Efficient extracellular production of type I secretion pathway-dependent Pseudomonas fluorescens lipase in recombinant Escherichia coli by heterologous ABC protein exporters.

PubMed

Eom, Gyeong Tae; Lee, Seung Hwan; Oh, Young Hoon; Choi, Ji Eun; Park, Si Jae; Song, Jae Kwang

2014-10-01

Heterologous ABC protein exporters, the apparatus of type I secretion pathway in Gram-negative bacteria, were used for extracellular production of Pseudomonas fluorescens lipase (TliA) in recombinant Escherichia coli. The effect of the expression of different ABC protein exporter gene clusters (P. fluorescens tliDEF, Pseudomonas aeruginosa aprDEF, Erwinia chrysanthemi prtDEF, and Serratia marcescens lipBCD genes) was examined on the secretion of TliA at growth temperatures of 20, 25, 30 and 35 °C. TliA secretion in recombinant E. coli XL10-Gold varied depending upon type of ABC protein exporter and culture temperature. E. coli expressing S. marcescens lipBCD genes showed the highest secretion level of TliA (122.8 U ml(-1)) when cultured at 25 °C. Thus, optimized culture conditions for efficient extracellular production of lipase in recombinant E. coli can be designed by changing the type of ABC protein exporter and the growth temperature.

The Combinatorics of Local Constraints in Model-Based Recognition and Localization,

DTIC Science & Technology

1984-04-01

InCn ted alo0ng! th le 1111;i nor nria. T[hus, a secornd sensory point can 1,e cli aractcr: it’o ti this spare ’)N ;a uni~t vector in offset by some...number of independent constraints, defining the swept volume IVu,,ne! then the expec ted bound on the number of interpretations for k ± 1 sensory points...t It ll r I to our oart icu r c:1,:CCC;i ’erivv -xplicrt xpe(’ ted Counts Cit the or2 f C .Tlterpret at ons conitiro \\\\2tli ~ ’CCF pOilnts, 1C C I)YI

2000-centiGray total lymphoid irradiation for refractory rheumatoid arthritis

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

Trentham, D.E.; Belli, J.A.; Bloomer, W.D.

Because toxicity with the use of 3000 centiGray (cGy) of total lymphoid irradiation (TLI) was observed in an earlier study, 2000-cGy treatments were delivered in a 2-portal format to 7 patients and in a modified 3-portal fashion to 6 patients, as part of a randomized, investigator-blinded trial of TLI treatment for refractory rheumatoid arthritis. Analysis of combined data from the 13 patients revealed statistically significant improvement in 5 clinical indicators of disease activity at the end of TLI and 6 and 12 months later, accompanied by T4-specific immunosuppression. Management considerations resulted in the introduction of prednisone therapy in 5 patients,more » methotrexate in 4, and azathioprine in 1 during the interval of 8-12 months post-TLI. Herpes zoster occurred in 5 patients prior to the initiation of this additional therapy. These data indicate that, in patients with rheumatoid arthritis, a TLI dose of 2000 cGy is sufficient to produce measurable benefit that lasts for 6 months, and that the improvement can be maintained at 12 months by the use of prednisone and methotrexate.« less

Bridging automatic speech recognition and psycholinguistics: Extending Shortlist to an end-to-end model of human speech recognition (L)

NASA Astrophysics Data System (ADS)

Scharenborg, Odette; ten Bosch, Louis; Boves, Lou; Norris, Dennis

2003-12-01

This letter evaluates potential benefits of combining human speech recognition (HSR) and automatic speech recognition by building a joint model of an automatic phone recognizer (APR) and a computational model of HSR, viz., Shortlist [Norris, Cognition 52, 189-234 (1994)]. Experiments based on ``real-life'' speech highlight critical limitations posed by some of the simplifying assumptions made in models of human speech recognition. These limitations could be overcome by avoiding hard phone decisions at the output side of the APR, and by using a match between the input and the internal lexicon that flexibly copes with deviations from canonical phonemic representations.

Application of image recognition-based automatic hyphae detection in fungal keratitis.

PubMed

Wu, Xuelian; Tao, Yuan; Qiu, Qingchen; Wu, Xinyi

2018-03-01

The purpose of this study is to evaluate the accuracy of two methods in diagnosis of fungal keratitis, whereby one method is automatic hyphae detection based on images recognition and the other method is corneal smear. We evaluate the sensitivity and specificity of the method in diagnosis of fungal keratitis, which is automatic hyphae detection based on image recognition. We analyze the consistency of clinical symptoms and the density of hyphae, and perform quantification using the method of automatic hyphae detection based on image recognition. In our study, 56 cases with fungal keratitis (just single eye) and 23 cases with bacterial keratitis were included. All cases underwent the routine inspection of slit lamp biomicroscopy, corneal smear examination, microorganism culture and the assessment of in vivo confocal microscopy images before starting medical treatment. Then, we recognize the hyphae images of in vivo confocal microscopy by using automatic hyphae detection based on image recognition to evaluate its sensitivity and specificity and compare with the method of corneal smear. The next step is to use the index of density to assess the severity of infection, and then find the correlation with the patients' clinical symptoms and evaluate consistency between them. The accuracy of this technology was superior to corneal smear examination (p < 0.05). The sensitivity of the technology of automatic hyphae detection of image recognition was 89.29%, and the specificity was 95.65%. The area under the ROC curve was 0.946. The correlation coefficient between the grading of the severity in the fungal keratitis by the automatic hyphae detection based on image recognition and the clinical grading is 0.87. The technology of automatic hyphae detection based on image recognition was with high sensitivity and specificity, able to identify fungal keratitis, which is better than the method of corneal smear examination. This technology has the advantages when compared with the conventional artificial identification of confocal microscope corneal images, of being accurate, stable and does not rely on human expertise. It was the most useful to the medical experts who are not familiar with fungal keratitis. The technology of automatic hyphae detection based on image recognition can quantify the hyphae density and grade this property. Being noninvasive, it can provide an evaluation criterion to fungal keratitis in a timely, accurate, objective and quantitative manner.

A Limited-Vocabulary, Multi-Speaker Automatic Isolated Word Recognition System.

ERIC Educational Resources Information Center

Paul, James E., Jr.

Techniques for automatic recognition of isolated words are investigated, and a computer simulation of a word recognition system is effected. Considered in detail are data acquisition and digitizing, word detection, amplitude and time normalization, short-time spectral estimation including spectral windowing, spectral envelope approximation,…

Tl(I) and Tl(III) activate both mitochondrial and extrinsic pathways of apoptosis in rat pheochromocytoma (PC12) cells

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

Hanzel, Cecilia Eliana; Verstraeten, Sandra Viviana

2009-04-01

Thallium (Tl) is a highly toxic metal though yet its mechanisms are poorly understood. Previously, we demonstrated that rat pheochromocytoma (PC12) cells exposure to thallous (Tl(I)) or thallic (Tl(III)) cations leads to mitochondrial damage and reduced cell viability. In the present work we comparatively characterized the possible pathways involved in Tl(I)- and Tl(III)- (10-100 {mu}M) mediated decrease in PC12 cells viability. We observed that these cations do not cause cell necrosis but significantly increased the number of cells with apoptotic features. Both cations lead to Bax oligomerization and caused apoptosis inducing factor (AIF), endonuclease G (Endo G), and cytochrome cmore » release from mitochondria, but they did not activate caspase dependent DNAse (CAD). Tl(I)- and Tl(III)-dependent caspases 9 and 3 activation followed similar kinetics, with maximal effects at 18 h of incubation. In addition, Tl(I) promoted phosphatidylserine (PS) exposure. Tl(III) induced 2- and 18-fold increase in Fas content and caspase 8 activity, respectively. Together, experimental results show that Tl(I) and Tl(III) induce PC12 cells apoptosis, although differential pathways are involved. While Tl(I)-mediated cell apoptosis was mainly associated with mitochondrial damage, Tl(III) showed a mixed effect triggering both the intrinsic and extrinsic pathways of apoptosis. These findings contribute to a better understanding of the mechanisms underlying Tl-induced loss of cell viability in PC12 cells.« less

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

Bass, H.; Mosmann, T.; Strober, S.

Purified CD4+ BALB/c spleen T cells obtained 4-6 wk after total lymphoid irradiation (TLI) helped normal syngeneic B cells to produce a vigorous antibody response to TNP keyhole limpet hemocyanin in adoptive cell transfer experiments. However, the same cells failed to transfer delayed-type hypersensitivity to the adoptive hosts as measured by a foot pad swelling assay. In addition, purified CD4+ cells from TLI-treated mice were unable to induce graft vs. host disease in lethally irradiated allogeneic C57BL/Ka recipient mice. In response to mitogen stimulation, unfractionated spleen cells obtained from TLI mice secreted normal levels of IL-4 and IL-5, but markedlymore » reduced levels of IL-2 and INF-gamma. A total of 229 CD4+ clones from spleen cells of both normal and TLI-treated mice were established, and the cytokine secretion pattern from each clone was analyzed. The results demonstrate that the ratio of Th1- and Th2-like clones in the spleens of normal BALB/c mice is 1:0.6, whereas the ratio in TLI mice is approximately 1:7. These results suggest that Th2-like cells recover rapidly (at approximately 4-6 wk) after TLI treatment and account for the early return of antibody helper activity and secretion of IL-4 and IL-5, but Th1-like cells recover more slowly (in approximately 3 mo) after irradiation, and this accounts for the deficit in cell-mediated immunity and the reduced amount of IL-2 and IFN-gamma secretion.« less

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

ERIC Educational Resources Information Center

Makhoul, John I.

