognition.

PubMed

Kauppinen, Tomi A; Kaipio, Johanna; Koivikko, Mika P

2013-12-01

Speech recognition (SR) speeds patient care processes by reducing report turnaround times. However, concerns have emerged about prolonged training and an added secretarial burden for radiologists. We assessed how much proofing radiologists who have years of experience with SR and radiologists new to SR must perform, and estimated how quickly the new users become as skilled as the experienced users. We studied SR log entries for 0.25 million reports from 154 radiologists and after careful exclusions, defined a group of 11 experienced radiologists and 71 radiologists new to SR (24,833 and 122,093 reports, respectively). Data were analyzed for sound file and report lengths, character-based error rates, and words unknown to the SR's dictionary. Experienced radiologists corrected 6 characters for each report and for new users, 11. Some users presented a very unfavorable learning curve, with error rates not declining as expected. New users' reports were longer, and data for the experienced users indicates that their reports, initially equally lengthy, shortened over a period of several years. For most radiologists, only minor corrections of dictated reports were necessary. While new users adopted SR quickly, with a subset outperforming experienced users from the start, identification of users struggling with SR will help facilitate troubleshooting and support.

Analytic study of the Tadoma method: background and preliminary results.

PubMed

Norton, S J; Schultz, M C; Reed, C M; Braida, L D; Durlach, N I; Rabinowitz, W M; Chomsky, C

1977-09-01

Certain deaf-blind persons have been taught, through the Tadoma method of speechreading, to use vibrotactile cues from the face and neck to understand speech. This paper reports the results of preliminary tests of the speechreading ability of one adult Tadoma user. The tests were of four major types: (1) discrimination of speech stimuli; (2) recognition of words in isolation and in sentences; (3) interpretation of prosodic and syntactic features in sentences; and (4) comprehension of written (Braille) and oral speech. Words in highly contextual environments were much better perceived than were words in low-context environments. Many of the word errors involved phonemic substitutions which shared articulatory features with the target phonemes, with a higher error rate for vowels than consonants. Relative to performance on word-recognition tests, performance on some of the discrimination tests was worse than expected. Perception of sentences appeared to be mildly sensitive to rate of talking and to speaker differences. Results of the tests on perception of prosodic and syntactic features, while inconclusive, indicate that many of the features tested were not used in interpreting sentences. On an English comprehension test, a higher score was obtained for items administered in Braille than through oral presentation.

Structural analysis of online handwritten mathematical symbols based on support vector machines

NASA Astrophysics Data System (ADS)

Simistira, Foteini; Papavassiliou, Vassilis; Katsouros, Vassilis; Carayannis, George

2013-01-01

Mathematical expression recognition is still a very challenging task for the research community mainly because of the two-dimensional (2d) structure of mathematical expressions (MEs). In this paper, we present a novel approach for the structural analysis between two on-line handwritten mathematical symbols of a ME, based on spatial features of the symbols. We introduce six features to represent the spatial affinity of the symbols and compare two multi-class classification methods that employ support vector machines (SVMs): one based on the "one-against-one" technique and one based on the "one-against-all", in identifying the relation between a pair of symbols (i.e. subscript, numerator, etc). A dataset containing 1906 spatial relations derived from the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2012 training dataset is constructed to evaluate the classifiers and compare them with the rule-based classifier of the ILSP-1 system participated in the contest. The experimental results give an overall mean error rate of 2.61% for the "one-against-one" SVM approach, 6.57% for the "one-against-all" SVM technique and 12.31% error rate for the ILSP-1 classifier.

Voice recognition technology implementation in surgical pathology: advantages and limitations.

PubMed

Singh, Meenakshi; Pal, Timothy R

2011-11-01

Voice recognition technology (VRT) has been in use for medical transcription outside of laboratories for many years, and in recent years it has evolved to a level where it merits consideration by surgical pathologists. To determine the feasibility and impact of making a transition from a transcriptionist-based service to VRT in surgical pathology. We have evaluated VRT in a phased manner for sign out of general and subspecialty surgical pathology cases after conducting a pilot study. We evaluated the effect on turnaround time, workflow, staffing, typographical error rates, and the overall ability of VRT to be adapted for use in surgical pathology. The stepwise implementation of VRT has resulted in real-time sign out of cases and improvement in average turnaround time from 4 to 3 days. The percentage of cases signed out in 1 day improved from 22% to 37%. Amendment rates for typographical errors have decreased. Use of templates and synoptic reports has been facilitated. The transcription staff has been reassigned to other duties and is successfully assisting in other areas. Resident involvement and exposure to complete case sign out has been achieved resulting in a positive impact on resident education. Voice recognition technology allows for a seamless workflow in surgical pathology, with improvements in turnaround time and a positive impact on competency-based resident education. Individual practices may assess the value of VRT and decide to implement it, potentially with gains in many aspects of their practice.

Cognitive mechanisms of false facial recognition in older adults.

PubMed

Edmonds, Emily C; Glisky, Elizabeth L; Bartlett, James C; Rapcsak, Steven Z

2012-03-01

Older adults show elevated false alarm rates on recognition memory tests involving faces in comparison to younger adults. It has been proposed that this age-related increase in false facial recognition reflects a deficit in recollection and a corresponding increase in the use of familiarity when making memory decisions. To test this hypothesis, we examined the performance of 40 older adults and 40 younger adults on a face recognition memory paradigm involving three different types of lures with varying levels of familiarity. A robust age effect was found, with older adults demonstrating a markedly heightened false alarm rate in comparison to younger adults for "familiarized lures" that were exact repetitions of faces encountered earlier in the experiment, but outside the study list, and therefore required accurate recollection of contextual information to reject. By contrast, there were no age differences in false alarms to "conjunction lures" that recombined parts of study list faces, or to entirely new faces. Overall, the pattern of false recognition errors observed in older adults was consistent with excessive reliance on a familiarity-based response strategy. Specifically, in the absence of recollection older adults appeared to base their memory decisions on item familiarity, as evidenced by a linear increase in false alarm rates with increasing familiarity of the lures. These findings support the notion that automatic memory processes such as familiarity remain invariant with age, while more controlled memory processes such as recollection show age-related decline.

The Magnet Nursing Services Recognition Program

PubMed Central

Aiken, Linda H.; Havens, Donna S.; Sloane, Douglas M.

2015-01-01

OVERVIEW In an environment rife with controversy about patient safety in hospitals, medical error rates, and nursing shortages, consumers need to know how good the care is at their local hospitals. Nursing’s best kept secret is the single most effective mechanism for providing that type of comparative information to consumers, a seal of approval for quality nursing care: designation of magnet hospital status by the American Nurses Credentialing Center (ANCC). Magnet designation, or recognition of the “best” hospitals, was conceived in the early 1980s when the American Academy of Nursing (AAN) conducted a study to identify which hospitals attracted and retained nurses and which organizational features were shared by these successful hospitals, referred to as magnet hospitals. In the 1990s, the American Nurses Association (ANA), through the ANCC, established a formal program to acknowledge excellence in nursing services: the Magnet Nursing Services Recognition Program. The purpose of the current study is to examine whether hospitals selected for recognition by the ANCC application process—ANCC-accredited hospitals—are as successful in creating environments in which excellent nursing care is provided as the original AAN magnet hospitals were. We found that at ANCC-recognized magnet hospitals nurses had lower burnout rates and higher levels of job satisfaction and gave the quality of care provided at their hospitals higher ratings than did nurses at the AAN magnet hospitals. Our findings validate the ability of the Magnet Nursing Services Recognition Program to successfully identify hospitals that provide high-quality nursing care. PMID:19641439

Super-resolution method for face recognition using nonlinear mappings on coherent features.

PubMed

Huang, Hua; He, Huiting

2011-01-01

Low-resolution (LR) of face images significantly decreases the performance of face recognition. To address this problem, we present a super-resolution method that uses nonlinear mappings to infer coherent features that favor higher recognition of the nearest neighbor (NN) classifiers for recognition of single LR face image. Canonical correlation analysis is applied to establish the coherent subspaces between the principal component analysis (PCA) based features of high-resolution (HR) and LR face images. Then, a nonlinear mapping between HR/LR features can be built by radial basis functions (RBFs) with lower regression errors in the coherent feature space than in the PCA feature space. Thus, we can compute super-resolved coherent features corresponding to an input LR image according to the trained RBF model efficiently and accurately. And, face identity can be obtained by feeding these super-resolved features to a simple NN classifier. Extensive experiments on the Facial Recognition Technology, University of Manchester Institute of Science and Technology, and Olivetti Research Laboratory databases show that the proposed method outperforms the state-of-the-art face recognition algorithms for single LR image in terms of both recognition rate and robustness to facial variations of pose and expression.

Word-level recognition of multifont Arabic text using a feature vector matching approach

NASA Astrophysics Data System (ADS)

Erlandson, Erik J.; Trenkle, John M.; Vogt, Robert C., III

1996-03-01

Many text recognition systems recognize text imagery at the character level and assemble words from the recognized characters. An alternative approach is to recognize text imagery at the word level, without analyzing individual characters. This approach avoids the problem of individual character segmentation, and can overcome local errors in character recognition. A word-level recognition system for machine-printed Arabic text has been implemented. Arabic is a script language, and is therefore difficult to segment at the character level. Character segmentation has been avoided by recognizing text imagery of complete words. The Arabic recognition system computes a vector of image-morphological features on a query word image. This vector is matched against a precomputed database of vectors from a lexicon of Arabic words. Vectors from the database with the highest match score are returned as hypotheses for the unknown image. Several feature vectors may be stored for each word in the database. Database feature vectors generated using multiple fonts and noise models allow the system to be tuned to its input stream. Used in conjunction with database pruning techniques, this Arabic recognition system has obtained promising word recognition rates on low-quality multifont text imagery.

Acquisition of Malay word recognition skills: lessons from low-progress early readers.

PubMed

Lee, Lay Wah; Wheldall, Kevin

2011-02-01

Malay is a consistent alphabetic orthography with complex syllable structures. The focus of this research was to investigate word recognition performance in order to inform reading interventions for low-progress early readers. Forty-six Grade 1 students were sampled and 11 were identified as low-progress readers. The results indicated that both syllable awareness and phoneme blending were significant predictors of word recognition, suggesting that both syllable and phonemic grain-sizes are important in Malay word recognition. Item analysis revealed a hierarchical pattern of difficulty based on the syllable and the phonic structure of the words. Error analysis identified the sources of errors to be errors due to inefficient syllable segmentation, oversimplification of syllables, insufficient grapheme-phoneme knowledge and inefficient phonemic code assembly. Evidence also suggests that direct instruction in syllable segmentation, phonemic awareness and grapheme-phoneme correspondence is necessary for low-progress readers to acquire word recognition skills. Finally, a logical sequence to teach grapheme-phoneme decoding in Malay is suggested. Copyright © 2010 John Wiley & Sons, Ltd.

DCT-based iris recognition.

PubMed

Monro, Donald M; Rakshit, Soumyadip; Zhang, Dexin

2007-04-01

This paper presents a novel iris coding method based on differences of discrete cosine transform (DCT) coefficients of overlapped angular patches from normalized iris images. The feature extraction capabilities of the DCT are optimized on the two largest publicly available iris image data sets, 2,156 images of 308 eyes from the CASIA database and 2,955 images of 150 eyes from the Bath database. On this data, we achieve 100 percent Correct Recognition Rate (CRR) and perfect Receiver-Operating Characteristic (ROC) Curves with no registered false accepts or rejects. Individual feature bit and patch position parameters are optimized for matching through a product-of-sum approach to Hamming distance calculation. For verification, a variable threshold is applied to the distance metric and the False Acceptance Rate (FAR) and False Rejection Rate (FRR) are recorded. A new worst-case metric is proposed for predicting practical system performance in the absence of matching failures, and the worst case theoretical Equal Error Rate (EER) is predicted to be as low as 2.59 x 10(-4) on the available data sets.

The Effects of Aging and IQ on Item and Associative Memory

PubMed Central

Ratcliff, Roger; Thapar, Anjali; McKoon, Gail

2011-01-01

The effects of aging and IQ on performance were examined in four memory tasks: item recognition, associative recognition, cued recall, and free recall. For item and associative recognition, accuracy and the response time distributions for correct and error responses were explained by Ratcliff’s (1978) diffusion model, at the level of individual participants. The values of the components of processing identified by the model for the recognition tasks, as well as accuracy for cued and free recall, were compared across levels of IQ ranging from 85 to 140 and age (college-age, 60-74 year olds, and 75-90 year olds). IQ had large effects on the quality of the evidence from memory on which decisions were based in the recognition tasks and accuracy in the recall tasks, except for the oldest participants for whom some of the measures were near floor values. Drift rates in the recognition tasks, accuracy in the recall tasks, and IQ all correlated strongly with each other. However, there was a small decline in drift rates for item recognition and a large decline for associative recognition and accuracy in cued recall (about 70 percent). In contrast, there were large age effects on boundary separation and nondecision time (which correlated across tasks), but little effect of IQ. The implications of these results for single- and dual- process models of item recognition are discussed and it is concluded that models that deal with both RTs and accuracy are subject to many more constraints than models that deal with only one of these measures. Overall, the results of the study show a complicated but interpretable pattern of interactions that present important targets for response time and memory models. PMID:21707207

Effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication.

PubMed

Dhir, L; Habib, N E; Monro, D M; Rakshit, S

2010-06-01

The purpose of this study was to investigate the effect of cataract surgery and pupil dilation on iris pattern recognition for personal authentication. Prospective non-comparative cohort study. Images of 15 subjects were captured before (enrolment), and 5, 10, and 15 min after instillation of mydriatics before routine cataract surgery. After cataract surgery, images were captured 2 weeks thereafter. Enrolled and test images (after pupillary dilation and after cataract surgery) were segmented to extract the iris. This was then unwrapped onto a rectangular format for normalization and a novel method using the Discrete Cosine Transform was applied to encode the image into binary bits. The numerical difference between two iris codes (Hamming distance, HD) was calculated. The HD between identification and enrolment codes was used as a score and was compared with a confidence threshold for specific equipment, giving a match or non-match result. The Correct Recognition Rate (CRR) and Equal Error Rates (EERs) were calculated to analyse overall system performance. After cataract surgery, perfect identification and verification was achieved, with zero false acceptance rate, zero false rejection rate, and zero EER. After pupillary dilation, non-elastic deformation occurs and a CRR of 86.67% and EER of 9.33% were obtained. Conventional circle-based localization methods are inadequate. Matching reliability decreases considerably with increase in pupillary dilation. Cataract surgery has no effect on iris pattern recognition, whereas pupil dilation may be used to defeat an iris-based authentication system.

Parallel photonic information processing at gigabyte per second data rates using transient states

NASA Astrophysics Data System (ADS)

Brunner, Daniel; Soriano, Miguel C.; Mirasso, Claudio R.; Fischer, Ingo

2013-01-01

The increasing demands on information processing require novel computational concepts and true parallelism. Nevertheless, hardware realizations of unconventional computing approaches never exceeded a marginal existence. While the application of optics in super-computing receives reawakened interest, new concepts, partly neuro-inspired, are being considered and developed. Here we experimentally demonstrate the potential of a simple photonic architecture to process information at unprecedented data rates, implementing a learning-based approach. A semiconductor laser subject to delayed self-feedback and optical data injection is employed to solve computationally hard tasks. We demonstrate simultaneous spoken digit and speaker recognition and chaotic time-series prediction at data rates beyond 1Gbyte/s. We identify all digits with very low classification errors and perform chaotic time-series prediction with 10% error. Our approach bridges the areas of photonic information processing, cognitive and information science.

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

Cross-cultural emotional prosody recognition: evidence from Chinese and British listeners.

PubMed

Paulmann, Silke; Uskul, Ayse K

2014-01-01

This cross-cultural study of emotional tone of voice recognition tests the in-group advantage hypothesis (Elfenbein & Ambady, 2002) employing a quasi-balanced design. Individuals of Chinese and British background were asked to recognise pseudosentences produced by Chinese and British native speakers, displaying one of seven emotions (anger, disgust, fear, happy, neutral tone of voice, sad, and surprise). Findings reveal that emotional displays were recognised at rates higher than predicted by chance; however, members of each cultural group were more accurate in recognising the displays communicated by a member of their own cultural group than a member of the other cultural group. Moreover, the evaluation of error matrices indicates that both culture groups relied on similar mechanism when recognising emotional displays from the voice. Overall, the study reveals evidence for both universal and culture-specific principles in vocal emotion recognition.

Pattern recognition of electronic bit-sequences using a semiconductor mode-locked laser and spatial light modulators

NASA Astrophysics Data System (ADS)

Bhooplapur, Sharad; Akbulut, Mehmetkan; Quinlan, Franklyn; Delfyett, Peter J.

2010-04-01

A novel scheme for recognition of electronic bit-sequences is demonstrated. Two electronic bit-sequences that are to be compared are each mapped to a unique code from a set of Walsh-Hadamard codes. The codes are then encoded in parallel on the spectral phase of the frequency comb lines from a frequency-stabilized mode-locked semiconductor laser. Phase encoding is achieved by using two independent spatial light modulators based on liquid crystal arrays. Encoded pulses are compared using interferometric pulse detection and differential balanced photodetection. Orthogonal codes eight bits long are compared, and matched codes are successfully distinguished from mismatched codes with very low error rates, of around 10-18. This technique has potential for high-speed, high accuracy recognition of bit-sequences, with applications in keyword searches and internet protocol packet routing.

Comparing source-based and gist-based false recognition in aging and Alzheimer's disease.

PubMed

Pierce, Benton H; Sullivan, Alison L; Schacter, Daniel L; Budson, Andrew E

2005-07-01

This study examined 2 factors contributing to false recognition of semantic associates: errors based on confusion of source and errors based on general similarity information or gist. The authors investigated these errors in patients with Alzheimer's disease (AD), age-matched control participants, and younger adults, focusing on each group's ability to use recollection of source information to suppress false recognition. The authors used a paradigm consisting of both deep and shallow incidental encoding tasks, followed by study of a series of categorized lists in which several typical exemplars were omitted. Results showed that healthy older adults were able to use recollection from the deep processing task to some extent but less than that used by younger adults. In contrast, false recognition in AD patients actually increased following the deep processing task, suggesting that they were unable to use recollection to oppose familiarity arising from incidental presentation. (c) 2005 APA, all rights reserved.

Specificity and false positive rates of the Test of Memory Malingering, Rey 15-item Test, and Rey Word Recognition Test among forensic inpatients with intellectual disabilities.

PubMed

Love, Christopher M; Glassmire, David M; Zanolini, Shanna Jordan; Wolf, Amanda

2014-10-01

This study evaluated the specificity and false positive (FP) rates of the Rey 15-Item Test (FIT), Word Recognition Test (WRT), and Test of Memory Malingering (TOMM) in a sample of 21 forensic inpatients with mild intellectual disability (ID). The FIT demonstrated an FP rate of 23.8% with the standard quantitative cutoff score. Certain qualitative error types on the FIT showed promise and had low FP rates. The WRT obtained an FP rate of 0.0% with previously reported cutoff scores. Finally, the TOMM demonstrated low FP rates of 4.8% and 0.0% on Trial 2 and the Retention Trial, respectively, when applying the standard cutoff score. FP rates are reported for a range of cutoff scores and compared with published research on individuals diagnosed with ID. Results indicated that although the quantitative variables on the FIT had unacceptably high FP rates, the TOMM and WRT had low FP rates, increasing the confidence clinicians can place in scores reflecting poor effort on these measures during ID evaluations. © The Author(s) 2014.

Scene Text Recognition using Similarity and a Lexicon with Sparse Belief Propagation

PubMed Central

Weinman, Jerod J.; Learned-Miller, Erik; Hanson, Allen R.

2010-01-01

Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and store fronts. Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions. Much information available to solve this problem is frequently ignored or used sequentially. Similarity between character images is often overlooked as useful information. Because of language priors, a recognizer may assign different labels to identical characters. Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label. Lexicons improve recognition accuracy but are used post hoc. We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision. Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses. By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy. In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19%, the lexicon reduces word recognition error by 35%, and sparse belief propagation reduces the lexicon words considered by 99.9% with a 12X speedup and no loss in accuracy. PMID:19696446

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).

Detecting paroxysmal coughing from pertussis cases using voice recognition technology.

PubMed

Parker, Danny; Picone, Joseph; Harati, Amir; Lu, Shuang; Jenkyns, Marion H; Polgreen, Philip M

2013-01-01

Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs. We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented. After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively. Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms.

Syntactic error modeling and scoring normalization in speech recognition: Error modeling and scoring normalization in the speech recognition task for adult literacy training

NASA Technical Reports Server (NTRS)

Olorenshaw, Lex; Trawick, David

1991-01-01

The purpose was to develop a speech recognition system to be able to detect speech which is pronounced incorrectly, given that the text of the spoken speech is known to the recognizer. Better mechanisms are provided for using speech recognition in a literacy tutor application. Using a combination of scoring normalization techniques and cheater-mode decoding, a reasonable acceptance/rejection threshold was provided. In continuous speech, the system was tested to be able to provide above 80 pct. correct acceptance of words, while correctly rejecting over 80 pct. of incorrectly pronounced words.

Affect Recognition in Adults with Attention-Deficit/Hyperactivity Disorder

PubMed Central

Miller, Meghan; Hanford, Russell B.; Fassbender, Catherine; Duke, Marshall; Schweitzer, Julie B.

2014-01-01

Objective This study compared affect recognition abilities between adults with and without Attention-Deficit/Hyperactivity Disorder (ADHD). Method The sample included 51 participants (34 men, 17 women) divided into 3 groups: ADHD-Combined Type (ADHD-C; n = 17), ADHD-Predominantly Inattentive Type (ADHD-I; n = 16), and controls (n = 18). The mean age was 34 years. Affect recognition abilities were assessed by the Diagnostic Analysis of Nonverbal Accuracy (DANVA). Results Analyses of Variance showed that the ADHD-I group made more fearful emotion errors relative to the control group. Inattentive symptoms were positively correlated while hyperactive-impulsive symptoms were negatively correlated with affect recognition errors. Conclusion These results suggest that affect recognition abilities may be impaired in adults with ADHD and that affect recognition abilities are more adversely affected by inattentive than hyperactive-impulsive symptoms. PMID:20555036

Neuropsychology of selective attention and magnetic cortical stimulation.

PubMed

Sabatino, M; Di Nuovo, S; Sardo, P; Abbate, C S; La Grutta, V

1996-01-01

Informed volunteers were asked to perform different neuropsychological tests involving selective attention under control conditions and during transcranial magnetic cortical stimulation. The tests chosen involved the recognition of a specific letter among different letters (verbal test) and the search for three different spatial orientations of an appendage to a square (visuo-spatial test). For each test the total time taken and the error rate were calculated. Results showed that cortical stimulation did not cause a worsening in performance. Moreover, magnetic stimulation of the temporal lobe neither modified completion time in both verbal and visuo-spatial tests nor changed error rate. In contrast, magnetic stimulation of the pre-frontal area induced a significant reduction in the performance time of both the verbal and visuo-spatial tests always without an increase in the number of errors. The experimental findings underline the importance of the pre-frontal area in performing tasks requiring a high level of controlled attention and suggest the need to adopt an interdisciplinary approach towards the study of neurone/mind interface mechanisms.

On the limits of Kagan's impulsive reflective distinction.

PubMed

Jones, B; McIntyre, L

1976-05-01

A logical analysis is made of the Matching Familiar Figures (MFF) Test on the basis of which children have been classified as "impulsive" or "reflective." The reflective strategy is implicitly preferred to the impulsive because the reflective child makes fewer errors though generally taking longer to make his first response. We show that the test allows the choice of a number of "game plans" and speed-accuracy tradeoffs which in practice may not be very different. Error rates may not indicate perceptual sensitivity, in any case, since sensitivity and response factors may be confounded in the error rate. Using a visual running-memory-span task to avoid the inherent difficulties of the MFF test, we found that children previously classified on the basis of that test as impulsive or reflective did not differ in recognition accuracy but did differ in response bias and response latency. Accuracy and bias are estimated by way of Luce's choice theory (Luce, 1963), and the results are discussed in those terms.

Individual identification via electrocardiogram analysis.

PubMed

Fratini, Antonio; Sansone, Mario; Bifulco, Paolo; Cesarelli, Mario

2015-08-14

During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.

Boost OCR accuracy using iVector based system combination approach

NASA Astrophysics Data System (ADS)

Peng, Xujun; Cao, Huaigu; Natarajan, Prem

2015-01-01

Optical character recognition (OCR) is a challenging task because most existing preprocessing approaches are sensitive to writing style, writing material, noises and image resolution. Thus, a single recognition system cannot address all factors of real document images. In this paper, we describe an approach to combine diverse recognition systems by using iVector based features, which is a newly developed method in the field of speaker verification. Prior to system combination, document images are preprocessed and text line images are extracted with different approaches for each system, where iVector is transformed from a high-dimensional supervector of each text line and is used to predict the accuracy of OCR. We merge hypotheses from multiple recognition systems according to the overlap ratio and the predicted OCR score of text line images. We present evaluation results on an Arabic document database where the proposed method is compared against the single best OCR system using word error rate (WER) metric.

Case study of 3D fingerprints applications

PubMed Central

Liu, Feng; Liang, Jinrong; Shen, Linlin; Yang, Meng; Zhang, David; Lai, Zhihui

2017-01-01

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. The applications of these features are finally discussed. A series of experiments are conducted to demonstrate the helpfulness of 3D information to fingerprint recognition. Results show that a quick alignment can be easily implemented under the guidance of 3D finger shape feature even though this feature does not work for fingerprint recognition directly. The newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%. Also, it is helpful to remove false core point. Furthermore, a promising of EER ~1.3% is realized by combining this feature with 2D features for fingerprint recognition which indicates the prospect of 3D fingerprint recognition. PMID:28399141

Case study of 3D fingerprints applications.

PubMed

Liu, Feng; Liang, Jinrong; Shen, Linlin; Yang, Meng; Zhang, David; Lai, Zhihui

2017-01-01

Human fingers are 3D objects. More information will be provided if three dimensional (3D) fingerprints are available compared with two dimensional (2D) fingerprints. Thus, this paper firstly collected 3D finger point cloud data by Structured-light Illumination method. Additional features from 3D fingerprint images are then studied and extracted. The applications of these features are finally discussed. A series of experiments are conducted to demonstrate the helpfulness of 3D information to fingerprint recognition. Results show that a quick alignment can be easily implemented under the guidance of 3D finger shape feature even though this feature does not work for fingerprint recognition directly. The newly defined distinctive 3D shape ridge feature can be used for personal authentication with Equal Error Rate (EER) of ~8.3%. Also, it is helpful to remove false core point. Furthermore, a promising of EER ~1.3% is realized by combining this feature with 2D features for fingerprint recognition which indicates the prospect of 3D fingerprint recognition.

Componential Network for the Recognition of Tool-Associated Actions: Evidence from Voxel-based Lesion-Symptom Mapping in Acute Stroke Patients.

PubMed

Martin, Markus; Dressing, Andrea; Bormann, Tobias; Schmidt, Charlotte S M; Kümmerer, Dorothee; Beume, Lena; Saur, Dorothee; Mader, Irina; Rijntjes, Michel; Kaller, Christoph P; Weiller, Cornelius

2017-08-01

The study aimed to elucidate areas involved in recognizing tool-associated actions, and to characterize the relationship between recognition and active performance of tool use.We performed voxel-based lesion-symptom mapping in a prospective cohort of 98 acute left-hemisphere ischemic stroke patients (68 male, age mean ± standard deviation, 65 ± 13 years; examination 4.4 ± 2 days post-stroke). In a video-based test, patients distinguished correct tool-related actions from actions with spatio-temporal (incorrect grip, kinematics, or tool orientation) or conceptual errors (incorrect tool-recipient matching, e.g., spreading jam on toast with a paintbrush). Moreover, spatio-temporal and conceptual errors were determined during actual tool use.Deficient spatio-temporal error discrimination followed lesions within a dorsal network in which the inferior parietal lobule (IPL) and the lateral temporal cortex (sLTC) were specifically relevant for assessing functional hand postures and kinematics, respectively. Conversely, impaired recognition of conceptual errors resulted from damage to ventral stream regions including anterior temporal lobe. Furthermore, LTC and IPL lesions impacted differently on action recognition and active tool use, respectively.In summary, recognition of tool-associated actions relies on a componential network. Our study particularly highlights the dissociable roles of LTC and IPL for the recognition of action kinematics and functional hand postures, respectively. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

PubMed Central

Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity. PMID:24683317

Efficient iris recognition based on optimal subfeature selection and weighted subregion fusion.

PubMed

Chen, Ying; Liu, Yuanning; Zhu, Xiaodong; He, Fei; Wang, Hongye; Deng, Ning

2014-01-01

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region's weights and then weighted different subregions' matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, and MMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity.

Score-level fusion of two-dimensional and three-dimensional palmprint for personal recognition systems

NASA Astrophysics Data System (ADS)

Chaa, Mourad; Boukezzoula, Naceur-Eddine; Attia, Abdelouahab

2017-01-01

Two types of scores extracted from two-dimensional (2-D) and three-dimensional (3-D) palmprint for personal recognition systems are merged, introducing a local image descriptor for 2-D palmprint-based recognition systems, named bank of binarized statistical image features (B-BSIF). The main idea of B-BSIF is that the extracted histograms from the binarized statistical image features (BSIF) code images (the results of applying the different BSIF descriptor size with the length 12) are concatenated into one to produce a large feature vector. 3-D palmprint contains the depth information of the palm surface. The self-quotient image (SQI) algorithm is applied for reconstructing illumination-invariant 3-D palmprint images. To extract discriminative Gabor features from SQI images, Gabor wavelets are defined and used. Indeed, the dimensionality reduction methods have shown their ability in biometrics systems. Given this, a principal component analysis (PCA)+linear discriminant analysis (LDA) technique is employed. For the matching process, the cosine Mahalanobis distance is applied. Extensive experiments were conducted on a 2-D and 3-D palmprint database with 10,400 range images from 260 individuals. Then, a comparison was made between the proposed algorithm and other existing methods in the literature. Results clearly show that the proposed framework provides a higher correct recognition rate. Furthermore, the best results were obtained by merging the score of B-BSIF descriptor with the score of the SQI+Gabor wavelets+PCA+LDA method, yielding an equal error rate of 0.00% and a recognition rate of rank-1=100.00%.

Word Recognition Error Analysis: Comparing Isolated Word List and Oral Passage Reading

ERIC Educational Resources Information Center

Flynn, Lindsay J.; Hosp, John L.; Hosp, Michelle K.; Robbins, Kelly P.

2011-01-01

The purpose of this study was to determine the relation between word recognition errors made at a letter-sound pattern level on a word list and on a curriculum-based measurement oral reading fluency measure (CBM-ORF) for typical and struggling elementary readers. The participants were second, third, and fourth grade typical and struggling readers…

Fusion of Dependent and Independent Biometric Information Sources

DTIC Science & Technology

2005-03-01

palmprint , DNA, ECG, signature, etc. The comparison of various biometric techniques is given in [13] and is presented in Table 1. Since, each...theory. Experimental studies on the M2VTS database [32] showed that a reduction in error rates is up to about 40%. Four combination strategies are...taken from the CEDAR benchmark database . The word recognition results were the highest (91%) among published results for handwritten words (before 2001

Analysis of Factors Affecting System Performance in the ASpIRE Challenge

DTIC Science & Technology

2015-12-13

performance in the ASpIRE (Automatic Speech recognition In Reverberant Environments) challenge. In particular, overall word error rate (WER) of the solver...systems is analyzed as a function of room, distance between talker and microphone, and microphone type. We also analyze speech activity detection...analysis will inform the design of future challenges and provide insight into the efficacy of current solutions addressing noisy reverberant speech

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.

Speech variability effects on recognition accuracy associated with concurrent task performance by pilots

NASA Technical Reports Server (NTRS)

Simpson, C. A.

1985-01-01

In the present study of the responses of pairs of pilots to aircraft warning classification tasks using an isolated word, speaker-dependent speech recognition system, the induced stress was manipulated by means of different scoring procedures for the classification task and by the inclusion of a competitive manual control task. Both speech patterns and recognition accuracy were analyzed, and recognition errors were recorded by type for an isolated word speaker-dependent system and by an offline technique for a connected word speaker-dependent system. While errors increased with task loading for the isolated word system, there was no such effect for task loading in the case of the connected word system.

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.

Syntactic error modeling and scoring normalization in speech recognition

NASA Technical Reports Server (NTRS)

Olorenshaw, Lex

1991-01-01

The objective was to develop the speech recognition system to be able to detect speech which is pronounced incorrectly, given that the text of the spoken speech is known to the recognizer. Research was performed in the following areas: (1) syntactic error modeling; (2) score normalization; and (3) phoneme error modeling. The study into the types of errors that a reader makes will provide the basis for creating tests which will approximate the use of the system in the real world. NASA-Johnson will develop this technology into a 'Literacy Tutor' in order to bring innovative concepts to the task of teaching adults to read.

Emotional face recognition in adolescent suicide attempters and adolescents engaging in non-suicidal self-injury.

PubMed

Seymour, Karen E; Jones, Richard N; Cushman, Grace K; Galvan, Thania; Puzia, Megan E; Kim, Kerri L; Spirito, Anthony; Dickstein, Daniel P

2016-03-01

Little is known about the bio-behavioral mechanisms underlying and differentiating suicide attempts from non-suicidal self-injury (NSSI) in adolescents. Adolescents who attempt suicide or engage in NSSI often report significant interpersonal and social difficulties. Emotional face recognition ability is a fundamental skill required for successful social interactions, and deficits in this ability may provide insight into the unique brain-behavior interactions underlying suicide attempts versus NSSI in adolescents. Therefore, we examined emotional face recognition ability among three mutually exclusive groups: (1) inpatient adolescents who attempted suicide (SA, n = 30); (2) inpatient adolescents engaged in NSSI (NSSI, n = 30); and (3) typically developing controls (TDC, n = 30) without psychiatric illness. Participants included adolescents aged 13-17 years, matched on age, gender and full-scale IQ. Emotional face recognition was evaluated using the diagnostic assessment of nonverbal accuracy (DANVA-2). Compared to TDC youth, adolescents with NSSI made more errors on child fearful and adult sad face recognition while controlling for psychopathology and medication status (ps < 0.05). No differences were found on emotional face recognition between NSSI and SA groups. Secondary analyses showed that compared to inpatients without major depression, those with major depression made fewer errors on adult sad face recognition even when controlling for group status (p < 0.05). Further, compared to inpatients without generalized anxiety, those with generalized anxiety made fewer recognition errors on adult happy faces even when controlling for group status (p < 0.05). Adolescent inpatients engaged in NSSI showed greater deficits in emotional face recognition than TDC, but not inpatient adolescents who attempted suicide. Further results suggest the importance of psychopathology in emotional face recognition. Replication of these preliminary results and examination of the role of context-dependent emotional processing are needed moving forward.

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

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

Dise, J; McDonald, D; Ashenafi, M

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels withmore » similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed positions would improve the algorithm speed.« less

26 CFR 1.1312-8 - Law applicable in determination of error.

Code of Federal Regulations, 2012 CFR

2012-04-01

... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition is determined under... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition, as the case may be, was made. The fact that the inclusion, exclusion, omission, allowance, disallowance, recognition, or...

26 CFR 1.1312-8 - Law applicable in determination of error.

Code of Federal Regulations, 2013 CFR

2013-04-01

... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition is determined under... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition, as the case may be, was made. The fact that the inclusion, exclusion, omission, allowance, disallowance, recognition, or...

26 CFR 1.1312-8 - Law applicable in determination of error.

Code of Federal Regulations, 2014 CFR

2014-04-01

... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition is determined under... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition, as the case may be, was made. The fact that the inclusion, exclusion, omission, allowance, disallowance, recognition, or...

26 CFR 1.1312-8 - Law applicable in determination of error.

Code of Federal Regulations, 2011 CFR

2011-04-01

... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition is determined under... inclusion, exclusion, omission, allowance, disallowance, recognition, or nonrecognition, as the case may be, was made. The fact that the inclusion, exclusion, omission, allowance, disallowance, recognition, or...

SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System.

PubMed

Lin, Feng; Zhuang, Yan; Song, Chen; Wang, Aosen; Li, Yiran; Gu, Changzhan; Li, Changzhi; Xu, Wenyao

2017-02-01

Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive to use. To overcome these obstacles, a noncontact and cost-effective sleep monitoring system, named SleepSense, is proposed for continuous recognition of the sleep status, including on-bed movement, bed exit, and breathing section. SleepSense consists of three parts: a Doppler radar-based sensor, a robust automated radar demodulation module, and a sleep status recognition framework. Herein, several time-domain and frequency-domain features are extracted for the sleep recognition framework. A prototype of SleepSense is presented and evaluated using two sets of experiments. In the short-term controlled experiment, the SleepSense achieves an overall 95.1% accuracy rate in identifying various sleep status. In the 75-minute sleep study, SleepSense demonstrates wide usability in real life. The error rate for breathing rate extraction in this study is only 6.65%. These experimental results indicate that SleepSense is an effective and promising solution for in-home sleep monitoring.

Structured Head and Neck CT Angiography Reporting Reduces Resident Revision Rates.

PubMed

Johnson, Tucker F; Brinjikji, Waleed; Doolittle, Derrick A; Nagelschneider, Alex A; Welch, Brian T; Kotsenas, Amy L

2018-04-12

This resident-driven quality improvement project was undertaken to assess the effectiveness of structured reporting to reduce revision rates for afterhours reports dictated by residents. The first part of the study assessed baseline revision rates for head and neck CT angiography (CTA) examinations dictated by residents during afterhours call. A structured report was subsequently created based on templates on the RSNA informatics reporting website and critical findings that should be assessed for on all CTA examinations. The template was made available to residents through the speech recognition software for all head and neck CTA examinations for a duration of 2 months. Report revision rates were then compared with and without use of the structured template. The structured template was found to reduce revision rates by approximately 50% with 10/41 unstructured reports revised and 2/17 structured reports revised. We believe that structured reporting can help reduce reporting errors, particularly in term of typographical errors, train residents to evaluate complex examinations in a systematic fashion, and assist them in recalling critical findings on these examinations. Copyright © 2018 Elsevier Inc. All rights reserved.

Analytical performance evaluation of SAR ATR with inaccurate or estimated models

NASA Astrophysics Data System (ADS)

DeVore, Michael D.

2004-09-01

Hypothesis testing algorithms for automatic target recognition (ATR) are often formulated in terms of some assumed distribution family. The parameter values corresponding to a particular target class together with the distribution family constitute a model for the target's signature. In practice such models exhibit inaccuracy because of incorrect assumptions about the distribution family and/or because of errors in the assumed parameter values, which are often determined experimentally. Model inaccuracy can have a significant impact on performance predictions for target recognition systems. Such inaccuracy often causes model-based predictions that ignore the difference between assumed and actual distributions to be overly optimistic. This paper reports on research to quantify the effect of inaccurate models on performance prediction and to estimate the effect using only trained parameters. We demonstrate that for large observation vectors the class-conditional probabilities of error can be expressed as a simple function of the difference between two relative entropies. These relative entropies quantify the discrepancies between the actual and assumed distributions and can be used to express the difference between actual and predicted error rates. Focusing on the problem of ATR from synthetic aperture radar (SAR) imagery, we present estimators of the probabilities of error in both ideal and plug-in tests expressed in terms of the trained model parameters. These estimators are defined in terms of unbiased estimates for the first two moments of the sample statistic. We present an analytical treatment of these results and include demonstrations from simulated radar data.

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.

Stereotype threat can reduce older adults' memory errors.

PubMed

Barber, Sarah J; Mather, Mara

2013-01-01

Stereotype threat often incurs the cost of reducing the amount of information that older adults accurately recall. In the current research, we tested whether stereotype threat can also benefit memory. According to the regulatory focus account of stereotype threat, threat induces a prevention focus in which people become concerned with avoiding errors of commission and are sensitive to the presence or absence of losses within their environment. Because of this, we predicted that stereotype threat might reduce older adults' memory errors. Results were consistent with this prediction. Older adults under stereotype threat had lower intrusion rates during free-recall tests (Experiments 1 and 2). They also reduced their false alarms and adopted more conservative response criteria during a recognition test (Experiment 2). Thus, stereotype threat can decrease older adults' false memories, albeit at the cost of fewer veridical memories, as well.

Music Recognition in Frontotemporal Lobar Degeneration and Alzheimer Disease

PubMed Central

Johnson, Julene K; Chang, Chiung-Chih; Brambati, Simona M; Migliaccio, Raffaella; Gorno-Tempini, Maria Luisa; Miller, Bruce L; Janata, Petr

2013-01-01

Objective To compare music recognition in patients with frontotemporal dementia, semantic dementia, Alzheimer disease, and controls and to evaluate the relationship between music recognition and brain volume. Background Recognition of familiar music depends on several levels of processing. There are few studies about how patients with dementia recognize familiar music. Methods Subjects were administered tasks that assess pitch and melody discrimination, detection of pitch errors in familiar melodies, and naming of familiar melodies. Results There were no group differences on pitch and melody discrimination tasks. However, patients with semantic dementia had considerable difficulty naming familiar melodies and also scored the lowest when asked to identify pitch errors in the same melodies. Naming familiar melodies, but not other music tasks, was strongly related to measures of semantic memory. Voxel-based morphometry analysis of brain MRI showed that difficulty in naming songs was associated with the bilateral temporal lobes and inferior frontal gyrus, whereas difficulty in identifying pitch errors in familiar melodies correlated with primarily the right temporal lobe. Conclusions The results support a view that the anterior temporal lobes play a role in familiar melody recognition, and that musical functions are affected differentially across forms of dementia. PMID:21617528

Recognition errors suggest fast familiarity and slow recollection in rhesus monkeys

PubMed Central

Basile, Benjamin M.; Hampton, Robert R.

2013-01-01

One influential model of recognition posits two underlying memory processes: recollection, which is detailed but relatively slow, and familiarity, which is quick but lacks detail. Most of the evidence for this dual-process model in nonhumans has come from analyses of receiver operating characteristic (ROC) curves in rats, but whether ROC analyses can demonstrate dual processes has been repeatedly challenged. Here, we present independent converging evidence for the dual-process model from analyses of recognition errors made by rhesus monkeys. Recognition choices were made in three different ways depending on processing duration. Short-latency errors were disproportionately false alarms to familiar lures, suggesting control by familiarity. Medium-latency responses were less likely to be false alarms and were more accurate, suggesting onset of a recollective process that could correctly reject familiar lures. Long-latency responses were guesses. A response deadline increased false alarms, suggesting that limiting processing time weakened the contribution of recollection and strengthened the contribution of familiarity. Together, these findings suggest fast familiarity and slow recollection in monkeys, that monkeys use a “recollect to reject” strategy to countermand false familiarity, and that primate recognition performance is well-characterized by a dual-process model consisting of recollection and familiarity. PMID:23864646

Computer discrimination procedures applicable to aerial and ERTS multispectral data

NASA Technical Reports Server (NTRS)

Richardson, A. J.; Torline, R. J.; Allen, W. A.

1970-01-01

Two statistical models are compared in the classification of crops recorded on color aerial photographs. A theory of error ellipses is applied to the pattern recognition problem. An elliptical boundary condition classification model (EBC), useful for recognition of candidate patterns, evolves out of error ellipse theory. The EBC model is compared with the minimum distance to the mean (MDM) classification model in terms of pattern recognition ability. The pattern recognition results of both models are interpreted graphically using scatter diagrams to represent measurement space. Measurement space, for this report, is determined by optical density measurements collected from Kodak Ektachrome Infrared Aero Film 8443 (EIR). The EBC model is shown to be a significant improvement over the MDM model.

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.

Vision-based mobile robot navigation through deep convolutional neural networks and end-to-end learning

NASA Astrophysics Data System (ADS)

Zhang, Yachu; Zhao, Yuejin; Liu, Ming; Dong, Liquan; Kong, Lingqin; Liu, Lingling

2017-09-01

In contrast to humans, who use only visual information for navigation, many mobile robots use laser scanners and ultrasonic sensors along with vision cameras to navigate. This work proposes a vision-based robot control algorithm based on deep convolutional neural networks. We create a large 15-layer convolutional neural network learning system and achieve the advanced recognition performance. Our system is trained from end to end to map raw input images to direction in supervised mode. The images of data sets are collected in a wide variety of weather conditions and lighting conditions. Besides, the data sets are augmented by adding Gaussian noise and Salt-and-pepper noise to avoid overfitting. The algorithm is verified by two experiments, which are line tracking and obstacle avoidance. The line tracking experiment is proceeded in order to track the desired path which is composed of straight and curved lines. The goal of obstacle avoidance experiment is to avoid the obstacles indoor. Finally, we get 3.29% error rate on the training set and 5.1% error rate on the test set in the line tracking experiment, 1.8% error rate on the training set and less than 5% error rate on the test set in the obstacle avoidance experiment. During the actual test, the robot can follow the runway centerline outdoor and avoid the obstacle in the room accurately. The result confirms the effectiveness of the algorithm and our improvement in the network structure and train parameters

Functions of graphemic and phonemic codes in visual word-recognition.

PubMed

Meyer, D E; Schvaneveldt, R W; Ruddy, M G

1974-03-01

Previous investigators have argued that printed words are recognized directly from visual representations and/or phonological representations obtained through phonemic recoding. The present research tested these hypotheses by manipulating graphemic and phonemic relations within various pairs of letter strings. Ss in two experiments classified the pairs as words or nonwords. Reaction times and error rates were relatively small for word pairs (e.g., BRIBE-TRIBE) that were both graphemically, and phonemically similar. Graphemic similarity alone inhibited performance on other word pairs (e.g., COUCH-TOUCH). These and other results suggest that phonological representations play a significant role in visual word recognition and that there is a dependence between successive phonemic-encoding operations. An encoding-bias model is proposed to explain the data.

Towards automated assistance for operating home medical devices.

PubMed

Gao, Zan; Detyniecki, Marcin; Chen, Ming-Yu; Wu, Wen; Hauptmann, Alexander G; Wactlar, Howard D

2010-01-01

To detect errors when subjects operate a home medical device, we observe them with multiple cameras. We then perform action recognition with a robust approach to recognize action information based on explicitly encoding motion information. This algorithm detects interest points and encodes not only their local appearance but also explicitly models local motion. Our goal is to recognize individual human actions in the operations of a home medical device to see if the patient has correctly performed the required actions in the prescribed sequence. Using a specific infusion pump as a test case, requiring 22 operation steps from 6 action classes, our best classifier selects high likelihood action estimates from 4 available cameras, to obtain an average class recognition rate of 69%.

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.

Older adults encode--but do not always use--perceptual details: intentional versus unintentional effects of detail on memory judgments.

PubMed

Koutstaal, Wilma

2003-03-01

Investigations of memory deficits in older individuals have concentrated on their increased likelihood of forgetting events or details of events that were actually encountered (errors of omission). However, mounting evidence demonstrates that normal cognitive aging also is associated with an increased propensity for errors of commission--shown in false alarms or false recognition. The present study examined the origins of this age difference. Older and younger adults each performed three types of memory tasks in which details of encountered items might influence performance. Although older adults showed greater false recognition of related lures on a standard (identical) old/new episodic recognition task, older and younger adults showed parallel effects of detail on repetition priming and meaning-based episodic recognition (decreased priming and decreased meaning-based recognition for different relative to same exemplars). The results suggest that the older adults encoded details but used them less effectively than the younger adults in the recognition context requiring their deliberate, controlled use.

False memory in aging resulting from self-referential processing.