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

Application of automatic threshold in dynamic target recognition with low contrast

NASA Astrophysics Data System (ADS)

Miao, Hua; Guo, Xiaoming; Chen, Yu

2014-11-01

Hybrid photoelectric joint transform correlator can realize automatic real-time recognition with high precision through the combination of optical devices and electronic devices. When recognizing targets with low contrast using photoelectric joint transform correlator, because of the difference of attitude, brightness and grayscale between target and template, only four to five frames of dynamic targets can be recognized without any processing. CCD camera is used to capture the dynamic target images and the capturing speed of CCD is 25 frames per second. Automatic threshold has many advantages like fast processing speed, effectively shielding noise interference, enhancing diffraction energy of useful information and better reserving outline of target and template, so this method plays a very important role in target recognition with optical correlation method. However, the automatic obtained threshold by program can not achieve the best recognition results for dynamic targets. The reason is that outline information is broken to some extent. Optimal threshold is obtained by manual intervention in most cases. Aiming at the characteristics of dynamic targets, the processing program of improved automatic threshold is finished by multiplying OTSU threshold of target and template by scale coefficient of the processed image, and combining with mathematical morphology. The optimal threshold can be achieved automatically by improved automatic threshold processing for dynamic low contrast target images. The recognition rate of dynamic targets is improved through decreased background noise effect and increased correlation information. A series of dynamic tank images with the speed about 70 km/h are adapted as target images. The 1st frame of this series of tanks can correlate only with the 3rd frame without any processing. Through OTSU threshold, the 80th frame can be recognized. By automatic threshold processing of the joint images, this number can be increased to 89 frames. Experimental results show that the improved automatic threshold processing has special application value for the recognition of dynamic target with low contrast.

Microcolumn-based speciation analysis of thallium in soil and green cabbage.

PubMed

Jia, Yanlong; Xiao, Tangfu; Sun, Jialong; Yang, Fei; Baveye, Philippe C

2018-07-15

Thallium (Tl) is a toxic trace metal, whose geochemical behavior and biological effects are closely controlled by its chemical speciation in the environment. However, little tends to be known about this speciation of Tl in soil and plant systems that directly affect the safety of food supplies. In this context, the objective of the present study was to elaborate an efficient method to separate and detect Tl(I) and Tl(III) species for soil and plant samples. This method involves the selective adsorption of Tl(I) on microcolumns filled with immobilized oxine, in the presence of DTPA (diethylenetriaminepentaacetic acid), followed by DTPA-enhanced ultrasonic and heating-induced extraction, coupled with ICP-MS detection. The method was characterized by a LOD of 0.037 μg/L for Tl(I) and 0.18 μg/L for Tl(III) in 10  mL samples. With this method, a second objective of the research was to assess the speciation of Tl in pot and field soils and in green cabbage crops. Experimental results suggest that DTPA extracted Tl was mainly present as Tl(I) in soils (>95%). Tl in hyperaccumulator plant green cabbage was also mainly present as Tl(I) (>90%). With respect to Tl uptake in plants, this study provides direct evidence that green cabbage mainly takes up Tl(I) from soil, and transports it into the aboveground organs. In soils, Tl(III) is reduced to Tl(I) even at the surface where the chemical environment promotes oxidation. This observation is conducive to understanding the mechanisms of Tl isotope fractionation in the soil-plant system. Based on geochemical fraction studies, the reducible fraction was the main source of Tl getting accumulated by plants. These results indicate that the improved analytical method presented in this study offers an economical, simple, fast, and sensitive approach for the separation of Tl species present in soils at trace levels. Copyright © 2018 Elsevier B.V. All rights reserved.

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…

Cardiac allograft prolongation in mice treated with combined posttransplantation total-lymphoid irradiation and anti-L3T4 antibody therapy

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

Trager, D.K.; Banks, B.A.; Rosenbaum, G.E.

1989-04-01

Neonatal cardiac allograft survival was examined in mice treated with anti-L3T4 antibody, posttransplantation total lymphoid irradiation (TLI) or a combination of both therapies. Independently, both posttransplantation TLI and short-course antibody treatment allowed minimal prolongation. However, synergistic prolongation in graft survival was observed with the combination (synergistic) therapy. Fluorescence-activated cell sorter analysis of peripheral blood lymphocytes from animals treated with combined anti-L3T4 and posttransplantation TLI additionally revealed ''synergy'' with respect to the degree of peripheral lymphocyte depletion.

Speech Processing and Recognition (SPaRe)

DTIC Science & Technology

2011-01-01

results in the areas of automatic speech recognition (ASR), speech processing, machine translation (MT), natural language processing ( NLP ), and...Processing ( NLP ), Information Retrieval (IR) 16. SECURITY CLASSIFICATION OF: UNCLASSIFED 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME...Figure 9, the IOC was only expected to provide document submission and search; automatic speech recognition (ASR) for English, Spanish, Arabic , and

Four-Channel Biosignal Analysis and Feature Extraction for Automatic Emotion Recognition

NASA Astrophysics Data System (ADS)

Kim, Jonghwa; André, Elisabeth

This paper investigates the potential of physiological signals as a reliable channel for automatic recognition of user's emotial state. For the emotion recognition, little attention has been paid so far to physiological signals compared to audio-visual emotion channels such as facial expression or speech. All essential stages of automatic recognition system using biosignals are discussed, from recording physiological dataset up to feature-based multiclass classification. Four-channel biosensors are used to measure electromyogram, electrocardiogram, skin conductivity and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to search the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by emotion recognition results.

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Ionizing radiation and autoimmunity: Induction of autoimmune disease in mice by high dose fractionated total lymphoid irradiation and its prevention by inoculating normal T cells

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

Sakaguchi, N.; Sakaguchi, S.; Miyai, K.

1992-11-01

Ionizing radiation can functionally alter the immune system and break self-tolerance. High dose (42.5 Gy), fractionated (2.5 Gy 17 times) total lymphoid irradiation (TLI) on mice caused various organ-specific autoimmune diseases, such as gastritis, thyroiditis, and orchitis, depending on the radiation dosages, the extent of lymphoid irradiation, and the genetic background of the mouse strains. Radiation-induced tissue damage is not the primary cause of the autoimmune disease because irradiation of the target organs alone failed to elicit the autoimmunity and shielding of the organs from irradiation was unable to prevent it. In contrast, irradiation of both the thymus and themore » peripheral lymphoid organs/tissues was required for efficient induction of autoimmune disease by TLI. TLI eliminated the majority of mature thymocytes and the peripheral T cells for 1 mo, and inoculation of spleen cell, thymocyte, or bone marrow cell suspensions (prepared from syngeneic nonirradiated mice) within 2 wk after TLI effectively prevented the autoimmune development. Depletion of T cells from the inocula abrogated the preventive activity. CD4[sup +] T cells mediated the autoimmune prevention but CD8[sup +] T cells did not. CD4[sup +] T cells also appeared to mediate the TLI-induced autoimmune disease because CD4[sup +] T cells from disease-bearing TLI mice adoptively transferred the autoimmune disease to syngeneic naive mice. Taken together, these results indicate that high dose, fractionated ionizing radiation on the lymphoid organs/tissues can cause autoimmune disease by affecting the T cell immune system, rather than the target self-Ags, presumably by altering T cell-dependent control of self-reactive T cells. 62 refs., 9 figs., 2 tabs.« less

Automatic recognition of postural allocations.

PubMed

Sazonov, Edward; Krishnamurthy, Vidya; Makeyev, Oleksandr; Browning, Ray; Schutz, Yves; Hill, James

2007-01-01

A significant part of daily energy expenditure may be attributed to non-exercise activity thermogenesis and exercise activity thermogenesis. Automatic recognition of postural allocations such as standing or sitting can be used in behavioral modification programs aimed at minimizing static postures. In this paper we propose a shoe-based device and related pattern recognition methodology for recognition of postural allocations. Inexpensive technology allows implementation of this methodology as a part of footwear. The experimental results suggest high efficiency and reliability of the proposed approach.

Digital signal processing algorithms for automatic voice recognition

NASA Technical Reports Server (NTRS)

Botros, Nazeih M.

1987-01-01

The current digital signal analysis algorithms are investigated that are implemented in automatic voice recognition algorithms. Automatic voice recognition means, the capability of a computer to recognize and interact with verbal commands. The digital signal is focused on, rather than the linguistic, analysis of speech signal. Several digital signal processing algorithms are available for voice recognition. Some of these algorithms are: Linear Predictive Coding (LPC), Short-time Fourier Analysis, and Cepstrum Analysis. Among these algorithms, the LPC is the most widely used. This algorithm has short execution time and do not require large memory storage. However, it has several limitations due to the assumptions used to develop it. The other 2 algorithms are frequency domain algorithms with not many assumptions, but they are not widely implemented or investigated. However, with the recent advances in the digital technology, namely signal processors, these 2 frequency domain algorithms may be investigated in order to implement them in voice recognition. This research is concerned with real time, microprocessor based recognition algorithms.

Effect of total lymphoid irradiation on levels of serum autoantibodies in systemic lupus erythematosus and in rheumatoid arthritis

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

Tanay, A.; Schiffman, G.; Strober, S.

1986-01-01

The effects of total lymphoid irradiation (TLI) on serum levels of autoantibodies, and of antibodies to diphtheria toxoid, tetanus toxoid, and pneumococcal polysaccharide in patients with lupus nephritis were compared with those previously observed in rheumatoid arthritis (RA) patients. Baseline levels of antibodies to diphtheria toxoid and tetanus toxoid decreased significantly after TLI in patients with lupus and RA, but antibody levels to pneumococcal polysaccharide remained unchanged. After TLI, the levels of antinuclear and anti-DNA antibodies were reduced significantly in lupus, but levels of rheumatoid factor, antinuclear, and antigranulocyte antibodies all tended to increase in RA.

Shape and texture fused recognition of flying targets

NASA Astrophysics Data System (ADS)

Kovács, Levente; Utasi, Ákos; Kovács, Andrea; Szirányi, Tamás

2011-06-01

This paper presents visual detection and recognition of flying targets (e.g. planes, missiles) based on automatically extracted shape and object texture information, for application areas like alerting, recognition and tracking. Targets are extracted based on robust background modeling and a novel contour extraction approach, and object recognition is done by comparisons to shape and texture based query results on a previously gathered real life object dataset. Application areas involve passive defense scenarios, including automatic object detection and tracking with cheap commodity hardware components (CPU, camera and GPS).