PubMed

Rosa, Nicole M; Gutchess, Angela H

2013-11-01

Referencing the self is known to enhance accurate memory, but less is known about how the strategy affects false memory, particularly for highly self-relevant information. Because older adults are more prone to false memories, we tested whether self-referencing increased false memories with age. In 2 studies, older and younger adults rated adjectives for self-descriptiveness and later completed a surprise recognition test comprised of words rated previously for self-descriptiveness and novel lure words. Lure words were subsequently rated for self-descriptiveness in order to assess the impact of self-relevance on false memory. Study 2 introduced commonness judgments as a control condition, such that participants completed a recognition test on adjectives rated for commonness in addition to adjectives in the self-descriptiveness condition. Across both studies, findings indicate an increased response bias to self-referencing that increased hit rates for both older and younger adults but also increased false alarms as information became more self-descriptive, particularly for older adults. Although the present study supports previous literature showing a boost in memory for self-referenced information, the increase in false alarms, especially in older adults, highlights the potential for memory errors, particularly for information that is strongly related to the self.

Accurate palm vein recognition based on wavelet scattering and spectral regression kernel discriminant analysis

NASA Astrophysics Data System (ADS)

Elnasir, Selma; Shamsuddin, Siti Mariyam; Farokhi, Sajad

2015-01-01

Palm vein recognition (PVR) is a promising new biometric that has been applied successfully as a method of access control by many organizations, which has even further potential in the field of forensics. The palm vein pattern has highly discriminative features that are difficult to forge because of its subcutaneous position in the palm. Despite considerable progress and a few practical issues, providing accurate palm vein readings has remained an unsolved issue in biometrics. We propose a robust and more accurate PVR method based on the combination of wavelet scattering (WS) with spectral regression kernel discriminant analysis (SRKDA). As the dimension of WS generated features is quite large, SRKDA is required to reduce the extracted features to enhance the discrimination. The results based on two public databases-PolyU Hyper Spectral Palmprint public database and PolyU Multi Spectral Palmprint-show the high performance of the proposed scheme in comparison with state-of-the-art methods. The proposed approach scored a 99.44% identification rate and a 99.90% verification rate [equal error rate (EER)=0.1%] for the hyperspectral database and a 99.97% identification rate and a 99.98% verification rate (EER=0.019%) for the multispectral database.

Contact-free palm-vein recognition based on local invariant features.

PubMed

Kang, Wenxiong; Liu, Yang; Wu, Qiuxia; Yue, Xishun

2014-01-01

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach.

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.

Contact-Free Palm-Vein Recognition Based on Local Invariant Features

PubMed Central

Kang, Wenxiong; Liu, Yang; Wu, Qiuxia; Yue, Xishun

2014-01-01

Contact-free palm-vein recognition is one of the most challenging and promising areas in hand biometrics. In view of the existing problems in contact-free palm-vein imaging, including projection transformation, uneven illumination and difficulty in extracting exact ROIs, this paper presents a novel recognition approach for contact-free palm-vein recognition that performs feature extraction and matching on all vein textures distributed over the palm surface, including finger veins and palm veins, to minimize the loss of feature information. First, a hierarchical enhancement algorithm, which combines a DOG filter and histogram equalization, is adopted to alleviate uneven illumination and to highlight vein textures. Second, RootSIFT, a more stable local invariant feature extraction method in comparison to SIFT, is adopted to overcome the projection transformation in contact-free mode. Subsequently, a novel hierarchical mismatching removal algorithm based on neighborhood searching and LBP histograms is adopted to improve the accuracy of feature matching. Finally, we rigorously evaluated the proposed approach using two different databases and obtained 0.996% and 3.112% Equal Error Rates (EERs), respectively, which demonstrate the effectiveness of the proposed approach. PMID:24866176

Two-dimensional shape recognition using oriented-polar representation

NASA Astrophysics Data System (ADS)

Hu, Neng-Chung; Yu, Kuo-Kan; Hsu, Yung-Li

1997-10-01

To deal with such a problem as object recognition of position, scale, and rotation invariance (PSRI), we utilize some PSRI properties of images obtained from objects, for example, the centroid of the image. The corresponding position of the centroid to the boundary of the image is invariant in spite of rotation, scale, and translation of the image. To obtain the information of the image, we use the technique similar to Radon transform, called the oriented-polar representation of a 2D image. In this representation, two specific points, the centroid and the weighted mean point, are selected to form an initial ray, then the image is sampled with N angularly equispaced rays departing from the initial rays. Each ray contains a number of intersections and the distance information obtained from the centroid to the intersections. The shape recognition algorithm is based on the least total error of these two items of information. Together with a simple noise removal and a typical backpropagation neural network, this algorithm is simple, but the PSRI is achieved with a high recognition rate.

Recognizing Whispered Speech Produced by an Individual with Surgically Reconstructed Larynx Using Articulatory Movement Data

PubMed Central

Cao, Beiming; Kim, Myungjong; Mau, Ted; Wang, Jun

2017-01-01

Individuals with larynx (vocal folds) impaired have problems in controlling their glottal vibration, producing whispered speech with extreme hoarseness. Standard automatic speech recognition using only acoustic cues is typically ineffective for whispered speech because the corresponding spectral characteristics are distorted. Articulatory cues such as the tongue and lip motion may help in recognizing whispered speech since articulatory motion patterns are generally not affected. In this paper, we investigated whispered speech recognition for patients with reconstructed larynx using articulatory movement data. A data set with both acoustic and articulatory motion data was collected from a patient with surgically reconstructed larynx using an electromagnetic articulograph. Two speech recognition systems, Gaussian mixture model-hidden Markov model (GMM-HMM) and deep neural network-HMM (DNN-HMM), were used in the experiments. Experimental results showed adding either tongue or lip motion data to acoustic features such as mel-frequency cepstral coefficient (MFCC) significantly reduced the phone error rates on both speech recognition systems. Adding both tongue and lip data achieved the best performance. PMID:29423453

Understanding native Russian listeners' errors on an English word recognition test: model-based analysis of phoneme confusion.

PubMed

Shi, Lu-Feng; Morozova, Natalia

2012-08-01

Word recognition is a basic component in a comprehensive hearing evaluation, but data are lacking for listeners speaking two languages. This study obtained such data for Russian natives in the US and analysed the data using the perceptual assimilation model (PAM) and speech learning model (SLM). Listeners were randomly presented 200 NU-6 words in quiet. Listeners responded verbally and in writing. Performance was scored on words and phonemes (word-initial consonants, vowels, and word-final consonants). Seven normal-hearing, adult monolingual English natives (NM), 16 English-dominant (ED), and 15 Russian-dominant (RD) Russian natives participated. ED and RD listeners differed significantly in their language background. Consistent with the SLM, NM outperformed ED listeners and ED outperformed RD listeners, whether responses were scored on words or phonemes. NM and ED listeners shared similar phoneme error patterns, whereas RD listeners' errors had unique patterns that could be largely understood via the PAM. RD listeners had particular difficulty differentiating vowel contrasts /i-I/, /æ-ε/, and /ɑ-Λ/, word-initial consonant contrasts /p-h/ and /b-f/, and word-final contrasts /f-v/. Both first-language phonology and second-language learning history affect word and phoneme recognition. Current findings may help clinicians differentiate word recognition errors due to language background from hearing pathologies.

Model and algorithmic framework for detection and correction of cognitive errors.

PubMed

Feki, Mohamed Ali; Biswas, Jit; Tolstikov, Andrei

2009-01-01

This paper outlines an approach that we are taking for elder-care applications in the smart home, involving cognitive errors and their compensation. Our approach involves high level modeling of daily activities of the elderly by breaking down these activities into smaller units, which can then be automatically recognized at a low level by collections of sensors placed in the homes of the elderly. This separation allows us to employ plan recognition algorithms and systems at a high level, while developing stand-alone activity recognition algorithms and systems at a low level. It also allows the mixing and matching of multi-modality sensors of various kinds that go to support the same high level requirement. Currently our plan recognition algorithms are still at a conceptual stage, whereas a number of low level activity recognition algorithms and systems have been developed. Herein we present our model for plan recognition, providing a brief survey of the background literature. We also present some concrete results that we have achieved for activity recognition, emphasizing how these results are incorporated into the overall plan recognition system.

Facial emotion recognition deficits in relatives of children with autism are not associated with 5HTTLPR.

PubMed

Neves, Maila de Castro Lourenço das; Tremeau, Fabien; Nicolato, Rodrigo; Lauar, Hélio; Romano-Silva, Marco Aurélio; Correa, Humberto

2011-09-01

A large body of evidence suggests that several aspects of face processing are impaired in autism and that this impairment might be hereditary. This study was aimed at assessing facial emotion recognition in parents of children with autism and its associations with a functional polymorphism of the serotonin transporter (5HTTLPR). We evaluated 40 parents of children with autism and 41 healthy controls. All participants were administered the Penn Emotion Recognition Test (ER40) and were genotyped for 5HTTLPR. Our study showed that parents of children with autism performed worse in the facial emotion recognition test than controls. Analyses of error patterns showed that parents of children with autism over-attributed neutral to emotional faces. We found evidence that 5HTTLPR polymorphism did not influence the performance in the Penn Emotion Recognition Test, but that it may determine different error patterns. Facial emotion recognition deficits are more common in first-degree relatives of autistic patients than in the general population, suggesting that facial emotion recognition is a candidate endophenotype for autism.

Hemispheric Asymmetries in the Activation and Monitoring of Memory Errors

ERIC Educational Resources Information Center

Giammattei, Jeannette; Arndt, Jason

2012-01-01

Previous research on the lateralization of memory errors suggests that the right hemisphere's tendency to produce more memory errors than the left hemisphere reflects hemispheric differences in semantic activation. However, all prior research that has examined the lateralization of memory errors has used self-paced recognition judgments. Because…

Attitude errors arising from antenna/satellite altitude errors - Recognition and reduction

NASA Technical Reports Server (NTRS)

Godbey, T. W.; Lambert, R.; Milano, G.

1972-01-01

A review is presented of the three basic types of pulsed radar altimeter designs, as well as the source and form of altitude bias errors arising from antenna/satellite attitude errors in each design type. A quantitative comparison of the three systems was also made.

Using Gaussian mixture models to detect and classify dolphin whistles and pulses.

PubMed

Peso Parada, Pablo; Cardenal-López, Antonio

2014-06-01

In recent years, a number of automatic detection systems for free-ranging cetaceans have been proposed that aim to detect not just surfaced, but also submerged, individuals. These systems are typically based on pattern-recognition techniques applied to underwater acoustic recordings. Using a Gaussian mixture model, a classification system was developed that detects sounds in recordings and classifies them as one of four types: background noise, whistles, pulses, and combined whistles and pulses. The classifier was tested using a database of underwater recordings made off the Spanish coast during 2011. Using cepstral-coefficient-based parameterization, a sound detection rate of 87.5% was achieved for a 23.6% classification error rate. To improve these results, two parameters computed using the multiple signal classification algorithm and an unpredictability measure were included in the classifier. These parameters, which helped to classify the segments containing whistles, increased the detection rate to 90.3% and reduced the classification error rate to 18.1%. Finally, the potential of the multiple signal classification algorithm and unpredictability measure for estimating whistle contours and classifying cetacean species was also explored, with promising results.

Pilot study on the feasibility of a computerized speech recognition charting system.

PubMed

Feldman, C A; Stevens, D

1990-08-01

The objective of this study was to determine the feasibility of developing and using a voice recognition computerized charting system to record dental clinical examination data. More specifically, the study was designed to analyze the time and error differential between the traditional examiner/recorder method (ASSISTANT) and computerized voice recognition method (VOICE). DMFS examinations were performed twice on 20 patients using the traditional ASSISTANT and the VOICE charting system. A statistically significant difference was found when comparing the mean ASSISTANT time of 2.69 min to the VOICE time of 3.72 min (P less than 0.001). No statistically significant difference was found when comparing the mean ASSISTANT recording errors of 0.1 to VOICE recording errors of 0.6 (P = 0.059). 90% of the patients indicated they felt comfortable with the dentist talking to a computer and only 5% of the sample indicated they opposed VOICE. Results from this pilot study indicate that a charting system utilizing voice recognition technology could be considered a viable alternative to traditional examiner/recorder methods of clinical charting.

Consonant-recognition patterns and self-assessment of hearing handicap.

PubMed

Hustedde, C G; Wiley, T L

1991-12-01

Two companion experiments were conducted with normal-hearing subjects and subjects with high-frequency, sensorineural hearing loss. In Experiment 1, the validity of a self-assessment device of hearing handicap was evaluated in two groups of hearing-impaired listeners with significantly different consonant-recognition ability. Data for the Hearing Performance Inventory--Revised (Lamb, Owens, & Schubert, 1983) did not reveal differences in self-perceived handicap for the two groups of hearing-impaired listeners; it was sensitive to perceived differences in hearing abilities for listeners who did and did not have a hearing loss. Experiment 2 was aimed at evaluation of consonant error patterns that accounted for observed group differences in consonant-recognition ability. Error patterns on the Nonsense-Syllable Test (NST) across the two subject groups differed in both degree and type of error. Listeners in the group with poorer NST performance always demonstrated greater difficulty with selected low-frequency and high-frequency syllables than did listeners in the group with better NST performance. Overall, the NST was sensitive to differences in consonant-recognition ability for normal-hearing and hearing-impaired listeners.

Modelling Errors in Automatic Speech Recognition for Dysarthric Speakers

NASA Astrophysics Data System (ADS)

Caballero Morales, Santiago Omar; Cox, Stephen J.

2009-12-01

Dysarthria is a motor speech disorder characterized by weakness, paralysis, or poor coordination of the muscles responsible for speech. Although automatic speech recognition (ASR) systems have been developed for disordered speech, factors such as low intelligibility and limited phonemic repertoire decrease speech recognition accuracy, making conventional speaker adaptation algorithms perform poorly on dysarthric speakers. In this work, rather than adapting the acoustic models, we model the errors made by the speaker and attempt to correct them. For this task, two techniques have been developed: (1) a set of "metamodels" that incorporate a model of the speaker's phonetic confusion matrix into the ASR process; (2) a cascade of weighted finite-state transducers at the confusion matrix, word, and language levels. Both techniques attempt to correct the errors made at the phonetic level and make use of a language model to find the best estimate of the correct word sequence. Our experiments show that both techniques outperform standard adaptation techniques.

Optical character recognition: an illustrated guide to the frontier

NASA Astrophysics Data System (ADS)

Nagy, George; Nartker, Thomas A.; Rice, Stephen V.

1999-12-01

We offer a perspective on the performance of current OCR systems by illustrating and explaining actual OCR errors made by three commercial devices. After discussing briefly the character recognition abilities of humans and computers, we present illustrated examples of recognition errors. The top level of our taxonomy of the causes of errors consists of Imaging Defects, Similar Symbols, Punctuation, and Typography. The analysis of a series of 'snippets' from this perspective provides insight into the strengths and weaknesses of current systems, and perhaps a road map to future progress. The examples were drawn from the large-scale tests conducted by the authors at the Information Science Research Institute of the University of Nevada, Las Vegas. By way of conclusion, we point to possible approaches for improving the accuracy of today's systems. The talk is based on our eponymous monograph, recently published in The Kluwer International Series in Engineering and Computer Science, Kluwer Academic Publishers, 1999.

Documentation of procedures for textural/spatial pattern recognition techniques

NASA Technical Reports Server (NTRS)

Haralick, R. M.; Bryant, W. F.

1976-01-01

A C-130 aircraft was flown over the Sam Houston National Forest on March 21, 1973 at 10,000 feet altitude to collect multispectral scanner (MSS) data. Existing textural and spatial automatic processing techniques were used to classify the MSS imagery into specified timber categories. Several classification experiments were performed on this data using features selected from the spectral bands and a textural transform band. The results indicate that (1) spatial post-processing a classified image can cut the classification error to 1/2 or 1/3 of its initial value, (2) spatial post-processing the classified image using combined spectral and textural features produces a resulting image with less error than post-processing a classified image using only spectral features and (3) classification without spatial post processing using the combined spectral textural features tends to produce about the same error rate as a classification without spatial post processing using only spectral features.

Stereotype threat can reduce older adults' memory errors

PubMed Central

Barber, Sarah J.; Mather, Mara

2014-01-01

Stereotype threat often incurs the cost of reducing the amount of information that older adults accurately recall. In the current research we tested whether stereotype threat can also benefit memory. According to the regulatory focus account of stereotype threat, threat induces a prevention focus in which people become concerned with avoiding errors of commission and are sensitive to the presence or absence of losses within their environment (Seibt & Förster, 2004). Because of this, we predicted that stereotype threat might reduce older adults' memory errors. Results were consistent with this prediction. Older adults under stereotype threat had lower intrusion rates during free-recall tests (Experiments 1 & 2). They also reduced their false alarms and adopted more conservative response criteria during a recognition test (Experiment 2). Thus, stereotype threat can decrease older adults' false memories, albeit at the cost of fewer veridical memories, as well. PMID:24131297

Detecting Paroxysmal Coughing from Pertussis Cases Using Voice Recognition Technology

PubMed Central

Parker, Danny; Picone, Joseph; Harati, Amir; Lu, Shuang; Jenkyns, Marion H.; Polgreen, Philip M.

2013-01-01

Background Pertussis is highly contagious; thus, prompt identification of cases is essential to control outbreaks. Clinicians experienced with the disease can easily identify classic cases, where patients have bursts of rapid coughing followed by gasps, and a characteristic whooping sound. However, many clinicians have never seen a case, and thus may miss initial cases during an outbreak. The purpose of this project was to use voice-recognition software to distinguish pertussis coughs from croup and other coughs. Methods We collected a series of recordings representing pertussis, croup and miscellaneous coughing by children. We manually categorized coughs as either pertussis or non-pertussis, and extracted features for each category. We used Mel-frequency cepstral coefficients (MFCC), a sampling rate of 16 KHz, a frame Duration of 25 msec, and a frame rate of 10 msec. The coughs were filtered. Each cough was divided into 3 sections of proportion 3-4-3. The average of the 13 MFCCs for each section was computed and made into a 39-element feature vector used for the classification. We used the following machine learning algorithms: Neural Networks, K-Nearest Neighbor (KNN), and a 200 tree Random Forest (RF). Data were reserved for cross-validation of the KNN and RF. The Neural Network was trained 100 times, and the averaged results are presented. Results After categorization, we had 16 examples of non-pertussis coughs and 31 examples of pertussis coughs. Over 90% of all pertussis coughs were properly classified as pertussis. The error rates were: Type I errors of 7%, 12%, and 25% and Type II errors of 8%, 0%, and 0%, using the Neural Network, Random Forest, and KNN, respectively. Conclusion Our results suggest that we can build a robust classifier to assist clinicians and the public to help identify pertussis cases in children presenting with typical symptoms. PMID:24391730

Drug error in paediatric anaesthesia: current status and where to go now.

PubMed

Anderson, Brian J

2018-06-01

Medication errors in paediatric anaesthesia and the perioperative setting continue to occur despite widespread recognition of the problem and published advice for reduction of this predicament at international, national, local and individual levels. Current literature was reviewed to ascertain drug error rates and to appraise causes and proposed solutions to reduce these errors. The medication error incidence remains high. There is documentation of reduction through identification of causes with consequent education and application of safety analytics and quality improvement programs in anaesthesia departments. Children remain at higher risk than adults because of additional complexities such as drug dose calculations, increased susceptibility to some adverse effects and changes associated with growth and maturation. Major improvements are best made through institutional system changes rather than a commitment to do better on the part of each practitioner. Medication errors in paediatric anaesthesia represent an important risk to children and most are avoidable. There is now an understanding of the genesis of adverse drug events and this understanding should facilitate the implementation of known effective countermeasures. An institution-wide commitment and strategy are the basis for a worthwhile and sustained improvement in medication safety.

Deep neural network using color and synthesized three-dimensional shape for face recognition

NASA Astrophysics Data System (ADS)

Rhee, Seon-Min; Yoo, ByungIn; Han, Jae-Joon; Hwang, Wonjun

2017-03-01

We present an approach for face recognition using synthesized three-dimensional (3-D) shape information together with two-dimensional (2-D) color in a deep convolutional neural network (DCNN). As 3-D facial shape is hardly affected by the extrinsic 2-D texture changes caused by illumination, make-up, and occlusions, it could provide more reliable complementary features in harmony with the 2-D color feature in face recognition. Unlike other approaches that use 3-D shape information with the help of an additional depth sensor, our approach generates a personalized 3-D face model by using only face landmarks in the 2-D input image. Using the personalized 3-D face model, we generate a frontalized 2-D color facial image as well as 3-D facial images (e.g., a depth image and a normal image). In our DCNN, we first feed 2-D and 3-D facial images into independent convolutional layers, where the low-level kernels are successfully learned according to their own characteristics. Then, we merge them and feed into higher-level layers under a single deep neural network. Our proposed approach is evaluated with labeled faces in the wild dataset and the results show that the error rate of the verification rate at false acceptance rate 1% is improved by up to 32.1% compared with the baseline where only a 2-D color image is used.

Multilayer perceptron, fuzzy sets, and classification

NASA Technical Reports Server (NTRS)

Pal, Sankar K.; Mitra, Sushmita

1992-01-01

A fuzzy neural network model based on the multilayer perceptron, using the back-propagation algorithm, and capable of fuzzy classification of patterns is described. The input vector consists of membership values to linguistic properties while the output vector is defined in terms of fuzzy class membership values. This allows efficient modeling of fuzzy or uncertain patterns with appropriate weights being assigned to the backpropagated errors depending upon the membership values at the corresponding outputs. During training, the learning rate is gradually decreased in discrete steps until the network converges to a minimum error solution. The effectiveness of the algorithm is demonstrated on a speech recognition problem. The results are compared with those of the conventional MLP, the Bayes classifier, and the other related models.

Acceptance threshold hypothesis is supported by chemical similarity of cuticular hydrocarbons in a stingless bee, Melipona asilvai.

PubMed

Nascimento, D L; Nascimento, F S

2012-11-01

The ability to discriminate nestmates from non-nestmates in insect societies is essential to protect colonies from conspecific invaders. The acceptance threshold hypothesis predicts that organisms whose recognition systems classify recipients without errors should optimize the balance between acceptance and rejection. In this process, cuticular hydrocarbons play an important role as cues of recognition in social insects. The aims of this study were to determine whether guards exhibit a restrictive level of rejection towards chemically distinct individuals, becoming more permissive during the encounters with either nestmate or non-nestmate individuals bearing chemically similar profiles. The study demonstrates that Melipona asilvai (Hymenoptera: Apidae: Meliponini) guards exhibit a flexible system of nestmate recognition according to the degree of chemical similarity between the incoming forager and its own cuticular hydrocarbons profile. Guards became less restrictive in their acceptance rates when they encounter non-nestmates with highly similar chemical profiles, which they probably mistake for nestmates, hence broadening their acceptance level.

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

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

McClanahan, Richard; De Leon, Phillip L.

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, wemore » can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.« less

Reducing computation in an i-vector speaker recognition system using a tree-structured universal background model

DOE PAGES

McClanahan, Richard; De Leon, Phillip L.

2014-08-20

The majority of state-of-the-art speaker recognition systems (SR) utilize speaker models that are derived from an adapted universal background model (UBM) in the form of a Gaussian mixture model (GMM). This is true for GMM supervector systems, joint factor analysis systems, and most recently i-vector systems. In all of the identified systems, the posterior probabilities and sufficient statistics calculations represent a computational bottleneck in both enrollment and testing. We propose a multi-layered hash system, employing a tree-structured GMM–UBM which uses Runnalls’ Gaussian mixture reduction technique, in order to reduce the number of these calculations. Moreover, with this tree-structured hash, wemore » can trade-off reduction in computation with a corresponding degradation of equal error rate (EER). As an example, we also reduce this computation by a factor of 15× while incurring less than 10% relative degradation of EER (or 0.3% absolute EER) when evaluated with NIST 2010 speaker recognition evaluation (SRE) telephone data.« less

Can we recognize horses by their ocular biometric traits using deep convolutional neural networks?

NASA Astrophysics Data System (ADS)

Trokielewicz, Mateusz; Szadkowski, Mateusz

2017-08-01

This paper aims at determining the viability of horse recognition by the means of ocular biometrics and deep convolutional neural networks (deep CNNs). Fast and accurate identification of race horses before racing is crucial for ensuring that exactly the horses that were declared are participating, using methods that are non-invasive and friendly to these delicate animals. As typical iris recognition methods require lot of fine-tuning of the method parameters and high-quality data, CNNs seem like a natural candidate to be applied for recognition thanks to their potentially excellent abilities in describing texture, combined with ease of implementation in an end-to-end manner. Also, with such approach we can easily utilize both iris and periocular features without constructing complicated algorithms for each. We thus present a simple CNN classifier, able to correctly identify almost 80% of the samples in an identification scenario, and give equal error rate (EER) of less than 10% in a verification scenario.

Determining the optimal window length for pattern recognition-based myoelectric control: balancing the competing effects of classification error and controller delay.

PubMed

Smith, Lauren H; Hargrove, Levi J; Lock, Blair A; Kuiken, Todd A

2011-04-01

Pattern recognition-based control of myoelectric prostheses has shown great promise in research environments, but has not been optimized for use in a clinical setting. To explore the relationship between classification error, controller delay, and real-time controllability, 13 able-bodied subjects were trained to operate a virtual upper-limb prosthesis using pattern recognition of electromyogram (EMG) signals. Classification error and controller delay were varied by training different classifiers with a variety of analysis window lengths ranging from 50 to 550 ms and either two or four EMG input channels. Offline analysis showed that classification error decreased with longer window lengths (p < 0.01 ). Real-time controllability was evaluated with the target achievement control (TAC) test, which prompted users to maneuver the virtual prosthesis into various target postures. The results indicated that user performance improved with lower classification error (p < 0.01 ) and was reduced with longer controller delay (p < 0.01 ), as determined by the window length. Therefore, both of these effects should be considered when choosing a window length; it may be beneficial to increase the window length if this results in a reduced classification error, despite the corresponding increase in controller delay. For the system employed in this study, the optimal window length was found to be between 150 and 250 ms, which is within acceptable controller delays for conventional multistate amplitude controllers.

Online 3D Ear Recognition by Combining Global and Local Features.

PubMed

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%.

Online 3D Ear Recognition by Combining Global and Local Features

PubMed Central

Liu, Yahui; Zhang, Bob; Lu, Guangming; Zhang, David

2016-01-01

The three-dimensional shape of the ear has been proven to be a stable candidate for biometric authentication because of its desirable properties such as universality, uniqueness, and permanence. In this paper, a special laser scanner designed for online three-dimensional ear acquisition was described. Based on the dataset collected by our scanner, two novel feature classes were defined from a three-dimensional ear image: the global feature class (empty centers and angles) and local feature class (points, lines, and areas). These features are extracted and combined in an optimal way for three-dimensional ear recognition. Using a large dataset consisting of 2,000 samples, the experimental results illustrate the effectiveness of fusing global and local features, obtaining an equal error rate of 2.2%. PMID:27935955

Examination of soldier target recognition with direct view optics

NASA Astrophysics Data System (ADS)

Long, Frederick H.; Larkin, Gabriella; Bisordi, Danielle; Dorsey, Shauna; Marianucci, Damien; Goss, Lashawnta; Bastawros, Michael; Misiuda, Paul; Rodgers, Glenn; Mazz, John P.

2017-10-01

Target recognition and identification is a problem of great military and scientific importance. To examine the correlation between target recognition and optical magnification, ten U.S. Army soldiers were tasked with identifying letters on targets at 800 and 1300 meters away. Letters were used since they are a standard method for measuring visual acuity. The letters were approximately 90 cm high, which is the size of a well-known rifle. Four direct view optics with angular magnifications of 1.5x, 4x, 6x, and 9x were used. The goal of this approach was to measure actual probabilities for correct target identification. Previous scientific literature suggests that target recognition can be modeled as a linear response problem in angular frequency space using the established values for the contrast sensitivity function for a healthy human eye and the experimentally measured modulation transfer function of the optic. At the 9x magnification, the soldiers could identify the letters with almost no errors (i.e., 97% probability of correct identification). At lower magnification, errors in letter identification were more frequent. The identification errors were not random but occurred most frequently with a few pairs of letters (e.g., O and Q), which is consistent with the literature for letter recognition. In addition, in the small subject sample of ten soldiers, there was considerable variation in the observer recognition capability at 1.5x and a range of 800 meters. This can be directly attributed to the variation in the observer visual acuity.

Effects of Aging and IQ on Item and Associative Memory

ERIC Educational Resources Information Center

Ratcliff, Roger; Thapar, Anjali; McKoon, Gail

2011-01-01

The effects of aging and IQ on performance were examined in 4 memory tasks: item recognition, associative recognition, cued recall, and free recall. For item and associative recognition, accuracy and the response time (RT) distributions for correct and error responses were explained by Ratcliff's (1978) diffusion model at the level of individual…

The cingulo-opercular network provides word-recognition benefit.

PubMed

Vaden, Kenneth I; Kuchinsky, Stefanie E; Cute, Stephanie L; Ahlstrom, Jayne B; Dubno, Judy R; Eckert, Mark A

2013-11-27

Recognizing speech in difficult listening conditions requires considerable focus of attention that is often demonstrated by elevated activity in putative attention systems, including the cingulo-opercular network. We tested the prediction that elevated cingulo-opercular activity provides word-recognition benefit on a subsequent trial. Eighteen healthy, normal-hearing adults (10 females; aged 20-38 years) performed word recognition (120 trials) in multi-talker babble at +3 and +10 dB signal-to-noise ratios during a sparse sampling functional magnetic resonance imaging (fMRI) experiment. Blood oxygen level-dependent (BOLD) contrast was elevated in the anterior cingulate cortex, anterior insula, and frontal operculum in response to poorer speech intelligibility and response errors. These brain regions exhibited significantly greater correlated activity during word recognition compared with rest, supporting the premise that word-recognition demands increased the coherence of cingulo-opercular network activity. Consistent with an adaptive control network explanation, general linear mixed model analyses demonstrated that increased magnitude and extent of cingulo-opercular network activity was significantly associated with correct word recognition on subsequent trials. These results indicate that elevated cingulo-opercular network activity is not simply a reflection of poor performance or error but also supports word recognition in difficult listening conditions.

Interspecific aggression and character displacement of competitor recognition in Hetaerina damselflies.

PubMed

Anderson, Christopher N; Grether, Gregory F

2010-02-22

In zones of sympatry between closely related species, species recognition errors in a competitive context can cause character displacement in agonistic signals and competitor recognition functions, just as species recognition errors in a mating context can cause character displacement in mating signals and mate recognition. These two processes are difficult to distinguish because the same traits can serve as both agonistic and mating signals. One solution is to test for sympatric shifts in recognition functions. We studied competitor recognition in Hetaerina damselflies by challenging territory holders with live tethered conspecific and heterospecific intruders. Heterospecific intruders elicited less aggression than conspecific intruders in species pairs with dissimilar wing coloration (H. occisa/H. titia, H. americana/H. titia) but not in species pairs with similar wing coloration (H. occisa/H. cruentata, H. americana/H. cruentata). Natural variation in the area of black wing pigmentation on H. titia intruders correlated negatively with heterospecific aggression. To directly examine the role of wing coloration, we blackened the wings of H. occisa or H. americana intruders and measured responses of conspecific territory holders. This treatment reduced territorial aggression at multiple sites where H. titia is present, but not at allopatric sites. These results provide strong evidence for agonistic character displacement.

Impaired recognition of scary music following unilateral temporal lobe excision.

PubMed

Gosselin, Nathalie; Peretz, Isabelle; Noulhiane, Marion; Hasboun, Dominique; Beckett, Christine; Baulac, Michel; Samson, Séverine

2005-03-01

Music constitutes an ideal means to create a sense of suspense in films. However, there has been minimal investigation into the underlying cerebral organization for perceiving danger created by music. In comparison, the amygdala's role in recognition of fear in non-musical contexts has been well established. The present study sought to fill this gap in exploring how patients with amygdala resection recognize emotional expression in music. To this aim, we tested 16 patients with left (LTR; n = 8) or right (RTR; n = 8) medial temporal resection (including amygdala) for the relief of medically intractable seizures and 16 matched controls in an emotion recognition task involving instrumental music. The musical selections were purposely created to induce fear, peacefulness, happiness and sadness. Participants were asked to rate to what extent each musical passage expressed these four emotions on 10-point scales. In order to check for the presence of a perceptual problem, the same musical selections were presented to the participants in an error detection task. None of the patients was found to perform below controls in the perceptual task. In contrast, both LTR and RTR patients were found to be impaired in the recognition of scary music. Recognition of happy and sad music was normal. These findings suggest that the anteromedial temporal lobe (including the amygdala) plays a role in the recognition of danger in a musical context.

The face you recognize may not be the one you saw: memory conjunction errors in individuals with or without learning disability.

PubMed

Danielsson, Henrik; Rönnberg, Jerker; Leven, Anna; Andersson, Jan; Andersson, Karin; Lyxell, Björn

2006-06-01

Memory conjunction errors, that is, when a combination of two previously presented stimuli is erroneously recognized as previously having been seen, were investigated in a face recognition task with drawings and photographs in 23 individuals with learning disability, and 18 chronologically age-matched controls without learning disability. Compared to the controls, individuals with learning disability committed significantly more conjunction errors, feature errors (one old and one new component), but had lower correct recognition, when the results were adjusted for different guessing levels. A dual-processing approach gained more support than a binding approach. However, neither of the approaches could explain all of the results. The results of the learning disability group were only partly related to non-verbal intelligence.

Intelligent OCR Processing.

ERIC Educational Resources Information Center

Sun, Wei; And Others

1992-01-01

Identifies types and distributions of errors in text produced by optical character recognition (OCR) and proposes a process using machine learning techniques to recognize and correct errors in OCR texts. Results of experiments indicating that this strategy can reduce human interaction required for error correction are reported. (25 references)…

Speed and accuracy of dyslexic versus typical word recognition: an eye-movement investigation

PubMed Central

Kunert, Richard; Scheepers, Christoph

2014-01-01

Developmental dyslexia is often characterized by a dual deficit in both word recognition accuracy and general processing speed. While previous research into dyslexic word recognition may have suffered from speed-accuracy trade-off, the present study employed a novel eye-tracking task that is less prone to such confounds. Participants (10 dyslexics and 12 controls) were asked to look at real word stimuli, and to ignore simultaneously presented non-word stimuli, while their eye-movements were recorded. Improvements in word recognition accuracy over time were modeled in terms of a continuous non-linear function. The words' rhyme consistency and the non-words' lexicality (unpronounceable, pronounceable, pseudohomophone) were manipulated within-subjects. Speed-related measures derived from the model fits confirmed generally slower processing in dyslexics, and showed a rhyme consistency effect in both dyslexics and controls. In terms of overall error rate, dyslexics (but not controls) performed less accurately on rhyme-inconsistent words, suggesting a representational deficit for such words in dyslexics. Interestingly, neither group showed a pseudohomophone effect in speed or accuracy, which might call the task-independent pervasiveness of this effect into question. The present results illustrate the importance of distinguishing between speed- vs. accuracy-related effects for our understanding of dyslexic word recognition. PMID:25346708

When false recognition is out of control: the case of facial conjunctions.

PubMed

Jones, Todd C; Bartlett, James C

2009-03-01

In three experiments, a dual-process approach to face recognition memory is examined, with a specific focus on the idea that a recollection process can be used to retrieve configural information of a studied face. Subjects could avoid, with confidence, a recognition error to conjunction lure faces (each a reconfiguration of features from separate studied faces) or feature lure faces (each based on a set of old features and a set of new features) by recalling a studied configuration. In Experiment 1, study repetition (one vs. eight presentations) was manipulated, and in Experiments 2 and 3, retention interval over a short number of trials (0-20) was manipulated. Different measures converged on the conclusion that subjects were unable to use a recollection process to retrieve configural information in an effort to temper recognition errors for conjunction or feature lure faces. A single process, familiarity, appears to be the sole process underlying recognition of conjunction and feature faces, and familiarity contributes, perhaps in whole, to discrimination of old from conjunction faces.

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.

Familiarity and face emotion recognition in patients with schizophrenia.

PubMed

Lahera, Guillermo; Herrera, Sara; Fernández, Cristina; Bardón, Marta; de los Ángeles, Victoria; Fernández-Liria, Alberto

2014-01-01

To assess the emotion recognition in familiar and unknown faces in a sample of schizophrenic patients and healthy controls. Face emotion recognition of 18 outpatients diagnosed with schizophrenia (DSM-IVTR) and 18 healthy volunteers was assessed with two Emotion Recognition Tasks using familiar faces and unknown faces. Each subject was accompanied by 4 familiar people (parents, siblings or friends), which were photographed by expressing the 6 Ekman's basic emotions. Face emotion recognition in familiar faces was assessed with this ad hoc instrument. In each case, the patient scored (from 1 to 10) the subjective familiarity and affective valence corresponding to each person. Patients with schizophrenia not only showed a deficit in the recognition of emotions on unknown faces (p=.01), but they also showed an even more pronounced deficit on familiar faces (p=.001). Controls had a similar success rate in the unknown faces task (mean: 18 +/- 2.2) and the familiar face task (mean: 17.4 +/- 3). However, patients had a significantly lower score in the familiar faces task (mean: 13.2 +/- 3.8) than in the unknown faces task (mean: 16 +/- 2.4; p<.05). In both tests, the highest number of errors was with emotions of anger and fear. Subjectively, the patient group showed a lower level of familiarity and emotional valence to their respective relatives (p<.01). The sense of familiarity may be a factor involved in the face emotion recognition and it may be disturbed in schizophrenia. © 2013.

See-What-I-Do: Increasing Mentor and Trainee Sense of Co-Presence in Trauma Surgeries with the STAR Platform

DTIC Science & Technology

2016-04-01

publications, images, and videos.  Technologies or techniques . The technique for one shot gesture recognition is a result from the research activity... shot learning concept for gesture recognition. Name: Aditya Ajay Shanghavi Project Role: Master Student Researcher Identifier (e.g. ORCID ID...use case . The transparency error depends more on the x than the z head tracking error. Head tracking is typically accurate to less than 10mm in x

Effectiveness of feature and classifier algorithms in character recognition systems

NASA Astrophysics Data System (ADS)

Wilson, Charles L.

1993-04-01

At the first Census Optical Character Recognition Systems Conference, NIST generated accuracy data for more than character recognition systems. Most systems were tested on the recognition of isolated digits and upper and lower case alphabetic characters. The recognition experiments were performed on sample sizes of 58,000 digits, and 12,000 upper and lower case alphabetic characters. The algorithms used by the 26 conference participants included rule-based methods, image-based methods, statistical methods, and neural networks. The neural network methods included Multi-Layer Perceptron's, Learned Vector Quantitization, Neocognitrons, and cascaded neural networks. In this paper 11 different systems are compared using correlations between the answers of different systems, comparing the decrease in error rate as a function of confidence of recognition, and comparing the writer dependence of recognition. This comparison shows that methods that used different algorithms for feature extraction and recognition performed with very high levels of correlation. This is true for neural network systems, hybrid systems, and statistically based systems, and leads to the conclusion that neural networks have not yet demonstrated a clear superiority to more conventional statistical methods. Comparison of these results with the models of Vapnick (for estimation problems), MacKay (for Bayesian statistical models), Moody (for effective parameterization), and Boltzmann models (for information content) demonstrate that as the limits of training data variance are approached, all classifier systems have similar statistical properties. The limiting condition can only be approached for sufficiently rich feature sets because the accuracy limit is controlled by the available information content of the training set, which must pass through the feature extraction process prior to classification.

False Recognition in Behavioral Variant Frontotemporal Dementia and Alzheimer's Disease-Disinhibition or Amnesia?

PubMed

Flanagan, Emma C; Wong, Stephanie; Dutt, Aparna; Tu, Sicong; Bertoux, Maxime; Irish, Muireann; Piguet, Olivier; Rao, Sulakshana; Hodges, John R; Ghosh, Amitabha; Hornberger, Michael

2016-01-01

Episodic memory recall processes in Alzheimer's disease (AD) and behavioral variant frontotemporal dementia (bvFTD) can be similarly impaired, whereas recognition performance is more variable. A potential reason for this variability could be false-positive errors made on recognition trials and whether these errors are due to amnesia per se or a general over-endorsement of recognition items regardless of memory. The current study addressed this issue by analysing recognition performance on the Rey Auditory Verbal Learning Test (RAVLT) in 39 bvFTD, 77 AD and 61 control participants from two centers (India, Australia), as well as disinhibition assessed using the Hayling test. Whereas both AD and bvFTD patients were comparably impaired on delayed recall, bvFTD patients showed intact recognition performance in terms of the number of correct hits. However, both patient groups endorsed significantly more false-positives than controls, and bvFTD and AD patients scored equally poorly on a sensitivity index (correct hits-false-positives). Furthermore, measures of disinhibition were significantly associated with false positives in both groups, with a stronger relationship with false-positives in bvFTD. Voxel-based morphometry analyses revealed similar neural correlates of false positive endorsement across bvFTD and AD, with both patient groups showing involvement of prefrontal and Papez circuitry regions, such as medial temporal and thalamic regions, and a DTI analysis detected an emerging but non-significant trend between false positives and decreased fornix integrity in bvFTD only. These findings suggest that false-positive errors on recognition tests relate to similar mechanisms in bvFTD and AD, reflecting deficits in episodic memory processes and disinhibition. These findings highlight that current memory tests are not sufficient to accurately distinguish between bvFTD and AD patients.

False Memory in Aging Resulting From Self-Referential Processing

PubMed Central

2013-01-01

Objectives. Referencing the self is known to enhance accurate memory, but less is known about how the strategy affects false memory, particularly for highly self-relevant information. Because older adults are more prone to false memories, we tested whether self-referencing increased false memories with age. Method. In 2 studies, older and younger adults rated adjectives for self-descriptiveness and later completed a surprise recognition test comprised of words rated previously for self-descriptiveness and novel lure words. Lure words were subsequently rated for self-descriptiveness in order to assess the impact of self-relevance on false memory. Study 2 introduced commonness judgments as a control condition, such that participants completed a recognition test on adjectives rated for commonness in addition to adjectives in the self-descriptiveness condition. Results. Across both studies, findings indicate an increased response bias to self-referencing that increased hit rates for both older and younger adults but also increased false alarms as information became more self-descriptive, particularly for older adults. Discussion. Although the present study supports previous literature showing a boost in memory for self-referenced information, the increase in false alarms, especially in older adults, highlights the potential for memory errors, particularly for information that is strongly related to the self. PMID:23576449

Random Forest-Based Recognition of Isolated Sign Language Subwords Using Data from Accelerometers and Surface Electromyographic Sensors.

PubMed

Su, Ruiliang; Chen, Xiang; Cao, Shuai; Zhang, Xu

2016-01-14

Sign language recognition (SLR) has been widely used for communication amongst the hearing-impaired and non-verbal community. This paper proposes an accurate and robust SLR framework using an improved decision tree as the base classifier of random forests. This framework was used to recognize Chinese sign language subwords using recordings from a pair of portable devices worn on both arms consisting of accelerometers (ACC) and surface electromyography (sEMG) sensors. The experimental results demonstrated the validity of the proposed random forest-based method for recognition of Chinese sign language (CSL) subwords. With the proposed method, 98.25% average accuracy was obtained for the classification of a list of 121 frequently used CSL subwords. Moreover, the random forests method demonstrated a superior performance in resisting the impact of bad training samples. When the proportion of bad samples in the training set reached 50%, the recognition error rate of the random forest-based method was only 10.67%, while that of a single decision tree adopted in our previous work was almost 27.5%. Our study offers a practical way of realizing a robust and wearable EMG-ACC-based SLR systems.

Reduction of the dimension of neural network models in problems of pattern recognition and forecasting

NASA Astrophysics Data System (ADS)

Nasertdinova, A. D.; Bochkarev, V. V.

2017-11-01

Deep neural networks with a large number of parameters are a powerful tool for solving problems of pattern recognition, prediction and classification. Nevertheless, overfitting remains a serious problem in the use of such networks. A method of solving the problem of overfitting is proposed in this article. This method is based on reducing the number of independent parameters of a neural network model using the principal component analysis, and can be implemented using existing libraries of neural computing. The algorithm was tested on the problem of recognition of handwritten symbols from the MNIST database, as well as on the task of predicting time series (rows of the average monthly number of sunspots and series of the Lorentz system were used). It is shown that the application of the principal component analysis enables reducing the number of parameters of the neural network model when the results are good. The average error rate for the recognition of handwritten figures from the MNIST database was 1.12% (which is comparable to the results obtained using the "Deep training" methods), while the number of parameters of the neural network can be reduced to 130 times.

Robust recognition of degraded machine-printed characters using complementary similarity measure and error-correction learning

NASA Astrophysics Data System (ADS)

Hagita, Norihiro; Sawaki, Minako

1995-03-01

Most conventional methods in character recognition extract geometrical features such as stroke direction, connectivity of strokes, etc., and compare them with reference patterns in a stored dictionary. Unfortunately, geometrical features are easily degraded by blurs, stains and the graphical background designs used in Japanese newspaper headlines. This noise must be removed before recognition commences, but no preprocessing method is completely accurate. This paper proposes a method for recognizing degraded characters and characters printed on graphical background designs. This method is based on the binary image feature method and uses binary images as features. A new similarity measure, called the complementary similarity measure, is used as a discriminant function. It compares the similarity and dissimilarity of binary patterns with reference dictionary patterns. Experiments are conducted using the standard character database ETL-2 which consists of machine-printed Kanji, Hiragana, Katakana, alphanumeric, an special characters. The results show that this method is much more robust against noise than the conventional geometrical feature method. It also achieves high recognition rates of over 92% for characters with textured foregrounds, over 98% for characters with textured backgrounds, over 98% for outline fonts, and over 99% for reverse contrast characters.

Effect of Context and Hearing Loss on Time-Gated Word Recognition in Children.

PubMed

Lewis, Dawna; Kopun, Judy; McCreery, Ryan; Brennan, Marc; Nishi, Kanae; Cordrey, Evan; Stelmachowicz, Pat; Moeller, Mary Pat

The purpose of this study was to examine word recognition in children who are hard of hearing (CHH) and children with normal hearing (CNH) in response to time-gated words presented in high- versus low-predictability sentences (HP, LP), where semantic cues were manipulated. Findings inform our understanding of how CHH combine cognitive-linguistic and acoustic-phonetic cues to support spoken word recognition. It was hypothesized that both groups of children would be able to make use of linguistic cues provided by HP sentences to support word recognition. CHH were expected to require greater acoustic information (more gates) than CNH to correctly identify words in the LP condition. In addition, it was hypothesized that error patterns would differ across groups. Sixteen CHH with mild to moderate hearing loss and 16 age-matched CNH participated (5 to 12 years). Test stimuli included 15 LP and 15 HP age-appropriate sentences. The final word of each sentence was divided into segments and recombined with the sentence frame to create series of sentences in which the final word was progressively longer by the gated increments. Stimuli were presented monaurally through headphones and children were asked to identify the target word at each successive gate. They also were asked to rate their confidence in their word choice using a five- or three-point scale. For CHH, the signals were processed through a hearing aid simulator. Standardized language measures were used to assess the contribution of linguistic skills. Analysis of language measures revealed that the CNH and CHH performed within the average range on language abilities. Both groups correctly recognized a significantly higher percentage of words in the HP condition than in the LP condition. Although CHH performed comparably with CNH in terms of successfully recognizing the majority of words, differences were observed in the amount of acoustic-phonetic information needed to achieve accurate word recognition. CHH needed more gates than CNH to identify words in the LP condition. CNH were significantly lower in rating their confidence in the LP condition than in the HP condition. CHH, however, were not significantly different in confidence between the conditions. Error patterns for incorrect word responses across gates and predictability varied depending on hearing status. The results of this study suggest that CHH with age-appropriate language abilities took advantage of context cues in the HP sentences to guide word recognition in a manner similar to CNH. However, in the LP condition, they required more acoustic information (more gates) than CNH for word recognition. Differences in the structure of incorrect word responses and their nomination patterns across gates for CHH compared with their peers with NH suggest variations in how these groups use limited acoustic information to select word candidates.