Concept Recognition in an Automatic Text-Processing System for the Life Sciences.

ERIC Educational Resources Information Center

Vleduts-Stokolov, Natasha

1987-01-01

Describes a system developed for the automatic recognition of biological concepts in titles of scientific articles; reports results of several pilot experiments which tested the system's performance; analyzes typical ambiguity problems encountered by the system; describes a disambiguation technique that was developed; and discusses future plans…

Fuzzy Logic-Based Audio Pattern Recognition

NASA Astrophysics Data System (ADS)

Malcangi, M.

2008-11-01

Audio and audio-pattern recognition is becoming one of the most important technologies to automatically control embedded systems. Fuzzy logic may be the most important enabling methodology due to its ability to rapidly and economically model such application. An audio and audio-pattern recognition engine based on fuzzy logic has been developed for use in very low-cost and deeply embedded systems to automate human-to-machine and machine-to-machine interaction. This engine consists of simple digital signal-processing algorithms for feature extraction and normalization, and a set of pattern-recognition rules manually tuned or automatically tuned by a self-learning process.

Automatic violence detection in digital movies

NASA Astrophysics Data System (ADS)

Fischer, Stephan

1996-11-01

Research on computer-based recognition of violence is scant. We are working on the automatic recognition of violence in digital movies, a first step towards the goal of a computer- assisted system capable of protecting children against TV programs containing a great deal of violence. In the video domain a collision detection and a model-mapping to locate human figures are run, while the creation and comparison of fingerprints to find certain events are run int he audio domain. This article centers on the recognition of fist- fights in the video domain and on the recognition of shots, explosions and cries in the audio domain.

Developing and Evaluating an Oral Skills Training Website Supported by Automatic Speech Recognition Technology

ERIC Educational Resources Information Center

Chen, Howard Hao-Jan

2011-01-01

Oral communication ability has become increasingly important to many EFL students. Several commercial software programs based on automatic speech recognition (ASR) technologies are available but their prices are not affordable for many students. This paper will demonstrate how the Microsoft Speech Application Software Development Kit (SASDK), a…

Automatic speech recognition in air traffic control

NASA Technical Reports Server (NTRS)

Karlsson, Joakim

1990-01-01

Automatic Speech Recognition (ASR) technology and its application to the Air Traffic Control system are described. The advantages of applying ASR to Air Traffic Control, as well as criteria for choosing a suitable ASR system are presented. Results from previous research and directions for future work at the Flight Transportation Laboratory are outlined.

Automatic Speech Recognition: Reliability and Pedagogical Implications for Teaching Pronunciation

ERIC Educational Resources Information Center

Kim, In-Seok

2006-01-01

This study examines the reliability of automatic speech recognition (ASR) software used to teach English pronunciation, focusing on one particular piece of software, "FluSpeak, as a typical example." Thirty-six Korean English as a Foreign Language (EFL) college students participated in an experiment in which they listened to 15 sentences…

Automatic Speech Recognition Technology as an Effective Means for Teaching Pronunciation

ERIC Educational Resources Information Center

Elimat, Amal Khalil; AbuSeileek, Ali Farhan

2014-01-01

This study aimed to explore the effect of using automatic speech recognition technology (ASR) on the third grade EFL students' performance in pronunciation, whether teaching pronunciation through ASR is better than regular instruction, and the most effective teaching technique (individual work, pair work, or group work) in teaching pronunciation…

Automatization and Orthographic Development in Second Language Visual Word Recognition

ERIC Educational Resources Information Center

Kida, Shusaku

2016-01-01

The present study investigated second language (L2) learners' acquisition of automatic word recognition and the development of L2 orthographic representation in the mental lexicon. Participants in the study were Japanese university students enrolled in a compulsory course involving a weekly 30-minute sustained silent reading (SSR) activity with…

Evaluating Automatic Speech Recognition-Based Language Learning Systems: A Case Study

ERIC Educational Resources Information Center

van Doremalen, Joost; Boves, Lou; Colpaert, Jozef; Cucchiarini, Catia; Strik, Helmer

2016-01-01

The purpose of this research was to evaluate a prototype of an automatic speech recognition (ASR)-based language learning system that provides feedback on different aspects of speaking performance (pronunciation, morphology and syntax) to students of Dutch as a second language. We carried out usability reviews, expert reviews and user tests to…

The Effect of Automatic Speech Recognition Eyespeak Software on Iraqi Students' English Pronunciation: A Pilot Study

ERIC Educational Resources Information Center

Sidgi, Lina Fathi Sidig; Shaari, Ahmad Jelani

2017-01-01

The use of technology, such as computer-assisted language learning (CALL), is used in teaching and learning in the foreign language classrooms where it is most needed. One promising emerging technology that supports language learning is automatic speech recognition (ASR). Integrating such technology, especially in the instruction of pronunciation…

Efficacy of a Classroom Integrated Intervention of Phonological Awareness and Word Recognition in "Double-Deficit Children" Learning a Regular Orthography

ERIC Educational Resources Information Center

Mayer, Andreas; Motsch, Hans-Joachim

2015-01-01

This study analysed the effects of a classroom intervention focusing on phonological awareness and/or automatized word recognition in children with a deficit in the domains of phonological awareness and rapid automatized naming ("double deficit"). According to the double-deficit hypothesis (Wolf & Bowers, 1999), these children belong…

Using Automatic Speech Recognition Technology with Elicited Oral Response Testing

ERIC Educational Resources Information Center

Cox, Troy L.; Davies, Randall S.

2012-01-01

This study examined the use of automatic speech recognition (ASR) scored elicited oral response (EOR) tests to assess the speaking ability of English language learners. It also examined the relationship between ASR-scored EOR and other language proficiency measures and the ability of the ASR to rate speakers without bias to gender or native…

Formal thought disorder in people at ultra-high risk of psychosis

PubMed Central

Weinstein, Sara; Stahl, Daniel; Day, Fern; Valmaggia, Lucia; Rutigliano, Grazia; De Micheli, Andrea; Fusar-Poli, Paolo; McGuire, Philip

2017-01-01

Background Formal thought disorder is a cardinal feature of psychosis. However, the extent to which formal thought disorder is evident in ultra-high-risk individuals and whether it is linked to the progression to psychosis remains unclear. Aims Examine the severity of formal thought disorder in ultra-high-risk participants and its association with future psychosis. Method The Thought and Language Index (TLI) was used to assess 24 ultra-high-risk participants, 16 people with first-episode psychosis and 13 healthy controls. Ultra-high-risk individuals were followed up for a mean duration of 7 years (s.d.=1.5) to determine the relationship between formal thought disorder at baseline and transition to psychosis. Results TLI scores were significantly greater in the ultra-high-risk group compared with the healthy control group (effect size (ES)=1.2), but lower than in people with first-episode psychosis (ES=0.8). Total and negative TLI scores were higher in ultra-high-risk individuals who developed psychosis, but this was not significant. Combining negative TLI scores with attenuated psychotic symptoms and basic symptoms predicted transition to psychosis (P=0.04; ES=1.04). Conclusions TLI is beneficial in evaluating formal thought disorder in ultra-high-risk participants, and complements existing instruments for the evaluation of psychopathology in this group. Declaration of interests None. Copyright and usage © The Royal College of Psychiatrists 2017. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license. PMID:28713586

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

Tanaka, T.; Kimata, S.; Hirosawa, K.

To determine whether thallium lung uptake images (TLI) can be used as a noninvasive method to estimate any of hemodynamic changes in patients (pts) with acute myocardial infarction (AMI) TLI were evaluated in 23 pts with AMI. All pts underwent multigated blood pool imaging and cardiac catheterization. TLI were estimated by comparing the intensity of T1-201 activity in the lower right lung with maximal myocardial count (thallium lung heart ratio; LHR). Pts with AMI were classified to 3 grades according to LHR. The classifications were hemodynamically significant. The specificity of LHR <0.6 for mPw <18mmHg was 100% (10/10). The specificitymore » of LHR greater than or equal to 0.8 for mPw greater than or equal to 18mmHg was 85% (11/13) and for EF greater than or equal to 30% was 100% (13/13). The pts with LHR 0.8 showed high mortality (4/9) and high morbidity (all survivors were in NYHA class 2-3 and receiving digitalis and diuretics). TLI were easily obtained after routine T1-myocardial imaging, i.e. another 5 minutes imaging yielded clinically useful information for separating high and low-risk groups of pts with AMI.« less

Face Recognition From One Example View.

DTIC Science & Technology

1995-09-01

Proceedings, International Workshop on Automatic Face- and Gesture-Recognition, pages 248{253, Zurich, 1995. [32] Yael Moses, Shimon Ullman, and Shimon...recognition. Journal of Cognitive Neuroscience, 3(1):71{86, 1991. [49] Shimon Ullman and Ronen Basri. Recognition by linear combinations of models

Automatic Target Recognition Based on Cross-Plot

PubMed Central

Wong, Kelvin Kian Loong; Abbott, Derek

2011-01-01

Automatic target recognition that relies on rapid feature extraction of real-time target from photo-realistic imaging will enable efficient identification of target patterns. To achieve this objective, Cross-plots of binary patterns are explored as potential signatures for the observed target by high-speed capture of the crucial spatial features using minimal computational resources. Target recognition was implemented based on the proposed pattern recognition concept and tested rigorously for its precision and recall performance. We conclude that Cross-plotting is able to produce a digital fingerprint of a target that correlates efficiently and effectively to signatures of patterns having its identity in a target repository. PMID:21980508

Presentation video retrieval using automatically recovered slide and spoken text

NASA Astrophysics Data System (ADS)

Cooper, Matthew

2013-03-01

Video is becoming a prevalent medium for e-learning. Lecture videos contain text information in both the presentation slides and lecturer's speech. This paper examines the relative utility of automatically recovered text from these sources for lecture video retrieval. To extract the visual information, we automatically detect slides within the videos and apply optical character recognition to obtain their text. Automatic speech recognition is used similarly to extract spoken text from the recorded audio. We perform controlled experiments with manually created ground truth for both the slide and spoken text from more than 60 hours of lecture video. We compare the automatically extracted slide and spoken text in terms of accuracy relative to ground truth, overlap with one another, and utility for video retrieval. Results reveal that automatically recovered slide text and spoken text contain different content with varying error profiles. Experiments demonstrate that automatically extracted slide text enables higher precision video retrieval than automatically recovered spoken text.