Predictive codes of familiarity and context during the perceptual learning of facial identities

NASA Astrophysics Data System (ADS)

Apps, Matthew A. J.; Tsakiris, Manos

2013-11-01

Face recognition is a key component of successful social behaviour. However, the computational processes that underpin perceptual learning and recognition as faces transition from unfamiliar to familiar are poorly understood. In predictive coding, learning occurs through prediction errors that update stimulus familiarity, but recognition is a function of both stimulus and contextual familiarity. Here we show that behavioural responses on a two-option face recognition task can be predicted by the level of contextual and facial familiarity in a computational model derived from predictive-coding principles. Using fMRI, we show that activity in the superior temporal sulcus varies with the contextual familiarity in the model, whereas activity in the fusiform face area covaries with the prediction error parameter that updated facial familiarity. Our results characterize the key computations underpinning the perceptual learning of faces, highlighting that the functional properties of face-processing areas conform to the principles of predictive coding.

Multi-layer cube sampling for liver boundary detection in PET-CT images.

PubMed

Liu, Xinxin; Yang, Jian; Song, Shuang; Song, Hong; Ai, Danni; Zhu, Jianjun; Jiang, Yurong; Wang, Yongtian

2018-06-01

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

A multi-views multi-learners approach towards dysarthric speech recognition using multi-nets artificial neural networks.

PubMed

Shahamiri, Seyed Reza; Salim, Siti Salwah Binti

2014-09-01

Automatic speech recognition (ASR) can be very helpful for speakers who suffer from dysarthria, a neurological disability that damages the control of motor speech articulators. Although a few attempts have been made to apply ASR technologies to sufferers of dysarthria, previous studies show that such ASR systems have not attained an adequate level of performance. In this study, a dysarthric multi-networks speech recognizer (DM-NSR) model is provided using a realization of multi-views multi-learners approach called multi-nets artificial neural networks, which tolerates variability of dysarthric speech. In particular, the DM-NSR model employs several ANNs (as learners) to approximate the likelihood of ASR vocabulary words and to deal with the complexity of dysarthric speech. The proposed DM-NSR approach was presented as both speaker-dependent and speaker-independent paradigms. In order to highlight the performance of the proposed model over legacy models, multi-views single-learner models of the DM-NSRs were also provided and their efficiencies were compared in detail. Moreover, a comparison among the prominent dysarthric ASR methods and the proposed one is provided. The results show that the DM-NSR recorded improved recognition rate by up to 24.67% and the error rate was reduced by up to 8.63% over the reference model.

Package Design Affects Accuracy Recognition for Medications.

PubMed

Endestad, Tor; Wortinger, Laura A; Madsen, Steinar; Hortemo, Sigurd

2016-12-01

Our aim was to test if highlighting and placement of substance name on medication package have the potential to reduce patient errors. An unintentional overdose of medication is a large health issue that might be linked to medication package design. In two experiments, placement, background color, and the active ingredient of generic medication packages were manipulated according to best human factors guidelines to reduce causes of labeling-related patient errors. In two experiments, we compared the original packaging with packages where we varied placement of the name, dose, and background of the active ingredient. Age-relevant differences and the effect of color on medication recognition error were tested. In Experiment 1, 59 volunteers (30 elderly and 29 young students), participated. In Experiment 2, 25 volunteers participated. The most common error was the inability to identify that two different packages contained the same active ingredient (young, 41%, and elderly, 68%). This kind of error decreased with the redesigned packages (young, 8%, and elderly, 16%). Confusion errors related to color design were reduced by two thirds in the redesigned packages compared with original generic medications. Prominent placement of substance name and dose with a band of high-contrast color support recognition of the active substance in medications. A simple modification including highlighting and placing the name of the active ingredient in the upper right-hand corner of the package helps users realize that two different packages can contain the same active substance, thus reducing the risk of inadvertent medication overdose. © 2016, Human Factors and Ergonomics Society.

Package Design Affects Accuracy Recognition for Medications

PubMed Central

Endestad, Tor; Wortinger, Laura A.; Madsen, Steinar; Hortemo, Sigurd

2016-01-01

Objective: Our aim was to test if highlighting and placement of substance name on medication package have the potential to reduce patient errors. Background: An unintentional overdose of medication is a large health issue that might be linked to medication package design. In two experiments, placement, background color, and the active ingredient of generic medication packages were manipulated according to best human factors guidelines to reduce causes of labeling-related patient errors. Method: In two experiments, we compared the original packaging with packages where we varied placement of the name, dose, and background of the active ingredient. Age-relevant differences and the effect of color on medication recognition error were tested. In Experiment 1, 59 volunteers (30 elderly and 29 young students), participated. In Experiment 2, 25 volunteers participated. Results: The most common error was the inability to identify that two different packages contained the same active ingredient (young, 41%, and elderly, 68%). This kind of error decreased with the redesigned packages (young, 8%, and elderly, 16%). Confusion errors related to color design were reduced by two thirds in the redesigned packages compared with original generic medications. Conclusion: Prominent placement of substance name and dose with a band of high-contrast color support recognition of the active substance in medications. Application: A simple modification including highlighting and placing the name of the active ingredient in the upper right-hand corner of the package helps users realize that two different packages can contain the same active substance, thus reducing the risk of inadvertent medication overdose. PMID:27591209

Use of simulation to assess electronic health record safety in the intensive care unit: a pilot study

PubMed Central

March, Christopher A; Steiger, David; Scholl, Gretchen; Mohan, Vishnu; Hersh, William R; Gold, Jeffrey A

2013-01-01

Objective To establish the role of high-fidelity simulation training to test the efficacy and safety of the electronic health record (EHR)–user interface within the intensive care unit (ICU) environment. Design Prospective pilot study. Setting Medical ICU in an academic medical centre. Participants Postgraduate medical trainees. Interventions A 5-day-simulated ICU patient was developed in the EHR including labs, hourly vitals, medication administration, ventilator settings, nursing and notes. Fourteen medical issues requiring recognition and subsequent changes in management were included. Issues were chosen based on their frequency of occurrence within the ICU and their ability to test different aspects of the EHR–user interface. ICU residents, blinded to the presence of medical errors within the case, were provided a sign-out and given 10 min to review the case in the EHR. They then presented the case with their management suggestions to an attending physician. Participants were graded on the number of issues identified. All participants were provided with immediate feedback upon completion of the simulation. Primary and secondary outcomes To determine the frequency of error recognition in an EHR simulation. To determine factors associated with improved performance in the simulation. Results 38 participants including 9 interns, 10 residents and 19 fellows were tested. The average error recognition rate was 41% (range 6–73%), which increased slightly with the level of training (35%, 41% and 50% for interns, residents, and fellows, respectively). Over-sedation was the least-recognised error (16%); poor glycemic control was most often recognised (68%). Only 32% of the participants recognised inappropriate antibiotic dosing. Performance correlated with the total number of screens used (p=0.03). Conclusions Despite development of comprehensive EHRs, there remain significant gaps in identifying dangerous medical management issues. This gap remains despite high levels of medical training, suggesting that EHR-specific training may be beneficial. Simulation provides a novel tool in order to both identify these gaps as well as foster EHR-specific training. PMID:23578685

Dependency of Optimal Parameters of the IRIS Template on Image Quality and Border Detection Error

NASA Astrophysics Data System (ADS)

Matveev, I. A.; Novik, V. P.

2017-05-01

Generation of a template containing spatial-frequency features of iris is an important stage of identification. The template is obtained by a wavelet transform in an image region specified by iris borders. One of the main characteristics of the identification system is the value of recognition error, equal error rate (EER) is used as criterion here. The optimal values (in sense of minimizing the EER) of wavelet transform parameters depend on many factors: image quality, sharpness, size of characteristic objects, etc. It is hard to isolate these factors and their influences. The work presents an attempt to study an influence of following factors to EER: iris segmentation precision, defocus level, noise level. Several public domain iris image databases were involved in experiments. The images were subjected to modelled distortions of said types. The dependencies of wavelet parameter and EER values from the distortion levels were build. It is observed that the increase of the segmentation error and image noise leads to the increase of the optimal wavelength of the wavelets, whereas the increase of defocus level leads to decreasing of this value.

The relevance of error analysis in graphical symbols evaluation.

PubMed

Piamonte, D P

1999-01-01

In an increasing number of modern tools and devices, small graphical symbols appear simultaneously in sets as parts of the human-machine interfaces. The presence of each symbol can influence the other's recognizability and correct association to its intended referents. Thus, aside from correct associations, it is equally important to perform certain error analysis of the wrong answers, misses, confusions, and even lack of answers. This research aimed to show how such error analyses could be valuable in evaluating graphical symbols especially across potentially different user groups. The study tested 3 sets of icons representing 7 videophone functions. The methods involved parameters such as hits, confusions, missing values, and misses. The association tests showed similar hit rates of most symbols across the majority of the participant groups. However, exploring the error patterns helped detect differences in the graphical symbols' performances between participant groups, which otherwise seemed to have similar levels of recognition. These are very valuable not only in determining the symbols to be retained, replaced or re-designed, but also in formulating instructions and other aids in learning to use new products faster and more satisfactorily.

Clustering of tethered satellite system simulation data by an adaptive neuro-fuzzy algorithm

NASA Technical Reports Server (NTRS)

Mitra, Sunanda; Pemmaraju, Surya

1992-01-01

Recent developments in neuro-fuzzy systems indicate that the concepts of adaptive pattern recognition, when used to identify appropriate control actions corresponding to clusters of patterns representing system states in dynamic nonlinear control systems, may result in innovative designs. A modular, unsupervised neural network architecture, in which fuzzy learning rules have been embedded is used for on-line identification of similar states. The architecture and control rules involved in Adaptive Fuzzy Leader Clustering (AFLC) allow this system to be incorporated in control systems for identification of system states corresponding to specific control actions. We have used this algorithm to cluster the simulation data of Tethered Satellite System (TSS) to estimate the range of delta voltages necessary to maintain the desired length rate of the tether. The AFLC algorithm is capable of on-line estimation of the appropriate control voltages from the corresponding length error and length rate error without a priori knowledge of their membership functions and familarity with the behavior of the Tethered Satellite System.

Reducing errors from the electronic transcription of data collected on paper forms: a research data case study.

PubMed

Wahi, Monika M; Parks, David V; Skeate, Robert C; Goldin, Steven B

2008-01-01

We conducted a reliability study comparing single data entry (SE) into a Microsoft Excel spreadsheet to entry using the existing forms (EF) feature of the Teleforms software system, in which optical character recognition is used to capture data off of paper forms designed in non-Teleforms software programs. We compared the transcription of data from multiple paper forms from over 100 research participants representing almost 20,000 data entry fields. Error rates for SE were significantly lower than those for EF, so we chose SE for data entry in our study. Data transcription strategies from paper to electronic format should be chosen based on evidence from formal evaluations, and their design should be contemplated during the paper forms development stage.

Reducing Errors from the Electronic Transcription of Data Collected on Paper Forms: A Research Data Case Study

PubMed Central

Wahi, Monika M.; Parks, David V.; Skeate, Robert C.; Goldin, Steven B.

2008-01-01

We conducted a reliability study comparing single data entry (SE) into a Microsoft Excel spreadsheet to entry using the existing forms (EF) feature of the Teleforms software system, in which optical character recognition is used to capture data off of paper forms designed in non-Teleforms software programs. We compared the transcription of data from multiple paper forms from over 100 research participants representing almost 20,000 data entry fields. Error rates for SE were significantly lower than those for EF, so we chose SE for data entry in our study. Data transcription strategies from paper to electronic format should be chosen based on evidence from formal evaluations, and their design should be contemplated during the paper forms development stage. PMID:18308994

Recognizing familiar objects by hand and foot: Haptic shape perception generalizes to inputs from unusual locations and untrained body parts.

PubMed

Lawson, Rebecca

2014-02-01

The limits of generalization of our 3-D shape recognition system to identifying objects by touch was investigated by testing exploration at unusual locations and using untrained effectors. In Experiments 1 and 2, people found identification by hand of real objects, plastic 3-D models of objects, and raised line drawings placed in front of themselves no easier than when exploration was behind their back. Experiment 3 compared one-handed, two-handed, one-footed, and two-footed haptic object recognition of familiar objects. Recognition by foot was slower (7 vs. 13 s) and much less accurate (9 % vs. 47 % errors) than recognition by either one or both hands. Nevertheless, item difficulty was similar across hand and foot exploration, and there was a strong correlation between an individual's hand and foot performance. Furthermore, foot recognition was better with the largest 20 of the 80 items (32 % errors), suggesting that physical limitations hampered exploration by foot. Thus, object recognition by hand generalized efficiently across the spatial location of stimuli, while object recognition by foot seemed surprisingly good given that no prior training was provided. Active touch (haptics) thus efficiently extracts 3-D shape information and accesses stored representations of familiar objects from novel modes of input.

Source memory errors in schizophrenia, hallucinations and negative symptoms: a synthesis of research findings.

PubMed

Brébion, G; Ohlsen, R I; Bressan, R A; David, A S

2012-12-01

Previous research has shown associations between source memory errors and hallucinations in patients with schizophrenia. We bring together here findings from a broad memory investigation to specify better the type of source memory failure that is associated with auditory and visual hallucinations. Forty-one patients with schizophrenia and 43 healthy participants underwent a memory task involving recall and recognition of lists of words, recognition of pictures, memory for temporal and spatial context of presentation of the stimuli, and remembering whether target items were presented as words or pictures. False recognition of words and pictures was associated with hallucination scores. The extra-list intrusions in free recall were associated with verbal hallucinations whereas the intra-list intrusions were associated with a global hallucination score. Errors in discriminating the temporal context of word presentation and the spatial context of picture presentation were associated with auditory hallucinations. The tendency to remember verbal labels of items as pictures of these items was associated with visual hallucinations. Several memory errors were also inversely associated with affective flattening and anhedonia. Verbal and visual hallucinations are associated with confusion between internal verbal thoughts or internal visual images and perception. In addition, auditory hallucinations are associated with failure to process or remember the context of presentation of the events. Certain negative symptoms have an opposite effect on memory errors.

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications

PubMed Central

Park, Keunyeol; Song, Minkyu

2018-01-01

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm2 with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency. PMID:29495273

The Design of a Single-Bit CMOS Image Sensor for Iris Recognition Applications.

PubMed

Park, Keunyeol; Song, Minkyu; Kim, Soo Youn

2018-02-24

This paper presents a single-bit CMOS image sensor (CIS) that uses a data processing technique with an edge detection block for simple iris segmentation. In order to recognize the iris image, the image sensor conventionally captures high-resolution image data in digital code, extracts the iris data, and then compares it with a reference image through a recognition algorithm. However, in this case, the frame rate decreases by the time required for digital signal conversion of multi-bit digital data through the analog-to-digital converter (ADC) in the CIS. In order to reduce the overall processing time as well as the power consumption, we propose a data processing technique with an exclusive OR (XOR) logic gate to obtain single-bit and edge detection image data instead of multi-bit image data through the ADC. In addition, we propose a logarithmic counter to efficiently measure single-bit image data that can be applied to the iris recognition algorithm. The effective area of the proposed single-bit image sensor (174 × 144 pixel) is 2.84 mm² with a 0.18 μm 1-poly 4-metal CMOS image sensor process. The power consumption of the proposed single-bit CIS is 2.8 mW with a 3.3 V of supply voltage and 520 frame/s of the maximum frame rates. The error rate of the ADC is 0.24 least significant bit (LSB) on an 8-bit ADC basis at a 50 MHz sampling frequency.

Invention and validation of an automated camera system that uses optical character recognition to identify patient name mislabeled samples.

PubMed

Hawker, Charles D; McCarthy, William; Cleveland, David; Messinger, Bonnie L

2014-03-01

Mislabeled samples are a serious problem in most clinical laboratories. Published error rates range from 0.39/1000 to as high as 1.12%. Standardization of bar codes and label formats has not yet achieved the needed improvement. The mislabel rate in our laboratory, although low compared with published rates, prompted us to seek a solution to achieve zero errors. To reduce or eliminate our mislabeled samples, we invented an automated device using 4 cameras to photograph the outside of a sample tube. The system uses optical character recognition (OCR) to look for discrepancies between the patient name in our laboratory information system (LIS) vs the patient name on the customer label. All discrepancies detected by the system's software then require human inspection. The system was installed on our automated track and validated with production samples. We obtained 1 009 830 images during the validation period, and every image was reviewed. OCR passed approximately 75% of the samples, and no mislabeled samples were passed. The 25% failed by the system included 121 samples actually mislabeled by patient name and 148 samples with spelling discrepancies between the patient name on the customer label and the patient name in our LIS. Only 71 of the 121 mislabeled samples detected by OCR were found through our normal quality assurance process. We have invented an automated camera system that uses OCR technology to identify potential mislabeled samples. We have validated this system using samples transported on our automated track. Full implementation of this technology offers the possibility of zero mislabeled samples in the preanalytic stage.

Correlation-based pattern recognition for implantable defibrillators.

PubMed Central

Wilkins, J.

1996-01-01

An estimated 300,000 Americans die each year from cardiac arrhythmias. Historically, drug therapy or surgery were the only treatment options available for patients suffering from arrhythmias. Recently, implantable arrhythmia management devices have been developed. These devices allow abnormal cardiac rhythms to be sensed and corrected in vivo. Proper arrhythmia classification is critical to selecting the appropriate therapeutic intervention. The classification problem is made more challenging by the power/computation constraints imposed by the short battery life of implantable devices. Current devices utilize heart rate-based classification algorithms. Although easy to implement, rate-based approaches have unacceptably high error rates in distinguishing supraventricular tachycardia (SVT) from ventricular tachycardia (VT). Conventional morphology assessment techniques used in ECG analysis often require too much computation to be practical for implantable devices. In this paper, a computationally-efficient, arrhythmia classification architecture using correlation-based morphology assessment is presented. The architecture classifies individuals heart beats by assessing similarity between an incoming cardiac signal vector and a series of prestored class templates. A series of these beat classifications are used to make an overall rhythm assessment. The system makes use of several new results in the field of pattern recognition. The resulting system achieved excellent accuracy in discriminating SVT and VT. PMID:8947674

Emotional facial recognition in proactive and reactive violent offenders.

PubMed

Philipp-Wiegmann, Florence; Rösler, Michael; Retz-Junginger, Petra; Retz, Wolfgang

2017-10-01

The purpose of this study is to analyse individual differences in the ability of emotional facial recognition in violent offenders, who were characterised as either reactive or proactive in relation to their offending. In accordance with findings of our previous study, we expected higher impairments in facial recognition in reactive than proactive violent offenders. To assess the ability to recognize facial expressions, the computer-based Facial Emotional Expression Labeling Test (FEEL) was performed. Group allocation of reactive und proactive violent offenders and assessment of psychopathic traits were performed by an independent forensic expert using rating scales (PROREA, PCL-SV). Compared to proactive violent offenders and controls, the performance of emotion recognition in the reactive offender group was significantly lower, both in total and especially in recognition of negative emotions such as anxiety (d = -1.29), sadness (d = -1.54), and disgust (d = -1.11). Furthermore, reactive violent offenders showed a tendency to interpret non-anger emotions as anger. In contrast, proactive violent offenders performed as well as controls. General and specific deficits in reactive violent offenders are in line with the results of our previous study and correspond to predictions of the Integrated Emotion System (IES, 7) and the hostile attribution processes (21). Due to the different error pattern in the FEEL test, the theoretical distinction between proactive and reactive aggression can be supported based on emotion recognition, even though aggression itself is always a heterogeneous act rather than a distinct one-dimensional concept.

Modeling guidance and recognition in categorical search: bridging human and computer object detection.

PubMed

Zelinsky, Gregory J; Peng, Yifan; Berg, Alexander C; Samaras, Dimitris

2013-10-08

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery.

Modeling guidance and recognition in categorical search: Bridging human and computer object detection

PubMed Central

Zelinsky, Gregory J.; Peng, Yifan; Berg, Alexander C.; Samaras, Dimitris

2013-01-01

Search is commonly described as a repeating cycle of guidance to target-like objects, followed by the recognition of these objects as targets or distractors. Are these indeed separate processes using different visual features? We addressed this question by comparing observer behavior to that of support vector machine (SVM) models trained on guidance and recognition tasks. Observers searched for a categorically defined teddy bear target in four-object arrays. Target-absent trials consisted of random category distractors rated in their visual similarity to teddy bears. Guidance, quantified as first-fixated objects during search, was strongest for targets, followed by target-similar, medium-similarity, and target-dissimilar distractors. False positive errors to first-fixated distractors also decreased with increasing dissimilarity to the target category. To model guidance, nine teddy bear detectors, using features ranging in biological plausibility, were trained on unblurred bears then tested on blurred versions of the same objects appearing in each search display. Guidance estimates were based on target probabilities obtained from these detectors. To model recognition, nine bear/nonbear classifiers, trained and tested on unblurred objects, were used to classify the object that would be fixated first (based on the detector estimates) as a teddy bear or a distractor. Patterns of categorical guidance and recognition accuracy were modeled almost perfectly by an HMAX model in combination with a color histogram feature. We conclude that guidance and recognition in the context of search are not separate processes mediated by different features, and that what the literature knows as guidance is really recognition performed on blurred objects viewed in the visual periphery. PMID:24105460

Dereverberation and denoising based on generalized spectral subtraction by multi-channel LMS algorithm using a small-scale microphone array

NASA Astrophysics Data System (ADS)

Wang, Longbiao; Odani, Kyohei; Kai, Atsuhiko

2012-12-01

A blind dereverberation method based on power spectral subtraction (SS) using a multi-channel least mean squares algorithm was previously proposed to suppress the reverberant speech without additive noise. The results of isolated word speech recognition experiments showed that this method achieved significant improvements over conventional cepstral mean normalization (CMN) in a reverberant environment. In this paper, we propose a blind dereverberation method based on generalized spectral subtraction (GSS), which has been shown to be effective for noise reduction, instead of power SS. Furthermore, we extend the missing feature theory (MFT), which was initially proposed to enhance the robustness of additive noise, to dereverberation. A one-stage dereverberation and denoising method based on GSS is presented to simultaneously suppress both the additive noise and nonstationary multiplicative noise (reverberation). The proposed dereverberation method based on GSS with MFT is evaluated on a large vocabulary continuous speech recognition task. When the additive noise was absent, the dereverberation method based on GSS with MFT using only 2 microphones achieves a relative word error reduction rate of 11.4 and 32.6% compared to the dereverberation method based on power SS and the conventional CMN, respectively. For the reverberant and noisy speech, the dereverberation and denoising method based on GSS achieves a relative word error reduction rate of 12.8% compared to the conventional CMN with GSS-based additive noise reduction method. We also analyze the effective factors of the compensation parameter estimation for the dereverberation method based on SS, such as the number of channels (the number of microphones), the length of reverberation to be suppressed, and the length of the utterance used for parameter estimation. The experimental results showed that the SS-based method is robust in a variety of reverberant environments for both isolated and continuous speech recognition and under various parameter estimation conditions.

A post-processing algorithm for time domain pitch trackers

NASA Astrophysics Data System (ADS)

Specker, P.

1983-01-01

This paper describes a powerful post-processing algorithm for time-domain pitch trackers. On two successive passes, the post-processing algorithm eliminates errors produced during a first pass by a time-domain pitch tracker. During the second pass, incorrect pitch values are detected as outliers by computing the distribution of values over a sliding 80 msec window. During the third pass (based on artificial intelligence techniques), remaining pitch pulses are used as anchor points to reconstruct the pitch train from the original waveform. The algorithm produced a decrease in the error rate from 21% obtained with the original time domain pitch tracker to 2% for isolated words and sentences produced in an office environment by 3 male and 3 female talkers. In a noisy computer room errors decreased from 52% to 2.9% for the same stimuli produced by 2 male talkers. The algorithm is efficient, accurate, and resistant to noise. The fundamental frequency micro-structure is tracked sufficiently well to be used in extracting phonetic features in a feature-based recognition system.

Fast Face-Recognition Optical Parallel Correlator Using High Accuracy Correlation Filter

NASA Astrophysics Data System (ADS)

Watanabe, Eriko; Kodate, Kashiko

2005-11-01

We designed and fabricated a fully automatic fast face recognition optical parallel correlator [E. Watanabe and K. Kodate: Appl. Opt. 44 (2005) 5666] based on the VanderLugt principle. The implementation of an as-yet unattained ultra high-speed system was aided by reconfiguring the system to make it suitable for easier parallel processing, as well as by composing a higher accuracy correlation filter and high-speed ferroelectric liquid crystal-spatial light modulator (FLC-SLM). In running trial experiments using this system (dubbed FARCO), we succeeded in acquiring remarkably low error rates of 1.3% for false match rate (FMR) and 2.6% for false non-match rate (FNMR). Given the results of our experiments, the aim of this paper is to examine methods of designing correlation filters and arranging database image arrays for even faster parallel correlation, underlining the issues of calculation technique, quantization bit rate, pixel size and shift from optical axis. The correlation filter has proved its excellent performance and higher precision than classical correlation and joint transform correlator (JTC). Moreover, arrangement of multi-object reference images leads to 10-channel correlation signals, as sharply marked as those of a single channel. This experiment result demonstrates great potential for achieving the process speed of 10000 face/s.

Joint Denoising/Compression of Image Contours via Shape Prior and Context Tree

NASA Astrophysics Data System (ADS)

Zheng, Amin; Cheung, Gene; Florencio, Dinei

2018-07-01

With the advent of depth sensing technologies, the extraction of object contours in images---a common and important pre-processing step for later higher-level computer vision tasks like object detection and human action recognition---has become easier. However, acquisition noise in captured depth images means that detected contours suffer from unavoidable errors. In this paper, we propose to jointly denoise and compress detected contours in an image for bandwidth-constrained transmission to a client, who can then carry out aforementioned application-specific tasks using the decoded contours as input. We first prove theoretically that in general a joint denoising / compression approach can outperform a separate two-stage approach that first denoises then encodes contours lossily. Adopting a joint approach, we first propose a burst error model that models typical errors encountered in an observed string y of directional edges. We then formulate a rate-constrained maximum a posteriori (MAP) problem that trades off the posterior probability p(x'|y) of an estimated string x' given y with its code rate R(x'). We design a dynamic programming (DP) algorithm that solves the posed problem optimally, and propose a compact context representation called total suffix tree (TST) that can reduce complexity of the algorithm dramatically. Experimental results show that our joint denoising / compression scheme outperformed a competing separate scheme in rate-distortion performance noticeably.

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…

On-chip learning of hyper-spectral data for real time target recognition

NASA Technical Reports Server (NTRS)

Duong, T. A.; Daud, T.; Thakoor, A.

2000-01-01

As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in target recognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for target recognition can be achieved.

Guidelines for the assessment and acceptance of potential brain-dead organ donors

PubMed Central

Westphal, Glauco Adrieno; Garcia, Valter Duro; de Souza, Rafael Lisboa; Franke, Cristiano Augusto; Vieira, Kalinca Daberkow; Birckholz, Viviane Renata Zaclikevis; Machado, Miriam Cristine; de Almeida, Eliana Régia Barbosa; Machado, Fernando Osni; Sardinha, Luiz Antônio da Costa; Wanzuita, Raquel; Silvado, Carlos Eduardo Soares; Costa, Gerson; Braatz, Vera; Caldeira Filho, Milton; Furtado, Rodrigo; Tannous, Luana Alves; de Albuquerque, André Gustavo Neves; Abdala, Edson; Gonçalves, Anderson Ricardo Roman; Pacheco-Moreira, Lúcio Filgueiras; Dias, Fernando Suparregui; Fernandes, Rogério; Giovanni, Frederico Di; de Carvalho, Frederico Bruzzi; Fiorelli, Alfredo; Teixeira, Cassiano; Feijó, Cristiano; Camargo, Spencer Marcantonio; de Oliveira, Neymar Elias; David, André Ibrahim; Prinz, Rafael Augusto Dantas; Herranz, Laura Brasil; de Andrade, Joel

2016-01-01

Organ transplantation is the only alternative for many patients with terminal diseases. The increasing disproportion between the high demand for organ transplants and the low rate of transplants actually performed is worrisome. Some of the causes of this disproportion are errors in the identification of potential organ donors and in the determination of contraindications by the attending staff. Therefore, the aim of the present document is to provide guidelines for intensive care multi-professional staffs for the recognition, assessment and acceptance of potential organ donors. PMID:27737418

Discriminant WSRC for Large-Scale Plant Species Recognition.

PubMed

Zhang, Shanwen; Zhang, Chuanlei; Zhu, Yihai; You, Zhuhong

2017-01-01

In sparse representation based classification (SRC) and weighted SRC (WSRC), it is time-consuming to solve the global sparse representation problem. A discriminant WSRC (DWSRC) is proposed for large-scale plant species recognition, including two stages. Firstly, several subdictionaries are constructed by dividing the dataset into several similar classes, and a subdictionary is chosen by the maximum similarity between the test sample and the typical sample of each similar class. Secondly, the weighted sparse representation of the test image is calculated with respect to the chosen subdictionary, and then the leaf category is assigned through the minimum reconstruction error. Different from the traditional SRC and its improved approaches, we sparsely represent the test sample on a subdictionary whose base elements are the training samples of the selected similar class, instead of using the generic overcomplete dictionary on the entire training samples. Thus, the complexity to solving the sparse representation problem is reduced. Moreover, DWSRC is adapted to newly added leaf species without rebuilding the dictionary. Experimental results on the ICL plant leaf database show that the method has low computational complexity and high recognition rate and can be clearly interpreted.

Printed Arabic optical character segmentation

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Ayyesh, Muna; Qaroush, Aziz; Tumar, Iyad

2015-03-01

A considerable progress in recognition techniques for many non-Arabic characters has been achieved. In contrary, few efforts have been put on the research of Arabic characters. In any Optical Character Recognition (OCR) system the segmentation step is usually the essential stage in which an extensive portion of processing is devoted and a considerable share of recognition errors is attributed. In this research, a novel segmentation approach for machine Arabic printed text with diacritics is proposed. The proposed method reduces computation, errors, gives a clear description for the sub-word and has advantages over using the skeleton approach in which the data and information of the character can be lost. Both of initial evaluation and testing of the proposed method have been developed using MATLAB and shows 98.7% promising results.

Fraudulent ID using face morphs: Experiments on human and automatic recognition

PubMed Central

Robertson, David J.; Kramer, Robin S. S.

2017-01-01

Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people’s ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to ‘trained’ human viewers—i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security. PMID:28328928

Initial Usability Testing of a Hand-Held Electronic Logbook Prototype for the Human Research Facility

NASA Technical Reports Server (NTRS)

Berman, Andrea H.; Whitmore, Mihriban

1996-01-01

The Apple(R) Newton(TM) MessagePad 110 was flown aboard the KC-135 reduced gravity aircraft for microgravity usability testing. The Newton served as the initial hand-held electronic logbook prototype for the International Space Station (ISS) Human Research Facility (HRF). Subjects performed three different tasks with the Newton: (1) using the stylus to tap on different sections of the screen in order to launch an application and to select options within it; (2) using the stylus to write, and; (3) correcting handwriting recognition errors in a handwriting-intensive application. Subjects rated handwriting in microgravity 'Borderline' and had great difficulties finding a way in which to adequately restrain themselves at the lower body in order to have their hands free for the Newton. Handwriting recognition was rated 'Unacceptable,' but this issue is hardware-related and not unique to the microgravity environment. It is suggested that the restraint and handwriting issues are related and require further joint research with the current Handheld Electronic Logbook prototype: the Norand Pen*key Model #6300.

Fraudulent ID using face morphs: Experiments on human and automatic recognition.

PubMed

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

2017-01-01

Matching unfamiliar faces is known to be difficult, and this can give an opportunity to those engaged in identity fraud. Here we examine a relatively new form of fraud, the use of photo-ID containing a graphical morph between two faces. Such a document may look sufficiently like two people to serve as ID for both. We present two experiments with human viewers, and a third with a smartphone face recognition system. In Experiment 1, viewers were asked to match pairs of faces, without being warned that one of the pair could be a morph. They very commonly accepted a morphed face as a match. However, in Experiment 2, following very short training on morph detection, their acceptance rate fell considerably. Nevertheless, there remained large individual differences in people's ability to detect a morph. In Experiment 3 we show that a smartphone makes errors at a similar rate to 'trained' human viewers-i.e. accepting a small number of morphs as genuine ID. We discuss these results in reference to the use of face photos for security.

Deep features for efficient multi-biometric recognition with face and ear images

NASA Astrophysics Data System (ADS)

Omara, Ibrahim; Xiao, Gang; Amrani, Moussa; Yan, Zifei; Zuo, Wangmeng

2017-07-01

Recently, multimodal biometric systems have received considerable research interest in many applications especially in the fields of security. Multimodal systems can increase the resistance to spoof attacks, provide more details and flexibility, and lead to better performance and lower error rate. In this paper, we present a multimodal biometric system based on face and ear, and propose how to exploit the extracted deep features from Convolutional Neural Networks (CNNs) on the face and ear images to introduce more powerful discriminative features and robust representation ability for them. First, the deep features for face and ear images are extracted based on VGG-M Net. Second, the extracted deep features are fused by using a traditional concatenation and a Discriminant Correlation Analysis (DCA) algorithm. Third, multiclass support vector machine is adopted for matching and classification. The experimental results show that the proposed multimodal system based on deep features is efficient and achieves a promising recognition rate up to 100 % by using face and ear. In addition, the results indicate that the fusion based on DCA is superior to traditional fusion.

Orthographic recognition in late adolescents: an assessment through event-related brain potentials.

PubMed

González-Garrido, Andrés Antonio; Gómez-Velázquez, Fabiola Reveca; Rodríguez-Santillán, Elizabeth

2014-04-01

Reading speed and efficiency are achieved through the automatic recognition of written words. Difficulties in learning and recognizing the orthography of words can arise despite reiterative exposure to texts. This study aimed to investigate, in native Spanish-speaking late adolescents, how different levels of orthographic knowledge might result in behavioral and event-related brain potential differences during the recognition of orthographic errors. Forty-five healthy high school students were selected and divided into 3 equal groups (High, Medium, Low) according to their performance on a 5-test battery of orthographic knowledge. All participants performed an orthographic recognition task consisting of the sequential presentation of a picture (object, fruit, or animal) followed by a correctly, or incorrectly, written word (orthographic mismatch) that named the picture just shown. Electroencephalogram (EEG) recording took place simultaneously. Behavioral results showed that the Low group had a significantly lower number of correct responses and increased reaction times while processing orthographical errors. Tests showed significant positive correlations between higher performance on the experimental task and faster and more accurate reading. The P150 and P450 components showed higher voltages in the High group when processing orthographic errors, whereas N170 seemed less lateralized to the left hemisphere in the lower orthographic performers. Also, trials with orthographic errors elicited a frontal P450 component that was only evident in the High group. The present results show that higher levels of orthographic knowledge correlate with high reading performance, likely because of faster and more accurate perceptual processing, better visual orthographic representations, and top-down supervision, as the event-related brain potential findings seem to suggest.

Participation of the Classical Speech Areas in Auditory Long-Term Memory

PubMed Central

Karabanov, Anke Ninija; Paine, Rainer; Chao, Chi Chao; Schulze, Katrin; Scott, Brian; Hallett, Mark; Mishkin, Mortimer

2015-01-01

Accumulating evidence suggests that storing speech sounds requires transposing rapidly fluctuating sound waves into more easily encoded oromotor sequences. If so, then the classical speech areas in the caudalmost portion of the temporal gyrus (pSTG) and in the inferior frontal gyrus (IFG) may be critical for performing this acoustic-oromotor transposition. We tested this proposal by applying repetitive transcranial magnetic stimulation (rTMS) to each of these left-hemisphere loci, as well as to a nonspeech locus, while participants listened to pseudowords. After 5 minutes these stimuli were re-presented together with new ones in a recognition test. Compared to control-site stimulation, pSTG stimulation produced a highly significant increase in recognition error rate, without affecting reaction time. By contrast, IFG stimulation led only to a weak, non-significant, trend toward recognition memory impairment. Importantly, the impairment after pSTG stimulation was not due to interference with perception, since the same stimulation failed to affect pseudoword discrimination examined with short interstimulus intervals. Our findings suggest that pSTG is essential for transforming speech sounds into stored motor plans for reproducing the sound. Whether or not the IFG also plays a role in speech-sound recognition could not be determined from the present results. PMID:25815813

Participation of the classical speech areas in auditory long-term memory.

PubMed

Karabanov, Anke Ninija; Paine, Rainer; Chao, Chi Chao; Schulze, Katrin; Scott, Brian; Hallett, Mark; Mishkin, Mortimer

2015-01-01

Accumulating evidence suggests that storing speech sounds requires transposing rapidly fluctuating sound waves into more easily encoded oromotor sequences. If so, then the classical speech areas in the caudalmost portion of the temporal gyrus (pSTG) and in the inferior frontal gyrus (IFG) may be critical for performing this acoustic-oromotor transposition. We tested this proposal by applying repetitive transcranial magnetic stimulation (rTMS) to each of these left-hemisphere loci, as well as to a nonspeech locus, while participants listened to pseudowords. After 5 minutes these stimuli were re-presented together with new ones in a recognition test. Compared to control-site stimulation, pSTG stimulation produced a highly significant increase in recognition error rate, without affecting reaction time. By contrast, IFG stimulation led only to a weak, non-significant, trend toward recognition memory impairment. Importantly, the impairment after pSTG stimulation was not due to interference with perception, since the same stimulation failed to affect pseudoword discrimination examined with short interstimulus intervals. Our findings suggest that pSTG is essential for transforming speech sounds into stored motor plans for reproducing the sound. Whether or not the IFG also plays a role in speech-sound recognition could not be determined from the present results.

Reading laterally: the cerebral hemispheric use of spatial frequencies in visual word recognition.

PubMed

Tadros, Karine; Dupuis-Roy, Nicolas; Fiset, Daniel; Arguin, Martin; Gosselin, Frédéric

2013-01-04

It is generally accepted that the left hemisphere (LH) is more capable for reading than the right hemisphere (RH). Left hemifield presentations (initially processed by the RH) lead to a globally higher error rate, slower word identification, and a significantly stronger word length effect (i.e., slower reaction times for longer words). Because the visuo-perceptual mechanisms of the brain for word recognition are primarily localized in the LH (Cohen et al., 2003), it is possible that this part of the brain possesses better spatial frequency (SF) tuning for processing the visual properties of words than the RH. The main objective of this study is to determine the SF tuning functions of the LH and RH for word recognition. Each word image was randomly sampled in the SF domain using the SF bubbles method (Willenbockel et al., 2010) and was presented laterally to the left or right visual hemifield. As expected, the LH requires less visual information than the RH to reach the same level of performance, illustrating the well-known LH advantage for word recognition. Globally, the SF tuning of both hemispheres is similar. However, these seemingly identical tuning functions hide important differences. Most importantly, we argue that the RH requires higher SFs to identify longer words because of crowding.

[Research on the application of grey system theory in the pattern recognition for chromatographic fingerprints of traditional Chinese medicine].

PubMed

Wei, Hang; Lin, Li; Zhang, Yuan; Wang, Lianjing; Chen, Qinqun

2013-02-01

A model based on grey system theory was proposed for pattern recognition in chromatographic fingerprints (CF) of traditional Chinese medicine (TCM). The grey relational grade among the data series of each testing CF and the ideal CF was obtained by entropy and norm respectively, then the principle of "maximal matching degree" was introduced to make judgments, so as to achieve the purpose of variety identification and quality evaluation. A satisfactory result in the high performance liquid chromatographic (HPLC) analysis of 56 batches of different varieties of Exocarpium Citrus Grandis was achieved with this model. The errors in the chromatographic fingerprint analysis caused by traditional similarity method or grey correlation method were overcome, as the samples of Citrus grandis 'Tomentosa' and Citrus grandis (L.) Osbeck were correctly distinguished in the experiment. Furthermore in the study on the variety identification of Citrus grandis 'Tomentosa', the recognition rates were up to 92.85%, although the types and the contents of the chemical compositions of the samples were very close. At the same time, the model had the merits of low computation complexity and easy operation by computer programming. The research indicated that the grey system theory has good applicability to pattern recognition in the chromatographic fingerprints of TCM.

Robust recognition of loud and Lombard speech in the fighter cockpit environment

NASA Astrophysics Data System (ADS)

Stanton, Bill J., Jr.

1988-08-01

There are a number of challenges associated with incorporating speech recognition technology into the fighter cockpit. One of the major problems is the wide range of variability in the pilot's voice. That can result from changing levels of stress and workload. Increasing the training set to include abnormal speech is not an attractive option because of the innumerable conditions that would have to be represented and the inordinate amount of time to collect such a training set. A more promising approach is to study subsets of abnormal speech that have been produced under controlled cockpit conditions with the purpose of characterizing reliable shifts that occur relative to normal speech. Such was the initiative of this research. Analyses were conducted for 18 features on 17671 phoneme tokens across eight speakers for normal, loud, and Lombard speech. It was discovered that there was a consistent migration of energy in the sonorants. This discovery of reliable energy shifts led to the development of a method to reduce or eliminate these shifts in the Euclidean distances between LPC log magnitude spectra. This combination significantly improved recognition performance of loud and Lombard speech. Discrepancies in recognition error rates between normal and abnormal speech were reduced by approximately 50 percent for all eight speakers combined.

Northeast Artificial Intelligence Consortium Annual Report. Volume 7. 1988 Research in Automated Photointerpretation

DTIC Science & Technology

1989-10-01

weight based on how powerful the corresponding feature is for object recognition and discrimination. For example, consider an arbitrary weight, denoted...quality of the segmentation, how powerful the features and spatial constraints in the knowledge base are (as far as object recognition is concern...that are powerful for object recognition and discrimination. At this point, this selection is performed heuristically through trial-and-error. As a

Investigating the variability of memory distortion for an analogue trauma.

PubMed

Strange, Deryn; Takarangi, Melanie K T

2015-01-01

In this paper, we examine whether source monitoring (SM) errors might be one mechanism that accounts for traumatic memory distortion. Participants watched a traumatic film with some critical (crux) and non-critical (non-crux) scenes removed. Twenty-four hours later, they completed a memory test. To increase the likelihood participants would notice the film's gaps, we inserted visual static for the length of each missing scene. We then added manipulations designed to affect people's SM behaviour. To encourage systematic SM, before watching the film, we warned half the participants that we had removed some scenes. To encourage heuristic SM some participants also saw labels describing the missing scenes. Adding static highlighting, the missing scenes did not affect false recognition of those missing scenes. However, a warning decreased, while labels increased, participants' false recognition rates. We conclude that manipulations designed to affect SM behaviour also affect the degree of memory distortion in our paradigm.

Hand biometric recognition based on fused hand geometry and vascular patterns.

PubMed

Park, GiTae; Kim, Soowon

2013-02-28

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%.

Hand Biometric Recognition Based on Fused Hand Geometry and Vascular Patterns

PubMed Central

Park, GiTae; Kim, Soowon

2013-01-01

A hand biometric authentication method based on measurements of the user's hand geometry and vascular pattern is proposed. To acquire the hand geometry, the thickness of the side view of the hand, the K-curvature with a hand-shaped chain code, the lengths and angles of the finger valleys, and the lengths and profiles of the fingers were used, and for the vascular pattern, the direction-based vascular-pattern extraction method was used, and thus, a new multimodal biometric approach is proposed. The proposed multimodal biometric system uses only one image to extract the feature points. This system can be configured for low-cost devices. Our multimodal biometric-approach hand-geometry (the side view of the hand and the back of hand) and vascular-pattern recognition method performs at the score level. The results of our study showed that the equal error rate of the proposed system was 0.06%. PMID:23449119

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.

Corneal topography measurements for biometric applications

NASA Astrophysics Data System (ADS)

Lewis, Nathan D.

The term biometrics is used to describe the process of analyzing biological and behavioral traits that are unique to an individual in order to confirm or determine his or her identity. Many biometric modalities are currently being researched and implemented including, fingerprints, hand and facial geometry, iris recognition, vein structure recognition, gait, voice recognition, etc... This project explores the possibility of using corneal topography measurements as a trait for biometric identification. Two new corneal topographers were developed for this study. The first was designed to function as an operator-free device that will allow a user to approach the device and have his or her corneal topography measured. Human subject topography data were collected with this device and compared to measurements made with the commercially available Keratron Piccolo topographer (Optikon, Rome, Italy). A third topographer that departs from the standard Placido disk technology allows for arbitrary pattern illumination through the use of LCD monitors. This topographer was built and tested to be used in future research studies. Topography data was collected from 59 subjects and modeled using Zernike polynomials, which provide for a simple method of compressing topography data and comparing one topographical measurement with a database for biometric identification. The data were analyzed to determine the biometric error rates associated with corneal topography measurements. Reasonably accurate results, between three to eight percent simultaneous false match and false non-match rates, were achieved.

76 FR 39757 - Filing Procedures

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-06

... an optical character recognition process, such a document may contain recognition errors. CAUTION... network speed e-filing of these documents may be difficult. Pursuant to section II(C) above, the Secretary... optical scan format or a typed ``electronic signature,'' e.g., ``/s/Jane Doe.'' (3) In the case of a...

Effect of Context and Hearing Loss on Time-Gated Word Recognition in Children

PubMed Central

Lewis, Dawna E.; Kopun, Judy; McCreery, Ryan; Brennan, Marc; Nishi, Kanae; Cordrey, Evan; Stelmachowicz, Pat; Moeller, Mary Pat

2016-01-01

Objectives The purpose of this study was to examine word recognition in children who are hard of hearing (CHH) and children with normal hearing (CNH) in response to time-gated words presented in high- vs. low-predictability sentences (HP, LP), where semantic cues were manipulated. Findings inform our understanding of how CHH combine cognitive-linguistic and acoustic-phonetic cues to support spoken word recognition. It was hypothesized that both groups of children would be able to make use of linguistic cues provided by HP sentences to support word recognition. CHH were expected to require greater acoustic information (more gates) than CNH to correctly identify words in the LP condition. In addition, it was hypothesized that error patterns would differ across groups. Design Sixteen CHH with mild-to-moderate hearing loss and 16 age-matched CNH participated (5–12 yrs). Test stimuli included 15 LP and 15 HP age-appropriate sentences. The final word of each sentence was divided into segments and recombined with the sentence frame to create series of sentences in which the final word was progressively longer by the gated increments. Stimuli were presented monaurally through headphones and children were asked to identify the target word at each successive gate. They also were asked to rate their confidence in their word choice using a 5- or 3-point scale. For CHH, the signals were processed through a hearing aid simulator. Standardized language measures were used to assess the contribution of linguistic skills. Results Analysis of language measures revealed that the CNH and CHH performed within the average range on language abilities. Both groups correctly recognized a significantly higher percentage of words in the HP condition than in the LP condition. Although CHH performed comparably to CNH in terms of successfully recognizing the majority of words, differences were observed in the amount of acoustic-phonetic information needed to achieve accurate word recognition. CHH needed more gates than CNH to identify words in the LP condition. CNH were significantly lower in rating their confidence in the LP condition than in the HP condition. CHH, however, were not significantly different in confidence between the conditions. Error patterns for incorrect word responses across gates and predictability varied depending on hearing status. Conclusions The results of this study suggest that CHH with age-appropriate language abilities took advantage of context cues in the HP sentences to guide word recognition in a manner similar to CNH. However, in the LP condition, they required more acoustic information (more gates) than CNH for word recognition. Differences in the structure of incorrect word responses and their nomination patterns across gates for CHH compared to their peers with normal hearing suggest variations in how these groups use limited acoustic information to select word candidates. PMID:28045838

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

PubMed

Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases.

Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition

PubMed Central

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the state-of-the-art results on AR, FERET, FRGC and LFW databases. PMID:26571112

Mechanisms of object recognition: what we have learned from pigeons

PubMed Central

Soto, Fabian A.; Wasserman, Edward A.

2014-01-01

Behavioral studies of object recognition in pigeons have been conducted for 50 years, yielding a large body of data. Recent work has been directed toward synthesizing this evidence and understanding the visual, associative, and cognitive mechanisms that are involved. The outcome is that pigeons are likely to be the non-primate species for which the computational mechanisms of object recognition are best understood. Here, we review this research and suggest that a core set of mechanisms for object recognition might be present in all vertebrates, including pigeons and people, making pigeons an excellent candidate model to study the neural mechanisms of object recognition. Behavioral and computational evidence suggests that error-driven learning participates in object category learning by pigeons and people, and recent neuroscientific research suggests that the basal ganglia, which are homologous in these species, may implement error-driven learning of stimulus-response associations. Furthermore, learning of abstract category representations can be observed in pigeons and other vertebrates. Finally, there is evidence that feedforward visual processing, a central mechanism in models of object recognition in the primate ventral stream, plays a role in object recognition by pigeons. We also highlight differences between pigeons and people in object recognition abilities, and propose candidate adaptive specializations which may explain them, such as holistic face processing and rule-based category learning in primates. From a modern comparative perspective, such specializations are to be expected regardless of the model species under study. The fact that we have a good idea of which aspects of object recognition differ in people and pigeons should be seen as an advantage over other animal models. From this perspective, we suggest that there is much to learn about human object recognition from studying the “simple” brains of pigeons. PMID:25352784

Dealing with Common Mistakes Using an Error Corpus for EFL Students to Increase Their Autonomy in Error Recognition and Correction in Every Day Class Tasks

ERIC Educational Resources Information Center

Terreros Lazo, Oscar

2012-01-01

In this article, you will find how autonomous students of EFL in Lima, Peru can be when they recognize and correct their errors based on the teachers' guidance about what to look for and how to do it in a process that I called "Error Hunting" during regular class activities without interfering with these activities.

What is the evidence for retrieval problems in the elderly?

PubMed

White, N; Cunningham, W R

1982-01-01

To determine whether older adults experience particular problems with retrieval, groups of young and elderly adults were given free recall and recognition tests of supraspan lists of unrelated words. Analysis of number of words correctly recalled and recognized yielded a significant age by retention test interaction: greater age differences were observed for recall than for recognition. In a second analysis of words recalled and recognized, corrected for guessing, the interaction disappeared. It was concluded that previous interpretations that age by retention test interactions are indicative of retrieval problems of the elderly may have been confounded by methodological problems. Furthermore, it was suggested that researchers in aging and memory need to be explicit in identifying their underlying models of error processes when analyzing recognition scores: different error models may lead to different results and interpretations.

INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network

NASA Astrophysics Data System (ADS)

Chang, Wen-Li

2010-01-01

We investigate the influence of blurred ways on pattern recognition of a Barabási-Albert scale-free Hopfield neural network (SFHN) with a small amount of errors. Pattern recognition is an important function of information processing in brain. Due to heterogeneous degree of scale-free network, different blurred ways have different influences on pattern recognition with same errors. Simulation shows that among partial recognition, the larger loading ratio (the number of patterns to average degree P/langlekrangle) is, the smaller the overlap of SFHN is. The influence of directed (large) way is largest and the directed (small) way is smallest while random way is intermediate between them. Under the ratio of the numbers of stored patterns to the size of the network P/N is less than 0. 1 conditions, there are three families curves of the overlap corresponding to directed (small), random and directed (large) blurred ways of patterns and these curves are not associated with the size of network and the number of patterns. This phenomenon only occurs in the SFHN. These conclusions are benefit for understanding the relation between neural network structure and brain function.

Bilevel Model-Based Discriminative Dictionary Learning for Recognition.

PubMed

Zhou, Pan; Zhang, Chao; Lin, Zhouchen

2017-03-01

Most supervised dictionary learning methods optimize the combinations of reconstruction error, sparsity prior, and discriminative terms. Thus, the learnt dictionaries may not be optimal for recognition tasks. Also, the sparse codes learning models in the training and the testing phases are inconsistent. Besides, without utilizing the intrinsic data structure, many dictionary learning methods only employ the l 0 or l 1 norm to encode each datum independently, limiting the performance of the learnt dictionaries. We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks. The upper level directly minimizes the classification error, while the lower level uses the sparsity term and the Laplacian term to characterize the intrinsic data structure. The lower level is subordinate to the upper level. Therefore, our model achieves an overall optimality for recognition in that the learnt dictionary is directly tailored for recognition. Moreover, the sparse codes learning models in the training and the testing phases can be the same. We further propose a novel method to solve our bilevel optimization problem. It first replaces the lower level with its Karush-Kuhn-Tucker conditions and then applies the alternating direction method of multipliers to solve the equivalent problem. Extensive experiments demonstrate the effectiveness and robustness of our method.

Validation of a short-term memory test for the recognition of people and faces.

PubMed

Leyk, D; Sievert, A; Heiss, A; Gorges, W; Ridder, D; Alexander, T; Wunderlich, M; Ruther, T

2008-08-01

Memorising and processing faces is a short-term memory dependent task of utmost importance in the security domain, in which constant and high performance is a must. Especially in access or passport control-related tasks, the timely identification of performance decrements is essential, margins of error are narrow and inadequate performance may have grave consequences. However, conventional short-term memory tests frequently use abstract settings with little relevance to working situations. They may thus be unable to capture task-specific decrements. The aim of the study was to devise and validate a new test, better reflecting job specifics and employing appropriate stimuli. After 1.5 s (short) or 4.5 s (long) presentation, a set of seven portraits of faces had to be memorised for comparison with two control stimuli. Stimulus appearance followed 2 s (first item) and 8 s (second item) after set presentation. Twenty eight subjects (12 male, 16 female) were tested at seven different times of day, 3 h apart. Recognition rates were above 60% even for the least favourable condition. Recognition was significantly better in the 'long' condition (+10%) and for the first item (+18%). Recognition time showed significant differences (10%) between items. Minor effects of learning were found for response latencies only. Based on occupationally relevant metrics, the test displayed internal and external validity, consistency and suitability for further use in test/retest scenarios. In public security, especially where access to restricted areas is monitored, margins of error are narrow and operator performance must remain high and level. Appropriate schedules for personnel, based on valid test results, are required. However, task-specific data and performance tests, permitting the description of task specific decrements, are not available. Commonly used tests may be unsuitable due to undue abstraction and insufficient reference to real-world conditions. Thus, tests are required that account for task-specific conditions and neurophysiological characteristics.

Type I Rehearsal and Recognition.

ERIC Educational Resources Information Center

Glenberg, Arthur; Adams, Frederick

1978-01-01

Rote, repetitive Type I Rehearsal is defined as the continuous maintenance of information in memory using the minimum cognitive capacity necessary for maintenance. An analysis of errors made on a forced-choice recognition test supported the hypothesis that acoustic-phonemic components of the memory trace are added or strengthened by this…

Evaluating Effects of Divided Hemispheric Processing on Word Recognition in Foveal and Extrafoveal Displays: The Evidence from Arabic

PubMed Central

Almabruk, Abubaker A. A.; Paterson, Kevin B.; McGowan, Victoria; Jordan, Timothy R.

2011-01-01

Background Previous studies have claimed that a precise split at the vertical midline of each fovea causes all words to the left and right of fixation to project to the opposite, contralateral hemisphere, and this division in hemispheric processing has considerable consequences for foveal word recognition. However, research in this area is dominated by the use of stimuli from Latinate languages, which may induce specific effects on performance. Consequently, we report two experiments using stimuli from a fundamentally different, non-Latinate language (Arabic) that offers an alternative way of revealing effects of split-foveal processing, if they exist. Methods and Findings Words (and pseudowords) were presented to the left or right of fixation, either close to fixation and entirely within foveal vision, or further from fixation and entirely within extrafoveal vision. Fixation location and stimulus presentations were carefully controlled using an eye-tracker linked to a fixation-contingent display. To assess word recognition, Experiment 1 used the Reicher-Wheeler task and Experiment 2 used the lexical decision task. Results Performance in both experiments indicated a functional division in hemispheric processing for words in extrafoveal locations (in recognition accuracy in Experiment 1 and in reaction times and error rates in Experiment 2) but no such division for words in foveal locations. Conclusions These findings from a non-Latinate language provide new evidence that although a functional division in hemispheric processing exists for word recognition outside the fovea, this division does not extend up to the point of fixation. Some implications for word recognition and reading are discussed. PMID:21559084

False-Positive Error Rates for Reliable Digit Span and Auditory Verbal Learning Test Performance Validity Measures in Amnestic Mild Cognitive Impairment and Early Alzheimer Disease.

PubMed

Loring, David W; Goldstein, Felicia C; Chen, Chuqing; Drane, Daniel L; Lah, James J; Zhao, Liping; Larrabee, Glenn J

2016-06-01

The objective is to examine failure on three embedded performance validity tests [Reliable Digit Span (RDS), Auditory Verbal Learning Test (AVLT) logistic regression, and AVLT recognition memory] in early Alzheimer disease (AD; n = 178), amnestic mild cognitive impairment (MCI; n = 365), and cognitively intact age-matched controls (n = 206). Neuropsychological tests scores were obtained from subjects participating in the Alzheimer's Disease Neuroimaging Initiative (ADNI). RDS failure using a ≤7 RDS threshold was 60/178 (34%) for early AD, 52/365 (14%) for MCI, and 17/206 (8%) for controls. A ≤6 RDS criterion reduced this rate to 24/178 (13%) for early AD, 15/365 (4%) for MCI, and 7/206 (3%) for controls. AVLT logistic regression probability of ≥.76 yielded unacceptably high false-positive rates in both clinical groups [early AD = 149/178 (79%); MCI = 159/365 (44%)] but not cognitively intact controls (13/206, 6%). AVLT recognition criterion of ≤9/15 classified 125/178 (70%) of early AD, 155/365 (42%) of MCI, and 18/206 (9%) of control scores as invalid, which decreased to 66/178 (37%) for early AD, 46/365 (13%) for MCI, and 10/206 (5%) for controls when applying a ≤5/15 criterion. Despite high false-positive rates across individual measures and thresholds, combining RDS ≤ 6 and AVLT recognition ≤9/15 classified only 9/178 (5%) of early AD and 4/365 (1%) of MCI patients as invalid performers. Embedded validity cutoffs derived from mixed clinical groups produce unacceptably high false-positive rates in MCI and early AD. Combining embedded PVT indicators lowers the false-positive rate. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

26 CFR 1.1312-8 - Law applicable in determination of error.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Law applicable in determination of error. The question whether there was an erroneous inclusion... provisions of the internal revenue laws applicable with respect to the year as to which the inclusion.... The fact that the inclusion, exclusion, omission, allowance, disallowance, recognition, or...

Speaker diarization system on the 2007 NIST rich transcription meeting recognition evaluation

NASA Astrophysics Data System (ADS)

Sun, Hanwu; Nwe, Tin Lay; Koh, Eugene Chin Wei; Bin, Ma; Li, Haizhou

2007-09-01

This paper presents a speaker diarization system developed at the Institute for Infocomm Research (I2R) for NIST Rich Transcription 2007 (RT-07) evaluation task. We describe in details our primary approaches for the speaker diarization on the Multiple Distant Microphones (MDM) conditions in conference room scenario. Our proposed system consists of six modules: 1). Least-mean squared (NLMS) adaptive filter for the speaker direction estimate via Time Difference of Arrival (TDOA), 2). An initial speaker clustering via two-stage TDOA histogram distribution quantization approach, 3). Multiple microphone speaker data alignment via GCC-PHAT Time Delay Estimate (TDE) among all the distant microphone channel signals, 4). A speaker clustering algorithm based on GMM modeling approach, 5). Non-speech removal via speech/non-speech verification mechanism and, 6). Silence removal via "Double-Layer Windowing"(DLW) method. We achieves error rate of 31.02% on the 2006 Spring (RT-06s) MDM evaluation task and a competitive overall error rate of 15.32% for the NIST Rich Transcription 2007 (RT-07) MDM evaluation task.

Judging the judges' performance in rhythmic gymnastics.

PubMed

Flessas, Konstantinos; Mylonas, Dimitris; Panagiotaropoulou, Georgia; Tsopani, Despina; Korda, Alexandrea; Siettos, Constantinos; Di Cagno, Alessandra; Evdokimidis, Ioannis; Smyrnis, Nikolaos

2015-03-01

Rhythmic gymnastics (RG) is an aesthetic event balancing between art and sport that also has a performance rating system (Code of Points) given by the International Gymnastics Federation. It is one of the sports in which competition results greatly depend on the judges' evaluation. In the current study, we explored the judges' performance in a five-gymnast ensemble routine. An expert-novice paradigm (10 international-level, 10 national-level, and 10 novice-level judges) was implemented under a fully simulated procedure of judgment in a five-gymnast ensemble routine of RG using two videos of routines performed by the Greek national team of RG. Simultaneous recordings of two-dimensional eye movements were taken during the judgment procedure to assess the percentage of time spent by each judge viewing the videos and fixation performance of each judge when an error in gymnast performance had occurred. All judge level groups had very modest performance of error recognition on gymnasts' routines, and the best international judges reported approximately 40% of true errors. Novice judges spent significantly more time viewing the videos compared with national and international judges and spent significantly more time fixating detected errors than the other two groups. National judges were the only group that made efficient use of fixation to detect errors. The fact that international-level judges outperformed both other groups, while not relying on visual fixation to detect errors, suggests that these experienced judges probably make use of other cognitive strategies, increasing their overall error detection efficiency, which was, however, still far below optimum.

Miscoding-induced stalling of substrate translocation on the bacterial ribosome.

PubMed

Alejo, Jose L; Blanchard, Scott C

2017-10-10

Directional transit of the ribosome along the messenger RNA (mRNA) template is a key determinant of the rate and processivity of protein synthesis. Imaging of the multistep translocation mechanism using single-molecule FRET has led to the hypothesis that substrate movements relative to the ribosome resolve through relatively long-lived late intermediates wherein peptidyl-tRNA enters the P site of the small ribosomal subunit via reversible, swivel-like motions of the small subunit head domain within the elongation factor G (GDP)-bound ribosome complex. Consistent with translocation being rate-limited by recognition and productive engagement of peptidyl-tRNA within the P site, we now show that base-pairing mismatches between the peptidyl-tRNA anticodon and the mRNA codon dramatically delay this rate-limiting, intramolecular process. This unexpected relationship between aminoacyl-tRNA decoding and translocation suggests that miscoding antibiotics may impact protein synthesis by impairing the recognition of peptidyl-tRNA in the small subunit P site during EF-G-catalyzed translocation. Strikingly, we show that elongation factor P (EF-P), traditionally known to alleviate ribosome stalling at polyproline motifs, can efficiently rescue translocation defects arising from miscoding. These findings help reveal the nature and origin of the rate-limiting steps in substrate translocation on the bacterial ribosome and indicate that EF-P can aid in resuming translation elongation stalled by miscoding errors.

Miscoding-induced stalling of substrate translocation on the bacterial ribosome

PubMed Central

Alejo, Jose L.; Blanchard, Scott C.

2017-01-01

Directional transit of the ribosome along the messenger RNA (mRNA) template is a key determinant of the rate and processivity of protein synthesis. Imaging of the multistep translocation mechanism using single-molecule FRET has led to the hypothesis that substrate movements relative to the ribosome resolve through relatively long-lived late intermediates wherein peptidyl-tRNA enters the P site of the small ribosomal subunit via reversible, swivel-like motions of the small subunit head domain within the elongation factor G (GDP)-bound ribosome complex. Consistent with translocation being rate-limited by recognition and productive engagement of peptidyl-tRNA within the P site, we now show that base-pairing mismatches between the peptidyl-tRNA anticodon and the mRNA codon dramatically delay this rate-limiting, intramolecular process. This unexpected relationship between aminoacyl-tRNA decoding and translocation suggests that miscoding antibiotics may impact protein synthesis by impairing the recognition of peptidyl-tRNA in the small subunit P site during EF-G–catalyzed translocation. Strikingly, we show that elongation factor P (EF-P), traditionally known to alleviate ribosome stalling at polyproline motifs, can efficiently rescue translocation defects arising from miscoding. These findings help reveal the nature and origin of the rate-limiting steps in substrate translocation on the bacterial ribosome and indicate that EF-P can aid in resuming translation elongation stalled by miscoding errors. PMID:28973849

Pubface: Celebrity face identification based on deep learning

NASA Astrophysics Data System (ADS)

Ouanan, H.; Ouanan, M.; Aksasse, B.

2018-05-01

In this paper, we describe a new real time application called PubFace, which allows to recognize celebrities in public spaces by employs a new pose invariant face recognition deep neural network algorithm with an extremely low error rate. To build this application, we make the following contributions: firstly, we build a novel dataset with over five million faces labelled. Secondly, we fine tuning the deep convolutional neural network (CNN) VGG-16 architecture on our new dataset that we have built. Finally, we deploy this model on the Raspberry Pi 3 model B using the OpenCv dnn module (OpenCV 3.3).

Phoneme Error Pattern by Heritage Speakers of Spanish on an English Word Recognition Test.

PubMed

Shi, Lu-Feng

2017-04-01

Heritage speakers acquire their native language from home use in their early childhood. As the native language is typically a minority language in the society, these individuals receive their formal education in the majority language and eventually develop greater competency with the majority than their native language. To date, there have not been specific research attempts to understand word recognition by heritage speakers. It is not clear if and to what degree we may infer from evidence based on bilingual listeners in general. This preliminary study investigated how heritage speakers of Spanish perform on an English word recognition test and analyzed their phoneme errors. A prospective, cross-sectional, observational design was employed. Twelve normal-hearing adult Spanish heritage speakers (four men, eight women, 20-38 yr old) participated in the study. Their language background was obtained through the Language Experience and Proficiency Questionnaire. Nine English monolingual listeners (three men, six women, 20-41 yr old) were also included for comparison purposes. Listeners were presented with 200 Northwestern University Auditory Test No. 6 words in quiet. They repeated each word orally and in writing. Their responses were scored by word, word-initial consonant, vowel, and word-final consonant. Performance was compared between groups with Student's t test or analysis of variance. Group-specific error patterns were primarily descriptive, but intergroup comparisons were made using 95% or 99% confidence intervals for proportional data. The two groups of listeners yielded comparable scores when their responses were examined by word, vowel, and final consonant. However, heritage speakers of Spanish misidentified significantly more word-initial consonants and had significantly more difficulty with initial /p, b, h/ than their monolingual peers. The two groups yielded similar patterns for vowel and word-final consonants, but heritage speakers made significantly fewer errors with /e/ and more errors with word-final /p, k/. Data reported in the present study lead to a twofold conclusion. On the one hand, normal-hearing heritage speakers of Spanish may misidentify English phonemes in patterns different from those of English monolingual listeners. Not all phoneme errors can be readily understood by comparing Spanish and English phonology, suggesting that Spanish heritage speakers differ in performance from other Spanish-English bilingual listeners. On the other hand, the absolute number of errors and the error pattern of most phonemes were comparable between English monolingual listeners and Spanish heritage speakers, suggesting that audiologists may assess word recognition in quiet in the same way for these two groups of listeners, if diagnosis is based on words, not phonemes. American Academy of Audiology

Abnormal Facial Emotion Recognition in Depression: Serial Testing in an Ultra-Rapid-Cycling Patient.

ERIC Educational Resources Information Center

George, Mark S.; Huggins, Teresa; McDermut, Wilson; Parekh, Priti I.; Rubinow, David; Post, Robert M.

1998-01-01

Mood disorder subjects have a selective deficit in recognizing human facial emotion. Whether the facial emotion recognition errors persist during normal mood states (i.e., are state vs. trait dependent) was studied in one male bipolar II patient. Results of five sessions are presented and discussed. (Author/EMK)

Schematic Influences on Category Learning and Recognition Memory

ERIC Educational Resources Information Center

Sakamoto, Yasuaki; Love, Bradley C.

2004-01-01

The results from 3 category learning experiments suggest that items are better remembered when they violate a salient knowledge structure such as a rule. The more salient the knowledge structure, the stronger the memory for deviant items. The effect of learning errors on subsequent recognition appears to be mediated through the imposed knowledge…

Recognition Errors Suggest Fast Familiarity and Slow Recollection in Rhesus Monkeys

ERIC Educational Resources Information Center

Basile, Benjamin M.; Hampton, Robert R.

2013-01-01

One influential model of recognition posits two underlying memory processes: recollection, which is detailed but relatively slow, and familiarity, which is quick but lacks detail. Most of the evidence for this dual-process model in nonhumans has come from analyses of receiver operating characteristic (ROC) curves in rats, but whether ROC analyses…

Recognition Memory for Movement in Photographs: A Developmental Study.

ERIC Educational Resources Information Center

Futterweit, Lorelle R.; Beilin, Harry

1994-01-01

Investigated whether children's recognition memory for movement in photographs is distorted forward in the direction of implied motion. When asked whether the second photograph was the same as or different from the first, subjects made more errors for test photographs showing the action slightly forward in time, compared with slightly backward in…

[Multi-Target Recognition of Internal and External Defects of Potato by Semi-Transmission Hyperspectral Imaging and Manifold Learning Algorithm].

PubMed

Huang, Tao; Li, Xiao-yu; Jin, Rui; Ku, Jing; Xu, Sen-miao; Xu, Meng-ling; Wu, Zhen-zhong; Kong, De-guo

2015-04-01

The present paper put forward a non-destructive detection method which combines semi-transmission hyperspectral imaging technology with manifold learning dimension reduction algorithm and least squares support vector machine (LSSVM) to recognize internal and external defects in potatoes simultaneously. Three hundred fifteen potatoes were bought in farmers market as research object, and semi-transmission hyperspectral image acquisition system was constructed to acquire the hyperspectral images of normal external defects (bud and green rind) and internal defect (hollow heart) potatoes. In order to conform to the actual production, defect part is randomly put right, side and back to the acquisition probe when the hyperspectral images of external defects potatoes are acquired. The average spectrums (390-1,040 nm) were extracted from the region of interests for spectral preprocessing. Then three kinds of manifold learning algorithm were respectively utilized to reduce the dimension of spectrum data, including supervised locally linear embedding (SLLE), locally linear embedding (LLE) and isometric mapping (ISOMAP), the low-dimensional data gotten by manifold learning algorithms is used as model input, Error Correcting Output Code (ECOC) and LSSVM were combined to develop the multi-target classification model. By comparing and analyzing results of the three models, we concluded that SLLE is the optimal manifold learning dimension reduction algorithm, and the SLLE-LSSVM model is determined to get the best recognition rate for recognizing internal and external defects potatoes. For test set data, the single recognition rate of normal, bud, green rind and hollow heart potato reached 96.83%, 86.96%, 86.96% and 95% respectively, and he hybrid recognition rate was 93.02%. The results indicate that combining the semi-transmission hyperspectral imaging technology with SLLE-LSSVM is a feasible qualitative analytical method which can simultaneously recognize the internal and external defects potatoes and also provide technical reference for rapid on-line non-destructive detecting of the internal and external defects potatoes.

Forecasting Financial Extremes: A Network Degree Measure of Super-Exponential Growth.

PubMed

Yan, Wanfeng; van Tuyll van Serooskerken, Edgar

2015-01-01

Investors in stock market are usually greedy during bull markets and scared during bear markets. The greed or fear spreads across investors quickly. This is known as the herding effect, and often leads to a fast movement of stock prices. During such market regimes, stock prices change at a super-exponential rate and are normally followed by a trend reversal that corrects the previous overreaction. In this paper, we construct an indicator to measure the magnitude of the super-exponential growth of stock prices, by measuring the degree of the price network, generated from the price time series. Twelve major international stock indices have been investigated. Error diagram tests show that this new indicator has strong predictive power for financial extremes, both peaks and troughs. By varying the parameters used to construct the error diagram, we show the predictive power is very robust. The new indicator has a better performance than the LPPL pattern recognition indicator.

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.

Hypercorrection of high confidence errors in lexical representations.

PubMed

Iwaki, Nobuyoshi; Matsushima, Hiroko; Kodaira, Kazumasa

2013-08-01

Memory errors associated with higher confidence are more likely to be corrected than errors made with lower confidence, a phenomenon called the hypercorrection effect. This study investigated whether the hypercorrection effect occurs with phonological information of lexical representations. In Experiment 1, 15 participants performed a Japanese Kanji word-reading task, in which the words had several possible pronunciations. In the initial task, participants were required to read aloud each word and indicate their confidence in their response; this was followed by receipt of visual feedback of the correct response. A hypercorrection effect was observed, indicating generality of this effect beyond previous observations in memories based upon semantic or episodic representations. This effect was replicated in Experiment 2, in which 40 participants performed the same task as in Experiment 1. When the participant's ratings of the practical value of the words were controlled, a partial correlation between confidence and likelihood of later correcting the initial mistaken response was reduced. This suggests that the hypercorrection effect may be partially caused by an individual's recognition of the practical value of reading the words correctly.

Theoretical Aspects of the Patterns Recognition Statistical Theory Used for Developing the Diagnosis Algorithms for Complicated Technical Systems

NASA Astrophysics Data System (ADS)

Obozov, A. A.; Serpik, I. N.; Mihalchenko, G. S.; Fedyaeva, G. A.

2017-01-01

In the article, the problem of application of the pattern recognition (a relatively young area of engineering cybernetics) for analysis of complicated technical systems is examined. It is shown that the application of a statistical approach for hard distinguishable situations could be the most effective. The different recognition algorithms are based on Bayes approach, which estimates posteriori probabilities of a certain event and an assumed error. Application of the statistical approach to pattern recognition is possible for solving the problem of technical diagnosis complicated systems and particularly big powered marine diesel engines.

Effective connectivity of visual word recognition and homophone orthographic errors

PubMed Central

Guàrdia-Olmos, Joan; Peró-Cebollero, Maribel; Zarabozo-Hurtado, Daniel; González-Garrido, Andrés A.; Gudayol-Ferré, Esteve

2015-01-01

The study of orthographic errors in a transparent language like Spanish is an important topic in relation to writing acquisition. The development of neuroimaging techniques, particularly functional magnetic resonance imaging (fMRI), has enabled the study of such relationships between brain areas. The main objective of the present study was to explore the patterns of effective connectivity by processing pseudohomophone orthographic errors among subjects with high and low spelling skills. Two groups of 12 Mexican subjects each, matched by age, were formed based on their results in a series of ad hoc spelling-related out-scanner tests: a high spelling skills (HSSs) group and a low spelling skills (LSSs) group. During the f MRI session, two experimental tasks were applied (spelling recognition task and visuoperceptual recognition task). Regions of Interest and their signal values were obtained for both tasks. Based on these values, structural equation models (SEMs) were obtained for each group of spelling competence (HSS and LSS) and task through maximum likelihood estimation, and the model with the best fit was chosen in each case. Likewise, dynamic causal models (DCMs) were estimated for all the conditions across tasks and groups. The HSS group’s SEM results suggest that, in the spelling recognition task, the right middle temporal gyrus, and, to a lesser extent, the left parahippocampal gyrus receive most of the significant effects, whereas the DCM results in the visuoperceptual recognition task show less complex effects, but still congruent with the previous results, with an important role in several areas. In general, these results are consistent with the major findings in partial studies about linguistic activities but they are the first analyses of statistical effective brain connectivity in transparent languages. PMID:26042070

Nurses' attitude and intention of medication administration error reporting.

PubMed

Hung, Chang-Chiao; Chu, Tsui-Ping; Lee, Bih-O; Hsiao, Chia-Chi

2016-02-01

The Aims of this study were to explore the effects of nurses' attitudes and intentions regarding medication administration error reporting on actual reporting behaviours. Underreporting of medication errors is still a common occurrence. Whether attitude and intention towards medication administration error reporting connect to actual reporting behaviours remain unclear. This study used a cross-sectional design with self-administered questionnaires, and the theory of planned behaviour was used as the framework for this study. A total of 596 staff nurses who worked in general wards and intensive care units in a hospital were invited to participate in this study. The researchers used the instruments measuring nurses' attitude, nurse managers' and co-workers' attitude, report control, and nurses' intention to predict nurses' actual reporting behaviours. Data were collected from September-November 2013. Path analyses were used to examine the hypothesized model. Of the 596 nurses invited to participate, 548 (92%) completed and returned a valid questionnaire. The findings indicated that nurse managers' and co-workers' attitudes are predictors for nurses' attitudes towards medication administration error reporting. Nurses' attitudes also influenced their intention to report medication administration errors; however, no connection was found between intention and actual reporting behaviour. The findings reflected links among colleague perspectives, nurses' attitudes, and intention to report medication administration errors. The researchers suggest that hospitals should increase nurses' awareness and recognition of error occurrence. Regardless of nurse managers' and co-workers' attitudes towards medication administration error reporting, nurses are likely to report medication administration errors if they detect them. Management of medication administration errors should focus on increasing nurses' awareness and recognition of error occurrence. © 2015 John Wiley & Sons Ltd.

The biometric recognition on contactless multi-spectrum finger images

NASA Astrophysics Data System (ADS)

Kang, Wenxiong; Chen, Xiaopeng; Wu, Qiuxia

2015-01-01

This paper presents a novel multimodal biometric system based on contactless multi-spectrum finger images, which aims to deal with the limitations of unimodal biometrics. The chief merits of the system are the richness of the permissible texture and the ease of data access. We constructed a multi-spectrum instrument to simultaneously acquire three different types of biometrics from a finger: contactless fingerprint, finger vein, and knuckleprint. On the basis of the samples with these characteristics, a moderate database was built for the evaluation of our system. Considering the real-time requirements and the respective characteristics of the three biometrics, the block local binary patterns algorithm was used to extract features and match for the fingerprints and finger veins, while the Oriented FAST and Rotated BRIEF algorithm was applied for knuckleprints. Finally, score-level fusion was performed on the matching results from the aforementioned three types of biometrics. The experiments showed that our proposed multimodal biometric recognition system achieves an equal error rate of 0.109%, which is 88.9%, 94.6%, and 89.7% lower than the individual fingerprint, knuckleprint, and finger vein recognitions, respectively. Nevertheless, our proposed system also satisfies the real-time requirements of the applications.

False recollection of the role played by an actor in an event

PubMed Central

Earles, Julie L.; Upshaw, Christin

2013-01-01

Two experiments demonstrated that eyewitnesses more frequently associate an actor with the actions of another person when those two people had appeared together in the same event, rather than in different events. This greater likelihood of binding an actor with the actions of another person from the same event was associated with high-confidence recognition judgments and “remember” responses in a remember–know task, suggesting that viewing an actor together with the actions of another person led participants to falsely recollect having seen that actor perform those actions. An analysis of age differences provided evidence that familiarity also contributed to false recognition independently of a false-recollection mechanism. In particular, older adults were more likely than young adults to falsely recognize a novel conjunction of a familiar actor and action, regardless of whether that actor and action were from the same or from different events. Older adults’ elevated rate of false recognition was associated with intermediate confidence levels, suggesting that it stemmed from increased reliance on familiarity rather than from false recollection. The implications of these results are discussed for theories of conjunction errors in memory and of unconscious transference in eyewitness testimony. PMID:23722927

Iris recognition based on robust principal component analysis

NASA Astrophysics Data System (ADS)

Karn, Pradeep; He, Xiao Hai; Yang, Shuai; Wu, Xiao Hong

2014-11-01

Iris images acquired under different conditions often suffer from blur, occlusion due to eyelids and eyelashes, specular reflection, and other artifacts. Existing iris recognition systems do not perform well on these types of images. To overcome these problems, we propose an iris recognition method based on robust principal component analysis. The proposed method decomposes all training images into a low-rank matrix and a sparse error matrix, where the low-rank matrix is used for feature extraction. The sparsity concentration index approach is then applied to validate the recognition result. Experimental results using CASIA V4 and IIT Delhi V1iris image databases showed that the proposed method achieved competitive performances in both recognition accuracy and computational efficiency.

Face recognition using total margin-based adaptive fuzzy support vector machines.

PubMed

Liu, Yi-Hung; Chen, Yen-Ting

2007-01-01

This paper presents a new classifier called total margin-based adaptive fuzzy support vector machines (TAF-SVM) that deals with several problems that may occur in support vector machines (SVMs) when applied to the face recognition. The proposed TAF-SVM not only solves the overfitting problem resulted from the outlier with the approach of fuzzification of the penalty, but also corrects the skew of the optimal separating hyperplane due to the very imbalanced data sets by using different cost algorithm. In addition, by introducing the total margin algorithm to replace the conventional soft margin algorithm, a lower generalization error bound can be obtained. Those three functions are embodied into the traditional SVM so that the TAF-SVM is proposed and reformulated in both linear and nonlinear cases. By using two databases, the Chung Yuan Christian University (CYCU) multiview and the facial recognition technology (FERET) face databases, and using the kernel Fisher's discriminant analysis (KFDA) algorithm to extract discriminating face features, experimental results show that the proposed TAF-SVM is superior to SVM in terms of the face-recognition accuracy. The results also indicate that the proposed TAF-SVM can achieve smaller error variances than SVM over a number of tests such that better recognition stability can be obtained.

Pattern of eyelid motion predictive of decision errors during drowsiness: oculomotor indices of altered states.

PubMed

Lobb, M L; Stern, J A

1986-08-01

Sequential patterns of eye and eyelid motion were identified in seven subjects performing a modified serial probe recognition task under drowsy conditions. Using simultaneous EOG and video recordings, eyelid motion was divided into components above, within, and below the pupil and the durations in sequence were recorded. A serial probe recognition task was modified to allow for distinguishing decision errors from attention errors. Decision errors were found to be more frequent following a downward shift in the gaze angle which the eyelid closing sequence was reduced from a five element to a three element sequence. The velocity of the eyelid moving over the pupil during decision errors was slow in the closing and fast in the reopening phase, while on decision correct trials it was fast in closing and slower in reopening. Due to the high variability of eyelid motion under drowsy conditions these findings were only marginally significant. When a five element blink occurred, the velocity of the lid over pupil motion component of these endogenous eye blinks was significantly faster on decision correct than on decision error trials. Furthermore, the highly variable, long duration closings associated with the decision response produced slow eye movements in the horizontal plane (SEM) which were more frequent and significantly longer in duration on decision error versus decision correct responses.

Privacy protection schemes for fingerprint recognition systems

NASA Astrophysics Data System (ADS)

Marasco, Emanuela; Cukic, Bojan

2015-05-01

The deployment of fingerprint recognition systems has always raised concerns related to personal privacy. A fingerprint is permanently associated with an individual and, generally, it cannot be reset if compromised in one application. Given that fingerprints are not a secret, potential misuses besides personal recognition represent privacy threats and may lead to public distrust. Privacy mechanisms control access to personal information and limit the likelihood of intrusions. In this paper, image- and feature-level schemes for privacy protection in fingerprint recognition systems are reviewed. Storing only key features of a biometric signature can reduce the likelihood of biometric data being used for unintended purposes. In biometric cryptosystems and biometric-based key release, the biometric component verifies the identity of the user, while the cryptographic key protects the communication channel. Transformation-based approaches only a transformed version of the original biometric signature is stored. Different applications can use different transforms. Matching is performed in the transformed domain which enable the preservation of low error rates. Since such templates do not reveal information about individuals, they are referred to as cancelable templates. A compromised template can be re-issued using a different transform. At image-level, de-identification schemes can remove identifiers disclosed for objectives unrelated to the original purpose, while permitting other authorized uses of personal information. Fingerprint images can be de-identified by, for example, mixing fingerprints or removing gender signature. In both cases, degradation of matching performance is minimized.

Report on Automated Semantic Analysis of Scientific and Engineering Codes

NASA Technical Reports Server (NTRS)

Stewart. Maark E. M.; Follen, Greg (Technical Monitor)

2001-01-01

The loss of the Mars Climate Orbiter due to a software error reveals what insiders know: software development is difficult and risky because, in part, current practices do not readily handle the complex details of software. Yet, for scientific software development the MCO mishap represents the tip of the iceberg; few errors are so public, and many errors are avoided with a combination of expertise, care, and testing during development and modification. Further, this effort consumes valuable time and resources even when hardware costs and execution time continually decrease. Software development could use better tools! This lack of tools has motivated the semantic analysis work explained in this report. However, this work has a distinguishing emphasis; the tool focuses on automated recognition of the fundamental mathematical and physical meaning of scientific code. Further, its comprehension is measured by quantitatively evaluating overall recognition with practical codes. This emphasis is necessary if software errors-like the MCO error-are to be quickly and inexpensively avoided in the future. This report evaluates the progress made with this problem. It presents recommendations, describes the approach, the tool's status, the challenges, related research, and a development strategy.

Repetition increases false recollection in older people.

PubMed

Pitarque, Alfonso; Sales, Alicia; Meléndez, Juan Carlos; Algarabel, Salvador

2015-02-01

Aging is accompanied by an increase in false alarms on recognition tasks, and these false alarms increase with repetition in older people (but not in young people). Traditionally, this increase was thought to be due to a greater use of familiarity in older people, but it was recently pointed out that false alarms also have a clear recollection component in these people. The main objective of our study is to analyze whether the expected increase in the rate of false alarms in older people due to stimulus repetition is produced by an inadequate use of familiarity, recollection, or both processes. To do so, we carried out an associative recognition experiment using pairs of words and pairs of images (faces associated with everyday contexts), in which we analyzed whether the repetition of some of the pairs increases the rate of false alarms in older people (compared to what was found in a sample of young people), and whether this increase is due to familiarity or recollection (using a remember-know paradigm). Our results show that the increase in false alarms in older people due to repetition is produced by false recollection, calling into question both dual and single-process models of recognition. Also, older people falsely recollect details of never studied stimuli, a clear case of perceptual illusions. These results are better explained in terms of source-monitoring errors, mediated by people's retrieval expectations. © 2014 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Digital holographic-based cancellable biometric for personal authentication

NASA Astrophysics Data System (ADS)

Verma, Gaurav; Sinha, Aloka

2016-05-01

In this paper, we propose a new digital holographic-based cancellable biometric scheme for personal authentication and verification. The realization of cancellable biometric is presented by using an optoelectronic experimental approach, in which an optically recorded hologram of the fingerprint of a person is numerically reconstructed. Each reconstructed feature has its own perspective, which is utilized to generate user-specific fingerprint features by using a feature-extraction process. New representations of the user-specific fingerprint features can be obtained from the same hologram, by changing the reconstruction distance (d) by an amount Δd between the recording plane and the reconstruction plane. This parameter is the key to make the cancellable user-specific fingerprint features using a digital holographic technique, which allows us to choose different reconstruction distances when reissuing the user-specific fingerprint features in the event of compromise. We have shown theoretically that each user-specific fingerprint feature has a unique identity with a high discrimination ability, and the chances of a match between them are minimal. In this aspect, a recognition system has also been demonstrated using the fingerprint biometric of the enrolled person at a particular reconstruction distance. For the performance evaluation of a fingerprint recognition system—the false acceptance ratio, the false rejection ratio and the equal error rate are calculated using correlation. The obtained results show good discrimination ability between the genuine and the impostor populations with the highest recognition rate of 98.23%.

Driver landmark and traffic sign identification in early Alzheimer's disease.

PubMed

Uc, E Y; Rizzo, M; Anderson, S W; Shi, Q; Dawson, J D

2005-06-01

To assess visual search and recognition of roadside targets and safety errors during a landmark and traffic sign identification task in drivers with Alzheimer's disease. 33 drivers with probable Alzheimer's disease of mild severity and 137 neurologically normal older adults underwent a battery of visual and cognitive tests and were asked to report detection of specific landmarks and traffic signs along a segment of an experimental drive. The drivers with mild Alzheimer's disease identified significantly fewer landmarks and traffic signs and made more at-fault safety errors during the task than control subjects. Roadside target identification performance and safety errors were predicted by scores on standardised tests of visual and cognitive function. Drivers with Alzheimer's disease are impaired in a task of visual search and recognition of roadside targets; the demands of these targets on visual perception, attention, executive functions, and memory probably increase the cognitive load, worsening driving safety.

Measuring Error Identification and Recovery Skills in Surgical Residents.

PubMed

Sternbach, Joel M; Wang, Kevin; El Khoury, Rym; Teitelbaum, Ezra N; Meyerson, Shari L

2017-02-01

Although error identification and recovery skills are essential for the safe practice of surgery, they have not traditionally been taught or evaluated in residency training. This study validates a method for assessing error identification and recovery skills in surgical residents using a thoracoscopic lobectomy simulator. We developed a 5-station, simulator-based examination containing the most commonly encountered cognitive and technical errors occurring during division of the superior pulmonary vein for left upper lobectomy. Successful completion of each station requires identification and correction of these errors. Examinations were video recorded and scored in a blinded fashion using an examination-specific rating instrument evaluating task performance as well as error identification and recovery skills. Evidence of validity was collected in the categories of content, response process, internal structure, and relationship to other variables. Fifteen general surgical residents (9 interns and 6 third-year residents) completed the examination. Interrater reliability was high, with an intraclass correlation coefficient of 0.78 between 4 trained raters. Station scores ranged from 64% to 84% correct. All stations adequately discriminated between high- and low-performing residents, with discrimination ranging from 0.35 to 0.65. The overall examination score was significantly higher for intermediate residents than for interns (mean, 74 versus 64 of 90 possible; p = 0.03). The described simulator-based examination with embedded errors and its accompanying assessment tool can be used to measure error identification and recovery skills in surgical residents. This examination provides a valid method for comparing teaching strategies designed to improve error recognition and recovery to enhance patient safety. Copyright © 2017 The Society of Thoracic Surgeons. Published by Elsevier Inc. All rights reserved.

Inconsistent emotion recognition deficits across stimulus modalities in Huntington׳s disease.

PubMed

Rees, Elin M; Farmer, Ruth; Cole, James H; Henley, Susie M D; Sprengelmeyer, Reiner; Frost, Chris; Scahill, Rachael I; Hobbs, Nicola Z; Tabrizi, Sarah J

2014-11-01

Recognition of negative emotions is impaired in Huntington׳s Disease (HD). It is unclear whether these emotion-specific problems are driven by dissociable cognitive deficits, emotion complexity, test cue difficulty, or visuoperceptual impairments. This study set out to further characterise emotion recognition in HD by comparing patterns of deficits across stimulus modalities; notably including for the first time in HD, the more ecologically and clinically relevant modality of film clips portraying dynamic facial expressions. Fifteen early HD and 17 control participants were tested on emotion recognition from static facial photographs, non-verbal vocal expressions and one second dynamic film clips, all depicting different emotions. Statistically significant evidence of impairment of anger, disgust and fear recognition was seen in HD participants compared with healthy controls across multiple stimulus modalities. The extent of the impairment, as measured by the difference in the number of errors made between HD participants and controls, differed according to the combination of emotion and modality (p=0.013, interaction test). The largest between-group difference was seen in the recognition of anger from film clips. Consistent with previous reports, anger, disgust and fear were the most poorly recognised emotions by the HD group. This impairment did not appear to be due to task demands or expression complexity as the pattern of between-group differences did not correspond to the pattern of errors made by either group; implicating emotion-specific cognitive processing pathology. There was however evidence that the extent of emotion recognition deficits significantly differed between stimulus modalities. The implications in terms of designing future tests of emotion recognition and care giving are discussed. Copyright © 2014 Elsevier Ltd. All rights reserved.

"Context and Spoken Word Recognition in a Novel Lexicon": Correction

ERIC Educational Resources Information Center

Revill, Kathleen Pirog; Tanenhaus, Michael K.; Aslin, Richard N.

2009-01-01

Reports an error in "Context and spoken word recognition in a novel lexicon" by Kathleen Pirog Revill, Michael K. Tanenhaus and Richard N. Aslin ("Journal of Experimental Psychology: Learning, Memory, and Cognition," 2008[Sep], Vol 34[5], 1207-1223). Figure 9 was inadvertently duplicated as Figure 10. Figure 9 in the original article was correct.…

Component Structure of Individual Differences in True and False Recognition of Faces

ERIC Educational Resources Information Center

Bartlett, James C.; Shastri, Kalyan K.; Abdi, Herve; Neville-Smith, Marsha

2009-01-01

Principal-component analyses of 4 face-recognition studies uncovered 2 independent components. The first component was strongly related to false-alarm errors with new faces as well as to facial "conjunctions" that recombine features of previously studied faces. The second component was strongly related to hits as well as to the conjunction/new…

Destabilization of the MutSα's protein-protein interface due to binding to the DNA adduct induced by anticancer agent carboplatin via molecular dynamics simulations.

PubMed

Negureanu, Lacramioara; Salsbury, Freddie R

2013-11-01

DNA mismatch repair (MMR) proteins maintain genetic integrity in all organisms by recognizing and repairing DNA errors. Such alteration of hereditary information can lead to various diseases, including cancer. Besides their role in DNA repair, MMR proteins detect and initiate cellular responses to certain type of DNA damage. Its response to the damaged DNA has made the human MMR pathway a useful target for anticancer agents such as carboplatin. This study indicates that strong, specific interactions at the interface of MutSα in response to the mismatched DNA recognition are replaced by weak, non-specific interactions in response to the damaged DNA recognition. Data suggest a severe impairment of the dimerization of MutSα in response to the damaged DNA recognition. While the core of MutSα is preserved in response to the damaged DNA recognition, the loss of contact surface and the rearrangement of contacts at the protein interface suggest a different packing in response to the damaged DNA recognition. Coupled in response to the mismatched DNA recognition, interaction energies, hydrogen bonds, salt bridges, and solvent accessible surface areas at the interface of MutSα and within the subunits are uncoupled or asynchronously coupled in response to the damaged DNA recognition. These pieces of evidence suggest that the loss of a synchronous mode of response in the MutSα's surveillance for DNA errors would possibly be one of the mechanism(s) of signaling the MMR-dependent programed cell death much wanted in anticancer therapies. The analysis was drawn from dynamics simulations.

Emitter location errors in electronic recognition system

NASA Astrophysics Data System (ADS)

Matuszewski, Jan; Dikta, Anna

2017-04-01

The paper describes some of the problems associated with emitter location calculations. This aspect is the most important part of the series of tasks in the electronic recognition systems. The basic tasks include: detection of emission of electromagnetic signals, tracking (determining the direction of emitter sources), signal analysis in order to classify different emitter types and the identification of the sources of emission of the same type. The paper presents a brief description of the main methods of emitter localization and the basic mathematical formulae for calculating their location. The errors' estimation has been made to determine the emitter location for three different methods and different scenarios of emitters and direction finding (DF) sensors deployment in the electromagnetic environment. The emitter has been established using a special computer program. On the basis of extensive numerical calculations, the evaluation of precise emitter location in the recognition systems for different configuration alignment of bearing devices and emitter was conducted. The calculations which have been made based on the simulated data for different methods of location are presented in the figures and respective tables. The obtained results demonstrate that calculation of the precise emitter location depends on: the number of DF sensors, the distances between emitter and DF sensors, their mutual location in the reconnaissance area and bearing errors. The precise emitter location varies depending on the number of obtained bearings. The higher the number of bearings, the better the accuracy of calculated emitter location in spite of relatively high bearing errors for each DF sensor.

Body-object interaction ratings for 1,618 monosyllabic nouns.

PubMed

Tillotson, Sherri M; Siakaluk, Paul D; Pexman, Penny M

2008-11-01

Body-object interaction (BOI) assesses the ease with which a human body can physically interact with a word's referent. Recent research has shown that BOI influences visual word recognition processes in such a way that responses to high-BOI words (e.g., couch) are faster and less error prone than responses to low-BOI words (e.g., cliff). Importantly, the high-BOI words and the low-BOI words that were used in those studies were matched on imageability. In the present study, we collected BOI ratings for a large set of words. BOI ratings, on a 1-7 scale, were obtained for 1,618 monosyllabic nouns. These ratings allowed us to test the generalizability of BOI effects to a large set of items, and they should be useful to researchers who are interested in manipulating or controlling for the effects of BOI. The body-object interaction ratings for this study may be downloaded from the Psychonomic Society's Archive of Norms, Stimuli, and Data, www.psychonomic.org/archive.

Evaluation of the leap motion controller as a new contact-free pointing device.

PubMed

Bachmann, Daniel; Weichert, Frank; Rinkenauer, Gerhard

2014-12-24

This paper presents a Fitts' law-based analysis of the user's performance in selection tasks with the Leap Motion Controller compared with a standard mouse device. The Leap Motion Controller (LMC) is a new contact-free input system for gesture-based human-computer interaction with declared sub-millimeter accuracy. Up to this point, there has hardly been any systematic evaluation of this new system available. With an error rate of 7.8% for the LMC and 2.8% for the mouse device, movement times twice as large as for a mouse device and high overall effort ratings, the Leap Motion Controller's performance as an input device for everyday generic computer pointing tasks is rather limited, at least with regard to the selection recognition provided by the LMC.

Evaluation of the Leap Motion Controller as a New Contact-Free Pointing Device

PubMed Central

Bachmann, Daniel; Weichert, Frank; Rinkenauer, Gerhard

2015-01-01

This paper presents a Fitts' law-based analysis of the user's performance in selection tasks with the Leap Motion Controller compared with a standard mouse device. The Leap Motion Controller (LMC) is a new contact-free input system for gesture-based human-computer interaction with declared sub-millimeter accuracy. Up to this point, there has hardly been any systematic evaluation of this new system available. With an error rate of 7.8 % for the LMC and 2.8% for the mouse device, movement times twice as large as for a mouse device and high overall effort ratings, the Leap Motion Controller's performance as an input device for everyday generic computer pointing tasks is rather limited, at least with regard to the selection recognition provided by the LMC. PMID:25609043

Children with ADHD Symptoms Are Less Susceptible to Gap-Filling Errors than Typically Developing Children

ERIC Educational Resources Information Center

Mirandola, C.; Paparella, G.; Re, A. M.; Ghetti, S.; Cornoldi, C.

2012-01-01

Enhanced semantic processing is associated with increased false recognition of items consistent with studied material, suggesting that children with poor semantic skills could produce fewer false memories. We examined whether memory errors differed in children with Attention Deficit/Hyperactivity Disorder (ADHD) and controls. Children viewed 18…

A Generalized Process Model of Human Action Selection and Error and its Application to Error Prediction

DTIC Science & Technology

2014-07-01

Macmillan & Creelman , 2005). This is a quite high degree of discriminability and it means that when the decision model predicts a probability of...ROC analysis. Pattern Recognition Letters, 27(8), 861-874. Retrieved from Google Scholar. Macmillan, N. A., & Creelman , C. D. (2005). Detection

Effects of OCR Errors on Ranking and Feedback Using the Vector Space Model.

ERIC Educational Resources Information Center

Taghva, Kazem; And Others

1996-01-01

Reports on the performance of the vector space model in the presence of OCR (optical character recognition) errors in information retrieval. Highlights include precision and recall, a full-text test collection, smart vector representation, impact of weighting parameters, ranking variability, and the effect of relevance feedback. (Author/LRW)

Diagnosis of Cognitive Errors by Statistical Pattern Recognition Methods.

ERIC Educational Resources Information Center

Tatsuoka, Kikumi K.; Tatsuoka, Maurice M.

The rule space model permits measurement of cognitive skill acquisition, diagnosis of cognitive errors, and detection of the strengths and weaknesses of knowledge possessed by individuals. Two ways to classify an individual into his or her most plausible latent state of knowledge include: (1) hypothesis testing--Bayes' decision rules for minimum…

Automatic Facial Expression Recognition and Operator Functional State

NASA Technical Reports Server (NTRS)

Blanson, Nina

2012-01-01

The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions

Automatic Facial Expression Recognition and Operator Functional State

NASA Technical Reports Server (NTRS)

Blanson, Nina

2011-01-01

The prevalence of human error in safety-critical occupations remains a major challenge to mission success despite increasing automation in control processes. Although various methods have been proposed to prevent incidences of human error, none of these have been developed to employ the detection and regulation of Operator Functional State (OFS), or the optimal condition of the operator while performing a task, in work environments due to drawbacks such as obtrusiveness and impracticality. A video-based system with the ability to infer an individual's emotional state from facial feature patterning mitigates some of the problems associated with other methods of detecting OFS, like obtrusiveness and impracticality in integration with the mission environment. This paper explores the utility of facial expression recognition as a technology for inferring OFS by first expounding on the intricacies of OFS and the scientific background behind emotion and its relationship with an individual's state. Then, descriptions of the feedback loop and the emotion protocols proposed for the facial recognition program are explained. A basic version of the facial expression recognition program uses Haar classifiers and OpenCV libraries to automatically locate key facial landmarks during a live video stream. Various methods of creating facial expression recognition software are reviewed to guide future extensions of the program. The paper concludes with an examination of the steps necessary in the research of emotion and recommendations for the creation of an automatic facial expression recognition program for use in real-time, safety-critical missions.

Automated smartphone audiometry: Validation of a word recognition test app.

PubMed

Dewyer, Nicholas A; Jiradejvong, Patpong; Henderson Sabes, Jennifer; Limb, Charles J

2018-03-01

Develop and validate an automated smartphone word recognition test. Cross-sectional case-control diagnostic test comparison. An automated word recognition test was developed as an app for a smartphone with earphones. English-speaking adults with recent audiograms and various levels of hearing loss were recruited from an audiology clinic and were administered the smartphone word recognition test. Word recognition scores determined by the smartphone app and the gold standard speech audiometry test performed by an audiologist were compared. Test scores for 37 ears were analyzed. Word recognition scores determined by the smartphone app and audiologist testing were in agreement, with 86% of the data points within a clinically acceptable margin of error and a linear correlation value between test scores of 0.89. The WordRec automated smartphone app accurately determines word recognition scores. 3b. Laryngoscope, 128:707-712, 2018. © 2017 The American Laryngological, Rhinological and Otological Society, Inc.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

2017-02-09

In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

Human Activity Recognition by Combining a Small Number of Classifiers.

PubMed

Nazabal, Alfredo; Garcia-Moreno, Pablo; Artes-Rodriguez, Antonio; Ghahramani, Zoubin

2016-09-01

We consider the problem of daily human activity recognition (HAR) using multiple wireless inertial sensors, and specifically, HAR systems with a very low number of sensors, each one providing an estimation of the performed activities. We propose new Bayesian models to combine the output of the sensors. The models are based on a soft outputs combination of individual classifiers to deal with the small number of sensors. We also incorporate the dynamic nature of human activities as a first-order homogeneous Markov chain. We develop both inductive and transductive inference methods for each model to be employed in supervised and semisupervised situations, respectively. Using different real HAR databases, we compare our classifiers combination models against a single classifier that employs all the signals from the sensors. Our models exhibit consistently a reduction of the error rate and an increase of robustness against sensor failures. Our models also outperform other classifiers combination models that do not consider soft outputs and an Markovian structure of the human activities.

Automatically Detecting Likely Edits in Clinical Notes Created Using Automatic Speech Recognition

PubMed Central

Lybarger, Kevin; Ostendorf, Mari; Yetisgen, Meliha

2017-01-01

The use of automatic speech recognition (ASR) to create clinical notes has the potential to reduce costs associated with note creation for electronic medical records, but at current system accuracy levels, post-editing by practitioners is needed to ensure note quality. Aiming to reduce the time required to edit ASR transcripts, this paper investigates novel methods for automatic detection of edit regions within the transcripts, including both putative ASR errors but also regions that are targets for cleanup or rephrasing. We create detection models using logistic regression and conditional random field models, exploring a variety of text-based features that consider the structure of clinical notes and exploit the medical context. Different medical text resources are used to improve feature extraction. Experimental results on a large corpus of practitioner-edited clinical notes show that 67% of sentence-level edits and 45% of word-level edits can be detected with a false detection rate of 15%. PMID:29854187

Language Model Combination and Adaptation Using Weighted Finite State Transducers

NASA Technical Reports Server (NTRS)

Liu, X.; Gales, M. J. F.; Hieronymus, J. L.; Woodland, P. C.

2010-01-01

In speech recognition systems language model (LMs) are often constructed by training and combining multiple n-gram models. They can be either used to represent different genres or tasks found in diverse text sources, or capture stochastic properties of different linguistic symbol sequences, for example, syllables and words. Unsupervised LM adaption may also be used to further improve robustness to varying styles or tasks. When using these techniques, extensive software changes are often required. In this paper an alternative and more general approach based on weighted finite state transducers (WFSTs) is investigated for LM combination and adaptation. As it is entirely based on well-defined WFST operations, minimum change to decoding tools is needed. A wide range of LM combination configurations can be flexibly supported. An efficient on-the-fly WFST decoding algorithm is also proposed. Significant error rate gains of 7.3% relative were obtained on a state-of-the-art broadcast audio recognition task using a history dependently adapted multi-level LM modelling both syllable and word sequences

Design of a portable electronic nose for real-fake detection of liquors

NASA Astrophysics Data System (ADS)

Qi, Pei-Feng; Zeng, Ming; Li, Zhi-Hua; Sun, Biao; Meng, Qing-Hao

2017-09-01

Portability is a major issue that influences the practical application of electronic noses (e-noses). For liquors detection, an e-nose must preprocess the liquid samples (e.g., using evaporation and thermal desorption), which makes the portable design even more difficult. To realize convenient and rapid detection of liquors, we designed a portable e-nose platform that consists of hardware and software systems. The hardware system contains an evaporation/sampling module, a reaction module, a control/data acquisition and analysis module, and a power module. The software system provides a user-friendly interface and can achieve automatic sampling and data processing. This e-nose platform has been applied to the real-fake recognition of Chinese liquors. Through parameter optimization of a one-class support vector machine classifier, the error rate of the negative samples is greatly reduced, and the overall recognition accuracy is improved. The results validated the feasibility of the designed portable e-nose platform.

A biologically inspired neural network model to transformation invariant object recognition

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. Further, the residual critic error in DHP is generally smaller than that of HDP, and DHP achieves a 100% success rate more frequently than HDP for individual objects/subjects. On the other hand, HDP is more robust than the DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational time involved in DHP is more.

Do Recognition and Priming Index a Unitary Knowledge Base? Comment on Shanks et al. (2003)

ERIC Educational Resources Information Center

Runger, Dennis; Nagy, Gabriel; Frensch, Peter A.

2009-01-01

Whether sequence learning entails a single or multiple memory systems is a moot issue. Recently, D. R. Shanks, L. Wilkinson, and S. Channon advanced a single-system model that predicts a perfect correlation between true (i.e., error free) response time priming and recognition. The Shanks model is contrasted with a dual-process model that…

Photonics

NASA Astrophysics Data System (ADS)

Roh, Won B.

Photonic technologies-based computational systems are projected to be able to offer order-of-magnitude improvements in processing speed, due to their intrinsic architectural parallelism and ultrahigh switching speeds; these architectures also minimize connectors, thereby enhancing reliability, and preclude EMP vulnerability. The use of optoelectronic ICs would also extend weapons capabilities in such areas as automated target recognition, systems-state monitoring, and detection avoidance. Fiber-optics technologies have an information-carrying capacity fully five orders of magnitude greater than copper-wire-based systems; energy loss in transmission is two orders of magnitude lower, and error rates one order of magnitude lower. Attention is being given to ZrF glasses for optical fibers with unprecedentedly low scattering levels.

Evidence from the lamarck granodiorite for rapid late cretaceous crust formation in California

USGS Publications Warehouse

Coleman, D.S.; Frost, T.P.; Glazner, A.F.

1992-01-01

Strontium and neodymium isotopic data for rocks from the voluminous 90-million-year-old Lamarck intrusive suite in the Sierra Nevada batholith, California, show little variation across a compositional range from gabbro to granite. Data for three different gabbro intrusions within the suite are identical within analytical error and are consistent with derivation from an enriched mantle source. Recognition of local involvement of enriched mantle during generation of the Sierran batholith modifies estimates of crustal growth rates in the United States. These data indicate that parts of the Sierra Nevada batholith may consist almost entirely of juvenile crust added during Cretaceous magmatism.

Brain State Before Error Making in Young Patients With Mild Spastic Cerebral Palsy.

PubMed

Hakkarainen, Elina; Pirilä, Silja; Kaartinen, Jukka; van der Meere, Jaap J

2015-10-01

In the present experiment, children with mild spastic cerebral palsy and a control group carried out a memory recognition task. The key question was if errors of the patient group are foreshadowed by attention lapses, by weak motor preparation, or by both. Reaction times together with event-related potentials associated with motor preparation (frontal late contingent negative variation), attention (parietal P300), and response evaluation (parietal error-preceding positivity) were investigated in instances where 3 subsequent correct trials preceded an error. The findings indicated that error responses of the patient group are foreshadowed by weak motor preparation in correct trials directly preceding an error. © The Author(s) 2015.

Reader error, object recognition, and visual search

NASA Astrophysics Data System (ADS)

Kundel, Harold L.

2004-05-01

Small abnormalities such as hairline fractures, lung nodules and breast tumors are missed by competent radiologists with sufficient frequency to make them a matter of concern to the medical community; not only because they lead to litigation but also because they delay patient care. It is very easy to attribute misses to incompetence or inattention. To do so may be placing an unjustified stigma on the radiologists involved and may allow other radiologists to continue a false optimism that it can never happen to them. This review presents some of the fundamentals of visual system function that are relevant to understanding the search for and the recognition of small targets embedded in complicated but meaningful backgrounds like chests and mammograms. It presents a model for visual search that postulates a pre-attentive global analysis of the retinal image followed by foveal checking fixations and eventually discovery scanning. The model will be used to differentiate errors of search, recognition and decision making. The implications for computer aided diagnosis and for functional workstation design are discussed.

Influence of quality of images recorded in far infrared on pattern recognition based on neural networks and Eigenfaces algorithm

NASA Astrophysics Data System (ADS)

Jelen, Lukasz; Kobel, Joanna; Podbielska, Halina

2003-11-01

This paper discusses the possibility of exploiting of the tennovision registration and artificial neural networks for facial recognition systems. A biometric system that is able to identify people from thermograms is presented. To identify a person we used the Eigenfaces algorithm. For the face detection in the picture the backpropagation neural network was designed. For this purpose thermograms of 10 people in various external conditions were studies. The Eigenfaces algorithm calculated an average face and then the set of characteristic features for each studied person was produced. The neural network has to detect the face in the image before it actually can be identified. We used five hidden layers for that purpose. It was shown that the errors in recognition depend on the feature extraction, for low quality pictures the error was so high as 30%. However, for pictures with a good feature extraction the results of proper identification higher then 90%, were obtained.

Automated extraction of radiation dose information from CT dose report images.

PubMed

Li, Xinhua; Zhang, Da; Liu, Bob

2011-06-01

The purpose of this article is to describe the development of an automated tool for retrieving texts from CT dose report images. Optical character recognition was adopted to perform text recognitions of CT dose report images. The developed tool is able to automate the process of analyzing multiple CT examinations, including text recognition, parsing, error correction, and exporting data to spreadsheets. The results were precise for total dose-length product (DLP) and were about 95% accurate for CT dose index and DLP of scanned series.

Approach to recognition of flexible form for credit card expiration date recognition as example

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Nikolaev, Dmitry P.; Ingacheva, Anastasia; Skoryukina, Natalya

2015-12-01

In this paper we consider a task of finding information fields within document with flexible form for credit card expiration date field as example. We discuss main difficulties and suggest possible solutions. In our case this task is to be solved on mobile devices therefore computational complexity has to be as low as possible. In this paper we provide results of the analysis of suggested algorithm. Error distribution of the recognition system shows that suggested algorithm solves the task with required accuracy.

Erring and learning in clinical practice.

PubMed Central

Hurwitz, Brian

2002-01-01

This paper discusses error type their possible consequences and the doctors who make them. There is no single, all-encompassing typology of medical errors. They are frequently multifactorial in origin and use from the mental processes of individuals; from defects in perception, thinking reasoning planning and interpretation and from failures of team-working omissions and poorly executed actions. They also arise from inadequately designed and operated healthcare systems or procedures. The paper considers error-truth relatedness, the approach of UK courts to medical errors, the learning opportunities which flow from error recognition and the need for personal and professional self awareness of clinical fallibilities. PMID:12389767

On a problematic procedure to manipulate response biases in recognition experiments: the case of "implied" base rates.

PubMed

Bröder, Arndt; Malejka, Simone

2017-07-01

The experimental manipulation of response biases in recognition-memory tests is an important means for testing recognition models and for estimating their parameters. The textbook manipulations for binary-response formats either vary the payoff scheme or the base rate of targets in the recognition test, with the latter being the more frequently applied procedure. However, some published studies reverted to implying different base rates by instruction rather than actually changing them. Aside from unnecessarily deceiving participants, this procedure may lead to cognitive conflicts that prompt response strategies unknown to the experimenter. To test our objection, implied base rates were compared to actual base rates in a recognition experiment followed by a post-experimental interview to assess participants' response strategies. The behavioural data show that recognition-memory performance was estimated to be lower in the implied base-rate condition. The interview data demonstrate that participants used various second-order response strategies that jeopardise the interpretability of the recognition data. We thus advice researchers against substituting actual base rates with implied base rates.

Security and matching of partial fingerprint recognition systems

NASA Astrophysics Data System (ADS)

Jea, Tsai-Yang; Chavan, Viraj S.; Govindaraju, Venu; Schneider, John K.

2004-08-01

Despite advances in fingerprint identification techniques, matching incomplete or partial fingerprints still poses a difficult challenge. While the introduction of compact silicon chip-based sensors that capture only a part of the fingerprint area have made this problem important from a commercial perspective, there is also considerable interest on the topic for processing partial and latent fingerprints obtained at crime scenes. Attempts to match partial fingerprints using singular ridge structures-based alignment techniques fail when the partial print does not include such structures (e.g., core or delta). We present a multi-path fingerprint matching approach that utilizes localized secondary features derived using only the relative information of minutiae. Since the minutia-based fingerprint representation, is an ANSI-NIST standard, our approach has the advantage of being directly applicable to already existing databases. We also analyze the vulnerability of partial fingerprint identification systems to brute force attacks. The described matching approach has been tested on one of FVC2002"s DB1 database11. The experimental results show that our approach achieves an equal error rate of 1.25% and a total error rate of 1.8% (with FAR at 0.2% and FRR at 1.6%).

Combining multiple thresholding binarization values to improve OCR output

NASA Astrophysics Data System (ADS)

Lund, William B.; Kennard, Douglas J.; Ringger, Eric K.

2013-01-01

For noisy, historical documents, a high optical character recognition (OCR) word error rate (WER) can render the OCR text unusable. Since image binarization is often the method used to identify foreground pixels, a body of research seeks to improve image-wide binarization directly. Instead of relying on any one imperfect binarization technique, our method incorporates information from multiple simple thresholding binarizations of the same image to improve text output. Using a new corpus of 19th century newspaper grayscale images for which the text transcription is known, we observe WERs of 13.8% and higher using current binarization techniques and a state-of-the-art OCR engine. Our novel approach combines the OCR outputs from multiple thresholded images by aligning the text output and producing a lattice of word alternatives from which a lattice word error rate (LWER) is calculated. Our results show a LWER of 7.6% when aligning two threshold images and a LWER of 6.8% when aligning five. From the word lattice we commit to one hypothesis by applying the methods of Lund et al. (2011) achieving an improvement over the original OCR output and a 8.41% WER result on this data set.

Deep neural networks for texture classification-A theoretical analysis.

PubMed

Basu, Saikat; Mukhopadhyay, Supratik; Karki, Manohar; DiBiano, Robert; Ganguly, Sangram; Nemani, Ramakrishna; Gayaka, Shreekant

2018-01-01

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Children and Adults Both See "Pirates" in "Parties": Letter-Position Effects for Developing Readers and Skilled Adult Readers

ERIC Educational Resources Information Center

Paterson, Kevin B.; Read, Josephine; McGowan, Victoria A.; Jordan, Timothy R.

2015-01-01

Developing readers often make anagrammatical errors (e.g. misreading pirates as parties), suggesting they use letter position flexibly during word recognition. However, while it is widely assumed that the occurrence of these errors decreases with increases in reading skill, empirical evidence to support this distinction is lacking. Accordingly, we…

Emotion recognition in fathers and mothers at high-risk for child physical abuse.

PubMed

Asla, Nagore; de Paúl, Joaquín; Pérez-Albéniz, Alicia

2011-09-01

The present study was designed to determine whether parents at high risk for physical child abuse, in comparison with parents at low risk, show deficits in emotion recognition, as well as to examine the moderator effect of gender and stress on the relationship between risk for physical child abuse and emotion recognition. Based on their scores on the Abuse Scale of the CAP Inventory (Milner, 1986), 64 parents at high risk (24 fathers and 40 mothers) and 80 parents at low risk (40 fathers and 40 mothers) for physical child abuse were selected. The Subtle Expression Training Tool/Micro Expression Training Tool (Ekman, 2004a, 2004b) and the Diagnostic Analysis of Nonverbal Accuracy II (Nowicki & Carton, 1993) were used to assess emotion recognition. As expected, parents at high risk, in contrast to parents at low risk, showed deficits in emotion recognition. However, differences between high- and low-risk participants were observed only for fathers, but not for mothers. Whereas fathers at high risk for physical child abuse made more errors than mothers at high risk, no differences between mothers at low risk and fathers at low risk were found. No interaction between stress, gender, and risk status was observed for errors in emotion recognition. The present findings, if confirmed with physical abusers, could be helpful to further our understanding of deficits in processing information of physically abusive parents and to develop treatment strategies specifically focused on emotion recognition. Moreover, if gender differences can be confirmed, the findings could be helpful to develop specific treatment programs for abusive fathers. Copyright © 2011 Elsevier Ltd. All rights reserved.

Destabilization of the MutSα’s protein-protein interface due to binding to the DNA adduct induced by anticancer agent Carboplatin via molecular dynamics simulations

PubMed Central

Negureanu, Lacramioara; Salsbury, Freddie R

2013-01-01

DNA mismatch repair (MMR) proteins maintain genetic integrity in all organisms by recognizing and repairing DNA errors. Such alteration of hereditary information can lead to various diseases, including cancer. Besides their role in DNA repair, MMR proteins detect and initiate cellular responses to certain type of DNA damage. Its response to the damaged DNA has made the human MMR pathway a useful target for anticancer agents such as carboplatin. This study indicates that strong, specific interactions at the interface of MutSα in response to the mismatched DNA recognition are replaced by weak, non-specific interactions in response to the damaged DNA recognition. Data suggest a severe impairment of the dimerization of MutSα in response to the damaged DNA recognition. While the core of MutSα is preserved in response to the damaged DNA recognition, the loss of contact surface and the rearrangement of contacts at the protein interface suggest a different packing in response to the damaged DNA recognition. Coupled in response to the mismatched DNA recognition, interaction energies, hydrogen bonds, salt bridges, and solvent accessible surface areas at the interface of MutSα and within the subunits are uncoupled or asynchronously coupled in response to the damaged DNA recognition. These pieces of evidence suggest that the loss of a synchronous mode of response in the MutSα’s surveillance for DNA errors would possible be one of the mechanism(s) of signaling the MMR-dependent programed cell death much wanted in anticancer therapies. The analysis was drawn from dynamics simulations. PMID:24061854

Speech Recognition for Medical Dictation: Overview in Quebec and Systematic Review.

PubMed

Poder, Thomas G; Fisette, Jean-François; Déry, Véronique

2018-04-03

Speech recognition is increasingly used in medical reporting. The aim of this article is to identify in the literature the strengths and weaknesses of this technology, as well as barriers to and facilitators of its implementation. A systematic review of systematic reviews was performed using PubMed, Scopus, the Cochrane Library and the Center for Reviews and Dissemination through August 2017. The gray literature has also been consulted. The quality of systematic reviews has been assessed with the AMSTAR checklist. The main inclusion criterion was use of speech recognition for medical reporting (front-end or back-end). A survey has also been conducted in Quebec, Canada, to identify the dissemination of this technology in this province, as well as the factors leading to the success or failure of its implementation. Five systematic reviews were identified. These reviews indicated a high level of heterogeneity across studies. The quality of the studies reported was generally poor. Speech recognition is not as accurate as human transcription, but it can dramatically reduce turnaround times for reporting. In front-end use, medical doctors need to spend more time on dictation and correction than required with human transcription. With speech recognition, major errors occur up to three times more frequently. In back-end use, a potential increase in productivity of transcriptionists was noted. In conclusion, speech recognition offers several advantages for medical reporting. However, these advantages are countered by an increased burden on medical doctors and by risks of additional errors in medical reports. It is also hard to identify for which medical specialties and which clinical activities the use of speech recognition will be the most beneficial.

Errare machinale est: the use of error-related potentials in brain-machine interfaces

PubMed Central

Chavarriaga, Ricardo; Sobolewski, Aleksander; Millán, José del R.

2014-01-01

The ability to recognize errors is crucial for efficient behavior. Numerous studies have identified electrophysiological correlates of error recognition in the human brain (error-related potentials, ErrPs). Consequently, it has been proposed to use these signals to improve human-computer interaction (HCI) or brain-machine interfacing (BMI). Here, we present a review of over a decade of developments toward this goal. This body of work provides consistent evidence that ErrPs can be successfully detected on a single-trial basis, and that they can be effectively used in both HCI and BMI applications. We first describe the ErrP phenomenon and follow up with an analysis of different strategies to increase the robustness of a system by incorporating single-trial ErrP recognition, either by correcting the machine's actions or by providing means for its error-based adaptation. These approaches can be applied both when the user employs traditional HCI input devices or in combination with another BMI channel. Finally, we discuss the current challenges that have to be overcome in order to fully integrate ErrPs into practical applications. This includes, in particular, the characterization of such signals during real(istic) applications, as well as the possibility of extracting richer information from them, going beyond the time-locked decoding that dominates current approaches. PMID:25100937

Visual Recognition of the Elderly Concerning Risks of Falling or Stumbling Indoors in the Home

PubMed Central

Katsura, Toshiki; Miura, Norio; Hoshino, Akiko; Usui, Kanae; Takahashi, Yasuro; Hisamoto, Seiichi

2011-01-01

Objective: The objective of this study was to verify the recognition of dangers and obstacles within a house in the elderly when walking based on analyses of gaze point fixation. Materials and Methods: The rate of recognizing indoor dangers was compared among 30 elderly, 14 middle-aged and 11 young individuals using the Eye Mark Recorder. Results: 1) All of the elderly, middle-aged and young individuals showed a high recognition rate of 100% or near 100% when ascending outdoor steps but a low rate of recognizing obstacles placed on the steps. They showed a recognition rate of about 60% when descending steps from residential premises to the street. The rate of recognizing middle steps in the elderly was significantly lower than that in younger and middle-aged individuals. Regarding recognition indoors, when ascending stairs, all of the elderly, middle-aged and young individuals showed a high recognition rate of nearly 100%. When descending stairs, they showed a recognition rate of 70-90%. However, although the recognition rate in the elderly was lower than in younger and middle-aged individuals, no significant difference was observed. 2) When moving indoors, all of the elderly, middle-aged and young individuals showed a recognition rate of 70%-80%. The recognition rate was high regarding obstacles such as floors, televisions and chests of drawers but low for obstacles in the bathroom and steps on the path. The rate of recognizing steps of doorsills forming the division between a Japanese-style room and corridor as well as obstacles in a Japanese-style room was low, and the rate in the elderly was low, being 40% or less. Conclusion: The rate of recognizing steps of doorsills as well as obstacles in a Japanese-style room was lower in the elderly in comparison with middle-aged or young individuals. PMID:25648876

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.

A multistream model of visual word recognition.

PubMed

Allen, Philip A; Smith, Albert F; Lien, Mei-Ching; Kaut, Kevin P; Canfield, Angie

2009-02-01

Four experiments are reported that test a multistream model of visual word recognition, which associates letter-level and word-level processing channels with three known visual processing streams isolated in macaque monkeys: the magno-dominated (MD) stream, the interblob-dominated (ID) stream, and the blob-dominated (BD) stream (Van Essen & Anderson, 1995). We show that mixing the color of adjacent letters of words does not result in facilitation of response times or error rates when the spatial-frequency pattern of a whole word is familiar. However, facilitation does occur when the spatial-frequency pattern of a whole word is not familiar. This pattern of results is not due to different luminance levels across the different-colored stimuli and the background because isoluminant displays were used. Also, the mixed-case, mixed-hue facilitation occurred when different display distances were used (Experiments 2 and 3), so this suggests that image normalization can adjust independently of object size differences. Finally, we show that this effect persists in both spaced and unspaced conditions (Experiment 4)--suggesting that inappropriate letter grouping by hue cannot account for these results. These data support a model of visual word recognition in which lower spatial frequencies are processed first in the more rapid MD stream. The slower ID and BD streams may process some lower spatial frequency information in addition to processing higher spatial frequency information, but these channels tend to lose the processing race to recognition unless the letter string is unfamiliar to the MD stream--as with mixed-case presentation.

Margined winner-take-all: New learning rule for pattern recognition.

PubMed

Fukushima, Kunihiko

2018-01-01

The neocognitron is a deep (multi-layered) convolutional neural network that can be trained to recognize visual patterns robustly. In the intermediate layers of the neocognitron, local features are extracted from input patterns. In the deepest layer, based on the features extracted in the intermediate layers, input patterns are classified into classes. A method called IntVec (interpolating-vector) is used for this purpose. This paper proposes a new learning rule called margined Winner-Take-All (mWTA) for training the deepest layer. Every time when a training pattern is presented during the learning, if the result of recognition by WTA (Winner-Take-All) is an error, a new cell is generated in the deepest layer. Here we put a certain amount of margin to the WTA. In other words, only during the learning, a certain amount of handicap is given to cells of classes other than that of the training vector, and the winner is chosen under this handicap. By introducing the margin to the WTA, we can generate a compact set of cells, with which a high recognition rate can be obtained with a small computational cost. The ability of this mWTA is demonstrated by computer simulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

NASA Astrophysics Data System (ADS)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

Expeditious reconciliation for practical quantum key distribution

NASA Astrophysics Data System (ADS)

Nakassis, Anastase; Bienfang, Joshua C.; Williams, Carl J.

2004-08-01

The paper proposes algorithmic and environmental modifications to the extant reconciliation algorithms within the BB84 protocol so as to speed up reconciliation and privacy amplification. These algorithms have been known to be a performance bottleneck 1 and can process data at rates that are six times slower than the quantum channel they serve2. As improvements in single-photon sources and detectors are expected to improve the quantum channel throughput by two or three orders of magnitude, it becomes imperative to improve the performance of the classical software. We developed a Cascade-like algorithm that relies on a symmetric formulation of the problem, error estimation through the segmentation process, outright elimination of segments with many errors, Forward Error Correction, recognition of the distinct data subpopulations that emerge as the algorithm runs, ability to operate on massive amounts of data (of the order of 1 Mbit), and a few other minor improvements. The data from the experimental algorithm we developed show that by operating on massive arrays of data we can improve software performance by better than three orders of magnitude while retaining nearly as many bits (typically more than 90%) as the algorithms that were designed for optimal bit retention.

How well does multiple OCR error correction generalize?

NASA Astrophysics Data System (ADS)

Lund, William B.; Ringger, Eric K.; Walker, Daniel D.

2013-12-01

As the digitization of historical documents, such as newspapers, becomes more common, the need of the archive patron for accurate digital text from those documents increases. Building on our earlier work, the contributions of this paper are: 1. in demonstrating the applicability of novel methods for correcting optical character recognition (OCR) on disparate data sets, including a new synthetic training set, 2. enhancing the correction algorithm with novel features, and 3. assessing the data requirements of the correction learning method. First, we correct errors using conditional random fields (CRF) trained on synthetic training data sets in order to demonstrate the applicability of the methodology to unrelated test sets. Second, we show the strength of lexical features from the training sets on two unrelated test sets, yielding a relative reduction in word error rate on the test sets of 6.52%. New features capture the recurrence of hypothesis tokens and yield an additional relative reduction in WER of 2.30%. Further, we show that only 2.0% of the full training corpus of over 500,000 feature cases is needed to achieve correction results comparable to those using the entire training corpus, effectively reducing both the complexity of the training process and the learned correction model.

Humour experience in schizophrenia: relationship with executive dysfunction and psychosocial impairment.

PubMed

Tsoi, D T-Y; Lee, K-H; Gee, K A; Holden, K L; Parks, R W; Woodruff, P W R

2008-06-01

The ability to appreciate humour is essential to successful human interactions. In this study, we hypothesized that individuals with schizophrenia would have diminished ability to recognize and appreciate humour. The relationship between humour experience and clinical symptoms, cognitive and social functioning was examined. Thirty patients with a DSM-IV diagnosis of schizophrenia were compared with 30 age-, gender-, IQ- and ethnicity-matched healthy controls. Humour recognition was measured by identification of humorous moments in four silent slapstick comedy film clips and calculated as d-prime (d') according to signal detection theory. Humour appreciation was measured by self-report mood state and funniness ratings. Patients were assessed for clinical symptoms, theory of mind ability, executive function [using the Wisconsin Card Sorting Test (WCST)] and social functioning [using the Life Skills Profile (LSP)]. Patient and control groups did not differ in the funniness ratings they attributed to the video clips. Patients with schizophrenia had a lower d' (humour) compared to the controls, after controlling for (1) the performance of a baseline recognition task with a non-humorous video clip and (2) severity of depressive symptoms. In patients, d' (humour) had significant negative correlation with delusion and depression scores, the perseverative error score of the WCST and the total scores of the LSP. Compared with controls, patients with schizophrenia were less sensitive at detecting humour but similarly able to appreciate humour. The degree of humour recognition difficulty may be associated with the extent of executive dysfunction and thus contribute to the psychosocial impairment in patients with schizophrenia.

Label consistent K-SVD: learning a discriminative dictionary for recognition.

PubMed

Jiang, Zhuolin; Lin, Zhe; Davis, Larry S

2013-11-01

A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also associate label information with each dictionary item (columns of the dictionary matrix) to enforce discriminability in sparse codes during the dictionary learning process. More specifically, we introduce a new label consistency constraint called "discriminative sparse-code error" and combine it with the reconstruction error and the classification error to form a unified objective function. The optimal solution is efficiently obtained using the K-SVD algorithm. Our algorithm learns a single overcomplete dictionary and an optimal linear classifier jointly. The incremental dictionary learning algorithm is presented for the situation of limited memory resources. It yields dictionaries so that feature points with the same class labels have similar sparse codes. Experimental results demonstrate that our algorithm outperforms many recently proposed sparse-coding techniques for face, action, scene, and object category recognition under the same learning conditions.

Recurrent neural network approach to quantum signal: coherent state restoration for continuous-variable quantum key distribution

NASA Astrophysics Data System (ADS)

Lu, Weizhao; Huang, Chunhui; Hou, Kun; Shi, Liting; Zhao, Huihui; Li, Zhengmei; Qiu, Jianfeng

2018-05-01

In continuous-variable quantum key distribution (CV-QKD), weak signal carrying information transmits from Alice to Bob; during this process it is easily influenced by unknown noise which reduces signal-to-noise ratio, and strongly impacts reliability and stability of the communication. Recurrent quantum neural network (RQNN) is an artificial neural network model which can perform stochastic filtering without any prior knowledge of the signal and noise. In this paper, a modified RQNN algorithm with expectation maximization algorithm is proposed to process the signal in CV-QKD, which follows the basic rule of quantum mechanics. After RQNN, noise power decreases about 15 dBm, coherent signal recognition rate of RQNN is 96%, quantum bit error rate (QBER) drops to 4%, which is 6.9% lower than original QBER, and channel capacity is notably enlarged.

Medical simulation: a tool for recognition of and response to risk.

PubMed

Ruddy, Richard M; Patterson, Mary Deffner

2008-11-01

The use of simulation and team training has become an excellent tool to reduce errors in high-risk industry such as the commercial airlines and in the nuclear energy field. The health care industry has begun to use similar tools to improve the outcome of high-risk areas where events are relatively rare but where practice with a tactical team can significantly reduce the chance of bad outcome. There are two parts to this review: first, we review the rationale of why simulation is a key element in improving our error rate, and second, we describe specific tools that have great use at the clinical bedside for improving the care of patients. These cross different (i.e. medical and surgical) specialties and practices within specialties in the health care setting. Tools described will include the pinch, brief/debriefing, read-backs, call-outs, dynamic skepticism, assertive statements, two-challenge rules, checklists and step back (hold points). Examples will assist the clinician in practical daily use to improve their bedside care of children.

Predicting thunderstorm evolution using ground-based lightning detection networks

NASA Technical Reports Server (NTRS)

Goodman, Steven J.

1990-01-01

Lightning measurements acquired principally by a ground-based network of magnetic direction finders are used to diagnose and predict the existence, temporal evolution, and decay of thunderstorms over a wide range of space and time scales extending over four orders of magnitude. The non-linear growth and decay of thunderstorms and their accompanying cloud-to-ground lightning activity is described by the three parameter logistic growth model. The growth rate is shown to be a function of the storm size and duration, and the limiting value of the total lightning activity is related to the available energy in the environment. A new technique is described for removing systematic bearing errors from direction finder data where radar echoes are used to constrain site error correction and optimization (best point estimate) algorithms. A nearest neighbor pattern recognition algorithm is employed to cluster the discrete lightning discharges into storm cells and the advantages and limitations of different clustering strategies for storm identification and tracking are examined.

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition

PubMed Central

2017-01-01

Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user’s location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively. PMID:28817094

Combined Dynamic Time Warping with Multiple Sensors for 3D Gesture Recognition.

PubMed

Choi, Hyo-Rim; Kim, TaeYong

2017-08-17

Cyber-physical systems, which closely integrate physical systems and humans, can be applied to a wider range of applications through user movement analysis. In three-dimensional (3D) gesture recognition, multiple sensors are required to recognize various natural gestures. Several studies have been undertaken in the field of gesture recognition; however, gesture recognition was conducted based on data captured from various independent sensors, which rendered the capture and combination of real-time data complicated. In this study, a 3D gesture recognition method using combined information obtained from multiple sensors is proposed. The proposed method can robustly perform gesture recognition regardless of a user's location and movement directions by providing viewpoint-weighted values and/or motion-weighted values. In the proposed method, the viewpoint-weighted dynamic time warping with multiple sensors has enhanced performance by preventing joint measurement errors and noise due to sensor measurement tolerance, which has resulted in the enhancement of recognition performance by comparing multiple joint sequences effectively.

A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies.

PubMed

Benatti, Simone; Milosevic, Bojan; Farella, Elisabetta; Gruppioni, Emanuele; Benini, Luca

2017-04-15

Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller.

A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies

PubMed Central

Benatti, Simone; Milosevic, Bojan; Farella, Elisabetta; Gruppioni, Emanuele; Benini, Luca

2017-01-01

Poliarticulated prosthetic hands represent a powerful tool to restore functionality and improve quality of life for upper limb amputees. Such devices offer, on the same wearable node, sensing and actuation capabilities, which are not equally supported by natural interaction and control strategies. The control in state-of-the-art solutions is still performed mainly through complex encoding of gestures in bursts of contractions of the residual forearm muscles, resulting in a non-intuitive Human-Machine Interface (HMI). Recent research efforts explore the use of myoelectric gesture recognition for innovative interaction solutions, however there persists a considerable gap between research evaluation and implementation into successful complete systems. In this paper, we present the design of a wearable prosthetic hand controller, based on intuitive gesture recognition and a custom control strategy. The wearable node directly actuates a poliarticulated hand and wirelessly interacts with a personal gateway (i.e., a smartphone) for the training and personalization of the recognition algorithm. Through the whole system development, we address the challenge of integrating an efficient embedded gesture classifier with a control strategy tailored for an intuitive interaction between the user and the prosthesis. We demonstrate that this combined approach outperforms systems based on mere pattern recognition, since they target the accuracy of a classification algorithm rather than the control of a gesture. The system was fully implemented, tested on healthy and amputee subjects and compared against benchmark repositories. The proposed approach achieves an error rate of 1.6% in the end-to-end real time control of commonly used hand gestures, while complying with the power and performance budget of a low-cost microcontroller. PMID:28420135

Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees

PubMed Central

2012-01-01

Background Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Before these myoelectric prosthesis systems are clinically viable, it will be necessary to assess the effect of some disparities between the ideal laboratory setting and practical use on the control performance. One important obstacle is the impact of arm position variation that causes the changes of EMG pattern when performing identical motions in different arm positions. This study aimed to investigate the impacts of arm position variation on EMG pattern-recognition based motion classification in upper-limb amputees and the solutions for reducing these impacts. Methods With five unilateral transradial (TR) amputees, the EMG signals and tri-axial accelerometer mechanomyography (ACC-MMG) signals were simultaneously collected from both amputated and intact arms when performing six classes of arm and hand movements in each of five arm positions that were considered in the study. The effect of the arm position changes was estimated in terms of motion classification error and compared between amputated and intact arms. Then the performance of three proposed methods in attenuating the impact of arm positions was evaluated. Results With EMG signals, the average intra-position and inter-position classification errors across all five arm positions and five subjects were around 7.3% and 29.9% from amputated arms, respectively, about 1.0% and 10% low in comparison with those from intact arms. While ACC-MMG signals could yield a similar intra-position classification error (9.9%) as EMG, they had much higher inter-position classification error with an average value of 81.1% over the arm positions and the subjects. When the EMG data from all five arm positions were involved in the training set, the average classification error reached a value of around 10.8% for amputated arms. Using a two-stage cascade classifier, the average classification error was around 9.0% over all five arm positions. Reducing ACC-MMG channels from 8 to 2 only increased the average position classification error across all five arm positions from 0.7% to 1.0% in amputated arms. Conclusions The performance of EMG pattern-recognition based method in classifying movements strongly depends on arm positions. This dependency is a little stronger in intact arm than in amputated arm, which suggests that the investigations associated with practical use of a myoelectric prosthesis should use the limb amputees as subjects instead of using able-body subjects. The two-stage cascade classifier mode with ACC-MMG for limb position identification and EMG for limb motion classification may be a promising way to reduce the effect of limb position variation on classification performance. PMID:23036049

Step Detection and Activity Recognition Accuracy of Seven Physical Activity Monitors

PubMed Central

Storm, Fabio A.; Heller, Ben W.; Mazzà, Claudia

2015-01-01

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications. PMID:25789630

Step detection and activity recognition accuracy of seven physical activity monitors.

PubMed

Storm, Fabio A; Heller, Ben W; Mazzà, Claudia

2015-01-01

The aim of this study was to compare the seven following commercially available activity monitors in terms of step count detection accuracy: Movemonitor (Mc Roberts), Up (Jawbone), One (Fitbit), ActivPAL (PAL Technologies Ltd.), Nike+ Fuelband (Nike Inc.), Tractivity (Kineteks Corp.) and Sensewear Armband Mini (Bodymedia). Sixteen healthy adults consented to take part in the study. The experimental protocol included walking along an indoor straight walkway, descending and ascending 24 steps, free outdoor walking and free indoor walking. These tasks were repeated at three self-selected walking speeds. Angular velocity signals collected at both shanks using two wireless inertial measurement units (OPAL, ADPM Inc) were used as a reference for the step count, computed using previously validated algorithms. Step detection accuracy was assessed using the mean absolute percentage error computed for each sensor. The Movemonitor and the ActivPAL were also tested within a nine-minute activity recognition protocol, during which the participants performed a set of complex tasks. Posture classifications were obtained from the two monitors and expressed as a percentage of the total task duration. The Movemonitor, One, ActivPAL, Nike+ Fuelband and Sensewear Armband Mini underestimated the number of steps in all the observed walking speeds, whereas the Tractivity significantly overestimated step count. The Movemonitor was the best performing sensor, with an error lower than 2% at all speeds and the smallest error obtained in the outdoor walking. The activity recognition protocol showed that the Movemonitor performed best in the walking recognition, but had difficulty in discriminating between standing and sitting. Results of this study can be used to inform choice of a monitor for specific applications.

Speech recognition training for enhancing written language generation by a traumatic brain injury survivor.

PubMed

Manasse, N J; Hux, K; Rankin-Erickson, J L

2000-11-01

Impairments in motor functioning, language processing, and cognitive status may impact the written language performance of traumatic brain injury (TBI) survivors. One strategy to minimize the impact of these impairments is to use a speech recognition system. The purpose of this study was to explore the effect of mild dysarthria and mild cognitive-communication deficits secondary to TBI on a 19-year-old survivor's mastery and use of such a system-specifically, Dragon Naturally Speaking. Data included the % of the participant's words accurately perceived by the system over time, the participant's accuracy over time in using commands for navigation and error correction, and quantitative and qualitative changes in the participant's written texts generated with and without the use of the speech recognition system. Results showed that Dragon NaturallySpeaking was approximately 80% accurate in perceiving words spoken by the participant, and the participant quickly and easily mastered all navigation and error correction commands presented. Quantitatively, the participant produced a greater amount of text using traditional word processing and a standard keyboard than using the speech recognition system. Minimal qualitative differences appeared between writing samples. Discussion of factors that may have contributed to the obtained results and that may affect the generalization of the findings to other TBI survivors is provided.

No one way ticket from orthography to semantics in recognition memory: N400 and P200 effects of associations.

PubMed

Stuellein, Nicole; Radach, Ralph R; Jacobs, Arthur M; Hofmann, Markus J

2016-05-15

Computational models of word recognition already successfully used associative spreading from orthographic to semantic levels to account for false memories. But can they also account for semantic effects on event-related potentials in a recognition memory task? To address this question, target words in the present study had either many or few semantic associates in the stimulus set. We found larger P200 amplitudes and smaller N400 amplitudes for old words in comparison to new words. Words with many semantic associates led to larger P200 amplitudes and a smaller N400 in comparison to words with a smaller number of semantic associations. We also obtained inverted response time and accuracy effects for old and new words: faster response times and fewer errors were found for old words that had many semantic associates, whereas new words with a large number of semantic associates produced slower response times and more errors. Both behavioral and electrophysiological results indicate that semantic associations between words can facilitate top-down driven lexical access and semantic integration in recognition memory. Our results support neurophysiologically plausible predictions of the Associative Read-Out Model, which suggests top-down connections from semantic to orthographic layers. Copyright © 2016 Elsevier B.V. All rights reserved.

16p11.2 Deletion mice display cognitive deficits in touchscreen learning and novelty recognition tasks.