Image-based automatic recognition of larvae

NASA Astrophysics Data System (ADS)

Sang, Ru; Yu, Guiying; Fan, Weijun; Guo, Tiantai

2010-08-01

As the main objects, imagoes have been researched in quarantine pest recognition in these days. However, pests in their larval stage are latent, and the larvae spread abroad much easily with the circulation of agricultural and forest products. It is presented in this paper that, as the new research objects, larvae are recognized by means of machine vision, image processing and pattern recognition. More visional information is reserved and the recognition rate is improved as color image segmentation is applied to images of larvae. Along with the characteristics of affine invariance, perspective invariance and brightness invariance, scale invariant feature transform (SIFT) is adopted for the feature extraction. The neural network algorithm is utilized for pattern recognition, and the automatic identification of larvae images is successfully achieved with satisfactory results.

Offline Arabic handwriting recognition: a survey.

PubMed

Lorigo, Liana M; Govindaraju, Venu

2006-05-01

The automatic recognition of text on scanned images has enabled many applications such as searching for words in large volumes of documents, automatic sorting of postal mail, and convenient editing of previously printed documents. The domain of handwriting in the Arabic script presents unique technical challenges and has been addressed more recently than other domains. Many different methods have been proposed and applied to various types of images. This paper provides a comprehensive review of these methods. It is the first survey to focus on Arabic handwriting recognition and the first Arabic character recognition survey to provide recognition rates and descriptions of test data for the approaches discussed. It includes background on the field, discussion of the methods, and future research directions.

Automatic face recognition in HDR imaging

NASA Astrophysics Data System (ADS)

Pereira, Manuela; Moreno, Juan-Carlos; Proença, Hugo; Pinheiro, António M. G.

2014-05-01

The gaining popularity of the new High Dynamic Range (HDR) imaging systems is raising new privacy issues caused by the methods used for visualization. HDR images require tone mapping methods for an appropriate visualization on conventional and non-expensive LDR displays. These visualization methods might result in completely different visualization raising several issues on privacy intrusion. In fact, some visualization methods result in a perceptual recognition of the individuals, while others do not even show any identity. Although perceptual recognition might be possible, a natural question that can rise is how computer based recognition will perform using tone mapping generated images? In this paper, a study where automatic face recognition using sparse representation is tested with images that result from common tone mapping operators applied to HDR images. Its ability for the face identity recognition is described. Furthermore, typical LDR images are used for the face recognition training.

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

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Automatic forensic face recognition from digital images.

PubMed

Peacock, C; Goode, A; Brett, A

2004-01-01

Digital image evidence is now widely available from criminal investigations and surveillance operations, often captured by security and surveillance CCTV. This has resulted in a growing demand from law enforcement agencies for automatic person-recognition based on image data. In forensic science, a fundamental requirement for such automatic face recognition is to evaluate the weight that can justifiably be attached to this recognition evidence in a scientific framework. This paper describes a pilot study carried out by the Forensic Science Service (UK) which explores the use of digital facial images in forensic investigation. For the purpose of the experiment a specific software package was chosen (Image Metrics Optasia). The paper does not describe the techniques used by the software to reach its decision of probabilistic matches to facial images, but accepts the output of the software as though it were a 'black box'. In this way, the paper lays a foundation for how face recognition systems can be compared in a forensic framework. The aim of the paper is to explore how reliably and under what conditions digital facial images can be presented in evidence.

Automatic event recognition and anomaly detection with attribute grammar by learning scene semantics

NASA Astrophysics Data System (ADS)

Qi, Lin; Yao, Zhenyu; Li, Li; Dong, Junyu

2007-11-01

In this paper we present a novel framework for automatic event recognition and abnormal behavior detection with attribute grammar by learning scene semantics. This framework combines learning scene semantics by trajectory analysis and constructing attribute grammar-based event representation. The scene and event information is learned automatically. Abnormal behaviors that disobey scene semantics or event grammars rules are detected. By this method, an approach to understanding video scenes is achieved. Further more, with this prior knowledge, the accuracy of abnormal event detection is increased.

Automatic concept extraction from spoken medical reports.

PubMed

Happe, André; Pouliquen, Bruno; Burgun, Anita; Cuggia, Marc; Le Beux, Pierre

2003-07-01

The objective of this project is to investigate methods whereby a combination of speech recognition and automated indexing methods substitute for current transcription and indexing practices. We based our study on existing speech recognition software programs and on NOMINDEX, a tool that extracts MeSH concepts from medical text in natural language and that is mainly based on a French medical lexicon and on the UMLS. For each document, the process consists of three steps: (1) dictation and digital audio recording, (2) speech recognition, (3) automatic indexing. The evaluation consisted of a comparison between the set of concepts extracted by NOMINDEX after the speech recognition phase and the set of keywords manually extracted from the initial document. The method was evaluated on a set of 28 patient discharge summaries extracted from the MENELAS corpus in French, corresponding to in-patients admitted for coronarography. The overall precision was 73% and the overall recall was 90%. Indexing errors were mainly due to word sense ambiguity and abbreviations. A specific issue was the fact that the standard French translation of MeSH terms lacks diacritics. A preliminary evaluation of speech recognition tools showed that the rate of accurate recognition was higher than 98%. Only 3% of the indexing errors were generated by inadequate speech recognition. We discuss several areas to focus on to improve this prototype. However, the very low rate of indexing errors due to speech recognition errors highlights the potential benefits of combining speech recognition techniques and automatic indexing.

Automatic Activation of Phonological Code during Visual Word Recognition in Children: A Masked Priming Study in Grades 3 and 5

ERIC Educational Resources Information Center

Sauval, Karinne; Perre, Laetitia; Casalis, Séverine

2017-01-01

The present study aimed to investigate the development of automatic phonological processes involved in visual word recognition during reading acquisition in French. A visual masked priming lexical decision experiment was carried out with third, fifth graders and adult skilled readers. Three different types of partial overlap between the prime and…

Automatic Speech Recognition Predicts Speech Intelligibility and Comprehension for Listeners with Simulated Age-Related Hearing Loss

ERIC Educational Resources Information Center

Fontan, Lionel; Ferrané, Isabelle; Farinas, Jérôme; Pinquier, Julien; Tardieu, Julien; Magnen, Cynthia; Gaillard, Pascal; Aumont, Xavier; Füllgrabe, Christian

2017-01-01

Purpose: The purpose of this article is to assess speech processing for listeners with simulated age-related hearing loss (ARHL) and to investigate whether the observed performance can be replicated using an automatic speech recognition (ASR) system. The long-term goal of this research is to develop a system that will assist…

The Use of an Autonomous Pedagogical Agent and Automatic Speech Recognition for Teaching Sight Words to Students with Autism Spectrum Disorder

ERIC Educational Resources Information Center

Saadatzi, Mohammad Nasser; Pennington, Robert C.; Welch, Karla C.; Graham, James H.; Scott, Renee E.

2017-01-01

In the current study, we examined the effects of an instructional package comprised of an autonomous pedagogical agent, automatic speech recognition, and constant time delay during the instruction of reading sight words aloud to young adults with autism spectrum disorder. We used a concurrent multiple baseline across participants design to…

An Exploration of the Potential of Automatic Speech Recognition to Assist and Enable Receptive Communication in Higher Education

ERIC Educational Resources Information Center

Wald, Mike

2006-01-01

The potential use of Automatic Speech Recognition to assist receptive communication is explored. The opportunities and challenges that this technology presents students and staff to provide captioning of speech online or in classrooms for deaf or hard of hearing students and assist blind, visually impaired or dyslexic learners to read and search…

78 FR 72682 - Proposed Collection; 60-Day Comment Request; Rapid Throughput Standardized Evaluation of...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-03

... of 14-17 year-old students: Consent Form. 5,000 1 1/60 83 14-18 year-old students: School Survey (TLI.... The TLI is designed to identify the propensity for these and other substance abuse prior to... youths: Twins Survey (Demo/D&A).. 1 10/60 100 14-18 year-old youths: Twins Survey 1 10/60 100...

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

NASA Astrophysics Data System (ADS)

Heracleous, Panikos; Kaino, Tomomi; Saruwatari, Hiroshi; Shikano, Kiyohiro

2006-12-01

We present the use of stethoscope and silicon NAM (nonaudible murmur) microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible) speech, but also very quietly uttered speech (nonaudible murmur). As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc.) for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a[InlineEquation not available: see fulltext.] word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

Total lymphoid irradiation for multiple sclerosis

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

Devereux, C.K.; Vidaver, R.; Hafstein, M.P.

1988-01-01

Although chemical immunosuppression has been shown to benefit patients with chronic progressive multiple sclerosis (MS), it appears that chemotherapy has an appreciable oncogenic potential in patients with multiple sclerosis. Accordingly, we developed a modified total lymphoid irradiation (TLI) regimen designed to reduce toxicity and applied it to a randomized double blind trial of TLI or sham irradiation in MS. Standard TLI regimens were modified to reduce dose to 1,980 rad, lowering the superior mantle margin to midway between the thyroid cartilage and angle of the mandible (to avert xerostomia) and the lower margin of the mantle field to the inferiormore » margin of L1 (to reduce gastrointestinal toxicity by dividing abdominal radiation between mantle and inverted Y), limiting spinal cord dose to 1,000 rad by custom-made spine blocks in the mantle and upper 2 cm of inverted Y fields, and also protecting the left kidney even if part of the spleen were shielded. Clinical efficacy was documented by the less frequent functional scale deterioration of 20 TLI treated patients with chronic progressive MS compared to to 20 sham-irradiated progressive MS patients after 12 months (16% versus 55%, p less than 0.03), 18 months (28% versus 63%, p less than 0.03), and 24 months (44% versus 74%, N.S.). Therapeutic benefit during 3 years follow-up was related to the reduction in lymphocyte count 3 months post-irradiation (p less than 0.02). Toxicity was generally mild and transient, with no instance of xerostomia, pericarditis, herpes zoster, or need to terminate treatment in TLI patients. However, menopause was induced in 2 patients and staphylococcal pneumonia in one.« less

Serum and urine concentrations of trypsinogen-activation peptide as markers for acute pancreatitis in cats

PubMed Central

Allen, Heidi S.; Steiner, Jörg; Broussard, John; Mansfield, Caroline; Williams, David A.; Jones, Boyd

2006-01-01

The purpose of this study was to compare the clinical utility of the serum concentration of feline trypsin-like immunoreactivity (fTLI), the plasma and urine concentrations of trypsinogen-activation peptide (TAP), and the ratio of the urine TAP and creatinine concentrations (TAP:Cr) in the diagnosis of feline acute pancreatitis. We used 13 healthy cats and 10 cats with a diagnosis of acute pancreatitis. The mean serum fTLI and plasma TAP concentrations were significantly higher in the cats with acute pancreatitis than in the healthy cats (P < 0.05); the mean urine TAP concentrations and the median urine TAP:Cr ratios were not significantly different. Among the cats examined in this study, there was no benefit of plasma TAP over serum fTLI in the evaluation of suspected acute pancreatitis. PMID:17042387

Computational Modeling of Emotions and Affect in Social-Cultural Interaction

DTIC Science & Technology

2013-10-02

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

Constraining Source Terms, Regional Attenuation Models, and Site Effects (Postprint)

DTIC Science & Technology

2012-03-22

YAK ZRNK 10245 10831 11116 11346 12278...MDJ KAR NIL PDG QIZ RAYN SSE TATO TKM2TLG TLY UCHULHL ULN USP VOS WMQ WUS XAN YAK ZRNK 10245 10831 11116 11346 12278 12376 12574 12937 1311713130...LSA LZH MAKZUZ MDJ KAR NIL PDG QIZ RAYN SSE TATO TKM2TLG TLY UCHULHL ULN USP VOS WMQ WUS XAN YAK ZRNK 10245 10831 11116 11346 12278 12376 12574

Does stress mediate the development of substance use disorders among youth transitioning to young adulthood?

PubMed

Cornelius, Jack; Kirisci, Levent; Reynolds, Maureen; Tarter, Ralph

2014-05-01

Stress is a well-documented factor in the development of addiction. However, no longitudinal studies to date have assessed the role of stress in mediating the development of substance use disorders (SUD). Our previous results have demonstrated that a measure called Transmissible Liability Index (TLI) assessed during pre-adolescent years serves as a significant predictor of risk for substance use disorder among young adults. However, it remains unclear whether life stress mediates the relationship between TLI and SUD, or whether stress predicts SUD. We conducted a longitudinal study involving 191 male subjects to assess whether life stress mediates the relationship between TLI as assessed at age 10-12 and subsequent development of SUD at age 22, after controlling for other relevant factors. Logistic regression demonstrated that the development of SUD at age 22 was associated with stress at age 19. A path analysis demonstrated that stress at age 19 significantly predicted SUD at age 22. However, stress did not mediate the relationship between the TLI assessed at age 10-12 and SUD in young adulthood. These findings confirm that stress plays a role in the development of SUD, but also shows that stress does not mediate the development of SUD. Further studies are warranted to clarify the role of stress in the etiology of SUD.

Computer Recognition of Facial Profiles

DTIC Science & Technology

1974-08-01

facial recognition 20. ABSTRACT (Continue on reverse side It necessary and Identify by block number) A system for the recognition of human faces from...21 2.6 Classification Algorithms ........... ... 32 III FACIAL RECOGNITION AND AUTOMATIC TRAINING . . . 37 3.1 Facial Profile Recognition...provide a fair test of the classification system. The work of Goldstein, Harmon, and Lesk [81 indicates, however, that for facial recognition , a ten class

Automatic Mexican sign language and digits recognition using normalized central moments

NASA Astrophysics Data System (ADS)

Solís, Francisco; Martínez, David; Espinosa, Oscar; Toxqui, Carina

2016-09-01

This work presents a framework for automatic Mexican sign language and digits recognition based on computer vision system using normalized central moments and artificial neural networks. Images are captured by digital IP camera, four LED reflectors and a green background in order to reduce computational costs and prevent the use of special gloves. 42 normalized central moments are computed per frame and used in a Multi-Layer Perceptron to recognize each database. Four versions per sign and digit were used in training phase. 93% and 95% of recognition rates were achieved for Mexican sign language and digits respectively.

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

NASA Astrophysics Data System (ADS)

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

2017-08-01

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

Automatic Speech Recognition from Neural Signals: A Focused Review.

PubMed

Herff, Christian; Schultz, Tanja

2016-01-01

Speech interfaces have become widely accepted and are nowadays integrated in various real-life applications and devices. They have become a part of our daily life. However, speech interfaces presume the ability to produce intelligible speech, which might be impossible due to either loud environments, bothering bystanders or incapabilities to produce speech (i.e., patients suffering from locked-in syndrome). For these reasons it would be highly desirable to not speak but to simply envision oneself to say words or sentences. Interfaces based on imagined speech would enable fast and natural communication without the need for audible speech and would give a voice to otherwise mute people. This focused review analyzes the potential of different brain imaging techniques to recognize speech from neural signals by applying Automatic Speech Recognition technology. We argue that modalities based on metabolic processes, such as functional Near Infrared Spectroscopy and functional Magnetic Resonance Imaging, are less suited for Automatic Speech Recognition from neural signals due to low temporal resolution but are very useful for the investigation of the underlying neural mechanisms involved in speech processes. In contrast, electrophysiologic activity is fast enough to capture speech processes and is therefor better suited for ASR. Our experimental results indicate the potential of these signals for speech recognition from neural data with a focus on invasively measured brain activity (electrocorticography). As a first example of Automatic Speech Recognition techniques used from neural signals, we discuss the Brain-to-text system.

Direct Method Transcription for a Human-Class Translunar Injection Trajectory Optimization

NASA Technical Reports Server (NTRS)

Witzberger, Kevin E.; Zeiler, Tom

2012-01-01

This paper presents a new trajectory optimization software package developed in the framework of a low-to-high fidelity 3 degrees-of-freedom (DOF)/6-DOF vehicle simulation program named Mission Analysis Simulation Tool in Fortran (MASTIF) and its application to a translunar trajectory optimization problem. The functionality of the developed optimization package is implemented as a new "mode" in generalized settings to make it applicable for a general trajectory optimization problem. In doing so, a direct optimization method using collocation is employed for solving the problem. Trajectory optimization problems in MASTIF are transcribed to a constrained nonlinear programming (NLP) problem and solved with SNOPT, a commercially available NLP solver. A detailed description of the optimization software developed is provided as well as the transcription specifics for the translunar injection (TLI) problem. The analysis includes a 3-DOF trajectory TLI optimization and a 3-DOF vehicle TLI simulation using closed-loop guidance.

Quest Hierarchy for Hyperspectral Face Recognition

DTIC Science & Technology

2011-03-01

numerous face recognition algorithms available, several very good literature surveys are available that include Abate [29], Samal [110], Kong [18], Zou...Perception, Japan (January 1994). [110] Samal , Ashok and P. Iyengar, Automatic Recognition and Analysis of Human Faces and Facial Expressions: A Survey

The Suitability of Cloud-Based Speech Recognition Engines for Language Learning

ERIC Educational Resources Information Center

Daniels, Paul; Iwago, Koji

2017-01-01

As online automatic speech recognition (ASR) engines become more accurate and more widely implemented with call software, it becomes important to evaluate the effectiveness and the accuracy of these recognition engines using authentic speech samples. This study investigates two of the most prominent cloud-based speech recognition engines--Apple's…

Can a CNN recognize Catalan diet?

NASA Astrophysics Data System (ADS)

Herruzo, P.; Bolaños, M.; Radeva, P.

2016-10-01

Nowadays, we can find several diseases related to the unhealthy diet habits of the population, such as diabetes, obesity, anemia, bulimia and anorexia. In many cases, these diseases are related to the food consumption of people. Mediterranean diet is scientifically known as a healthy diet that helps to prevent many metabolic diseases. In particular, our work focuses on the recognition of Mediterranean food and dishes. The development of this methodology would allow to analise the daily habits of users with wearable cameras, within the topic of lifelogging. By using automatic mechanisms we could build an objective tool for the analysis of the patient's behavior, allowing specialists to discover unhealthy food patterns and understand the user's lifestyle. With the aim to automatically recognize a complete diet, we introduce a challenging multi-labeled dataset related to Mediter-ranean diet called FoodCAT. The first type of label provided consists of 115 food classes with an average of 400 images per dish, and the second one consists of 12 food categories with an average of 3800 pictures per class. This dataset will serve as a basis for the development of automatic diet recognition. In this context, deep learning and more specifically, Convolutional Neural Networks (CNNs), currently are state-of-the-art methods for automatic food recognition. In our work, we compare several architectures for image classification, with the purpose of diet recognition. Applying the best model for recognising food categories, we achieve a top-1 accuracy of 72.29%, and top-5 of 97.07%. In a complete diet recognition of dishes from Mediterranean diet, enlarged with the Food-101 dataset for international dishes recognition, we achieve a top-1 accuracy of 68.07%, and top-5 of 89.53%, for a total of 115+101 food classes.

Automatic vigilance for negative words in lexical decision and naming: comment on Larsen, Mercer, and Balota (2006).