PubMed

Yang, Mu; Lewis, Freeman C; Sarvi, Michael S; Foley, Gillian M; Crawley, Jacqueline N

2015-12-01

Chromosomal 16p11.2 deletion syndrome frequently presents with intellectual disabilities, speech delays, and autism. Here we investigated the Dolmetsch line of 16p11.2 heterozygous (+/-) mice on a range of cognitive tasks with different neuroanatomical substrates. Robust novel object recognition deficits were replicated in two cohorts of 16p11.2+/- mice, confirming previous findings. A similarly robust deficit in object location memory was discovered in +/-, indicating impaired spatial novelty recognition. Generalizability of novelty recognition deficits in +/- mice extended to preference for social novelty. Robust learning deficits and cognitive inflexibility were detected using Bussey-Saksida touchscreen operant chambers. During acquisition of pairwise visual discrimination, +/- mice required significantly more training trials to reach criterion than wild-type littermates (+/+), and made more errors and correction errors than +/+. In the reversal phase, all +/+ reached criterion, whereas most +/- failed to reach criterion by the 30-d cutoff. Contextual and cued fear conditioning were normal in +/-. These cognitive phenotypes may be relevant to some aspects of cognitive impairments in humans with 16p11.2 deletion, and support the use of 16p11.2+/- mice as a model system for discovering treatments for cognitive impairments in 16p11.2 deletion syndrome. © 2015 Yang et al.; Published by Cold Spring Harbor Laboratory Press.

Reading recognition of pointer meter based on pattern recognition and dynamic three-points on a line

NASA Astrophysics Data System (ADS)

Zhang, Yongqiang; Ding, Mingli; Fu, Wuyifang; Li, Yongqiang

2017-03-01

Pointer meters are frequently applied to industrial production for they are directly readable. They should be calibrated regularly to ensure the precision of the readings. Currently the method of manual calibration is most frequently adopted to accomplish the verification of the pointer meter, and professional skills and subjective judgment may lead to big measurement errors and poor reliability and low efficiency, etc. In the past decades, with the development of computer technology, the skills of machine vision and digital image processing have been applied to recognize the reading of the dial instrument. In terms of the existing recognition methods, all the parameters of dial instruments are supposed to be the same, which is not the case in practice. In this work, recognition of pointer meter reading is regarded as an issue of pattern recognition. We obtain the features of a small area around the detected point, make those features as a pattern, divide those certified images based on Gradient Pyramid Algorithm, train a classifier with the support vector machine (SVM) and complete the pattern matching of the divided mages. Then we get the reading of the pointer meter precisely under the theory of dynamic three points make a line (DTPML), which eliminates the error caused by tiny differences of the panels. Eventually, the result of the experiment proves that the proposed method in this work is superior to state-of-the-art works.

Fast, Simple and Accurate Handwritten Digit Classification by Training Shallow Neural Network Classifiers with the ‘Extreme Learning Machine’ Algorithm

PubMed Central

McDonnell, Mark D.; Tissera, Migel D.; Vladusich, Tony; van Schaik, André; Tapson, Jonathan

2015-01-01

Recent advances in training deep (multi-layer) architectures have inspired a renaissance in neural network use. For example, deep convolutional networks are becoming the default option for difficult tasks on large datasets, such as image and speech recognition. However, here we show that error rates below 1% on the MNIST handwritten digit benchmark can be replicated with shallow non-convolutional neural networks. This is achieved by training such networks using the ‘Extreme Learning Machine’ (ELM) approach, which also enables a very rapid training time (∼ 10 minutes). Adding distortions, as is common practise for MNIST, reduces error rates even further. Our methods are also shown to be capable of achieving less than 5.5% error rates on the NORB image database. To achieve these results, we introduce several enhancements to the standard ELM algorithm, which individually and in combination can significantly improve performance. The main innovation is to ensure each hidden-unit operates only on a randomly sized and positioned patch of each image. This form of random ‘receptive field’ sampling of the input ensures the input weight matrix is sparse, with about 90% of weights equal to zero. Furthermore, combining our methods with a small number of iterations of a single-batch backpropagation method can significantly reduce the number of hidden-units required to achieve a particular performance. Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems. PMID:26262687

Towards limb position invariant myoelectric pattern recognition using time-dependent spectral features.

PubMed

Khushaba, Rami N; Takruri, Maen; Miro, Jaime Valls; Kodagoda, Sarath

2014-07-01

Recent studies in Electromyogram (EMG) pattern recognition reveal a gap between research findings and a viable clinical implementation of myoelectric control strategies. One of the important factors contributing to the limited performance of such controllers in practice is the variation in the limb position associated with normal use as it results in different EMG patterns for the same movements when carried out at different positions. However, the end goal of the myoelectric control scheme is to allow amputees to control their prosthetics in an intuitive and accurate manner regardless of the limb position at which the movement is initiated. In an attempt to reduce the impact of limb position on EMG pattern recognition, this paper proposes a new feature extraction method that extracts a set of power spectrum characteristics directly from the time-domain. The end goal is to form a set of features invariant to limb position. Specifically, the proposed method estimates the spectral moments, spectral sparsity, spectral flux, irregularity factor, and signals power spectrum correlation. This is achieved through using Fourier transform properties to form invariants to amplification, translation and signal scaling, providing an efficient and accurate representation of the underlying EMG activity. Additionally, due to the inherent temporal structure of the EMG signal, the proposed method is applied on the global segments of EMG data as well as the sliced segments using multiple overlapped windows. The performance of the proposed features is tested on EMG data collected from eleven subjects, while implementing eight classes of movements, each at five different limb positions. Practical results indicate that the proposed feature set can achieve significant reduction in classification error rates, in comparison to other methods, with ≈8% error on average across all subjects and limb positions. A real-time implementation and demonstration is also provided and made available as a video supplement (see Appendix A). Copyright © 2014 Elsevier Ltd. All rights reserved.

EVENT-RELATED POTENTIAL STUDY OF ATTENTION REGULATION DURING ILLUSORY FIGURE CATEGORIZATION TASK IN ADHD, AUTISM SPECTRUM DISORDER, AND TYPICAL CHILDREN.

PubMed

Sokhadze, Estate M; Baruth, Joshua M; Sears, Lonnie; Sokhadze, Guela E; El-Baz, Ayman S; Williams, Emily; Klapheke, Robert; Casanova, Manuel F

2012-01-01

Autism spectrum disorders (ASD) and attention deficit/hyperactivity disorder (ADHD) are very common developmental disorders which share some similar symptoms of social, emotional, and attentional deficits. This study is aimed to help understand the differences and similarities of these deficits using analysis of dense-array event-related potentials (ERP) during an illusory figure recognition task. Although ADHD and ASD seem very distinct, they have been shown to share some similarities in their symptoms. Our hypothesis was that children with ASD will show less pronounced differences in ERP responses to target and non-target stimuli as compared to typical children, and to a lesser extent, ADHD. Participants were children with ASD (N=16), ADHD (N=16), and controls (N=16). EEG was collected using a 128 channel EEG system. The task involved the recognition of a specific illusory shape, in this case a square or triangle, created by three or four inducer disks. There were no between group differences in reaction time (RT) to target stimuli, but both ASD and ADHD committed more errors, specifically the ASD group had statistically higher commission error rate than controls. Post-error RT in ASD group was exhibited in a post-error speeding rather than corrective RT slowing typical for the controls. The ASD group also demonstrated an attenuated error-related negativity (ERN) as compared to ADHD and controls. The fronto-central P200, N200, and P300 were enhanced and less differentiated in response to target and non-target figures in the ASD group. The same ERP components were marked by more prolonged latencies in the ADHD group as compared to both ASD and typical controls. The findings are interpreted according to the "minicolumnar" hypothesis proposing existence of neuropathological differences in ASD and ADHD, specifically minicolumnar number/width morphometry spectrum differences. In autism, a model of local hyperconnectivity and long-range hypoconnectivity explains many of the behavioral and cognitive deficits present in the condition, while the inverse arrangement of local hypoconnectivity and long-range hyperconnectivity in ADHD explains some deficits typical for this disorder. The current ERP study supports the proposed suggestion that some between group differences could be manifested in the frontal ERP indices of executive functions during performance on an illusory figure categorization task.

Neural network for photoplethysmographic respiratory rate monitoring

NASA Astrophysics Data System (ADS)

Johansson, Anders

2001-10-01

The photoplethysmographic signal (PPG) includes respiratory components seen as frequency modulation of the heart rate (respiratory sinus arrhythmia, RSA), amplitude modulation of the cardiac pulse, and respiratory induced intensity variations (RIIV) in the PPG baseline. The aim of this study was to evaluate the accuracy of these components in determining respiratory rate, and to combine the components in a neural network for improved accuracy. The primary goal is to design a PPG ventilation monitoring system. PPG signals were recorded from 15 healthy subjects. From these signals, the systolic waveform, diastolic waveform, respiratory sinus arrhythmia, pulse amplitude and RIIV were extracted. By using simple algorithms, the rates of false positive and false negative detection of breaths were calculated for each of the five components in a separate analysis. Furthermore, a simple neural network (NN) was tried out in a combined pattern recognition approach. In the separate analysis, the error rates (sum of false positives and false negatives) ranged from 9.7% (pulse amplitude) to 14.5% (systolic waveform). The corresponding value of the NN analysis was 9.5-9.6%.

A cloud shadow detection method combined with cloud height iteration and spectral analysis for Landsat 8 OLI data

NASA Astrophysics Data System (ADS)

Sun, Lin; Liu, Xinyan; Yang, Yikun; Chen, TingTing; Wang, Quan; Zhou, Xueying

2018-04-01

Although enhanced over prior Landsat instruments, Landsat 8 OLI can obtain very high cloud detection precisions, but for the detection of cloud shadows, it still faces great challenges. Geometry-based cloud shadow detection methods are considered the most effective and are being improved constantly. The Function of Mask (Fmask) cloud shadow detection method is one of the most representative geometry-based methods that has been used for cloud shadow detection with Landsat 8 OLI. However, the Fmask method estimates cloud height employing fixed temperature rates, which are highly uncertain, and errors of large area cloud shadow detection can be caused by errors in estimations of cloud height. This article improves the geometry-based cloud shadow detection method for Landsat OLI from the following two aspects. (1) Cloud height no longer depends on the brightness temperature of the thermal infrared band but uses a possible dynamic range from 200 m to 12,000 m. In this case, cloud shadow is not a specific location but a possible range. Further analysis was carried out in the possible range based on the spectrum to determine cloud shadow location. This effectively avoids the cloud shadow leakage caused by the error in the height determination of a cloud. (2) Object-based and pixel spectral analyses are combined to detect cloud shadows, which can realize cloud shadow detection from two aspects of target scale and pixel scale. Based on the analysis of the spectral differences between the cloud shadow and typical ground objects, the best cloud shadow detection bands of Landsat 8 OLI were determined. The combined use of spectrum and shape can effectively improve the detection precision of cloud shadows produced by thin clouds. Several cloud shadow detection experiments were carried out, and the results were verified by the results of artificial recognition. The results of these experiments indicated that this method can identify cloud shadows in different regions with correct accuracy exceeding 80%, approximately 5% of the areas were wrongly identified, and approximately 10% of the cloud shadow areas were missing. The accuracy of this method is obviously higher than the recognition accuracy of Fmask, which has correct accuracy lower than 60%, and the missing recognition is approximately 40%.

"A violation of the conditional independence assumption in the two-high-threshold Model of recognition memory": Correction to Chen, Starns, and Rotello (2015).

PubMed

2016-01-01

Reports an error in "A violation of the conditional independence assumption in the two-high-threshold model of recognition memory" by Tina Chen, Jeffrey J. Starns and Caren M. Rotello (Journal of Experimental Psychology: Learning, Memory, and Cognition, 2015[Jul], Vol 41[4], 1215-1222). In the article, Chen et al. compared three models: a continuous signal detection model (SDT), a standard two-high-threshold discrete-state model in which detect states always led to correct responses (2HT), and a full-mapping version of the 2HT model in which detect states could lead to either correct or incorrect responses. After publication, Rani Moran (personal communication, April 21, 2015) identified two errors that impact the reported fit statistics for the Bayesian information criterion (BIC) metric of all models as well as the Akaike information criterion (AIC) results for the full-mapping model. The errors are described in the erratum. (The following abstract of the original article appeared in record 2014-56216-001.) The 2-high-threshold (2HT) model of recognition memory assumes that test items result in distinct internal states: they are either detected or not, and the probability of responding at a particular confidence level that an item is "old" or "new" depends on the state-response mapping parameters. The mapping parameters are independent of the probability that an item yields a particular state (e.g., both strong and weak items that are detected as old have the same probability of producing a highest-confidence "old" response). We tested this conditional independence assumption by presenting nouns 1, 2, or 4 times. To maximize the strength of some items, "superstrong" items were repeated 4 times and encoded in conjunction with pleasantness, imageability, anagram, and survival processing tasks. The 2HT model failed to simultaneously capture the response rate data for all item classes, demonstrating that the data violated the conditional independence assumption. In contrast, a Gaussian signal detection model, which posits that the level of confidence that an item is "old" or "new" is a function of its continuous strength value, provided a good account of the data. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Patients' perception of types of errors in palliative care - results from a qualitative interview study.

PubMed

Kiesewetter, Isabel; Schulz, Christian; Bausewein, Claudia; Fountain, Rita; Schmitz, Andrea

2016-08-11

Medical errors have been recognized as a relevant public health concern and research efforts to improve patient safety have increased. In palliative care, however, studies on errors are rare and mainly focus on quantitative measures. We aimed to explore how palliative care patients perceive and think about errors in palliative care and to generate an understanding of patients' perception of errors in that specialty. A semistructured qualitative interview study was conducted with patients who had received at least 1 week of palliative care in an inpatient or outpatient setting. All interviews were transcribed verbatim and analysed according to qualitative content analysis. Twelve patients from two centers were interviewed (7 women, median age 63.5 years, range 22-90 years). Eleven patients suffered from a malignancy. Days in palliative care ranged from 10 to 180 days (median 28 days). 96 categories emerged which were summed up under 11 umbrella terms definition, difference, type, cause, consequence, meaning, recognition, handling, prevention, person causing and affected person. A deductive model was developed assigning umbrella terms to error-theory-based factor levels (definition, type and process-related factors). 23 categories for type of error were identified, including 12 categories that can be considered as palliative care specific. On the level of process-related factors 3 palliative care specific categories emerged (recognition, meaning and consequence of errors). From the patients' perspective, there are some aspects of errors that could be considered as specific to palliative care. As the results of our study suggest, these palliative care-specific aspects seem to be very important from the patients' point of view and should receive further investigation. Moreover, the findings of this study can serve as a guide to further assess single aspects or categories of errors in palliative care in future research.

Error Tolerant Plan Recognition: An Empirical Investigation

DTIC Science & Technology

2015-05-01

structure can differ drastically in semantics. For instance, a plan to travel to a grocery store to buy milk might coincidentally be structurally...algorithm for its ability to tolerate input errors, and that storing and leveraging state information in its plan representation substantially...proposed a novel representation for storing and organizing plans in a plan library, based on action-state pairs and abstract states. It counts the

Nematode Damage Functions: The Problems of Experimental and Sampling Error

PubMed Central

Ferris, H.

1984-01-01

The development and use of pest damage functions involves measurement and experimental errors associated with cultural, environmental, and distributional factors. Damage predictions are more valuable if considered with associated probability. Collapsing population densities into a geometric series of population classes allows a pseudo-replication removal of experimental and sampling error in damage function development. Recognition of the nature of sampling error for aggregated populations allows assessment of probability associated with the population estimate. The product of the probabilities incorporated in the damage function and in the population estimate provides a basis for risk analysis of the yield loss prediction and the ensuing management decision. PMID:19295865

Left is where the L is right. Significantly delayed reaction time in limb laterality recognition in both CRPS and phantom limb pain patients.

PubMed

Reinersmann, Annika; Haarmeyer, Golo Sung; Blankenburg, Markus; Frettlöh, Jule; Krumova, Elena K; Ocklenburg, Sebastian; Maier, Christoph

2010-12-17

The body schema is based on an intact cortical body representation. Its disruption is indicated by delayed reaction times (RT) and high error rates when deciding on the laterality of a pictured hand in a limb laterality recognition task. Similarities in both cortical reorganisation and disrupted body schema have been found in two different unilateral pain syndromes, one with deafferentation (phantom limb pain, PLP) and one with pain-induced dysfunction (complex regional pain syndrome, CRPS). This study aims to compare the extent of impaired laterality recognition in these two groups. Performance on a test battery for attentional performance (TAP 2.0) and on a limb laterality recognition task was evaluated in CRPS (n=12), PLP (n=12) and healthy subjects (n=38). Differences between recognising affected and unaffected hands were analysed. CRPS patients and healthy subjects additionally completed a four-day training of limb laterality recognition. Reaction time was significantly delayed in both CRPS (2278±735.7ms) and PLP (2301.3±809.3ms) compared to healthy subjects (1826.5±517.0ms), despite normal TAP values in all groups. There were no differences between recognition of affected and unaffected hands in both patient groups. Both healthy subjects and CRPS patients improved during training, but RTs of CRPS patients (1874.5±613.3ms) remain slower (p<0.01) than those of healthy subjects (1280.6±343.2ms) after four-day training. Despite different pathomechanisms, the body schema is equally disrupted in PLP and CRPS patients, uninfluenced by attention and pain and cannot be fully reversed by training alone. This suggests the involvement of complex central nervous system mechanisms in the disruption of the body schema. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Congenital anosmia and emotion recognition: A case-control study.

PubMed

Lemogne, Cédric; Smadja, Julien; Zerdazi, El-Hadi; Soudry, Yaël; Robin, Marion; Berthoz, Sylvie; Limosin, Frédéric; Consoli, Silla M; Bonfils, Pierre

2015-06-01

Patients with anosmia are not able to detect volatile chemicals signaling the presence of infectious and non-infectious environmental hazards, which typically elicit disgust and fear, respectively. Social animals may compensate a loss of olfaction by taking advantage of signals of threat that are produced by their conspecifics. Among humans and other primates, body postures and facial expressions are powerful cues conveying emotional information, including fear and disgust. The aim of the present study was to examine whether humans with agenesis of the olfactory bulb, a rare disorder characterized by congenital anosmia, would be more accurate in recognizing facial expressions of fear and disgust. A total of 90 participants with no history of mental disorder or traumatic brain injury were recruited, including 17 patients with congenital anosmia (10 men, mean age ± standard deviation: 36.5 ± 14.8 years), 34 patients with acquired anosmia (18 men, mean age ± standard deviation: 57.2 ± 11.8 years) and 39 healthy subjects (22 men, mean age ± standard deviation: 36.7 ± 13.2 years). For each patient with congenital anosmia, the agenesis of the olfactory bulb was ascertained through magnetic resonance imaging. Emotion recognition abilities were examined with a dynamic paradigm in which a morphing technique allowed displaying emotional facial expressions increasing in intensity over time. Adjusting for age, education, depression and anxiety, patients with congenital anosmia required similar levels of intensity to correctly recognize fear and disgust than healthy subjects while they displayed decreased error rates for both fear (mean difference [95% confidence interval] = -28.3% [-46.3%, -10.2%], P = 0.003) and disgust (mean difference [95% confidence interval] = -15.8% [-31.5%, -0.2%], P = 0.048). Furthermore, among patients with acquired anosmia, there was a negative correlation between duration of anosmia and the rate of errors for fearful (Spearman's ρ = -0.531, P= 0.001) or disgust (Spearman's ρ = -0.719, P < 0.001) faces recognition. No significant difference was observed for the other primary emotions. Overall, these results suggest that patients with congenital anosmia and long-lasting acquired anosmia may compensate their inability to detect environmental hazards through olfaction by an increased ability to detect fear or disgust as facially expressed by others. Copyright © 2015 Elsevier Ltd. All rights reserved.

Fusing face-verification algorithms and humans.

PubMed

O'Toole, Alice J; Abdi, Hervé; Jiang, Fang; Phillips, P Jonathon

2007-10-01

It has been demonstrated recently that state-of-the-art face-recognition algorithms can surpass human accuracy at matching faces over changes in illumination. The ranking of algorithms and humans by accuracy, however, does not provide information about whether algorithms and humans perform the task comparably or whether algorithms and humans can be fused to improve performance. In this paper, we fused humans and algorithms using partial least square regression (PLSR). In the first experiment, we applied PLSR to face-pair similarity scores generated by seven algorithms participating in the Face Recognition Grand Challenge. The PLSR produced an optimal weighting of the similarity scores, which we tested for generality with a jackknife procedure. Fusing the algorithms' similarity scores using the optimal weights produced a twofold reduction of error rate over the most accurate algorithm. Next, human-subject-generated similarity scores were added to the PLSR analysis. Fusing humans and algorithms increased the performance to near-perfect classification accuracy. These results are discussed in terms of maximizing face-verification accuracy with hybrid systems consisting of multiple algorithms and humans.

Video indexing based on image and sound

NASA Astrophysics Data System (ADS)

Faudemay, Pascal; Montacie, Claude; Caraty, Marie-Jose

1997-10-01

Video indexing is a major challenge for both scientific and economic reasons. Information extraction can sometimes be easier from sound channel than from image channel. We first present a multi-channel and multi-modal query interface, to query sound, image and script through 'pull' and 'push' queries. We then summarize the segmentation phase, which needs information from the image channel. Detection of critical segments is proposed. It should speed-up both automatic and manual indexing. We then present an overview of the information extraction phase. Information can be extracted from the sound channel, through speaker recognition, vocal dictation with unconstrained vocabularies, and script alignment with speech. We present experiment results for these various techniques. Speaker recognition methods were tested on the TIMIT and NTIMIT database. Vocal dictation as experimented on newspaper sentences spoken by several speakers. Script alignment was tested on part of a carton movie, 'Ivanhoe'. For good quality sound segments, error rates are low enough for use in indexing applications. Major issues are the processing of sound segments with noise or music, and performance improvement through the use of appropriate, low-cost architectures or networks of workstations.

Comparative study of multimodal biometric recognition by fusion of iris and fingerprint.

PubMed

Benaliouche, Houda; Touahria, Mohamed

2014-01-01

This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical weighted sum rule and the classical sum rule in order. The performance evaluation of each method is reported in terms of matching time, error rates, and accuracy after doing exhaustive tests on the public CASIA-Iris databases V1 and V2 and the FVC 2004 fingerprint database. Experimental results prior to fusion and after fusion are presented followed by their comparison with related works in the current literature. The fusion by fuzzy logic decision mimics the human reasoning in a soft and simple way and gives enhanced results.

Comparative Study of Multimodal Biometric Recognition by Fusion of Iris and Fingerprint

PubMed Central

Benaliouche, Houda; Touahria, Mohamed

2014-01-01

This research investigates the comparative performance from three different approaches for multimodal recognition of combined iris and fingerprints: classical sum rule, weighted sum rule, and fuzzy logic method. The scores from the different biometric traits of iris and fingerprint are fused at the matching score and the decision levels. The scores combination approach is used after normalization of both scores using the min-max rule. Our experimental results suggest that the fuzzy logic method for the matching scores combinations at the decision level is the best followed by the classical weighted sum rule and the classical sum rule in order. The performance evaluation of each method is reported in terms of matching time, error rates, and accuracy after doing exhaustive tests on the public CASIA-Iris databases V1 and V2 and the FVC 2004 fingerprint database. Experimental results prior to fusion and after fusion are presented followed by their comparison with related works in the current literature. The fusion by fuzzy logic decision mimics the human reasoning in a soft and simple way and gives enhanced results. PMID:24605065

Recognition of the optical packet header for two channels utilizing the parallel reservoir computing based on a semiconductor ring laser

NASA Astrophysics Data System (ADS)

Bao, Xiurong; Zhao, Qingchun; Yin, Hongxi; Qin, Jie

2018-05-01

In this paper, an all-optical parallel reservoir computing (RC) system with two channels for the optical packet header recognition is proposed and simulated, which is based on a semiconductor ring laser (SRL) with the characteristic of bidirectional light paths. The parallel optical loops are built through the cross-feedback of the bidirectional light paths where every optical loop can independently recognize each injected optical packet header. Two input signals are mapped and recognized simultaneously by training all-optical parallel reservoir, which is attributed to the nonlinear states in the laser. The recognition of optical packet headers for two channels from 4 bits to 32 bits is implemented through the simulation optimizing system parameters and therefore, the optimal recognition error ratio is 0. Since this structure can combine with the wavelength division multiplexing (WDM) optical packet switching network, the wavelength of each channel of optical packet headers for recognition can be different, and a better recognition result can be obtained.

The processing of auditory and visual recognition of self-stimuli.

PubMed

Hughes, Susan M; Nicholson, Shevon E

2010-12-01

This study examined self-recognition processing in both the auditory and visual modalities by determining how comparable hearing a recording of one's own voice was to seeing photograph of one's own face. We also investigated whether the simultaneous presentation of auditory and visual self-stimuli would either facilitate or inhibit self-identification. Ninety-one participants completed reaction-time tasks of self-recognition when presented with their own faces, own voices, and combinations of the two. Reaction time and errors made when responding with both the right and left hand were recorded to determine if there were lateralization effects on these tasks. Our findings showed that visual self-recognition for facial photographs appears to be superior to auditory self-recognition for voice recordings. Furthermore, a combined presentation of one's own face and voice appeared to inhibit rather than facilitate self-recognition and there was a left-hand advantage for reaction time on the combined-presentation tasks. Copyright © 2010 Elsevier Inc. All rights reserved.

Fuzzy difference-of-Gaussian-based iris recognition method for noisy iris images

NASA Astrophysics Data System (ADS)

Kang, Byung Jun; Park, Kang Ryoung; Yoo, Jang-Hee; Moon, Kiyoung

2010-06-01

Iris recognition is used for information security with a high confidence level because it shows outstanding recognition accuracy by using human iris patterns with high degrees of freedom. However, iris recognition accuracy can be reduced by noisy iris images with optical and motion blurring. We propose a new iris recognition method based on the fuzzy difference-of-Gaussian (DOG) for noisy iris images. This study is novel in three ways compared to previous works: (1) The proposed method extracts iris feature values using the DOG method, which is robust to local variations of illumination and shows fine texture information, including various frequency components. (2) When determining iris binary codes, image noises that cause the quantization error of the feature values are reduced with the fuzzy membership function. (3) The optimal parameters of the DOG filter and the fuzzy membership function are determined in terms of iris recognition accuracy. Experimental results showed that the performance of the proposed method was better than that of previous methods for noisy iris images.

Optimal measurement of ice-sheet deformation from surface-marker arrays

NASA Astrophysics Data System (ADS)

Macayeal, D. R.

Surface strain rate is best observed by fitting a strain-rate ellipsoid to the measured movement of a stake network or other collection of surface features, using a least squares procedure. Error of the resulting fit varies as 1/(L delta t square root of N), where L is the stake separation, delta is the time period between initial and final stake survey, and n is the number of stakes in the network. This relation suggests that if n is sufficiently high, the traditional practice of revisiting stake-network sites on successive field seasons may be replaced by a less costly single year operation. A demonstration using Ross Ice Shelf data shows that reasonably accurate measurements are obtained from 12 stakes after only 4 days of deformation. It is possible for the least squares procedure to aid airborne photogrammetric surveys because reducing the time interval between survey and re-survey permits better surface feature recognition.

A novel approach for fire recognition using hybrid features and manifold learning-based classifier

NASA Astrophysics Data System (ADS)

Zhu, Rong; Hu, Xueying; Tang, Jiajun; Hu, Sheng

2018-03-01

Although image/video based fire recognition has received growing attention, an efficient and robust fire detection strategy is rarely explored. In this paper, we propose a novel approach to automatically identify the flame or smoke regions in an image. It is composed to three stages: (1) a block processing is applied to divide an image into several nonoverlapping image blocks, and these image blocks are identified as suspicious fire regions or not by using two color models and a color histogram-based similarity matching method in the HSV color space, (2) considering that compared to other information, the flame and smoke regions have significant visual characteristics, so that two kinds of image features are extracted for fire recognition, where local features are obtained based on the Scale Invariant Feature Transform (SIFT) descriptor and the Bags of Keypoints (BOK) technique, and texture features are extracted based on the Gray Level Co-occurrence Matrices (GLCM) and the Wavelet-based Analysis (WA) methods, and (3) a manifold learning-based classifier is constructed based on two image manifolds, which is designed via an improve Globular Neighborhood Locally Linear Embedding (GNLLE) algorithm, and the extracted hybrid features are used as input feature vectors to train the classifier, which is used to make decision for fire images or non fire images. Experiments and comparative analyses with four approaches are conducted on the collected image sets. The results show that the proposed approach is superior to the other ones in detecting fire and achieving a high recognition accuracy and a low error rate.

Sunglasses, traffic signals, and color vision deficiencies.

PubMed

Dain, Stephen J; Wood, Joanne M; Atchison, David A

2009-04-01

To determine (a) the effect of different sunglass tint colorations on traffic signal detection and recognition for color normal and color deficient observers, and (b) the adequacy of coloration requirements in current sunglass standards. Twenty color-normals and 49 color-deficient males performed a tracking task while wearing sunglasses of different colorations (clear, gray, green, yellow-green, yellow-brown, red-brown). At random intervals, simulated traffic light signals were presented against a white background at 5 degrees to the right or left and observers were instructed to identify signal color (red/yellow/green) by pressing a response button as quickly as possible; response times and response errors were recorded. Signal color and sunglass tint had significant effects on response times and error rates (p < 0.05), with significant between-color group differences and interaction effects. Response times for color deficient people were considerably slower than color normals for both red and yellow signals for all sunglass tints, but for green signals they were only noticeably slower with the green and yellow-green lenses. For most of the color deficient groups, there were recognition errors for yellow signals combined with the yellow-green and green tints. In addition, deuteranopes had problems for red signals combined with red-brown and yellow-brown tints, and protanopes had problems for green signals combined with the green tint and for red signals combined with the red-brown tint. Many sunglass tints currently permitted for drivers and riders cause a measurable decrement in the ability of color deficient observers to detect and recognize traffic signals. In general, combinations of signals and sunglasses of similar colors are of particular concern. This is prima facie evidence of a risk in the use of these tints for driving and cautions against the relaxation of coloration limits in sunglasses beyond those represented in the study.

Brain-to-text: decoding spoken phrases from phone representations in the brain.

PubMed

Herff, Christian; Heger, Dominic; de Pesters, Adriana; Telaar, Dominic; Brunner, Peter; Schalk, Gerwin; Schultz, Tanja

2015-01-01

It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step toward human-machine communication based on imagined speech.

Brain-to-text: decoding spoken phrases from phone representations in the brain

PubMed Central

Herff, Christian; Heger, Dominic; de Pesters, Adriana; Telaar, Dominic; Brunner, Peter; Schalk, Gerwin; Schultz, Tanja

2015-01-01

It has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To-Text system described in this paper represents an important step toward human-machine communication based on imagined speech. PMID:26124702

Automated Intelligibility Assessment of Pathological Speech Using Phonological Features

NASA Astrophysics Data System (ADS)

Middag, Catherine; Martens, Jean-Pierre; Van Nuffelen, Gwen; De Bodt, Marc

2009-12-01

It is commonly acknowledged that word or phoneme intelligibility is an important criterion in the assessment of the communication efficiency of a pathological speaker. People have therefore put a lot of effort in the design of perceptual intelligibility rating tests. These tests usually have the drawback that they employ unnatural speech material (e.g., nonsense words) and that they cannot fully exclude errors due to listener bias. Therefore, there is a growing interest in the application of objective automatic speech recognition technology to automate the intelligibility assessment. Current research is headed towards the design of automated methods which can be shown to produce ratings that correspond well with those emerging from a well-designed and well-performed perceptual test. In this paper, a novel methodology that is built on previous work (Middag et al., 2008) is presented. It utilizes phonological features, automatic speech alignment based on acoustic models that were trained on normal speech, context-dependent speaker feature extraction, and intelligibility prediction based on a small model that can be trained on pathological speech samples. The experimental evaluation of the new system reveals that the root mean squared error of the discrepancies between perceived and computed intelligibilities can be as low as 8 on a scale of 0 to 100.

[Association between intelligence development and facial expression recognition ability in children with autism spectrum disorder].

PubMed

Pan, Ning; Wu, Gui-Hua; Zhang, Ling; Zhao, Ya-Fen; Guan, Han; Xu, Cai-Juan; Jing, Jin; Jin, Yu

2017-03-01

To investigate the features of intelligence development, facial expression recognition ability, and the association between them in children with autism spectrum disorder (ASD). A total of 27 ASD children aged 6-16 years (ASD group, full intelligence quotient >70) and age- and gender-matched normally developed children (control group) were enrolled. Wechsler Intelligence Scale for Children Fourth Edition and Chinese Static Facial Expression Photos were used for intelligence evaluation and facial expression recognition test. Compared with the control group, the ASD group had significantly lower scores of full intelligence quotient, verbal comprehension index, perceptual reasoning index (PRI), processing speed index（PSI), and working memory index (WMI) (P<0.05). The ASD group also had a significantly lower overall accuracy rate of facial expression recognition and significantly lower accuracy rates of the recognition of happy, angry, sad, and frightened expressions than the control group (P<0.05). In the ASD group, the overall accuracy rate of facial expression recognition and the accuracy rates of the recognition of happy and frightened expressions were positively correlated with PRI (r=0.415, 0.455, and 0.393 respectively; P<0.05). The accuracy rate of the recognition of angry expression was positively correlated with WMI (r=0.397; P<0.05). ASD children have delayed intelligence development compared with normally developed children and impaired expression recognition ability. Perceptual reasoning and working memory abilities are positively correlated with expression recognition ability, which suggests that insufficient perceptual reasoning and working memory abilities may be important factors affecting facial expression recognition ability in ASD children.

Exploring Cultural Differences in the Recognition of the Self-Conscious Emotions.

PubMed

Chung, Joanne M; Robins, Richard W

2015-01-01

Recent research suggests that the self-conscious emotions of embarrassment, shame, and pride have distinct, nonverbal expressions that can be recognized in the United States at above-chance levels. However, few studies have examined the recognition of these emotions in other cultures, and little research has been conducted in Asia. Consequently the cross-cultural generalizability of self-conscious emotions has not been firmly established. Additionally, there is no research that examines cultural variability in the recognition of the self-conscious emotions. Cultural values and exposure to Western culture have been identified as contributors to variability in recognition rates for the basic emotions; we sought to examine this for the self-conscious emotions using the University of California, Davis Set of Emotion Expressions (UCDSEE). The present research examined recognition of the self-conscious emotion expressions in South Korean college students and found that recognition rates were very high for pride, low but above chance for shame, and near zero for embarrassment. To examine what might be underlying the recognition rates we found in South Korea, recognition of self-conscious emotions and several cultural values were examined in a U.S. college student sample of European Americans, Asian Americans, and Asian-born individuals. Emotion recognition rates were generally similar between the European Americans and Asian Americans, and higher than emotion recognition rates for Asian-born individuals. These differences were not explained by cultural values in an interpretable manner, suggesting that exposure to Western culture is a more important mediator than values.

Exploring Cultural Differences in the Recognition of the Self-Conscious Emotions

PubMed Central

Chung, Joanne M.; Robins, Richard W.

2015-01-01

Recent research suggests that the self-conscious emotions of embarrassment, shame, and pride have distinct, nonverbal expressions that can be recognized in the United States at above-chance levels. However, few studies have examined the recognition of these emotions in other cultures, and little research has been conducted in Asia. Consequently the cross-cultural generalizability of self-conscious emotions has not been firmly established. Additionally, there is no research that examines cultural variability in the recognition of the self-conscious emotions. Cultural values and exposure to Western culture have been identified as contributors to variability in recognition rates for the basic emotions; we sought to examine this for the self-conscious emotions using the University of California, Davis Set of Emotion Expressions (UCDSEE). The present research examined recognition of the self-conscious emotion expressions in South Korean college students and found that recognition rates were very high for pride, low but above chance for shame, and near zero for embarrassment. To examine what might be underlying the recognition rates we found in South Korea, recognition of self-conscious emotions and several cultural values were examined in a U.S. college student sample of European Americans, Asian Americans, and Asian-born individuals. Emotion recognition rates were generally similar between the European Americans and Asian Americans, and higher than emotion recognition rates for Asian-born individuals. These differences were not explained by cultural values in an interpretable manner, suggesting that exposure to Western culture is a more important mediator than values. PMID:26309215

Acceptance threshold theory can explain occurrence of homosexual behaviour.

PubMed

Engel, Katharina C; Männer, Lisa; Ayasse, Manfred; Steiger, Sandra

2015-01-01

Same-sex sexual behaviour (SSB) has been documented in a wide range of animals, but its evolutionary causes are not well understood. Here, we investigated SSB in the light of Reeve's acceptance threshold theory. When recognition is not error-proof, the acceptance threshold used by males to recognize potential mating partners should be flexibly adjusted to maximize the fitness pay-off between the costs of erroneously accepting males and the benefits of accepting females. By manipulating male burying beetles' search time for females and their reproductive potential, we influenced their perceived costs of making an acceptance or rejection error. As predicted, when the costs of rejecting females increased, males exhibited more permissive discrimination decisions and showed high levels of SSB; when the costs of accepting males increased, males were more restrictive and showed low levels of SSB. Our results support the idea that in animal species, in which the recognition cues of females and males overlap to a certain degree, SSB is a consequence of an adaptive discrimination strategy to avoid the costs of making rejection errors. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

A Novel Locally Linear KNN Method With Applications to Visual Recognition.

PubMed

Liu, Qingfeng; Liu, Chengjun

2017-09-01

A locally linear K Nearest Neighbor (LLK) method is presented in this paper with applications to robust visual recognition. Specifically, the concept of an ideal representation is first presented, which improves upon the traditional sparse representation in many ways. The objective function based on a host of criteria for sparsity, locality, and reconstruction is then optimized to derive a novel representation, which is an approximation to the ideal representation. The novel representation is further processed by two classifiers, namely, an LLK-based classifier and a locally linear nearest mean-based classifier, for visual recognition. The proposed classifiers are shown to connect to the Bayes decision rule for minimum error. Additional new theoretical analysis is presented, such as the nonnegative constraint, the group regularization, and the computational efficiency of the proposed LLK method. New methods such as a shifted power transformation for improving reliability, a coefficients' truncating method for enhancing generalization, and an improved marginal Fisher analysis method for feature extraction are proposed to further improve visual recognition performance. Extensive experiments are implemented to evaluate the proposed LLK method for robust visual recognition. In particular, eight representative data sets are applied for assessing the performance of the LLK method for various visual recognition applications, such as action recognition, scene recognition, object recognition, and face recognition.

Neural system for heartbeats recognition using genetically integrated ensemble of classifiers.

PubMed

Osowski, Stanislaw; Siwek, Krzysztof; Siroic, Robert

2011-03-01

This paper presents the application of genetic algorithm for the integration of neural classifiers combined in the ensemble for the accurate recognition of heartbeat types on the basis of ECG registration. The idea presented in this paper is that using many classifiers arranged in the form of ensemble leads to the increased accuracy of the recognition. In such ensemble the important problem is the integration of all classifiers into one effective classification system. This paper proposes the use of genetic algorithm. It was shown that application of the genetic algorithm is very efficient and allows to reduce significantly the total error of heartbeat recognition. This was confirmed by the numerical experiments performed on the MIT BIH Arrhythmia Database. Copyright © 2011 Elsevier Ltd. All rights reserved.

Investigation of Error Patterns in Geographical Databases

NASA Technical Reports Server (NTRS)

Dryer, David; Jacobs, Derya A.; Karayaz, Gamze; Gronbech, Chris; Jones, Denise R. (Technical Monitor)

2002-01-01

The objective of the research conducted in this project is to develop a methodology to investigate the accuracy of Airport Safety Modeling Data (ASMD) using statistical, visualization, and Artificial Neural Network (ANN) techniques. Such a methodology can contribute to answering the following research questions: Over a representative sampling of ASMD databases, can statistical error analysis techniques be accurately learned and replicated by ANN modeling techniques? This representative ASMD sample should include numerous airports and a variety of terrain characterizations. Is it possible to identify and automate the recognition of patterns of error related to geographical features? Do such patterns of error relate to specific geographical features, such as elevation or terrain slope? Is it possible to combine the errors in small regions into an error prediction for a larger region? What are the data density reduction implications of this work? ASMD may be used as the source of terrain data for a synthetic visual system to be used in the cockpit of aircraft when visual reference to ground features is not possible during conditions of marginal weather or reduced visibility. In this research, United States Geologic Survey (USGS) digital elevation model (DEM) data has been selected as the benchmark. Artificial Neural Networks (ANNS) have been used and tested as alternate methods in place of the statistical methods in similar problems. They often perform better in pattern recognition, prediction and classification and categorization problems. Many studies show that when the data is complex and noisy, the accuracy of ANN models is generally higher than those of comparable traditional methods.

A Novel Energy-Efficient Approach for Human Activity Recognition.

PubMed

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Peng, Ao; Tang, Biyu; Lu, Hai; Shi, Haibin; Zheng, Huiru

2017-09-08

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper.

Training a whole-book LSTM-based recognizer with an optimal training set

NASA Astrophysics Data System (ADS)

Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier

2018-04-01

Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.

Recognition of medical errors’ reporting system dimensions in educational hospitals

PubMed Central

Yarmohammadian, Mohammad H.; Mohammadinia, Leila; Tavakoli, Nahid; Ghalriz, Parvin; Haghshenas, Abbas

2014-01-01

Introduction and Objective: Nowadays medical errors are one of the serious issues in the health-care system and carry to account of the patient's safety threat. The most important step for achieving safety promotion is identifying errors and their causes in order to recognize, correct and omit them. Concerning about repeating medical errors and harms, which were received via theses errors concluded to designing and establishing medical error reporting systems for hospitals and centers that are presenting therapeutic services. The aim of this study is the recognition of medical errors’ reporting system dimensions in educational hospitals. Materials and Methods: This research is a descriptive-analytical and qualities’ study, which has been carried out in Shahid Beheshti educational therapeutic center in Isfahan during 2012. In this study, relevant information was collected through 15 face to face interviews. That each of interviews take place in about 1hr and creation of five focused discussion groups through 45 min for each section, they were composed of Metron, educational supervisor, health officer, health education, and all of the head nurses. Concluded data interviews and discussion sessions were coded, then achieved results were extracted in the presence of clear-sighted persons and after their feedback perception, they were categorized. In order to make sure of information correctness, tables were presented to the research's interviewers and final the corrections were confirmed based on their view. Finding: The extracted information from interviews and discussion groups have been divided into nine main categories after content analyzing and subject coding and their subsets have been completely expressed. Achieved dimensions are composed of nine domains of medical error concept, error cases according to nurses’ prospection, medical error reporting barriers, employees’ motivational factors for error reporting, purposes of medical error reporting system, error reporting's challenges and opportunities, a desired system characteristics, and the quality of error experiences’ transmission in the health-care system. Conclusion: Although, appropriate achievements have been assured in Shahid Beheshti Hospital, but it seems necessary that in order to immune promotion not only in this hospital, but in the other organizations, necessary infrastructures have been provided for an error reporting system performance. An appropriate medical error reporting system could be educated and prevent the occurrence of repeated errors. PMID:25250342

The current and ideal state of anatomic pathology patient safety.

PubMed

Raab, Stephen Spencer

2014-01-01

An anatomic pathology diagnostic error may be secondary to a number of active and latent technical and/or cognitive components, which may occur anywhere along the total testing process in clinical and/or laboratory domains. For the pathologist interpretive steps of diagnosis, we examine Kahneman's framework of slow and fast thinking to explain different causes of error in precision (agreement) and in accuracy (truth). The pathologist cognitive diagnostic process involves image pattern recognition and a slow thinking error may be caused by the application of different rationally-constructed mental maps of image criteria/patterns by different pathologists. This type of error is partly related to a system failure in standardizing the application of these maps. A fast thinking error involves the flawed leap from image pattern to incorrect diagnosis. In the ideal state, anatomic pathology systems would target these cognitive error causes as well as the technical latent factors that lead to error.

An enhanced feature set for pattern recognition based contrast enhancement of contact-less captured latent fingerprints in digitized crime scene forensics

NASA Astrophysics Data System (ADS)

Hildebrandt, Mario; Kiltz, Stefan; Dittmann, Jana; Vielhauer, Claus

2014-02-01

In crime scene forensics latent fingerprints are found on various substrates. Nowadays primarily physical or chemical preprocessing techniques are applied for enhancing the visibility of the fingerprint trace. In order to avoid altering the trace it has been shown that contact-less sensors offer a non-destructive acquisition approach. Here, the exploitation of fingerprint or substrate properties and the utilization of signal processing techniques are an essential requirement to enhance the fingerprint visibility. However, especially the optimal sensory is often substrate-dependent. An enhanced generic pattern recognition based contrast enhancement approach for scans of a chromatic white light sensor is introduced in Hildebrandt et al.1 using statistical, structural and Benford's law2 features for blocks of 50 micron. This approach achieves very good results for latent fingerprints on cooperative, non-textured, smooth substrates. However, on textured and structured substrates the error rates are very high and the approach thus unsuitable for forensic use cases. We propose the extension of the feature set with semantic features derived from known Gabor filter based exemplar fingerprint enhancement techniques by suggesting an Epsilon-neighborhood of each block in order to achieve an improved accuracy (called fingerprint ridge orientation semantics). Furthermore, we use rotation invariant Hu moments as an extension of the structural features and two additional preprocessing methods (separate X- and Y Sobel operators). This results in a 408-dimensional feature space. In our experiments we investigate and report the recognition accuracy for eight substrates, each with ten latent fingerprints: white furniture surface, veneered plywood, brushed stainless steel, aluminum foil, "Golden-Oak" veneer, non-metallic matte car body finish, metallic car body finish and blued metal. In comparison to Hildebrandt et al.,1 our evaluation shows a significant reduction of the error rates by 15.8 percent points on brushed stainless steel using the same classifier. This also allows for a successful biometric matching of 3 of the 8 latent fingerprint samples with the corresponding exemplar fingerprint on this particular substrate. For contrast enhancement analysis of classification results we suggest to use known Visual Quality Indexes (VQI)3 as a contrast enhancement quality indicator and discuss our first preliminary results using the exemplary chosen VQI Edge Similarity Score (ESS),4 showing a tendency that higher image differences between a substrate containing a fingerprint and a substrate with a blank surface correlate with a higher recognition accuracy between a latent fingerprint and an exemplar fingerprint. Those first preliminary results support further research into VQIs as contrast enhancement quality indicator for a given feature space.

A neuroimaging study of conflict during word recognition.

PubMed

Riba, Jordi; Heldmann, Marcus; Carreiras, Manuel; Münte, Thomas F

2010-08-04

Using functional magnetic resonance imaging the neural activity associated with error commission and conflict monitoring in a lexical decision task was assessed. In a cohort of 20 native speakers of Spanish conflict was introduced by presenting words with high and low lexical frequency and pseudo-words with high and low syllabic frequency for the first syllable. Erroneous versus correct responses showed activation in the frontomedial and left inferior frontal cortex. A similar pattern was found for correctly classified words of low versus high lexical frequency and for correctly classified pseudo-words of high versus low syllabic frequency. Conflict-related activations for language materials largely overlapped with error-induced activations. The effect of syllabic frequency underscores the role of sublexical processing in visual word recognition and supports the view that the initial syllable mediates between the letter and word level.

Anxiety disorders in adolescence are associated with impaired facial expression recognition to negative valence.

PubMed

Jarros, Rafaela Behs; Salum, Giovanni Abrahão; Belem da Silva, Cristiano Tschiedel; Toazza, Rudineia; de Abreu Costa, Marianna; Fumagalli de Salles, Jerusa; Manfro, Gisele Gus

2012-02-01

The aim of the present study was to test the ability of adolescents with a current anxiety diagnosis to recognize facial affective expressions, compared to those without an anxiety disorder. Forty cases and 27 controls were selected from a larger cross sectional community sample of adolescents, aged from 10 to 17 years old. Adolescent's facial recognition of six human emotions (sadness, anger, disgust, happy, surprise and fear) and neutral faces was assessed through a facial labeling test using Ekman's Pictures of Facial Affect (POFA). Adolescents with anxiety disorders had a higher mean number of errors in angry faces as compared to controls: 3.1 (SD=1.13) vs. 2.5 (SD=2.5), OR=1.72 (CI95% 1.02 to 2.89; p=0.040). However, they named neutral faces more accurately than adolescents without anxiety diagnosis: 15% of cases vs. 37.1% of controls presented at least one error in neutral faces, OR=3.46 (CI95% 1.02 to 11.7; p=0.047). No differences were found considering other human emotions or on the distribution of errors in each emotional face between the groups. Our findings support an anxiety-mediated influence on the recognition of facial expressions in adolescence. These difficulty in recognizing angry faces and more accuracy in naming neutral faces may lead to misinterpretation of social clues and can explain some aspects of the impairment in social interactions in adolescents with anxiety disorders. Copyright © 2011 Elsevier Ltd. All rights reserved.