PubMed

Estes, Zachary; Adelman, James S

2008-08-01

An automatic vigilance hypothesis states that humans preferentially attend to negative stimuli, and this attention to negative valence disrupts the processing of other stimulus properties. Thus, negative words typically elicit slower color naming, word naming, and lexical decisions than neutral or positive words. Larsen, Mercer, and Balota analyzed the stimuli from 32 published studies, and they found that word valence was confounded with several lexical factors known to affect word recognition. Indeed, with these lexical factors covaried out, Larsen et al. found no evidence of automatic vigilance. The authors report a more sensitive analysis of 1011 words. Results revealed a small but reliable valence effect, such that negative words (e.g., "shark") elicit slower lexical decisions and naming than positive words (e.g., "beach"). Moreover, the relation between valence and recognition was categorical rather than linear; the extremity of a word's valence did not affect its recognition. This valence effect was not attributable to word length, frequency, orthographic neighborhood size, contextual diversity, first phoneme, or arousal. Thus, the present analysis provides the most powerful demonstration of automatic vigilance to date.

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

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

NASA Astrophysics Data System (ADS)

Fernández Pozo, Rubén; Blanco Murillo, Jose Luis; Hernández Gómez, Luis; López Gonzalo, Eduardo; Alcázar Ramírez, José; Toledano, Doroteo T.

2009-12-01

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Early Detection of Severe Apnoea through Voice Analysis and Automatic Speaker Recognition Techniques

NASA Astrophysics Data System (ADS)

Fernández, Ruben; Blanco, Jose Luis; Díaz, David; Hernández, Luis A.; López, Eduardo; Alcázar, José

This study is part of an on-going collaborative effort between the medical and the signal processing communities to promote research on applying voice analysis and Automatic Speaker Recognition techniques (ASR) for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based diagnosis could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we present and discuss the possibilities of using generative Gaussian Mixture Models (GMMs), generally used in ASR systems, to model distinctive apnoea voice characteristics (i.e. abnormal nasalization). Finally, we present experimental findings regarding the discriminative power of speaker recognition techniques applied to severe apnoea detection. We have achieved an 81.25 % correct classification rate, which is very promising and underpins the interest in this line of inquiry.

Statistical Evaluation of Biometric Evidence in Forensic Automatic Speaker Recognition

NASA Astrophysics Data System (ADS)

Drygajlo, Andrzej

Forensic speaker recognition is the process of determining if a specific individual (suspected speaker) is the source of a questioned voice recording (trace). This paper aims at presenting forensic automatic speaker recognition (FASR) methods that provide a coherent way of quantifying and presenting recorded voice as biometric evidence. In such methods, the biometric evidence consists of the quantified degree of similarity between speaker-dependent features extracted from the trace and speaker-dependent features extracted from recorded speech of a suspect. The interpretation of recorded voice as evidence in the forensic context presents particular challenges, including within-speaker (within-source) variability and between-speakers (between-sources) variability. Consequently, FASR methods must provide a statistical evaluation which gives the court an indication of the strength of the evidence given the estimated within-source and between-sources variabilities. This paper reports on the first ENFSI evaluation campaign through a fake case, organized by the Netherlands Forensic Institute (NFI), as an example, where an automatic method using the Gaussian mixture models (GMMs) and the Bayesian interpretation (BI) framework were implemented for the forensic speaker recognition task.

Lidar as a Diagnostic of Smoke/Obscurants: Overview and Assessment of the Development with Recommendations

DTIC Science & Technology

1984-10-03

aerosol , and the name " laser radar" is used to denote systems de- signed for the detection of solid objects...34* MEASUREMENW OF T(Li) BY SEPARATE TRANSMISSOMETER . One method of measuring * T(Li) is by a separate transmissometer system operating at the lidar wavelength... transmissometer path may be radially outward from the lidar (along the lidar beam) to save the expense of scanning hardware in a developing lidar system ,

P Wave Velocity Structure Beneath the Baikal Rift Axis

NASA Astrophysics Data System (ADS)

Brazier, R. A.; Nyblade, A. A.; Boman, E. C.

2001-12-01

Over 100 p wave travel times from the 1500 km en echelon Baikal Rift system are used in this study.The events range 3 to 13 degrees from Talaya, Russia (TLY) along the axis of southwest northeast trending rift in East Siberia. A Herglotz Wiechert inversion of these events resolved a crust of 6.4 km/s and a gradient in the mantle starting at 35 km depth and 7.7 km/s down to 200 km depth and 8.2 km/s. This is compatible with Gao et al,1994 cross sectional structure which cuts the rift at about 400km from TLY. The Baikal Rift hosts the deepest lake and is the most seismically active rift in the world. It is one of the few continental rifts, it separates the Siberian craton and the Syan-Baikal mobile fold belt. Two events, the March 21 1999 magnitude 5.7 earthquake 638 km from TLY and the November 13th 1995 magnitude 5.9 earthquake 863 km from TLY were modeled for there PnL wave structure using the discrete wavenumber method and the Harvard CMT solutions with adjusted depths from p-pP times. The PnL signals match well. A genetic algorithm will used to perturb the velocity structure and compare to a selection of the events between 3 and 13 degrees many will require moment tensor solutions.

Advances in image compression and automatic target recognition; Proceedings of the Meeting, Orlando, FL, Mar. 30, 31, 1989

NASA Technical Reports Server (NTRS)

Tescher, Andrew G. (Editor)

1989-01-01

Various papers on image compression and automatic target recognition are presented. Individual topics addressed include: target cluster detection in cluttered SAR imagery, model-based target recognition using laser radar imagery, Smart Sensor front-end processor for feature extraction of images, object attitude estimation and tracking from a single video sensor, symmetry detection in human vision, analysis of high resolution aerial images for object detection, obscured object recognition for an ATR application, neural networks for adaptive shape tracking, statistical mechanics and pattern recognition, detection of cylinders in aerial range images, moving object tracking using local windows, new transform method for image data compression, quad-tree product vector quantization of images, predictive trellis encoding of imagery, reduced generalized chain code for contour description, compact architecture for a real-time vision system, use of human visibility functions in segmentation coding, color texture analysis and synthesis using Gibbs random fields.

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Speech recognition for embedded automatic positioner for laparoscope

NASA Astrophysics Data System (ADS)

Chen, Xiaodong; Yin, Qingyun; Wang, Yi; Yu, Daoyin

2014-07-01

In this paper a novel speech recognition methodology based on Hidden Markov Model (HMM) is proposed for embedded Automatic Positioner for Laparoscope (APL), which includes a fixed point ARM processor as the core. The APL system is designed to assist the doctor in laparoscopic surgery, by implementing the specific doctor's vocal control to the laparoscope. Real-time respond to the voice commands asks for more efficient speech recognition algorithm for the APL. In order to reduce computation cost without significant loss in recognition accuracy, both arithmetic and algorithmic optimizations are applied in the method presented. First, depending on arithmetic optimizations most, a fixed point frontend for speech feature analysis is built according to the ARM processor's character. Then the fast likelihood computation algorithm is used to reduce computational complexity of the HMM-based recognition algorithm. The experimental results show that, the method shortens the recognition time within 0.5s, while the accuracy higher than 99%, demonstrating its ability to achieve real-time vocal control to the APL.

Automatic recognition of ship types from infrared images using superstructure moment invariants

NASA Astrophysics Data System (ADS)

Li, Heng; Wang, Xinyu

2007-11-01

Automatic object recognition is an active area of interest for military and commercial applications. In this paper, a system addressing autonomous recognition of ship types in infrared images is proposed. Firstly, an approach of segmentation based on detection of salient features of the target with subsequent shadow removing is proposed, as is the base of the subsequent object recognition. Considering the differences between the shapes of various ships mainly lie in their superstructures, we then use superstructure moment functions invariant to translation, rotation and scale differences in input patterns and develop a robust algorithm of obtaining ship superstructure. Subsequently a back-propagation neural network is used as a classifier in the recognition stage and projection images of simulated three-dimensional ship models are used as the training sets. Our recognition model was implemented and experimentally validated using both simulated three-dimensional ship model images and real images derived from video of an AN/AAS-44V Forward Looking Infrared(FLIR) sensor.

Understanding Cognitive Development: Automaticity and the Early Years Child

ERIC Educational Resources Information Center

Gray, Colette

2004-01-01

In recent years a growing body of evidence has implicated deficits in the automaticity of fundamental facts such as word and number recognition in a range of disorders: including attention deficit hyperactivity disorder, dyslexia, apraxia and autism. Variously described as habits, fluency, chunking and over learning, automatic processes are best…

Does time-lapse imaging have favorable results for embryo incubation and selection compared with conventional methods in clinical in vitro fertilization? A meta-analysis and systematic review of randomized controlled trials.

PubMed

Chen, Minghao; Wei, Shiyou; Hu, Junyan; Yuan, Jing; Liu, Fenghua

2017-01-01

The present study aimed to undertake a review of available evidence assessing whether time-lapse imaging (TLI) has favorable outcomes for embryo incubation and selection compared with conventional methods in clinical in vitro fertilization (IVF). Using PubMed, EMBASE, Cochrane library and ClinicalTrial.gov up to February 2017 to search for randomized controlled trials (RCTs) comparing TLI versus conventional methods. Both studies randomized women and oocytes were included. For studies randomized women, the primary outcomes were live birth and ongoing pregnancy, the secondary outcomes were clinical pregnancy and miscarriage; for studies randomized oocytes, the primary outcome was blastocyst rate, the secondary outcome was good quality embryo on Day 2/3. Subgroup analysis was conducted based on different incubation and embryo selection between groups. Ten RCTs were included, four randomized oocytes and six randomized women. For oocyte-based review, the pool-analysis observed no significant difference between TLI group and control group for blastocyst rate [relative risk (RR) 1.08, 95% CI 0.94-1.25, I2 = 0%, two studies, including 1154 embryos]. The quality of evidence was moderate for all outcomes in oocyte-based review. For woman-based review, only one study provided live birth rate (RR 1,23, 95% CI 1.06-1.44,I2 N/A, one study, including 842 women), the pooled result showed no significant difference in ongoing pregnancy rate (RR 1.04, 95% CI 0.80-1.36, I2 = 59%, four studies, including 1403 women) between two groups. The quality of the evidence was low or very low for all outcomes in woman-based review. Currently there is insufficient evidence to support that TLI is superior to conventional methods for human embryo incubation and selection. In consideration of the limitations and flaws of included studies, more well designed RCTs are still in need to comprehensively evaluate the effectiveness of clinical TLI use.

Definition and automatic anatomy recognition of lymph node zones in the pelvis on CT images

NASA Astrophysics Data System (ADS)

Liu, Yu; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Guo, Shuxu; Attor, Rosemary; Reinicke, Danica; Torigian, Drew A.

2016-03-01

Currently, unlike IALSC-defined thoracic lymph node zones, no explicitly provided definitions for lymph nodes in other body regions are available. Yet, definitions are critical for standardizing the recognition, delineation, quantification, and reporting of lymphadenopathy in other body regions. Continuing from our previous work in the thorax, this paper proposes a standardized definition of the grouping of pelvic lymph nodes into 10 zones. We subsequently employ our earlier Automatic Anatomy Recognition (AAR) framework designed for body-wide organ modeling, recognition, and delineation to actually implement these zonal definitions where the zones are treated as anatomic objects. First, all 10 zones and key anatomic organs used as anchors are manually delineated under expert supervision for constructing fuzzy anatomy models of the assembly of organs together with the zones. Then, optimal hierarchical arrangement of these objects is constructed for the purpose of achieving the best zonal recognition. For actual localization of the objects, two strategies are used -- optimal thresholded search for organs and one-shot method for the zones where the known relationship of the zones to key organs is exploited. Based on 50 computed tomography (CT) image data sets for the pelvic body region and an equal division into training and test subsets, automatic zonal localization within 1-3 voxels is achieved.

Automatic recognition and analysis of synapses. [in brain tissue

NASA Technical Reports Server (NTRS)

Ungerleider, J. A.; Ledley, R. S.; Bloom, F. E.

1976-01-01

An automatic system for recognizing synaptic junctions would allow analysis of large samples of tissue for the possible classification of specific well-defined sets of synapses based upon structural morphometric indices. In this paper the three steps of our system are described: (1) cytochemical tissue preparation to allow easy recognition of the synaptic junctions; (2) transmitting the tissue information to a computer; and (3) analyzing each field to recognize the synapses and make measurements on them.

Health smart home for elders - a tool for automatic recognition of activities of daily living.

PubMed

Le, Xuan Hoa Binh; Di Mascolo, Maria; Gouin, Alexia; Noury, Norbert

2008-01-01

Elders live preferently in their own home, but with aging comes the loss of autonomy and associated risks. In order to help them live longer in safe conditions, we need a tool to automatically detect their loss of autonomy by assessing the degree of performance of activities of daily living. This article presents an approach enabling the activities recognition of an elder living alone in a home equipped with noninvasive sensors.

Unification of automatic target tracking and automatic target recognition

NASA Astrophysics Data System (ADS)

Schachter, Bruce J.

2014-06-01

The subject being addressed is how an automatic target tracker (ATT) and an automatic target recognizer (ATR) can be fused together so tightly and so well that their distinctiveness becomes lost in the merger. This has historically not been the case outside of biology and a few academic papers. The biological model of ATT∪ATR arises from dynamic patterns of activity distributed across many neural circuits and structures (including retina). The information that the brain receives from the eyes is "old news" at the time that it receives it. The eyes and brain forecast a tracked object's future position, rather than relying on received retinal position. Anticipation of the next moment - building up a consistent perception - is accomplished under difficult conditions: motion (eyes, head, body, scene background, target) and processing limitations (neural noise, delays, eye jitter, distractions). Not only does the human vision system surmount these problems, but it has innate mechanisms to exploit motion in support of target detection and classification. Biological vision doesn't normally operate on snapshots. Feature extraction, detection and recognition are spatiotemporal. When vision is viewed as a spatiotemporal process, target detection, recognition, tracking, event detection and activity recognition, do not seem as distinct as they are in current ATT and ATR designs. They appear as similar mechanism taking place at varying time scales. A framework is provided for unifying ATT and ATR.

Face averages enhance user recognition for smartphone security.

PubMed

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

2015-01-01

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

Rapid Word Recognition as a Measure of Word-Level Automaticity and Its Relation to Other Measures of Reading

ERIC Educational Resources Information Center

Frye, Elizabeth M.; Gosky, Ross

2012-01-01

The present study investigated the relationship between rapid recognition of individual words (Word Recognition Test) and two measures of contextual reading: (1) grade-level Passage Reading Test (IRI passage) and (2) performance on standardized STAR Reading Test. To establish if time of presentation on the word recognition test was a factor in…

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.

Voice reaction times with recognition for Commodore computers

NASA Technical Reports Server (NTRS)

Washburn, David A.; Putney, R. Thompson

1990-01-01

Hardware and software modifications are presented that allow for collection and recognition by a Commodore computer of spoken responses. Responses are timed with millisecond accuracy and automatically analyzed and scored. Accuracy data for this device from several experiments are presented. Potential applications and suggestions for improving recognition accuracy are also discussed.

Automatic Intention Recognition in Conversation Processing

ERIC Educational Resources Information Center

Holtgraves, Thomas

2008-01-01

A fundamental assumption of many theories of conversation is that comprehension of a speaker's utterance involves recognition of the speaker's intention in producing that remark. However, the nature of intention recognition is not clear. One approach is to conceptualize a speaker's intention in terms of speech acts [Searle, J. (1969). "Speech…

Recognition of surface lithologic and topographic patterns in southwest Colorado with ADP techniques

NASA Technical Reports Server (NTRS)

Melhorn, W. N.; Sinnock, S.

1973-01-01

Analysis of ERTS-1 multispectral data by automatic pattern recognition procedures is applicable toward grappling with current and future resource stresses by providing a means for refining existing geologic maps. The procedures used in the current analysis already yield encouraging results toward the eventual machine recognition of extensive surface lithologic and topographic patterns. Automatic mapping of a series of hogbacks, strike valleys, and alluvial surfaces along the northwest flank of the San Juan Basin in Colorado can be obtained by minimal man-machine interaction. The determination of causes for separable spectral signatures is dependent upon extensive correlation of micro- and macro field based ground truth observations and aircraft underflight data with the satellite data.

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.

Liver microRNA profile of induced allograft tolerance

PubMed Central

Vitalone, Matthew James; Wai, Liang; Fujiki, Masato; Lau, Audrey H.; Littau, Erik; Esquivel, Carlos; Martinez, Olivia M.; Krams, Sheri M.

2016-01-01

Introduction Although the liver is less immunogenic than other solid organs, most liver transplant recipients receive lifelong immunosuppression. In both experimental models and clinical transplantation, total Lymphoid Irradiation (TLI) has been shown to induce allograft tolerance. Our goal was to identify the microRNAs (miRNAs) expressed in tolerant liver allograft recipients in an experimental model of TLI-induced tolerance. Methods To identify the miRNAs associated with TLI-induced tolerance we examined syngeneic recipients (Lewis→Lewis) and allogeneic recipients (DA→Lewis) of orthotropic liver transplants that received post-transplant TLI, allogeneic recipients that were not treated post-transplantation and experienced acute rejection, and native DA livers. QPCR miRNA array cards were used to profile liver grafts. Results We identified 12 miRNAs that were specifically and significantly increased during acute rejection. In early tolerance, 33 miRNAs were altered compared to syngeneic livers, with 80% of the miRNAs increased. In established tolerance 42 miRNAs were altered. In addition, miR-142-5p and miR-181a demonstrated increased expression in tolerant livers (both early and established tolerance) as compared to syngeneic livers. A principal component analysis of all miRNAs assayed, demonstrated a profile in established tolerance that was closely related to that seen in syngeneic livers. Conclusions The miRNA profile of established tolerant allografts is very similar to syngeneic grafts suggesting tolerance may be a return to an immunological state of quiescence. PMID:26950716

Automatic integration of social information in emotion recognition.

PubMed

Mumenthaler, Christian; Sander, David

2015-04-01

This study investigated the automaticity of the influence of social inference on emotion recognition. Participants were asked to recognize dynamic facial expressions of emotion (fear or anger in Experiment 1 and blends of fear and surprise or of anger and disgust in Experiment 2) in a target face presented at the center of a screen while a subliminal contextual face appearing in the periphery expressed an emotion (fear or anger) or not (neutral) and either looked at the target face or not. Results of Experiment 1 revealed that recognition of the target emotion of fear was improved when a subliminal angry contextual face gazed toward-rather than away from-the fearful face. We replicated this effect in Experiment 2, in which facial expression blends of fear and surprise were more often and more rapidly categorized as expressing fear when the subliminal contextual face expressed anger and gazed toward-rather than away from-the target face. With the contextual face appearing for 30 ms in total, including only 10 ms of emotion expression, and being immediately masked, our data provide the first evidence that social influence on emotion recognition can occur automatically. (c) 2015 APA, all rights reserved).

On compensation of mismatched recording conditions in the Bayesian approach for forensic automatic speaker recognition.

PubMed

Botti, F; Alexander, A; Drygajlo, A

2004-12-02

This paper deals with a procedure to compensate for mismatched recording conditions in forensic speaker recognition, using a statistical score normalization. Bayesian interpretation of the evidence in forensic automatic speaker recognition depends on three sets of recordings in order to perform forensic casework: reference (R) and control (C) recordings of the suspect, and a potential population database (P), as well as a questioned recording (QR) . The requirement of similar recording conditions between suspect control database (C) and the questioned recording (QR) is often not satisfied in real forensic cases. The aim of this paper is to investigate a procedure of normalization of scores, which is based on an adaptation of the Test-normalization (T-norm) [2] technique used in the speaker verification domain, to compensate for the mismatch. Polyphone IPSC-02 database and ASPIC (an automatic speaker recognition system developed by EPFL and IPS-UNIL in Lausanne, Switzerland) were used in order to test the normalization procedure. Experimental results for three different recording condition scenarios are presented using Tippett plots and the effect of the compensation on the evaluation of the strength of the evidence is discussed.