Actively Paranoid Patients with Schizophrenia Over Attribute Anger to Neutral Faces

PubMed Central

Pinkham, Amy E.; Brensinger, Colleen; Kohler, Christian; Gur, Raquel E.; Gur, Ruben C.

2010-01-01

Previous investigations of the influence of paranoia on facial affect recognition in schizophrenia have been inconclusive as some studies demonstrate better performance for paranoid relative to non-paranoid patients and others show that paranoid patients display greater impairments. These studies have been limited by small sample sizes and inconsistencies in the criteria used to define groups. Here, we utilized an established emotion recognition task and a large sample to examine differential performance in emotion recognition ability between patients who were actively paranoid (AP) and those who were not actively paranoid (NAP). Accuracy and error patterns on the Penn Emotion Recognition test (ER40) were examined in 132 patients (64 NAP and 68 AP). Groups were defined based on the presence of paranoid ideation at the time of testing rather than diagnostic subtype. AP and NAP patients did not differ in overall task accuracy; however, an emotion by group interaction indicated that AP patients were significantly worse than NAP patients at correctly labeling neutral faces. A comparison of error patterns on neutral stimuli revealed that the groups differed only in misattributions of anger expressions, with AP patients being significantly more likely to misidentify a neutral expression as angry. The present findings suggest that paranoia is associated with a tendency to over attribute threat to ambiguous stimuli and also lend support to emerging hypotheses of amygdala hyperactivation as a potential neural mechanism for paranoid ideation. PMID:21112186

Performance of Language-Coordinated Collective Systems: A Study of Wine Recognition and Description

PubMed Central

Zubek, Julian; Denkiewicz, Michał; Dębska, Agnieszka; Radkowska, Alicja; Komorowska-Mach, Joanna; Litwin, Piotr; Stępień, Magdalena; Kucińska, Adrianna; Sitarska, Ewa; Komorowska, Krystyna; Fusaroli, Riccardo; Tylén, Kristian; Rączaszek-Leonardi, Joanna

2016-01-01

Most of our perceptions of and engagements with the world are shaped by our immersion in social interactions, cultural traditions, tools and linguistic categories. In this study we experimentally investigate the impact of two types of language-based coordination on the recognition and description of complex sensory stimuli: that of red wine. Participants were asked to taste, remember and successively recognize samples of wines within a larger set in a two-by-two experimental design: (1) either individually or in pairs, and (2) with or without the support of a sommelier card—a cultural linguistic tool designed for wine description. Both effectiveness of recognition and the kinds of errors in the four conditions were analyzed. While our experimental manipulations did not impact recognition accuracy, bias-variance decomposition of error revealed non-trivial differences in how participants solved the task. Pairs generally displayed reduced bias and increased variance compared to individuals, however the variance dropped significantly when they used the sommelier card. The effect of sommelier card reducing the variance was observed only in pairs, individuals did not seem to benefit from the cultural linguistic tool. Analysis of descriptions generated with the aid of sommelier cards shows that pairs were more coherent and discriminative than individuals. The findings are discussed in terms of global properties and dynamics of collective systems when constrained by different types of cultural practices. PMID:27729875

Measuring safety culture in the ambulatory setting: the safety attitudes questionnaire--ambulatory version.

PubMed

Modak, Isitri; Sexton, J Bryan; Lux, Thomas R; Helmreich, Robert L; Thomas, Eric J

2007-01-01

Provider attitudes about issues pertinent to patient safety may be related to errors and adverse events. We know of no instruments that measure safety-related attitudes in the outpatient setting. To adapt the safety attitudes questionnaire (SAQ) to the outpatient setting and compare attitudes among different types of providers in the outpatient setting. We modified the SAQ to create a 62-item SAQ-ambulatory version (SAQ-A). Patient care staff in a multispecialty, academic practice rated their agreement with the items using a 5-point Likert scale. Cronbach's alpha was calculated to determine reliability of scale scores. Differences in SAQ-A scores between providers were assessed using ANOVA. Of the 409 staff, 282 (69%) returned surveys. One hundred ninety (46%) surveys were included in the analyses. Cronbach's alpha ranged from 0.68 to 0.86 for the scales: teamwork climate, safety climate, perceptions of management, job satisfaction, working conditions, and stress recognition. Physicians had the least favorable attitudes about perceptions of management while managers had the most favorable attitudes (mean scores: 50.4 +/- 22.5 vs 72.5 +/- 19.6, P < 0.05; percent with positive attitudes 18% vs 70%, respectively). Nurses had the most positive stress recognition scores (mean score 66.0 +/- 24.0). All providers had similar attitudes toward teamwork climate, safety climate, job satisfaction, and working conditions. The SAQ-A is a reliable tool for eliciting provider attitudes about the ambulatory work setting. Attitudes relevant to medical error may differ among provider types and reflect behavior and clinic operations that could be improved.

Identifying hidden rate changes in the evolution of a binary morphological character: the evolution of plant habit in campanulid angiosperms.

PubMed

Beaulieu, Jeremy M; O'Meara, Brian C; Donoghue, Michael J

2013-09-01

The growth of phylogenetic trees in scope and in size is promising from the standpoint of understanding a wide variety of evolutionary patterns and processes. With trees comprised of larger, older, and globally distributed clades, it is likely that the lability of a binary character will differ significantly among lineages, which could lead to errors in estimating transition rates and the associated inference of ancestral states. Here we develop and implement a new method for identifying different rates of evolution in a binary character along different branches of a phylogeny. We illustrate this approach by exploring the evolution of growth habit in Campanulidae, a flowering plant clade containing some 35,000 species. The distribution of woody versus herbaceous species calls into question the use of traditional models of binary character evolution. The recognition and accommodation of changes in the rate of growth form evolution in different lineages demonstrates, for the first time, a robust picture of growth form evolution across a very large, very old, and very widespread flowering plant clade.

Are children's memory illusions created differently from those of adults? Evidence from levels-of-processing and divided attention paradigms.

PubMed

Wimmer, Marina C; Howe, Mark L

2010-09-01

In two experiments, we investigated the robustness and automaticity of adults' and children's generation of false memories by using a levels-of-processing paradigm (Experiment 1) and a divided attention paradigm (Experiment 2). The first experiment revealed that when information was encoded at a shallow level, true recognition rates decreased for all ages. For false recognition, when information was encoded on a shallow level, we found a different pattern for young children compared with that for older children and adults. False recognition rates were related to the overall amount of correctly remembered information for 7-year-olds, whereas no such association was found for the other age groups. In the second experiment, divided attention decreased true recognition for all ages. In contrast, children's (7- and 11-year-olds) false recognition rates were again dependent on the overall amount of correctly remembered information, whereas adults' false recognition was left unaffected. Overall, children's false recognition rates changed when levels of processing or divided attention was manipulated in comparison with adults. Together, these results suggest that there may be both quantitative and qualitative changes in false memory rates with age. Copyright 2010 Elsevier Inc. All rights reserved.

[Discriminant Analysis of Lavender Essential Oil by Attenuated Total Reflectance Infrared Spectroscopy].

PubMed

Tang, Jun; Wang, Qing; Tong, Hong; Liao, Xiang; Zhang, Zheng-fang

2016-03-01

This work aimed to use attenuated total reflectance Fourier transform infrared spectroscopy to identify the lavender essential oil by establishing a Lavender variety and quality analysis model. So, 96 samples were tested. For all samples, the raw spectra were pretreated as second derivative, and to determine the 1 750-900 cm(-1) wavelengths for pattern recognition analysis on the basis of the variance calculation. The results showed that principal component analysis (PCA) can basically discriminate lavender oil cultivar and the first three principal components mainly represent the ester, alcohol and terpenoid substances. When the orthogonal partial least-squares discriminant analysis (OPLS-DA) model was established, the 68 samples were used for the calibration set. Determination coefficients of OPLS-DA regression curve were 0.959 2, 0.976 4, and 0.958 8 respectively for three varieties of lavender essential oil. Three varieties of essential oil's the root mean square error of prediction (RMSEP) in validation set were 0.142 9, 0.127 3, and 0.124 9, respectively. The discriminant rate of calibration set and the prediction rate of validation set had reached 100%. The model has the very good recognition capability to detect the variety and quality of lavender essential oil. The result indicated that a model which provides a quick, intuitive and feasible method had been built to discriminate lavender oils.

Dual-Process Models of Associative Recognition in Young and Older Adults: Evidence from Receiver Operating Characteristics

ERIC Educational Resources Information Center

Healy, Michael R.; Light, Leah L.; Chung, Christie

2005-01-01

In 3 experiments, young and older adults studied lists of unrelated word pairs and were given confidence-rated item and associative recognition tests. Several different models of recognition were fit to the confidence-rating data using techniques described by S. Macho (2002, 2004). Concordant with previous findings, item recognition data were best…

Automatic feature design for optical character recognition using an evolutionary search procedure.

PubMed

Stentiford, F W

1985-03-01

An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers.

Counter-propagation network with variable degree variable step size LMS for single switch typing recognition.

PubMed

Yang, Cheng-Huei; Luo, Ching-Hsing; Yang, Cheng-Hong; Chuang, Li-Yeh

2004-01-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, including mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for disabled persons who suffer from muscle atrophy, cerebral palsy or other severe handicaps. A stable typing rate is strictly required for Morse code to be effective as a communication tool. This restriction is a major hindrance. Therefore, a switch adaptive automatic recognition method with a high recognition rate is needed. The proposed system combines counter-propagation networks with a variable degree variable step size LMS algorithm. It is divided into five stages: space recognition, tone recognition, learning process, adaptive processing, and character recognition. Statistical analyses demonstrated that the proposed method elicited a better recognition rate in comparison to alternative methods in the literature.

Correcting for sequencing error in maximum likelihood phylogeny inference.

PubMed

Kuhner, Mary K; McGill, James

2014-11-04

Accurate phylogenies are critical to taxonomy as well as studies of speciation processes and other evolutionary patterns. Accurate branch lengths in phylogenies are critical for dating and rate measurements. Such accuracy may be jeopardized by unacknowledged sequencing error. We use simulated data to test a correction for DNA sequencing error in maximum likelihood phylogeny inference. Over a wide range of data polymorphism and true error rate, we found that correcting for sequencing error improves recovery of the branch lengths, even if the assumed error rate is up to twice the true error rate. Low error rates have little effect on recovery of the topology. When error is high, correction improves topological inference; however, when error is extremely high, using an assumed error rate greater than the true error rate leads to poor recovery of both topology and branch lengths. The error correction approach tested here was proposed in 2004 but has not been widely used, perhaps because researchers do not want to commit to an estimate of the error rate. This study shows that correction with an approximate error rate is generally preferable to ignoring the issue. Copyright © 2014 Kuhner and McGill.

Effects of hydrocortisone on false memory recognition in healthy men and women.

PubMed

Duesenberg, Moritz; Weber, Juliane; Schaeuffele, Carmen; Fleischer, Juliane; Hellmann-Regen, Julian; Roepke, Stefan; Moritz, Steffen; Otte, Christian; Wingenfeld, Katja

2016-12-01

Most of the studies focusing on the effect of stress on false memories by using psychosocial and physiological stressors yielded diverse results. In the present study, we systematically tested the effect of exogenous hydrocortisone using a false memory paradigm. In this placebo-controlled study, 37 healthy men and 38 healthy women (mean age 24.59 years) received either 10 mg of hydrocortisone or placebo 75 min before using the false memory, that is, Deese-Roediger-McDermott (DRM), paradigm. We used emotionally charged and neutral DRM-based word lists to look for false recognition rates in comparison to true recognition rates. Overall, we expected an increase in false memory after hydrocortisone compared to placebo. No differences between the cortisol and the placebo group were revealed for false and for true recognition performance. In general, false recognition rates were lower compared to true recognition rates. Furthermore, we found a valence effect (neutral, positive, negative, disgust word stimuli), indicating higher rates of true and false recognition for emotional compared to neutral words. We further found an interaction effect between sex and recognition. Post hoc t tests showed that for true recognition women showed a significantly better memory performance than men, independent of treatment. This study does not support the hypothesis that cortisol decreases the ability to distinguish between old versus novel words in young healthy individuals. However, sex and emotional valence of word stimuli appear to be important moderators. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Cyclic volatile methylsiloxanes in human blood as markers for ruptured silicone gel-filled breast implants.

PubMed

Rosendahl, Pia; Hippler, Joerg; Schmitz, Oliver J; Hoffmann, Oliver; Rusch, Peter

2016-05-01

The replacement of medical-grade silicone with industrial-grade silicone material in some silicone gel-filled breast implants (SBI) manufactured by Poly Implant Prothèse and Rofil Medical Nederland B.V., reported in 2010, which resulted in a higher rupture tendency of these SBI, demonstrates the need for non-invasive, sensitive monitoring and screening methods. Therefore a sensitive method based on large volume injection-gas chromatography coupled to mass spectrometry (LVI-GC/MS) was developed to determine octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5), and dodecamethylcyclo-hexasiloxane (D6) in blood samples from women with intact (n = 13) and ruptured SBI (n = 11). With dichloromethane extraction, sample cooling during preparation, and analysis extraction efficiencies up to 100 % and limits of detection of 0.03-0.05 ng D4-D6/g blood were achieved. Blood samples from women with SBI were investigated. In contrast to women with intact SBI, in blood from women with ruptured SBI higher D4 and D6 concentrations up to 0.57 ng D4/g blood and 0.16 ng D6/g blood were detected. With concentrations above 0.18 D4 ng/blood and 0.10 ng D6/g blood as significant criteria for ruptured SBI, this developed analytical preoperative diagnostic method shows a significant increase of the recognition rate. Finally a higher precision (error rate 17%) than the commonly used clinical diagnostic method, mamma sonography (error rate 46%), was achieved.

Weighted Feature Gaussian Kernel SVM for Emotion Recognition

PubMed Central

Jia, Qingxuan

2016-01-01

Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods. PMID:27807443

Multicategory nets of single-layer perceptrons: complexity and sample-size issues.

PubMed

Raudys, Sarunas; Kybartas, Rimantas; Zavadskas, Edmundas Kazimieras

2010-05-01

The standard cost function of multicategory single-layer perceptrons (SLPs) does not minimize the classification error rate. In order to reduce classification error, it is necessary to: 1) refuse the traditional cost function, 2) obtain near to optimal pairwise linear classifiers by specially organized SLP training and optimal stopping, and 3) fuse their decisions properly. To obtain better classification in unbalanced training set situations, we introduce the unbalance correcting term. It was found that fusion based on the Kulback-Leibler (K-L) distance and the Wu-Lin-Weng (WLW) method result in approximately the same performance in situations where sample sizes are relatively small. The explanation for this observation is by theoretically known verity that an excessive minimization of inexact criteria becomes harmful at times. Comprehensive comparative investigations of six real-world pattern recognition (PR) problems demonstrated that employment of SLP-based pairwise classifiers is comparable and as often as not outperforming the linear support vector (SV) classifiers in moderate dimensional situations. The colored noise injection used to design pseudovalidation sets proves to be a powerful tool for facilitating finite sample problems in moderate-dimensional PR tasks.

Sign language ability in young deaf signers predicts comprehension of written sentences in English.

PubMed

Andrew, Kathy N; Hoshooley, Jennifer; Joanisse, Marc F

2014-01-01

We investigated the robust correlation between American Sign Language (ASL) and English reading ability in 51 young deaf signers ages 7;3 to 19;0. Signers were divided into 'skilled' and 'less-skilled' signer groups based on their performance on three measures of ASL. We next assessed reading comprehension of four English sentence structures (actives, passives, pronouns, reflexive pronouns) using a sentence-to-picture-matching task. Of interest was the extent to which ASL proficiency provided a foundation for lexical and syntactic processes of English. Skilled signers outperformed less-skilled signers overall. Error analyses further indicated greater single-word recognition difficulties in less-skilled signers marked by a higher rate of errors reflecting an inability to identify the actors and actions described in the sentence. Our findings provide evidence that increased ASL ability supports English sentence comprehension both at the levels of individual words and syntax. This is consistent with the theory that first language learning promotes second language through transference of linguistic elements irrespective of the transparency of mapping of grammatical structures between the two languages.

Sign Language Ability in Young Deaf Signers Predicts Comprehension of Written Sentences in English

PubMed Central

Andrew, Kathy N.; Hoshooley, Jennifer; Joanisse, Marc F.

2014-01-01

We investigated the robust correlation between American Sign Language (ASL) and English reading ability in 51 young deaf signers ages 7;3 to 19;0. Signers were divided into ‘skilled’ and ‘less-skilled’ signer groups based on their performance on three measures of ASL. We next assessed reading comprehension of four English sentence structures (actives, passives, pronouns, reflexive pronouns) using a sentence-to-picture-matching task. Of interest was the extent to which ASL proficiency provided a foundation for lexical and syntactic processes of English. Skilled signers outperformed less-skilled signers overall. Error analyses further indicated greater single-word recognition difficulties in less-skilled signers marked by a higher rate of errors reflecting an inability to identify the actors and actions described in the sentence. Our findings provide evidence that increased ASL ability supports English sentence comprehension both at the levels of individual words and syntax. This is consistent with the theory that first language learning promotes second language through transference of linguistic elements irrespective of the transparency of mapping of grammatical structures between the two languages. PMID:24587174

A Novel Energy-Efficient Approach for Human Activity Recognition

PubMed Central

Zheng, Lingxiang; Wu, Dihong; Ruan, Xiaoyang; Weng, Shaolin; Tang, Biyu; Lu, Hai; Shi, Haibin

2017-01-01

In this paper, we propose a novel energy-efficient approach for mobile activity recognition system (ARS) to detect human activities. The proposed energy-efficient ARS, using low sampling rates, can achieve high recognition accuracy and low energy consumption. A novel classifier that integrates hierarchical support vector machine and context-based classification (HSVMCC) is presented to achieve a high accuracy of activity recognition when the sampling rate is less than the activity frequency, i.e., the Nyquist sampling theorem is not satisfied. We tested the proposed energy-efficient approach with the data collected from 20 volunteers (14 males and six females) and the average recognition accuracy of around 96.0% was achieved. Results show that using a low sampling rate of 1Hz can save 17.3% and 59.6% of energy compared with the sampling rates of 5 Hz and 50 Hz. The proposed low sampling rate approach can greatly reduce the power consumption while maintaining high activity recognition accuracy. The composition of power consumption in online ARS is also investigated in this paper. PMID:28885560

Infrared Ship Classification Using A New Moment Pattern Recognition Concept

NASA Astrophysics Data System (ADS)

Casasent, David; Pauly, John; Fetterly, Donald

1982-03-01

An analysis of the statistics of the moments and the conventional invariant moments shows that the variance of the latter become quite large as the order of the moments and the degree of invariance increases. Moreso, the need to whiten the error volume increases with the order and degree, but so does the computational load associated with computing the whitening operator. We thus advance a new estimation approach to the use of moments in pattern recog-nition that overcomes these problems. This work is supported by experimental verification and demonstration on an infrared ship pattern recognition problem. The computational load associated with our new algorithm is also shown to be very low.

Error detection and response adjustment in youth with mild spastic cerebral palsy: an event-related brain potential study.

PubMed

Hakkarainen, Elina; Pirilä, Silja; Kaartinen, Jukka; van der Meere, Jaap J

2013-06-01

This study evaluated the brain activation state during error making in youth with mild spastic cerebral palsy and a peer control group while carrying out a stimulus recognition task. The key question was whether patients were detecting their own errors and subsequently improving their performance in a future trial. Findings indicated that error responses of the group with cerebral palsy were associated with weak motor preparation, as indexed by the amplitude of the late contingent negative variation. However, patients were detecting their errors as indexed by the amplitude of the response-locked negativity and thus improved their performance in a future trial. Findings suggest that the consequence of error making on future performance is intact in a sample of youth with mild spastic cerebral palsy. Because the study group is small, the present findings need replication using a larger sample.

Processing environmental stimuli in paranoid schizophrenia: recognizing facial emotions and performing executive functions.

PubMed

Yu, Shao Hua; Zhu, Jun Peng; Xu, You; Zheng, Lei Lei; Chai, Hao; He, Wei; Liu, Wei Bo; Li, Hui Chun; Wang, Wei

2012-12-01

To study the contribution of executive function to abnormal recognition of facial expressions of emotion in schizophrenia patients. Abnormal recognition of facial expressions of emotion was assayed according to Japanese and Caucasian facial expressions of emotion (JACFEE), Wisconsin card sorting test (WCST), positive and negative symptom scale, and Hamilton anxiety and depression scale, respectively, in 88 paranoid schizophrenia patients and 75 healthy volunteers. Patients scored higher on the Positive and Negative Symptom Scale and the Hamilton Anxiety and Depression Scales, displayed lower JACFEE recognition accuracies and poorer WCST performances. The JACFEE recognition accuracy of contempt and disgust was negatively correlated with the negative symptom scale score while the recognition accuracy of fear was positively with the positive symptom scale score and the recognition accuracy of surprise was negatively with the general psychopathology score in patients. Moreover, the WCST could predict the JACFEE recognition accuracy of contempt, disgust, and sadness in patients, and the perseverative errors negatively predicted the recognition accuracy of sadness in healthy volunteers. The JACFEE recognition accuracy of sadness could predict the WCST categories in paranoid schizophrenia patients. Recognition accuracy of social-/moral emotions, such as contempt, disgust and sadness is related to the executive function in paranoid schizophrenia patients, especially when regarding sadness. Copyright © 2012 The Editorial Board of Biomedical and Environmental Sciences. Published by Elsevier B.V. All rights reserved.

Real-time recognition of feedback error-related potentials during a time-estimation task.

PubMed

Lopez-Larraz, Eduardo; Iturrate, Iñaki; Montesano, Luis; Minguez, Javier

2010-01-01

Feedback error-related potentials are a promising brain process in the field of rehabilitation since they are related to human learning. Due to the fact that many therapeutic strategies rely on the presentation of feedback stimuli, potentials generated by these stimuli could be used to ameliorate the patient's progress. In this paper we propose a method that can identify, in real-time, feedback evoked potentials in a time-estimation task. We have tested our system with five participants in two different days with a separation of three weeks between them, achieving a mean single-trial detection performance of 71.62% for real-time recognition, and 78.08% in offline classification. Additionally, an analysis of the stability of the signal between the two days is performed, suggesting that the feedback responses are stable enough to be used without the needing of training again the user.

A new method for the assessment of patient safety competencies during a medical school clerkship using an objective structured clinical examination

PubMed Central

Daud-Gallotti, Renata Mahfuz; Morinaga, Christian Valle; Arlindo-Rodrigues, Marcelo; Velasco, Irineu Tadeu; Arruda Martins, Milton; Tiberio, Iolanda Calvo

2011-01-01

INTRODUCTION: Patient safety is seldom assessed using objective evaluations during undergraduate medical education. OBJECTIVE: To evaluate the performance of fifth-year medical students using an objective structured clinical examination focused on patient safety after implementation of an interactive program based on adverse events recognition and disclosure. METHODS: In 2007, a patient safety program was implemented in the internal medicine clerkship of our hospital. The program focused on human error theory, epidemiology of incidents, adverse events, and disclosure. Upon completion of the program, students completed an objective structured clinical examination with five stations and standardized patients. One station focused on patient safety issues, including medical error recognition/disclosure, the patient-physician relationship and humanism issues. A standardized checklist was completed by each standardized patient to assess the performance of each student. The student's global performance at each station and performance in the domains of medical error, the patient-physician relationship and humanism were determined. The correlations between the student performances in these three domains were calculated. RESULTS: A total of 95 students participated in the objective structured clinical examination. The mean global score at the patient safety station was 87.59±1.24 points. Students' performance in the medical error domain was significantly lower than their performance on patient-physician relationship and humanistic issues. Less than 60% of students (n = 54) offered the simulated patient an apology after a medical error occurred. A significant correlation was found between scores obtained in the medical error domains and scores related to both the patient-physician relationship and humanistic domains. CONCLUSIONS: An objective structured clinical examination is a useful tool to evaluate patient safety competencies during the medical student clerkship. PMID:21876976

Learning time-dependent noise to reduce logical errors: real time error rate estimation in quantum error correction

NASA Astrophysics Data System (ADS)

Huo, Ming-Xia; Li, Ying

2017-12-01

Quantum error correction is important to quantum information processing, which allows us to reliably process information encoded in quantum error correction codes. Efficient quantum error correction benefits from the knowledge of error rates. We propose a protocol for monitoring error rates in real time without interrupting the quantum error correction. Any adaptation of the quantum error correction code or its implementation circuit is not required. The protocol can be directly applied to the most advanced quantum error correction techniques, e.g. surface code. A Gaussian processes algorithm is used to estimate and predict error rates based on error correction data in the past. We find that using these estimated error rates, the probability of error correction failures can be significantly reduced by a factor increasing with the code distance.

Semantic and visual determinants of face recognition in a prosopagnosic patient.

PubMed

Dixon, M J; Bub, D N; Arguin, M

1998-05-01

Prosopagnosia is the neuropathological inability to recognize familiar people by their faces. It can occur in isolation or can coincide with recognition deficits for other nonface objects. Often, patients whose prosopagnosia is accompanied by object recognition difficulties have more trouble identifying certain categories of objects relative to others. In previous research, we demonstrated that objects that shared multiple visual features and were semantically close posed severe recognition difficulties for a patient with temporal lobe damage. We now demonstrate that this patient's face recognition is constrained by these same parameters. The prosopagnosic patient ELM had difficulties pairing faces to names when the faces shared visual features and the names were semantically related (e.g., Tonya Harding, Nancy Kerrigan, and Josee Chouinard -three ice skaters). He made tenfold fewer errors when the exact same faces were associated with semantically unrelated people (e.g., singer Celine Dion, actress Betty Grable, and First Lady Hillary Clinton). We conclude that prosopagnosia and co-occurring category-specific recognition problems both stem from difficulties disambiguating the stored representations of objects that share multiple visual features and refer to semantically close identities or concepts.

Robust kernel representation with statistical local features for face recognition.

PubMed

Yang, Meng; Zhang, Lei; Shiu, Simon Chi-Keung; Zhang, David

2013-06-01

Factors such as misalignment, pose variation, and occlusion make robust face recognition a difficult problem. It is known that statistical features such as local binary pattern are effective for local feature extraction, whereas the recently proposed sparse or collaborative representation-based classification has shown interesting results in robust face recognition. In this paper, we propose a novel robust kernel representation model with statistical local features (SLF) for robust face recognition. Initially, multipartition max pooling is used to enhance the invariance of SLF to image registration error. Then, a kernel-based representation model is proposed to fully exploit the discrimination information embedded in the SLF, and robust regression is adopted to effectively handle the occlusion in face images. Extensive experiments are conducted on benchmark face databases, including extended Yale B, AR (A. Martinez and R. Benavente), multiple pose, illumination, and expression (multi-PIE), facial recognition technology (FERET), face recognition grand challenge (FRGC), and labeled faces in the wild (LFW), which have different variations of lighting, expression, pose, and occlusions, demonstrating the promising performance of the proposed method.

Oxytocin increases bias, but not accuracy, in face recognition line-ups.

PubMed

Bate, Sarah; Bennetts, Rachel; Parris, Benjamin A; Bindemann, Markus; Udale, Robert; Bussunt, Amanda

2015-07-01

Previous work indicates that intranasal inhalation of oxytocin improves face recognition skills, raising the possibility that it may be used in security settings. However, it is unclear whether oxytocin directly acts upon the core face-processing system itself or indirectly improves face recognition via affective or social salience mechanisms. In a double-blind procedure, 60 participants received either an oxytocin or placebo nasal spray before completing the One-in-Ten task-a standardized test of unfamiliar face recognition containing target-present and target-absent line-ups. Participants in the oxytocin condition outperformed those in the placebo condition on target-present trials, yet were more likely to make false-positive errors on target-absent trials. Signal detection analyses indicated that oxytocin induced a more liberal response bias, rather than increasing accuracy per se. These findings support a social salience account of the effects of oxytocin on face recognition and indicate that oxytocin may impede face recognition in certain scenarios. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Recognition of names of eminent psychologists.

PubMed

Duncan, C P

1976-10-01

Faculty members, graduate students, undergraduate majors, and introductory psychology students checked those names they recognized in the list of 228 deceased psychologists, rated for eminence, provided by Annin, Boring, and Watson. Mean percentage recognition was less than 50% for the 128 American psychologists, and less than 25% for the 100 foreign psychologists, by the faculty subjects. The other three groups of subjects gave even lower recognition scores. Recognition was probably also influenced by recency; median year of death of the American psychologists was 1955, of the foreign psychologists, 1943. High recognition (defined as recognition by 80% or more of the faculty group) was achieved by only 34 psychologists, almost all of them American. These highly recognized psychologists also had high eminence ratings, but there was an equal number of psychologists with high eminence ratings that were poorly recognized.

Effects and modeling of phonetic and acoustic confusions in accented speech.

PubMed

Fung, Pascale; Liu, Yi

2005-11-01

Accented speech recognition is more challenging than standard speech recognition due to the effects of phonetic and acoustic confusions. Phonetic confusion in accented speech occurs when an expected phone is pronounced as a different one, which leads to erroneous recognition. Acoustic confusion occurs when the pronounced phone is found to lie acoustically between two baseform models and can be equally recognized as either one. We propose that it is necessary to analyze and model these confusions separately in order to improve accented speech recognition without degrading standard speech recognition. Since low phonetic confusion units in accented speech do not give rise to automatic speech recognition errors, we focus on analyzing and reducing phonetic and acoustic confusability under high phonetic confusion conditions. We propose using likelihood ratio test to measure phonetic confusion, and asymmetric acoustic distance to measure acoustic confusion. Only accent-specific phonetic units with low acoustic confusion are used in an augmented pronunciation dictionary, while phonetic units with high acoustic confusion are reconstructed using decision tree merging. Experimental results show that our approach is effective and superior to methods modeling phonetic confusion or acoustic confusion alone in accented speech, with a significant 5.7% absolute WER reduction, without degrading standard speech recognition.

Hierarchical singleton-type recurrent neural fuzzy networks for noisy speech recognition.

PubMed

Juang, Chia-Feng; Chiou, Chyi-Tian; Lai, Chun-Lung

2007-05-01

This paper proposes noisy speech recognition using hierarchical singleton-type recurrent neural fuzzy networks (HSRNFNs). The proposed HSRNFN is a hierarchical connection of two singleton-type recurrent neural fuzzy networks (SRNFNs), where one is used for noise filtering and the other for recognition. The SRNFN is constructed by recurrent fuzzy if-then rules with fuzzy singletons in the consequences, and their recurrent properties make them suitable for processing speech patterns with temporal characteristics. In n words recognition, n SRNFNs are created for modeling n words, where each SRNFN receives the current frame feature and predicts the next one of its modeling word. The prediction error of each SRNFN is used as recognition criterion. In filtering, one SRNFN is created, and each SRNFN recognizer is connected to the same SRNFN filter, which filters noisy speech patterns in the feature domain before feeding them to the SRNFN recognizer. Experiments with Mandarin word recognition under different types of noise are performed. Other recognizers, including multilayer perceptron (MLP), time-delay neural networks (TDNNs), and hidden Markov models (HMMs), are also tested and compared. These experiments and comparisons demonstrate good results with HSRNFN for noisy speech recognition tasks.

Changing predictions, stable recognition: Children's representations of downward incline motion.

PubMed

Hast, Michael; Howe, Christine

2017-11-01

Various studies to-date have demonstrated children hold ill-conceived expressed beliefs about the physical world such as that one ball will fall faster than another because it is heavier. At the same time, they also demonstrate accurate recognition of dynamic events. How these representations relate is still unresolved. This study examined 5- to 11-year-olds' (N = 130) predictions and recognition of motion down inclines. Predictions were typically in error, matching previous work, but children largely recognized correct events as correct and rejected incorrect ones. The results also demonstrate while predictions change with increasing age, recognition shows signs of stability. The findings provide further support for a hybrid model of object representations and argue in favour of stable core cognition existing alongside developmental changes. Statement of contribution What is already known on this subject? Children's predictions of physical events show limitations in accuracy Their recognition of such events suggests children may use different knowledge sources in their reasoning What the present study adds? Predictions fluctuate more strongly than recognition, suggesting stable core cognition But recognition also shows some fluctuation, arguing for a hybrid model of knowledge representation. © 2017 The British Psychological Society.

Recognition of visual stimuli and memory for spatial context in schizophrenic patients and healthy volunteers.

PubMed

Brébion, Gildas; David, Anthony S; Pilowsky, Lyn S; Jones, Hugh

2004-11-01

Verbal and visual recognition tasks were administered to 40 patients with schizophrenia and 40 healthy comparison subjects. The verbal recognition task consisted of discriminating between 16 target words and 16 new words. The visual recognition task consisted of discriminating between 16 target pictures (8 black-and-white and 8 color) and 16 new pictures (8 black-and-white and 8 color). Visual recognition was followed by a spatial context discrimination task in which subjects were required to remember the spatial location of the target pictures at encoding. Results showed that recognition deficit in patients was similar for verbal and visual material. In both schizophrenic and healthy groups, men, but not women, obtained better recognition scores for the colored than for the black-and-white pictures. However, men and women similarly benefited from color to reduce spatial context discrimination errors. Patients showed a significant deficit in remembering the spatial location of the pictures, independently of accuracy in remembering the pictures themselves. These data suggest that patients are impaired in the amount of visual information that they can encode. With regards to the perceptual attributes of the stimuli, memory for spatial information appears to be affected, but not processing of color information.

The role of unconscious memory errors in judgments of confidence for sentence recognition.

PubMed

Sampaio, Cristina; Brewer, William F

2009-03-01

The present experiment tested the hypothesis that unconscious reconstructive memory processing can lead to the breakdown of the relationship between memory confidence and memory accuracy. Participants heard deceptive schema-inference sentences and nondeceptive sentences and were tested with either simple or forced-choice recognition. The nondeceptive items showed a positive relation between confidence and accuracy in both simple and forced-choice recognition. However, the deceptive items showed a strong negative confidence/accuracy relationship in simple recognition and a low positive relationship in forced choice. The mean levels of confidence for erroneous responses for deceptive items were inappropriately high in simple recognition but lower in forced choice. These results suggest that unconscious reconstructive memory processes involved in memory for the deceptive schema-inference items led to inaccurate confidence judgments and that, when participants were made aware of the deceptive nature of the schema-inference items through the use of a forced-choice procedure, they adjusted their confidence accordingly.

Facial emotion recognition in childhood-onset bipolar I disorder: an evaluation of developmental differences between youths and adults.

PubMed

Wegbreit, Ezra; Weissman, Alexandra B; Cushman, Grace K; Puzia, Megan E; Kim, Kerri L; Leibenluft, Ellen; Dickstein, Daniel P

2015-08-01

Bipolar disorder (BD) is a severe mental illness with high healthcare costs and poor outcomes. Increasing numbers of youths are diagnosed with BD, and many adults with BD report that their symptoms started in childhood, suggesting that BD can be a developmental disorder. Studies advancing our understanding of BD have shown alterations in facial emotion recognition both in children and adults with BD compared to healthy comparison (HC) participants, but none have evaluated the development of these deficits. To address this, we examined the effect of age on facial emotion recognition in a sample that included children and adults with confirmed childhood-onset type-I BD, with the adults having been diagnosed and followed since childhood by the Course and Outcome in Bipolar Youth study. Using the Diagnostic Analysis of Non-Verbal Accuracy, we compared facial emotion recognition errors among participants with BD (n = 66; ages 7-26 years) and HC participants (n = 87; ages 7-25 years). Complementary analyses investigated errors for child and adult faces. A significant diagnosis-by-age interaction indicated that younger BD participants performed worse than expected relative to HC participants their own age. The deficits occurred both for child and adult faces and were particularly strong for angry child faces, which were most often mistaken as sad. Our results were not influenced by medications, comorbidities/substance use, or mood state/global functioning. Younger individuals with BD are worse than their peers at this important social skill. This deficit may be an important developmentally salient treatment target - that is, for cognitive remediation to improve BD youths' emotion recognition abilities. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Facial emotion recognition in childhood-onset bipolar I disorder: an evaluation of developmental differences between youths and adults

PubMed Central

Wegbreit, Ezra; Weissman, Alexandra B; Cushman, Grace K; Puzia, Megan E; Kim, Kerri L; Leibenluft, Ellen; Dickstein, Daniel P

2015-01-01

Objectives Bipolar disorder (BD) is a severe mental illness with high healthcare costs and poor outcomes. Increasing numbers of youths are diagnosed with BD, and many adults with BD report their symptoms started in childhood, suggesting BD can be a developmental disorder. Studies advancing our understanding of BD have shown alterations in facial emotion recognition in both children and adults with BD compared to healthy comparison (HC) participants, but none have evaluated the development of these deficits. To address this, we examined the effect of age on facial emotion recognition in a sample that included children and adults with confirmed childhood-onset type-I BD, with the adults having been diagnosed and followed since childhood by the Course and Outcome in Bipolar Youth study. Methods Using the Diagnostic Analysis of Non-Verbal Accuracy, we compared facial emotion recognition errors among participants with BD (n = 66; ages 7–26 years) and HC participants (n = 87; ages 7–25 years). Complementary analyses investigated errors for child and adult faces. Results A significant diagnosis-by-age interaction indicated that younger BD participants performed worse than expected relative to HC participants their own age. The deficits occurred for both child and adult faces and were particularly strong for angry child faces, which were most often mistaken as sad. Our results were not influenced by medications, comorbidities/substance use, or mood state/global functioning. Conclusions Younger individuals with BD are worse than their peers at this important social skill. This deficit may be an important developmentally salient treatment target, i.e., for cognitive remediation to improve BD youths’ emotion recognition abilities. PMID:25951752

NOBLE - Flexible concept recognition for large-scale biomedical natural language processing.

PubMed

Tseytlin, Eugene; Mitchell, Kevin; Legowski, Elizabeth; Corrigan, Julia; Chavan, Girish; Jacobson, Rebecca S

2016-01-14

Natural language processing (NLP) applications are increasingly important in biomedical data analysis, knowledge engineering, and decision support. Concept recognition is an important component task for NLP pipelines, and can be either general-purpose or domain-specific. We describe a novel, flexible, and general-purpose concept recognition component for NLP pipelines, and compare its speed and accuracy against five commonly used alternatives on both a biological and clinical corpus. NOBLE Coder implements a general algorithm for matching terms to concepts from an arbitrary vocabulary set. The system's matching options can be configured individually or in combination to yield specific system behavior for a variety of NLP tasks. The software is open source, freely available, and easily integrated into UIMA or GATE. We benchmarked speed and accuracy of the system against the CRAFT and ShARe corpora as reference standards and compared it to MMTx, MGrep, Concept Mapper, cTAKES Dictionary Lookup Annotator, and cTAKES Fast Dictionary Lookup Annotator. We describe key advantages of the NOBLE Coder system and associated tools, including its greedy algorithm, configurable matching strategies, and multiple terminology input formats. These features provide unique functionality when compared with existing alternatives, including state-of-the-art systems. On two benchmarking tasks, NOBLE's performance exceeded commonly used alternatives, performing almost as well as the most advanced systems. Error analysis revealed differences in error profiles among systems. NOBLE Coder is comparable to other widely used concept recognition systems in terms of accuracy and speed. Advantages of NOBLE Coder include its interactive terminology builder tool, ease of configuration, and adaptability to various domains and tasks. NOBLE provides a term-to-concept matching system suitable for general concept recognition in biomedical NLP pipelines.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

PubMed Central

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-01-01

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model

PubMed Central

Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

2014-01-01

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese–Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred–PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition. PMID:24992328

Blurred palmprint recognition based on stable-feature extraction using a Vese-Osher decomposition model.

PubMed

Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

2014-01-01

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese-Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred-PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

PubMed

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-02-26

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

The intercrater plains of Mercury and the Moon: Their nature, origin and role in terrestrial planet evolution. Measurement and errors of crater statistics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Leake, M. A.

1982-01-01

Planetary imagery techniques, errors in measurement or degradation assignment, and statistical formulas are presented with respect to cratering data. Base map photograph preparation, measurement of crater diameters and sampled area, and instruments used are discussed. Possible uncertainties, such as Sun angle, scale factors, degradation classification, and biases in crater recognition are discussed. The mathematical formulas used in crater statistics are presented.

Adaptive detection of missed text areas in OCR outputs: application to the automatic assessment of OCR quality in mass digitization projects

NASA Astrophysics Data System (ADS)

Ben Salah, Ahmed; Ragot, Nicolas; Paquet, Thierry

2013-01-01

The French National Library (BnF*) has launched many mass digitization projects in order to give access to its collection. The indexation of digital documents on Gallica (digital library of the BnF) is done through their textual content obtained thanks to service providers that use Optical Character Recognition softwares (OCR). OCR softwares have become increasingly complex systems composed of several subsystems dedicated to the analysis and the recognition of the elements in a page. However, the reliability of these systems is always an issue at stake. Indeed, in some cases, we can find errors in OCR outputs that occur because of an accumulation of several errors at different levels in the OCR process. One of the frequent errors in OCR outputs is the missed text components. The presence of such errors may lead to severe defects in digital libraries. In this paper, we investigate the detection of missed text components to control the OCR results from the collections of the French National Library. Our verification approach uses local information inside the pages based on Radon transform descriptors and Local Binary Patterns descriptors (LBP) coupled with OCR results to control their consistency. The experimental results show that our method detects 84.15% of the missed textual components, by comparing the OCR ALTO files outputs (produced by the service providers) to the images of the document.

Robust and Effective Component-based Banknote Recognition for the Blind

PubMed Central

Hasanuzzaman, Faiz M.; Yang, Xiaodong; Tian, YingLi

2012-01-01

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users. PMID:22661884

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.

Artificial neural network implementation of a near-ideal error prediction controller

NASA Technical Reports Server (NTRS)

Mcvey, Eugene S.; Taylor, Lynore Denise

1992-01-01

A theory has been developed at the University of Virginia which explains the effects of including an ideal predictor in the forward loop of a linear error-sampled system. It has been shown that the presence of this ideal predictor tends to stabilize the class of systems considered. A prediction controller is merely a system which anticipates a signal or part of a signal before it actually occurs. It is understood that an exact prediction controller is physically unrealizable. However, in systems where the input tends to be repetitive or limited, (i.e., not random) near ideal prediction is possible. In order for the controller to act as a stability compensator, the predictor must be designed in a way that allows it to learn the expected error response of the system. In this way, an unstable system will become stable by including the predicted error in the system transfer function. Previous and current prediction controller include pattern recognition developments and fast-time simulation which are applicable to the analysis of linear sampled data type systems. The use of pattern recognition techniques, along with a template matching scheme, has been proposed as one realizable type of near-ideal prediction. Since many, if not most, systems are repeatedly subjected to similar inputs, it was proposed that an adaptive mechanism be used to 'learn' the correct predicted error response. Once the system has learned the response of all the expected inputs, it is necessary only to recognize the type of input with a template matching mechanism and then to use the correct predicted error to drive the system. Suggested here is an alternate approach to the realization of a near-ideal error prediction controller, one designed using Neural Networks. Neural Networks are good at recognizing patterns such as system responses, and the back-propagation architecture makes use of a template matching scheme. In using this type of error prediction, it is assumed that the system error responses be known for a particular input and modeled plant. These responses are used in the error prediction controller. An analysis was done on the general dynamic behavior that results from including a digital error predictor in a control loop and these were compared to those including the near-ideal Neural Network error predictor. This analysis was done for a second and third order system.

Development of a Mandarin-English Bilingual Speech Recognition System for Real World Music Retrieval

NASA Astrophysics Data System (ADS)

Zhang, Qingqing; Pan, Jielin; Lin, Yang; Shao, Jian; Yan, Yonghong

In recent decades, there has been a great deal of research into the problem of bilingual speech recognition-to develop a recognizer that can handle inter- and intra-sentential language switching between two languages. This paper presents our recent work on the development of a grammar-constrained, Mandarin-English bilingual Speech Recognition System (MESRS) for real world music retrieval. Two of the main difficult issues in handling the bilingual speech recognition systems for real world applications are tackled in this paper. One is to balance the performance and the complexity of the bilingual speech recognition system; the other is to effectively deal with the matrix language accents in embedded language**. In order to process the intra-sentential language switching and reduce the amount of data required to robustly estimate statistical models, a compact single set of bilingual acoustic models derived by phone set merging and clustering is developed instead of using two separate monolingual models for each language. In our study, a novel Two-pass phone clustering method based on Confusion Matrix (TCM) is presented and compared with the log-likelihood measure method. Experiments testify that TCM can achieve better performance. Since potential system users' native language is Mandarin which is regarded as a matrix language in our application, their pronunciations of English as the embedded language usually contain Mandarin accents. In order to deal with the matrix language accents in embedded language, different non-native adaptation approaches are investigated. Experiments show that model retraining method outperforms the other common adaptation methods such as Maximum A Posteriori (MAP). With the effective incorporation of approaches on phone clustering and non-native adaptation, the Phrase Error Rate (PER) of MESRS for English utterances was reduced by 24.47% relatively compared to the baseline monolingual English system while the PER on Mandarin utterances was comparable to that of the baseline monolingual Mandarin system. The performance for bilingual utterances achieved 22.37% relative PER reduction.

Improved memory for error feedback.

PubMed

Van der Borght, Liesbet; Schouppe, Nathalie; Notebaert, Wim

2016-11-01

Surprising feedback in a general knowledge test leads to an improvement in memory for both the surface features and the content of the feedback (Psychon Bull Rev 16:88-92, 2009). Based on the idea that in cognitive tasks, error is surprising (the orienting account, Cognition 111:275-279, 2009), we tested whether error feedback would be better remembered than correct feedback. Colored words were presented as feedback signals in a flanker task, where the color indicated the accuracy. Subsequently, these words were again presented during a recognition task (Experiment 1) or a lexical decision task (Experiments 2 and 3). In all experiments, memory was improved for words seen as error feedback. These results are compared to the attentional boost effect (J Exp Psychol Learn Mem Cogn 39:1223-12231, 2013) and related to the orienting account for post-error slowing (Cognition 111:275-279, 2009).

Actively paranoid patients with schizophrenia over attribute anger to neutral faces.

PubMed

Pinkham, Amy E; Brensinger, Colleen; Kohler, Christian; Gur, Raquel E; Gur, Ruben C

2011-02-01

Previous investigations of the influence of paranoia on facial affect recognition in schizophrenia have been inconclusive as some studies demonstrate better performance for paranoid relative to non-paranoid patients and others show that paranoid patients display greater impairments. These studies have been limited by small sample sizes and inconsistencies in the criteria used to define groups. Here, we utilized an established emotion recognition task and a large sample to examine differential performance in emotion recognition ability between patients who were actively paranoid (AP) and those who were not actively paranoid (NAP). Accuracy and error patterns on the Penn Emotion Recognition test (ER40) were examined in 132 patients (64 NAP and 68 AP). Groups were defined based on the presence of paranoid ideation at the time of testing rather than diagnostic subtype. AP and NAP patients did not differ in overall task accuracy; however, an emotion by group interaction indicated that AP patients were significantly worse than NAP patients at correctly labeling neutral faces. A comparison of error patterns on neutral stimuli revealed that the groups differed only in misattributions of anger expressions, with AP patients being significantly more likely to misidentify a neutral expression as angry. The present findings suggest that paranoia is associated with a tendency to over attribute threat to ambiguous stimuli and also lend support to emerging hypotheses of amygdala hyperactivation as a potential neural mechanism for paranoid ideation. Copyright © 2010 Elsevier B.V. All rights reserved.

An investigation of the usability of sound recognition for source separation of packaging wastes in reverse vending machines.

PubMed

Korucu, M Kemal; Kaplan, Özgür; Büyük, Osman; Güllü, M Kemal

2016-10-01

In this study, we investigate the usability of sound recognition for source separation of packaging wastes in reverse vending machines (RVMs). For this purpose, an experimental setup equipped with a sound recording mechanism was prepared. Packaging waste sounds generated by three physical impacts such as free falling, pneumatic hitting and hydraulic crushing were separately recorded using two different microphones. To classify the waste types and sizes based on sound features of the wastes, a support vector machine (SVM) and a hidden Markov model (HMM) based sound classification systems were developed. In the basic experimental setup in which only free falling impact type was considered, SVM and HMM systems provided 100% classification accuracy for both microphones. In the expanded experimental setup which includes all three impact types, material type classification accuracies were 96.5% for dynamic microphone and 97.7% for condenser microphone. When both the material type and the size of the wastes were classified, the accuracy was 88.6% for the microphones. The modeling studies indicated that hydraulic crushing impact type recordings were very noisy for an effective sound recognition application. In the detailed analysis of the recognition errors, it was observed that most of the errors occurred in the hitting impact type. According to the experimental results, it can be said that the proposed novel approach for the separation of packaging wastes could provide a high classification performance for RVMs. Copyright © 2016 Elsevier Ltd. All rights reserved.

Attention and memory bias to facial emotions underlying negative symptoms of schizophrenia.

PubMed

Jang, Seon-Kyeong; Park, Seon-Cheol; Lee, Seung-Hwan; Cho, Yang Seok; Choi, Kee-Hong

2016-01-01

This study assessed bias in selective attention to facial emotions in negative symptoms of schizophrenia and its influence on subsequent memory for facial emotions. Thirty people with schizophrenia who had high and low levels of negative symptoms (n = 15, respectively) and 21 healthy controls completed a visual probe detection task investigating selective attention bias (happy, sad, and angry faces randomly presented for 50, 500, or 1000 ms). A yes/no incidental facial memory task was then completed. Attention bias scores and recognition errors were calculated. Those with high negative symptoms exhibited reduced attention to emotional faces relative to neutral faces; those with low negative symptoms showed the opposite pattern when faces were presented for 500 ms regardless of the valence. Compared to healthy controls, those with high negative symptoms made more errors for happy faces in the memory task. Reduced attention to emotional faces in the probe detection task was significantly associated with less pleasure and motivation and more recognition errors for happy faces in schizophrenia group only. Attention bias away from emotional information relatively early in the attentional process and associated diminished positive memory may relate to pathological mechanisms for negative symptoms.

Recursive least-squares learning algorithms for neural networks

NASA Astrophysics Data System (ADS)

Lewis, Paul S.; Hwang, Jenq N.

1990-11-01

This paper presents the development of a pair of recursive least squares (ItLS) algorithms for online training of multilayer perceptrons which are a class of feedforward artificial neural networks. These algorithms incorporate second order information about the training error surface in order to achieve faster learning rates than are possible using first order gradient descent algorithms such as the generalized delta rule. A least squares formulation is derived from a linearization of the training error function. Individual training pattern errors are linearized about the network parameters that were in effect when the pattern was presented. This permits the recursive solution of the least squares approximation either via conventional RLS recursions or by recursive QR decomposition-based techniques. The computational complexity of the update is 0(N2) where N is the number of network parameters. This is due to the estimation of the N x N inverse Hessian matrix. Less computationally intensive approximations of the ilLS algorithms can be easily derived by using only block diagonal elements of this matrix thereby partitioning the learning into independent sets. A simulation example is presented in which a neural network is trained to approximate a two dimensional Gaussian bump. In this example RLS training required an order of magnitude fewer iterations on average (527) than did training with the generalized delta rule (6 1 BACKGROUND Artificial neural networks (ANNs) offer an interesting and potentially useful paradigm for signal processing and pattern recognition. The majority of ANN applications employ the feed-forward multilayer perceptron (MLP) network architecture in which network parameters are " trained" by a supervised learning algorithm employing the generalized delta rule (GDIt) [1 2]. The GDR algorithm approximates a fixed step steepest descent algorithm using derivatives computed by error backpropagatiori. The GDII algorithm is sometimes referred to as the backpropagation algorithm. However in this paper we will use the term backpropagation to refer only to the process of computing error derivatives. While multilayer perceptrons provide a very powerful nonlinear modeling capability GDR training can be very slow and inefficient. In linear adaptive filtering the analog of the GDR algorithm is the leastmean- squares (LMS) algorithm. Steepest descent-based algorithms such as GDR or LMS are first order because they use only first derivative or gradient information about the training error to be minimized. To speed up the training process second order algorithms may be employed that take advantage of second derivative or Hessian matrix information. Second order information can be incorporated into MLP training in different ways. In many applications especially in the area of pattern recognition the training set is finite. In these cases block learning can be applied using standard nonlinear optimization techniques [3 4 5].

The death of marriage? The effects of new forms of legal recognition on marriage rates in the United States.

PubMed

Dillender, Marcus

2014-04-01

Some conservative groups argue that allowing same-sex couples to marry reduces the value of marriage to opposite-sex couples. This article examines how changes in U.S. legal recognition laws occurring between 1995 and 2010 designed to include same-sex couples have altered marriage rates in the United States. Using a difference-in-differences strategy that compares how marriage rates change after legal recognition in U.S. states that alter legal recognition versus states that do not, I find no evidence that allowing same-sex couples to marry reduces the opposite-sex marriage rate. Although the opposite-sex marriage rate is unaffected by same-sex couples marrying, it decreases when domestic partnerships are available to opposite-sex couples.

Classifying adolescent attention-deficit/hyperactivity disorder (ADHD) based on functional and structural imaging.

PubMed

Iannaccone, Reto; Hauser, Tobias U; Ball, Juliane; Brandeis, Daniel; Walitza, Susanne; Brem, Silvia

2015-10-01

Attention-deficit/hyperactivity disorder (ADHD) is a common disabling psychiatric disorder associated with consistent deficits in error processing, inhibition and regionally decreased grey matter volumes. The diagnosis is based on clinical presentation, interviews and questionnaires, which are to some degree subjective and would benefit from verification through biomarkers. Here, pattern recognition of multiple discriminative functional and structural brain patterns was applied to classify adolescents with ADHD and controls. Functional activation features in a Flanker/NoGo task probing error processing and inhibition along with structural magnetic resonance imaging data served to predict group membership using support vector machines (SVMs). The SVM pattern recognition algorithm correctly classified 77.78% of the subjects with a sensitivity and specificity of 77.78% based on error processing. Predictive regions for controls were mainly detected in core areas for error processing and attention such as the medial and dorsolateral frontal areas reflecting deficient processing in ADHD (Hart et al., in Hum Brain Mapp 35:3083-3094, 2014), and overlapped with decreased activations in patients in conventional group comparisons. Regions more predictive for ADHD patients were identified in the posterior cingulate, temporal and occipital cortex. Interestingly despite pronounced univariate group differences in inhibition-related activation and grey matter volumes the corresponding classifiers failed or only yielded a poor discrimination. The present study corroborates the potential of task-related brain activation for classification shown in previous studies. It remains to be clarified whether error processing, which performed best here, also contributes to the discrimination of useful dimensions and subtypes, different psychiatric disorders, and prediction of treatment success across studies and sites.

Acoustic landmarks contain more information about the phone string than other frames for automatic speech recognition with deep neural network acoustic model

NASA Astrophysics Data System (ADS)

He, Di; Lim, Boon Pang; Yang, Xuesong; Hasegawa-Johnson, Mark; Chen, Deming

2018-06-01

Most mainstream Automatic Speech Recognition (ASR) systems consider all feature frames equally important. However, acoustic landmark theory is based on a contradictory idea, that some frames are more important than others. Acoustic landmark theory exploits quantal non-linearities in the articulatory-acoustic and acoustic-perceptual relations to define landmark times at which the speech spectrum abruptly changes or reaches an extremum; frames overlapping landmarks have been demonstrated to be sufficient for speech perception. In this work, we conduct experiments on the TIMIT corpus, with both GMM and DNN based ASR systems and find that frames containing landmarks are more informative for ASR than others. We find that altering the level of emphasis on landmarks by re-weighting acoustic likelihood tends to reduce the phone error rate (PER). Furthermore, by leveraging the landmark as a heuristic, one of our hybrid DNN frame dropping strategies maintained a PER within 0.44% of optimal when scoring less than half (45.8% to be precise) of the frames. This hybrid strategy out-performs other non-heuristic-based methods and demonstrate the potential of landmarks for reducing computation.

Determination of geographical origin and icariin content of Herba Epimedii using near infrared spectroscopy and chemometrics

NASA Astrophysics Data System (ADS)

Yang, Yue; Wu, Yongjiang; Li, Weili; Liu, Xuesong; Zheng, Jiyu; Zhang, Wentao; Chen, Yong

2018-02-01

Near infrared (NIR) spectroscopy coupled with chemometrics was used to discriminate the geographical origin of Herba Epimedii in this work. Four different classification models, namely discriminant analysis (DA), back propagation neural network (BPNN), K-nearest neighbor (KNN), and support vector machine (SVM), were constructed, and their performances in terms of recognition accuracy were compared. The results indicated that the SVM model was superior over the other models in the geographical origin identification of Herba Epimedii. The recognition rates of the optimum SVM model were up to 100% for the calibration set and 94.44% for the prediction set, respectively. In addition, the feasibility of NIR spectroscopy with the CARS-PLSR calibration model in prediction of icariin content of Herba Epimedii was also investigated. The determination coefficient (RP2) and root-mean-square error (RMSEP) for prediction set were 0.9269 and 0.0480, respectively. It can be concluded that the NIR spectroscopy technique in combination with chemometrics has great potential in determination of geographical origin and icariin content of Herba Epimedii. This study can provide a valuable reference for rapid quality control of food products.

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.

Aspects of quality insurance in digitizing historical climate data in Germany

NASA Astrophysics Data System (ADS)

Mächel, H.; Behrends, J.; Kapala, A.

2010-09-01

This contribution presents some of the problems and offers solutions regarding the digitization of historical meteorological data, and explains the need for verification and quality control. For the assessment of changes in climate extremes, long-term and complete observational records with a high temporal resolution are needed. However, in most countries, including Germany, such climate data are rare. Therefore, in 2005, the German Weather Service launched a project to inventory and digitize historical daily climatic records in cooperation with the Meteorological Institute of the University of Bonn. Experience with Optical Character Recognition (OCR) show that it is only of very limited use, as even printed tables (e.g. yearbooks) are not sufficiently recognized (10-20% error). In hand-written records, the recognition rate is about 50%. By comparing daily and monthly values, it is possible to auto-detect errors, but they can not be automatically corrected, since there is often more than one error per month. These erroneous data must then be controlled manually on an individual basis, which is significantly more error-prone than direct manual input. Therefore, both precipitation and climate station data are digitized manually. The time required to digitize one year of precipitation data (including the recording of daily precipitation amount and type, snow amount and type, and weather events such as thunder storms, fog, etc.) is equivalent to about five hours for one year of data. This involves manually typing, reformatting and quality control of the digitized data, as well as creating a digital photograph. For climate stations with three observations per day, the working time is 30-50 hours for one year of data, depending on the number of parameters and the condition of the documents. Several other problems occur when creating the digital records from historical observational data, some of which are listed below. Older records often used varying units and different conventions. For example, a value of 100 was added to the observed temperatures to avoid negative values. Furthermore, because standardization of the observations was very low when measurements began up to 200 years ago, the data often reflect a greater part of non-climatic influences. Varying daily observation times make it difficult to calculate a representative daily value. Even unconventional completed tables cost labor and requires experienced and trained staff. Data homogenization as well as both manual and automatic quality control may address some of these problems.

Facial Emotion Recognition in Bipolar Disorder and Healthy Aging.

PubMed

Altamura, Mario; Padalino, Flavia A; Stella, Eleonora; Balzotti, Angela; Bellomo, Antonello; Palumbo, Rocco; Di Domenico, Alberto; Mammarella, Nicola; Fairfield, Beth

2016-03-01

Emotional face recognition is impaired in bipolar disorder, but it is not clear whether this is specific for the illness. Here, we investigated how aging and bipolar disorder influence dynamic emotional face recognition. Twenty older adults, 16 bipolar patients, and 20 control subjects performed a dynamic affective facial recognition task and a subsequent rating task. Participants pressed a key as soon as they were able to discriminate whether the neutral face was assuming a happy or angry facial expression and then rated the intensity of each facial expression. Results showed that older adults recognized happy expressions faster, whereas bipolar patients recognized angry expressions faster. Furthermore, both groups rated emotional faces more intensely than did the control subjects. This study is one of the first to compare how aging and clinical conditions influence emotional facial recognition and underlines the need to consider the role of specific and common factors in emotional face recognition.

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images

PubMed Central

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-01-01

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition. PMID:29786665

Automated Field-of-View, Illumination, and Recognition Algorithm Design of a Vision System for Pick-and-Place Considering Colour Information in Illumination and Images.

PubMed

Chen, Yibing; Ogata, Taiki; Ueyama, Tsuyoshi; Takada, Toshiyuki; Ota, Jun

2018-05-22

Machine vision is playing an increasingly important role in industrial applications, and the automated design of image recognition systems has been a subject of intense research. This study has proposed a system for automatically designing the field-of-view (FOV) of a camera, the illumination strength and the parameters in a recognition algorithm. We formulated the design problem as an optimisation problem and used an experiment based on a hierarchical algorithm to solve it. The evaluation experiments using translucent plastics objects showed that the use of the proposed system resulted in an effective solution with a wide FOV, recognition of all objects and 0.32 mm and 0.4° maximal positional and angular errors when all the RGB (red, green and blue) for illumination and R channel image for recognition were used. Though all the RGB illumination and grey scale images also provided recognition of all the objects, only a narrow FOV was selected. Moreover, full recognition was not achieved by using only G illumination and a grey-scale image. The results showed that the proposed method can automatically design the FOV, illumination and parameters in the recognition algorithm and that tuning all the RGB illumination is desirable even when single-channel or grey-scale images are used for recognition.

Effects of perceptual similarity but not semantic association on false recognition in aging

PubMed Central

Gill, Emma

2017-01-01

This study investigated semantic and perceptual influences on false recognition in older and young adults in a variant on the Deese-Roediger-McDermott paradigm. In two experiments, participants encoded intermixed sets of semantically associated words, and sets of unrelated words. Each set was presented in a shared distinctive font. Older adults were no more likely to falsely recognize semantically associated lure words compared to unrelated lures also presented in studied fonts. However, they showed an increase in false recognition of lures which were related to studied items only by a shared font. This increased false recognition was associated with recollective experience. The data show that older adults do not always rely more on prior knowledge in episodic memory tasks. They converge with other findings suggesting that older adults may also be more prone to perceptually-driven errors. PMID:29302398

Contralateral comparison of wavefront-guided LASIK surgery with iris recognition versus without iris recognition using the MEL80 Excimer laser system.

PubMed

Wu, Fang; Yang, Yabo; Dougherty, Paul J

2009-05-01

To compare outcomes in wavefront-guided LASIK performed with iris recognition software versus without iris recognition software in different eyes of the same patient. A randomised, prospective study of 104 myopic eyes of 52 patients undergoing LASIK surgery with the MEL80 excimer laser system was performed. Iris recognition software was used in one eye of each patient (study group) and not used in the other eye (control group). Higher order aberrations (HOAs), contrast sensitivity, uncorrected vision (UCV), visual acuity (VA) and corneal topography were measured and recorded pre-operatively and at one month and three months post-operatively for each eye. The mean post-operative sphere and cylinder between groups was similar, however the post-operative angles of error (AE) by refraction were significantly smaller in the study group compared to the control group both in arithmetic and absolute means (p = 0.03, p = 0.01). The mean logMAR UCV was significantly better in the study group than in the control group at one month (p = 0.01). The mean logMAR VA was significantly better in the study group than in control group at both one and three months (p = 0.01, p = 0.03). In addition, mean trefoil, total third-order aberration, total fourth-order aberration and the total scotopic root-mean-square (RMS) HOAs were significantly less in the study group than those in the control group at the third (p = 0.01, p = 0.05, p = 0.04, p = 0.02). By three months, the contrast sensitivity had recovered in both groups but the study group performed better at 2.6, 4.2 and 6.6 cpd (cycles per degree) than the control group (p = 0.01, p < 0.01, p = 0.01). LASIK performed with iris recognition results in better VA, lower mean higher-order aberrations, lower refractive post-operative angles of error and better contrast sensitivity at three months post-operatively than LASIK performed without iris recognition.

Learning optimal features for visual pattern recognition

NASA Astrophysics Data System (ADS)

Labusch, Kai; Siewert, Udo; Martinetz, Thomas; Barth, Erhardt

2007-02-01

The optimal coding hypothesis proposes that the human visual system has adapted to the statistical properties of the environment by the use of relatively simple optimality criteria. We here (i) discuss how the properties of different models of image coding, i.e. sparseness, decorrelation, and statistical independence are related to each other (ii) propose to evaluate the different models by verifiable performance measures (iii) analyse the classification performance on images of handwritten digits (MNIST data base). We first employ the SPARSENET algorithm (Olshausen, 1998) to derive a local filter basis (on 13 × 13 pixels windows). We then filter the images in the database (28 × 28 pixels images of digits) and reduce the dimensionality of the resulting feature space by selecting the locally maximal filter responses. We then train a support vector machine on a training set to classify the digits and report results obtained on a separate test set. Currently, the best state-of-the-art result on the MNIST data base has an error rate of 0,4%. This result, however, has been obtained by using explicit knowledge that is specific to the data (elastic distortion model for digits). We here obtain an error rate of 0,55% which is second best but does not use explicit data specific knowledge. In particular it outperforms by far all methods that do not use data-specific knowledge.

Automatic anatomy recognition in whole-body PET/CT images

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

Wang, Huiqian; Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity ofmore » anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process, to bring performance to the level achieved on diagnostic CT and MR images in body-region-wise approaches. The intermodality approach fosters the use of already existing fuzzy models, previously created from diagnostic CT images, on PET/CT and other derived images, thus truly separating the modality-independent object assembly anatomy from modality-specific tissue property portrayal in the image. Results: Key ways of combining the above three basic ideas lead them to 15 different strategies for recognizing objects in PET/CT images. Utilizing 50 diagnostic CT image data sets from the thoracic and abdominal body regions and 16 whole-body PET/CT image data sets, the authors compare the recognition performance among these 15 strategies on 18 objects from the thorax, abdomen, and pelvis in object localization error and size estimation error. Particularly on texture membership images, object localization is within three voxels on whole-body low-dose CT images and 2 voxels on body-region-wise low-dose images of known true locations. Surprisingly, even on direct body-region-wise PET images, localization error within 3 voxels seems possible. Conclusions: The previous body-region-wise approach can be extended to whole-body torso with similar object localization performance. Combined use of image texture and intensity property yields the best object localization accuracy. In both body-region-wise and whole-body approaches, recognition performance on low-dose CT images reaches levels previously achieved on diagnostic CT images. The best object recognition strategy varies among objects; the proposed framework however allows employing a strategy that is optimal for each object.« less

Effects of X-ray radiation on complex visual discrimination learning and social recognition memory in rats.

PubMed

Davis, Catherine M; Roma, Peter G; Armour, Elwood; Gooden, Virginia L; Brady, Joseph V; Weed, Michael R; Hienz, Robert D

2014-01-01

The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal). One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min), while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation.

Effects of X-Ray Radiation on Complex Visual Discrimination Learning and Social Recognition Memory in Rats

PubMed Central

Davis, Catherine M.; Roma, Peter G.; Armour, Elwood; Gooden, Virginia L.; Brady, Joseph V.; Weed, Michael R.; Hienz, Robert D.

2014-01-01

The present report describes an animal model for examining the effects of radiation on a range of neurocognitive functions in rodents that are similar to a number of basic human cognitive functions. Fourteen male Long-Evans rats were trained to perform an automated intra-dimensional set shifting task that consisted of their learning a basic discrimination between two stimulus shapes followed by more complex discrimination stages (e.g., a discrimination reversal, a compound discrimination, a compound reversal, a new shape discrimination, and an intra-dimensional stimulus discrimination reversal). One group of rats was exposed to head-only X-ray radiation (2.3 Gy at a dose rate of 1.9 Gy/min), while a second group received a sham-radiation exposure using the same anesthesia protocol. The irradiated group responded less, had elevated numbers of omitted trials, increased errors, and greater response latencies compared to the sham-irradiated control group. Additionally, social odor recognition memory was tested after radiation exposure by assessing the degree to which rats explored wooden beads impregnated with either their own odors or with the odors of novel, unfamiliar rats; however, no significant effects of radiation on social odor recognition memory were observed. These data suggest that rodent tasks assessing higher-level human cognitive domains are useful in examining the effects of radiation on the CNS, and may be applicable in approximating CNS risks from radiation exposure in clinical populations receiving whole brain irradiation. PMID:25099152

A Hybrid Acoustic and Pronunciation Model Adaptation Approach for Non-native Speech Recognition

NASA Astrophysics Data System (ADS)

Oh, Yoo Rhee; Kim, Hong Kook

In this paper, we propose a hybrid model adaptation approach in which pronunciation and acoustic models are adapted by incorporating the pronunciation and acoustic variabilities of non-native speech in order to improve the performance of non-native automatic speech recognition (ASR). Specifically, the proposed hybrid model adaptation can be performed at either the state-tying or triphone-modeling level, depending at which acoustic model adaptation is performed. In both methods, we first analyze the pronunciation variant rules of non-native speakers and then classify each rule as either a pronunciation variant or an acoustic variant. The state-tying level hybrid method then adapts pronunciation models and acoustic models by accommodating the pronunciation variants in the pronunciation dictionary and by clustering the states of triphone acoustic models using the acoustic variants, respectively. On the other hand, the triphone-modeling level hybrid method initially adapts pronunciation models in the same way as in the state-tying level hybrid method; however, for the acoustic model adaptation, the triphone acoustic models are then re-estimated based on the adapted pronunciation models and the states of the re-estimated triphone acoustic models are clustered using the acoustic variants. From the Korean-spoken English speech recognition experiments, it is shown that ASR systems employing the state-tying and triphone-modeling level adaptation methods can relatively reduce the average word error rates (WERs) by 17.1% and 22.1% for non-native speech, respectively, when compared to a baseline ASR system.

Analyzing handwriting biometrics in metadata context

NASA Astrophysics Data System (ADS)

Scheidat, Tobias; Wolf, Franziska; Vielhauer, Claus

2006-02-01

In this article, methods for user recognition by online handwriting are experimentally analyzed using a combination of demographic data of users in relation to their handwriting habits. Online handwriting as a biometric method is characterized by having high variations of characteristics that influences the reliance and security of this method. These variations have not been researched in detail so far. Especially in cross-cultural application it is urgent to reveal the impact of personal background to security aspects in biometrics. Metadata represent the background of writers, by introducing cultural, biological and conditional (changing) aspects like fist language, country of origin, gender, handedness, experiences the influence handwriting and language skills. The goal is the revelation of intercultural impacts on handwriting in order to achieve higher security in biometrical systems. In our experiments, in order to achieve a relatively high coverage, 48 different handwriting tasks have been accomplished by 47 users from three countries (Germany, India and Italy) have been investigated with respect to the relations of metadata and biometric recognition performance. For this purpose, hypotheses have been formulated and have been evaluated using the measurement of well-known recognition error rates from biometrics. The evaluation addressed both: system reliance and security threads by skilled forgeries. For the later purpose, a novel forgery type is introduced, which applies the personal metadata to security aspects and includes new methods of security tests. Finally in our paper, we formulate recommendations for specific user groups and handwriting samples.

Adaptive false memory: Imagining future scenarios increases false memories in the DRM paradigm.

PubMed

Dewhurst, Stephen A; Anderson, Rachel J; Grace, Lydia; van Esch, Lotte

2016-10-01

Previous research has shown that rating words for their relevance to a future scenario enhances memory for those words. The current study investigated the effect of future thinking on false memory using the Deese/Roediger-McDermott (DRM) procedure. In Experiment 1, participants rated words from 6 DRM lists for relevance to a past or future event (with or without planning) or in terms of pleasantness. In a surprise recall test, levels of correct recall did not vary between the rating tasks, but the future rating conditions led to significantly higher levels of false recall than the past and pleasantness conditions did. Experiment 2 found that future rating led to higher levels of false recognition than did past and pleasantness ratings but did not affect correct recognition. The effect in false recognition was, however, eliminated when DRM items were presented in random order. Participants in Experiment 3 were presented with both DRM lists and lists of unrelated words. Future rating increased levels of false recognition for DRM lures but did not affect correct recognition for DRM or unrelated lists. The findings are discussed in terms of the view that false memories can be associated with adaptive memory functions.

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

Forensic comparison and matching of fingerprints: using quantitative image measures for estimating error rates through understanding and predicting difficulty.

PubMed

Kellman, Philip J; Mnookin, Jennifer L; Erlikhman, Gennady; Garrigan, Patrick; Ghose, Tandra; Mettler, Everett; Charlton, David; Dror, Itiel E

2014-01-01

Latent fingerprint examination is a complex task that, despite advances in image processing, still fundamentally depends on the visual judgments of highly trained human examiners. Fingerprints collected from crime scenes typically contain less information than fingerprints collected under controlled conditions. Specifically, they are often noisy and distorted and may contain only a portion of the total fingerprint area. Expertise in fingerprint comparison, like other forms of perceptual expertise, such as face recognition or aircraft identification, depends on perceptual learning processes that lead to the discovery of features and relations that matter in comparing prints. Relatively little is known about the perceptual processes involved in making comparisons, and even less is known about what characteristics of fingerprint pairs make particular comparisons easy or difficult. We measured expert examiner performance and judgments of difficulty and confidence on a new fingerprint database. We developed a number of quantitative measures of image characteristics and used multiple regression techniques to discover objective predictors of error as well as perceived difficulty and confidence. A number of useful predictors emerged, and these included variables related to image quality metrics, such as intensity and contrast information, as well as measures of information quantity, such as the total fingerprint area. Also included were configural features that fingerprint experts have noted, such as the presence and clarity of global features and fingerprint ridges. Within the constraints of the overall low error rates of experts, a regression model incorporating the derived predictors demonstrated reasonable success in predicting objective difficulty for print pairs, as shown both in goodness of fit measures to the original data set and in a cross validation test. The results indicate the plausibility of using objective image metrics to predict expert performance and subjective assessment of difficulty in fingerprint comparisons.

Speech Clarity Index (Ψ): A Distance-Based Speech Quality Indicator and Recognition Rate Prediction for Dysarthric Speakers with Cerebral Palsy

NASA Astrophysics Data System (ADS)

Kayasith, Prakasith; Theeramunkong, Thanaruk

It is a tedious and subjective task to measure severity of a dysarthria by manually evaluating his/her speech using available standard assessment methods based on human perception. This paper presents an automated approach to assess speech quality of a dysarthric speaker with cerebral palsy. With the consideration of two complementary factors, speech consistency and speech distinction, a speech quality indicator called speech clarity index (Ψ) is proposed as a measure of the speaker's ability to produce consistent speech signal for a certain word and distinguished speech signal for different words. As an application, it can be used to assess speech quality and forecast speech recognition rate of speech made by an individual dysarthric speaker before actual exhaustive implementation of an automatic speech recognition system for the speaker. The effectiveness of Ψ as a speech recognition rate predictor is evaluated by rank-order inconsistency, correlation coefficient, and root-mean-square of difference. The evaluations had been done by comparing its predicted recognition rates with ones predicted by the standard methods called the articulatory and intelligibility tests based on the two recognition systems (HMM and ANN). The results show that Ψ is a promising indicator for predicting recognition rate of dysarthric speech. All experiments had been done on speech corpus composed of speech data from eight normal speakers and eight dysarthric speakers.

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.

Heuristic evaluation of eNote: an electronic notes system.

PubMed

Bright, Tiffani J; Bakken, Suzanne; Johnson, Stephen B

2006-01-01

eNote is an electronic health record (EHR) system based on semi-structured narrative documents. A heuristic evaluation was conducted with a sample of five usability experts. eNote performed highly in: 1)consistency with standards and 2)recognition rather than recall. eNote needs improvement in: 1)help and documentation, 2)aesthetic and minimalist design, 3)error prevention, 4)helping users recognize, diagnosis, and recover from errors, and 5)flexibility and efficiency of use. The heuristic evaluation was an efficient method of evaluating our interface.

Embodiment of Learning in Electro-Optical Signal Processors

NASA Astrophysics Data System (ADS)

Hermans, Michiel; Antonik, Piotr; Haelterman, Marc; Massar, Serge

2016-09-01

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

Embodiment of Learning in Electro-Optical Signal Processors.

PubMed

Hermans, Michiel; Antonik, Piotr; Haelterman, Marc; Massar, Serge

2016-09-16

Delay-coupled electro-optical systems have received much attention for their dynamical properties and their potential use in signal processing. In particular, it has recently been demonstrated, using the artificial intelligence algorithm known as reservoir computing, that photonic implementations of such systems solve complex tasks such as speech recognition. Here, we show how the backpropagation algorithm can be physically implemented on the same electro-optical delay-coupled architecture used for computation with only minor changes to the original design. We find that, compared to when the backpropagation algorithm is not used, the error rate of the resulting computing device, evaluated on three benchmark tasks, decreases considerably. This demonstrates that electro-optical analog computers can embody a large part of their own training process, allowing them to be applied to new, more difficult tasks.

Hierarchically Structured Non-Intrusive Sign Language Recognition. Chapter 2

NASA Technical Reports Server (NTRS)

Zieren, Jorg; Zieren, Jorg; Kraiss, Karl-Friedrich

2007-01-01

This work presents a hierarchically structured approach at the nonintrusive recognition of sign language from a monocular frontal view. Robustness is achieved through sophisticated localization and tracking methods, including a combined EM/CAMSHIFT overlap resolution procedure and the parallel pursuit of multiple hypotheses about hands position and movement. This allows handling of ambiguities and automatically corrects tracking errors. A biomechanical skeleton model and dynamic motion prediction using Kalman filters represents high level knowledge. Classification is performed by Hidden Markov Models. 152 signs from German sign language were recognized with an accuracy of 97.6%.

Semantic Network Adaptation Based on QoS Pattern Recognition for Multimedia Streams

NASA Astrophysics Data System (ADS)

Exposito, Ernesto; Gineste, Mathieu; Lamolle, Myriam; Gomez, Jorge

This article proposes an ontology based pattern recognition methodology to compute and represent common QoS properties of the Application Data Units (ADU) of multimedia streams. The use of this ontology by mechanisms located at different layers of the communication architecture will allow implementing fine per-packet self-optimization of communication services regarding the actual application requirements. A case study showing how this methodology is used by error control mechanisms in the context of wireless networks is presented in order to demonstrate the feasibility and advantages of this approach.

Analyzing communication errors in an air medical transport service.

PubMed

Dalto, Joseph D; Weir, Charlene; Thomas, Frank

2013-01-01

Poor communication can result in adverse events. Presently, no standards exist for classifying and analyzing air medical communication errors. This study sought to determine the frequency and types of communication errors reported within an air medical quality and safety assurance reporting system. Of 825 quality assurance reports submitted in 2009, 278 were randomly selected and analyzed for communication errors. Each communication error was classified and mapped to Clark's communication level hierarchy (ie, levels 1-4). Descriptive statistics were performed, and comparisons were evaluated using chi-square analysis. Sixty-four communication errors were identified in 58 reports (21% of 278). Of the 64 identified communication errors, only 18 (28%) were classified by the staff to be communication errors. Communication errors occurred most often at level 1 (n = 42/64, 66%) followed by level 4 (21/64, 33%). Level 2 and 3 communication failures were rare (, 1%). Communication errors were found in a fifth of quality and safety assurance reports. The reporting staff identified less than a third of these errors. Nearly all communication errors (99%) occurred at either the lowest level of communication (level 1, 66%) or the highest level (level 4, 33%). An air medical communication ontology is necessary to improve the recognition and analysis of communication errors. Copyright © 2013 Air Medical Journal Associates. Published by Elsevier Inc. All rights reserved.

Longitudinal changes in speech recognition in older persons.

PubMed

Dubno, Judy R; Lee, Fu-Shing; Matthews, Lois J; Ahlstrom, Jayne B; Horwitz, Amy R; Mills, John H

2008-01-01

Recognition of isolated monosyllabic words in quiet and recognition of key words in low- and high-context sentences in babble were measured in a large sample of older persons enrolled in a longitudinal study of age-related hearing loss. Repeated measures were obtained yearly or every 2 to 3 years. To control for concurrent changes in pure-tone thresholds and speech levels, speech-recognition scores were adjusted using an importance-weighted speech-audibility metric (AI). Linear-regression slope estimated the rate of change in adjusted speech-recognition scores. Recognition of words in quiet declined significantly faster with age than predicted by declines in speech audibility. As subjects aged, observed scores deviated increasingly from AI-predicted scores, but this effect did not accelerate with age. Rate of decline in word recognition was significantly faster for females than males and for females with high serum progesterone levels, whereas noise history had no effect. Rate of decline did not accelerate with age but increased with degree of hearing loss, suggesting that with more severe injury to the auditory system, impairments to auditory function other than reduced audibility resulted in faster declines in word recognition as subjects aged. Recognition of key words in low- and high-context sentences in babble did not decline significantly with age.

Is talking to an automated teller machine natural and fun?

PubMed

Chan, F Y; Khalid, H M

Usability and affective issues of using automatic speech recognition technology to interact with an automated teller machine (ATM) are investigated in two experiments. The first uncovered dialogue patterns of ATM users for the purpose of designing the user interface for a simulated speech ATM system. Applying the Wizard-of-Oz methodology, multiple mapping and word spotting techniques, the speech driven ATM accommodates bilingual users of Bahasa Melayu and English. The second experiment evaluates the usability of a hybrid speech ATM, comparing it with a simulated manual ATM. The aim is to investigate how natural and fun can talking to a speech ATM be for these first-time users. Subjects performed the withdrawal and balance enquiry tasks. The ANOVA was performed on the usability and affective data. The results showed significant differences between systems in the ability to complete the tasks as well as in transaction errors. Performance was measured on the time taken by subjects to complete the task and the number of speech recognition errors that occurred. On the basis of user emotions, it can be said that the hybrid speech system enabled pleasurable interaction. Despite the limitations of speech recognition technology, users are set to talk to the ATM when it becomes available for public use.

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.

A multimodal approach to emotion recognition ability in autism spectrum disorders.

PubMed

Jones, Catherine R G; Pickles, Andrew; Falcaro, Milena; Marsden, Anita J S; Happé, Francesca; Scott, Sophie K; Sauter, Disa; Tregay, Jenifer; Phillips, Rebecca J; Baird, Gillian; Simonoff, Emily; Charman, Tony

2011-03-01

Autism spectrum disorders (ASD) are characterised by social and communication difficulties in day-to-day life, including problems in recognising emotions. However, experimental investigations of emotion recognition ability in ASD have been equivocal, hampered by small sample sizes, narrow IQ range and over-focus on the visual modality. We tested 99 adolescents (mean age 15;6 years, mean IQ 85) with an ASD and 57 adolescents without an ASD (mean age 15;6 years, mean IQ 88) on a facial emotion recognition task and two vocal emotion recognition tasks (one verbal; one non-verbal). Recognition of happiness, sadness, fear, anger, surprise and disgust were tested. Using structural equation modelling, we conceptualised emotion recognition ability as a multimodal construct, measured by the three tasks. We examined how the mean levels of recognition of the six emotions differed by group (ASD vs. non-ASD) and IQ (≥ 80 vs. < 80). We found no evidence of a fundamental emotion recognition deficit in the ASD group and analysis of error patterns suggested that the ASD group were vulnerable to the same pattern of confusions between emotions as the non-ASD group. However, recognition ability was significantly impaired in the ASD group for surprise. IQ had a strong and significant effect on performance for the recognition of all six emotions, with higher IQ adolescents outperforming lower IQ adolescents. The findings do not suggest a fundamental difficulty with the recognition of basic emotions in adolescents with ASD. © 2010 The Authors. Journal of Child Psychology and Psychiatry © 2010 Association for Child and Adolescent Mental Health.

Some effects of stress on users of a voice recognition system: A preliminary inquiry

NASA Astrophysics Data System (ADS)

French, B. A.

1983-03-01

Recent work with Automatic Speech Recognition has focused on applications and productivity considerations in the man-machine interface. This thesis is an attempt to see if placing users of such equipment under time-induced stress has an effect on their percent correct recognition rates. Subjects were given a message-handling task of fixed length and allowed progressively shorter times to attempt to complete it. Questionnaire responses indicate stress levels increased with decreased time-allowance; recognition rates decreased as time was reduced.

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.

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.

[Automated Assessment for Bone Age of Left Wrist Joint in Uyghur Teenagers by Deep Learning].

PubMed

Hu, T H; Huo, Z; Liu, T A; Wang, F; Wan, L; Wang, M W; Chen, T; Wang, Y H

2018-02-01

To realize the automated bone age assessment by applying deep learning to digital radiography （DR） image recognition of left wrist joint in Uyghur teenagers, and explore its practical application value in forensic medicine bone age assessment. The X-ray films of left wrist joint after pretreatment, which were taken from 245 male and 227 female Uyghur nationality teenagers in Uygur Autonomous Region aged from 13.0 to 19.0 years old, were chosen as subjects. And AlexNet was as a regression model of image recognition. From the total samples above, 60％ of male and female DR images of left wrist joint were selected as net train set, and 10％ of samples were selected as validation set. As test set, the rest 30％ were used to obtain the image recognition accuracy with an error range in ±1.0 and ±0.7 age respectively, compared to the real age. The modelling results of deep learning algorithm showed that when the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the net train set was 81.4% and 75.6% in male, and 80.5% and 74.8% in female, respectively. When the error range was in ±1.0 and ±0.7 age respectively, the accuracy of the test set was 79.5% and 71.2% in male, and 79.4% and 66.2% in female, respectively. The combination of bone age research on teenagers' left wrist joint and deep learning, which has high accuracy and good feasibility, can be the research basis of bone age automatic assessment system for the rest joints of body. Copyright© by the Editorial Department of Journal of Forensic Medicine.

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

Patients with chronic insomnia have selective impairments in memory that are modulated by cortisol.

PubMed

Chen, Gui-Hai; Xia, Lan; Wang, Fang; Li, Xue-Wei; Jiao, Chuan-An

2016-10-01

Memory impairment is a frequent complaint in insomniacs; however, it is not consistently demonstrated. It is unknown whether memory impairment in insomniacs involves neuroendocrine dysfunction. The participants in this study were selected from the clinical setting and included 21 patients with chronic insomnia disorder (CID), 25 patients with insomnia and comorbid depressive disorder (CDD), and 20 control participants without insomnia. We evaluated spatial working and reference memory, object working and reference memory, and object recognition memory using the Nine Box Maze Test. We also evaluated serum neuroendocrine hormone levels. Compared to the controls, the CID patients made significantly more errors in spatial working and object recognition memory (p < .05), whereas the CDD patients performed poorly in all the assessed memory types (p < .05). In addition, the CID patients had higher levels (mean difference [95% CI]) of corticotrophin-releasing hormone, cortisol (31.98 [23.97, 39.98] μg/l), total triiodothyronine (667.58 [505.71, 829.45] μg/l), and total thyroxine (41.49 [33.23, 49.74] μg/l) (p < .05), and lower levels of thyrotropin-releasing hormone (-35.93 [-38.83, -33.02] ng/l), gonadotropin-releasing hormone (-4.50 [-5.02, -3.98] ng/l) (p < .05), and adrenocorticotropic hormone compared to the CDD patients. After controlling for confounding variables, the partial correlation analysis revealed that the levels of cortisol positively correlated with the errors in object working memory (r = .534, p = .033) and negatively correlated with the errors in object recognition memory (r = -.659, p = .006) in the CID patients. The results suggest that the CID patients had selective memory impairment, which may be mediated by increased cortisol levels. © 2016 Society for Psychophysiological Research.

A SEMantic and EPisodic Memory Test (SEMEP) Developed within the Embodied Cognition Framework: Application to Normal Aging, Alzheimer's Disease and Semantic Dementia.

PubMed

Vallet, Guillaume T; Hudon, Carol; Bier, Nathalie; Macoir, Joël; Versace, Rémy; Simard, Martine

2017-01-01

Embodiment has highlighted the importance of sensory-motor components in cognition. Perception and memory are thus very tightly bound together, and episodic and semantic memories should rely on the same grounded memory traces. Reduced perception should then directly reduce the ability to encode and retrieve an episodic memory, as in normal aging. Multimodal integration deficits, as in Alzheimer's disease, should lead to more severe episodic memory impairment. The present study introduces a new memory test developed to take into account these assumptions. The SEMEP (SEMantic-Episodic) memory test proposes to assess conjointly semantic and episodic knowledge across multiple tasks: semantic matching, naming, free recall, and recognition. The performance of young adults is compared to healthy elderly adults (HE), patients with Alzheimer's disease (AD), and patients with semantic dementia (SD). The results show specific patterns of performance between the groups. HE commit memory errors only for presented but not to be remembered items. AD patients present the worst episodic memory performance associated with intrusion errors (recall or recognition of items never presented). They were the only group to not benefit from a visual isolation (addition of a yellow background), a method known to increase the distinctiveness of the memory traces. Finally, SD patients suffer from the most severe semantic impairment. To conclude, confusion errors are common across all the elderly groups, whereas AD was the only group to exhibit regular intrusion errors and SD patients to show severe semantic impairment.

Impairment of perception and recognition of faces, mimic expression and gestures in schizophrenic patients.

PubMed

Berndl, K; von Cranach, M; Grüsser, O J

1986-01-01

The perception and recognition of faces, mimic expression and gestures were investigated in normal subjects and schizophrenic patients by means of a movie test described in a previous report (Berndl et al. 1986). The error scores were compared with results from a semi-quantitative evaluation of psychopathological symptoms and with some data from the case histories. The overall error scores found in the three groups of schizophrenic patients (paranoic, hebephrenic, schizo-affective) were significantly increased (7-fold) over those of normals. No significant difference in the distribution of the error scores in the three different patient groups was found. In 10 different sub-tests following the movie the deficiencies found in the schizophrenic patients were analysed in detail. The error score for the averbal test was on average higher in paranoic patients than in the two other groups of patients, while the opposite was true for the error scores found in the verbal tests. Age and sex had some impact on the test results. In normals, female subjects were somewhat better than male. In schizophrenic patients the reverse was true. Thus female patients were more affected by the disease than male patients with respect to the task performance. The correlation between duration of the disease and error score was small; less than 10% of the error scores could be attributed to factors related to the duration of illness. Evaluation of psychopathological symptoms indicated that the stronger the schizophrenic defect, the higher the error score, but again this relationship was responsible for not more than 10% of the errors. The estimated degree of acute psychosis and overall sum of psychopathological abnormalities as scored in a semi-quantitative exploration did not correlate with the error score, but with each other. Similarly, treatment with psychopharmaceuticals, previous misuse of drugs or of alcohol had practically no effect on the outcome of the test data. The analysis of performance and test data of schizophrenic patients indicated that our findings are most likely not due to a "non-specific" impairment of cognitive function in schizophrenia, but point to a fairly selective defect in elementary cognitive visual functions necessary for averbal social communication. Some possible explanations of the data are discussed in relation to neuropsychological and neurophysiological findings on "face-specific" cortical areas located in the primate temporal lobe.

Simultaneous Control of Error Rates in fMRI Data Analysis

PubMed Central

Kang, Hakmook; Blume, Jeffrey; Ombao, Hernando; Badre, David

2015-01-01

The key idea of statistical hypothesis testing is to fix, and thereby control, the Type I error (false positive) rate across samples of any size. Multiple comparisons inflate the global (family-wise) Type I error rate and the traditional solution to maintaining control of the error rate is to increase the local (comparison-wise) Type II error (false negative) rates. However, in the analysis of human brain imaging data, the number of comparisons is so large that this solution breaks down: the local Type II error rate ends up being so large that scientifically meaningful analysis is precluded. Here we propose a novel solution to this problem: allow the Type I error rate to converge to zero along with the Type II error rate. It works because when the Type I error rate per comparison is very small, the accumulation (or global) Type I error rate is also small. This solution is achieved by employing the Likelihood paradigm, which uses likelihood ratios to measure the strength of evidence on a voxel-by-voxel basis. In this paper, we provide theoretical and empirical justification for a likelihood approach to the analysis of human brain imaging data. In addition, we present extensive simulations that show the likelihood approach is viable, leading to ‘cleaner’ looking brain maps and operationally superiority (lower average error rate). Finally, we include a case study on cognitive control related activation in the prefrontal cortex of the human brain. PMID:26272730

Post processing of optically recognized text via second order hidden Markov model

NASA Astrophysics Data System (ADS)

Poudel, Srijana

In this thesis, we describe a postprocessing system on Optical Character Recognition(OCR) generated text. Second Order Hidden Markov Model (HMM) approach is used to detect and correct the OCR related errors. The reason for choosing the 2nd order HMM is to keep track of the bigrams so that the model can represent the system more accurately. Based on experiments with training data of 159,733 characters and testing of 5,688 characters, the model was able to correct 43.38 % of the errors with a precision of 75.34 %. However, the precision value indicates that the model introduced some new errors, decreasing the correction percentage to 26.4%.

Treatable newborn and infant seizures due to inborn errors of metabolism.

PubMed

Campistol, Jaume; Plecko, Barbara

2015-09-01

About 25% of seizures in the neonatal period have causes other than asphyxia, ischaemia or intracranial bleeding. Among these are primary genetic epileptic encephalopathies with sometimes poor prognosis and high mortality. In addition, some forms of neonatal infant seizures are due to inborn errors of metabolism that do not respond to common AEDs, but are amenable to specific treatment. In this situation, early recognition can allow seizure control and will prevent neurological deterioration and long-term sequelae. We review the group of inborn errors of metabolism that lead to newborn/infant seizures and epilepsy, of which the treatment with cofactors is very different to that used in typical epilepsy management.

Wavelet Types Comparison for Extracting Iris Feature Based on Energy Compaction

NASA Astrophysics Data System (ADS)

Rizal Isnanto, R.

2015-06-01

Human iris has a very unique pattern which is possible to be used as a biometric recognition. To identify texture in an image, texture analysis method can be used. One of method is wavelet that extract the image feature based on energy. Wavelet transforms used are Haar, Daubechies, Coiflets, Symlets, and Biorthogonal. In the research, iris recognition based on five mentioned wavelets was done and then comparison analysis was conducted for which some conclusions taken. Some steps have to be done in the research. First, the iris image is segmented from eye image then enhanced with histogram equalization. The features obtained is energy value. The next step is recognition using normalized Euclidean distance. Comparison analysis is done based on recognition rate percentage with two samples stored in database for reference images. After finding the recognition rate, some tests are conducted using Energy Compaction for all five types of wavelets above. As the result, the highest recognition rate is achieved using Haar, whereas for coefficients cutting for C(i) < 0.1, Haar wavelet has a highest percentage, therefore the retention rate or significan coefficient retained for Haaris lower than other wavelet types (db5, coif3, sym4, and bior2.4)