Automatic image database generation from CAD for 3D object recognition

NASA Astrophysics Data System (ADS)

Sardana, Harish K.; Daemi, Mohammad F.; Ibrahim, Mohammad K.

1993-06-01

The development and evaluation of Multiple-View 3-D object recognition systems is based on a large set of model images. Due to the various advantages of using CAD, it is becoming more and more practical to use existing CAD data in computer vision systems. Current PC- level CAD systems are capable of providing physical image modelling and rendering involving positional variations in cameras, light sources etc. We have formulated a modular scheme for automatic generation of various aspects (views) of the objects in a model based 3-D object recognition system. These views are generated at desired orientations on the unit Gaussian sphere. With a suitable network file sharing system (NFS), the images can directly be stored on a database located on a file server. This paper presents the image modelling solutions using CAD in relation to multiple-view approach. Our modular scheme for data conversion and automatic image database storage for such a system is discussed. We have used this approach in 3-D polyhedron recognition. An overview of the results, advantages and limitations of using CAD data and conclusions using such as scheme are also presented.

Automatic lip reading by using multimodal visual features

NASA Astrophysics Data System (ADS)

Takahashi, Shohei; Ohya, Jun

2013-12-01

Since long time ago, speech recognition has been researched, though it does not work well in noisy places such as in the car or in the train. In addition, people with hearing-impaired or difficulties in hearing cannot receive benefits from speech recognition. To recognize the speech automatically, visual information is also important. People understand speeches from not only audio information, but also visual information such as temporal changes in the lip shape. A vision based speech recognition method could work well in noisy places, and could be useful also for people with hearing disabilities. In this paper, we propose an automatic lip-reading method for recognizing the speech by using multimodal visual information without using any audio information such as speech recognition. First, the ASM (Active Shape Model) is used to track and detect the face and lip in a video sequence. Second, the shape, optical flow and spatial frequencies of the lip features are extracted from the lip detected by ASM. Next, the extracted multimodal features are ordered chronologically so that Support Vector Machine is performed in order to learn and classify the spoken words. Experiments for classifying several words show promising results of this proposed method.

I Hear You Eat and Speak: Automatic Recognition of Eating Condition and Food Type, Use-Cases, and Impact on ASR Performance

PubMed Central

Hantke, Simone; Weninger, Felix; Kurle, Richard; Ringeval, Fabien; Batliner, Anton; Mousa, Amr El-Desoky; Schuller, Björn

2016-01-01

We propose a new recognition task in the area of computational paralinguistics: automatic recognition of eating conditions in speech, i. e., whether people are eating while speaking, and what they are eating. To this end, we introduce the audio-visual iHEARu-EAT database featuring 1.6 k utterances of 30 subjects (mean age: 26.1 years, standard deviation: 2.66 years, gender balanced, German speakers), six types of food (Apple, Nectarine, Banana, Haribo Smurfs, Biscuit, and Crisps), and read as well as spontaneous speech, which is made publicly available for research purposes. We start with demonstrating that for automatic speech recognition (ASR), it pays off to know whether speakers are eating or not. We also propose automatic classification both by brute-forcing of low-level acoustic features as well as higher-level features related to intelligibility, obtained from an Automatic Speech Recogniser. Prediction of the eating condition was performed with a Support Vector Machine (SVM) classifier employed in a leave-one-speaker-out evaluation framework. Results show that the binary prediction of eating condition (i. e., eating or not eating) can be easily solved independently of the speaking condition; the obtained average recalls are all above 90%. Low-level acoustic features provide the best performance on spontaneous speech, which reaches up to 62.3% average recall for multi-way classification of the eating condition, i. e., discriminating the six types of food, as well as not eating. The early fusion of features related to intelligibility with the brute-forced acoustic feature set improves the performance on read speech, reaching a 66.4% average recall for the multi-way classification task. Analysing features and classifier errors leads to a suitable ordinal scale for eating conditions, on which automatic regression can be performed with up to 56.2% determination coefficient. PMID:27176486

Monitoring caustic injuries from emergency department databases using automatic keyword recognition software.

PubMed

Vignally, P; Fondi, G; Taggi, F; Pitidis, A

2011-03-31

In Italy the European Union Injury Database reports the involvement of chemical products in 0.9% of home and leisure accidents. The Emergency Department registry on domestic accidents in Italy and the Poison Control Centres record that 90% of cases of exposure to toxic substances occur in the home. It is not rare for the effects of chemical agents to be observed in hospitals, with a high potential risk of damage - the rate of this cause of hospital admission is double the domestic injury average. The aim of this study was to monitor the effects of injuries caused by caustic agents in Italy using automatic free-text recognition in Emergency Department medical databases. We created a Stata software program to automatically identify caustic or corrosive injury cases using an agent-specific list of keywords. We focused attention on the procedure's sensitivity and specificity. Ten hospitals in six regions of Italy participated in the study. The program identified 112 cases of injury by caustic or corrosive agents. Checking the cases by quality controls (based on manual reading of ED reports), we assessed 99 cases as true positive, i.e. 88.4% of the patients were automatically recognized by the software as being affected by caustic substances (99% CI: 80.6%- 96.2%), that is to say 0.59% (99% CI: 0.45%-0.76%) of the whole sample of home injuries, a value almost three times as high as that expected (p < 0.0001) from European codified information. False positives were 11.6% of the recognized cases (99% CI: 5.1%- 21.5%). Our automatic procedure for caustic agent identification proved to have excellent product recognition capacity with an acceptable level of excess sensitivity. Contrary to our a priori hypothesis, the automatic recognition system provided a level of identification of agents possessing caustic effects that was significantly much greater than was predictable on the basis of the values from current codifications reported in the European Database.

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC

PubMed Central

Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich

2015-01-01

Objective To create a multilingual gold-standard corpus for biomedical concept recognition. Materials and methods We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. Results The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. Discussion The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. Conclusion To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. PMID:25948699

Automatic Recognition of Fetal Facial Standard Plane in Ultrasound Image via Fisher Vector.

PubMed

Lei, Baiying; Tan, Ee-Leng; Chen, Siping; Zhuo, Liu; Li, Shengli; Ni, Dong; Wang, Tianfu

2015-01-01

Acquisition of the standard plane is the prerequisite of biometric measurement and diagnosis during the ultrasound (US) examination. In this paper, a new algorithm is developed for the automatic recognition of the fetal facial standard planes (FFSPs) such as the axial, coronal, and sagittal planes. Specifically, densely sampled root scale invariant feature transform (RootSIFT) features are extracted and then encoded by Fisher vector (FV). The Fisher network with multi-layer design is also developed to extract spatial information to boost the classification performance. Finally, automatic recognition of the FFSPs is implemented by support vector machine (SVM) classifier based on the stochastic dual coordinate ascent (SDCA) algorithm. Experimental results using our dataset demonstrate that the proposed method achieves an accuracy of 93.27% and a mean average precision (mAP) of 99.19% in recognizing different FFSPs. Furthermore, the comparative analyses reveal the superiority of the proposed method based on FV over the traditional methods.

Key features for ATA / ATR database design in missile systems

NASA Astrophysics Data System (ADS)

Özertem, Kemal Arda

2017-05-01

Automatic target acquisition (ATA) and automatic target recognition (ATR) are two vital tasks for missile systems, and having a robust detection and recognition algorithm is crucial for overall system performance. In order to have a robust target detection and recognition algorithm, an extensive image database is required. Automatic target recognition algorithms use the database of images in training and testing steps of algorithm. This directly affects the recognition performance, since the training accuracy is driven by the quality of the image database. In addition, the performance of an automatic target detection algorithm can be measured effectively by using an image database. There are two main ways for designing an ATA / ATR database. The first and easy way is by using a scene generator. A scene generator can model the objects by considering its material information, the atmospheric conditions, detector type and the territory. Designing image database by using a scene generator is inexpensive and it allows creating many different scenarios quickly and easily. However the major drawback of using a scene generator is its low fidelity, since the images are created virtually. The second and difficult way is designing it using real-world images. Designing image database with real-world images is a lot more costly and time consuming; however it offers high fidelity, which is critical for missile algorithms. In this paper, critical concepts in ATA / ATR database design with real-world images are discussed. Each concept is discussed in the perspective of ATA and ATR separately. For the implementation stage, some possible solutions and trade-offs for creating the database are proposed, and all proposed approaches are compared to each other with regards to their pros and cons.

Cost/benefit analysis of electronic license plates

DOT National Transportation Integrated Search

2008-06-01

The objective of this report is to determine whether electronic vehicle recognition systems (EVR) or automatic license plate recognition systems (ALPR) would be beneficial to the Arizona Department of Transportation (AZDOT). EVR uses radio frequency ...

An adaptive Hidden Markov Model for activity recognition based on a wearable multi-sensor device

USDA-ARS?s Scientific Manuscript database

Human activity recognition is important in the study of personal health, wellness and lifestyle. In order to acquire human activity information from the personal space, many wearable multi-sensor devices have been developed. In this paper, a novel technique for automatic activity recognition based o...

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish.

PubMed

Vieira, Manuel; Fonseca, Paulo J; Amorim, M Clara P; Teixeira, Carlos J C

2015-12-01

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Prosody's Contribution to Fluency: An Examination of the Theory of Automatic Information Processing

ERIC Educational Resources Information Center

Schrauben, Julie E.

2010-01-01

LaBerge and Samuels' (1974) theory of automatic information processing in reading offers a model that explains how and where the processing of information occurs and the degree to which processing of information occurs. These processes are dependent upon two criteria: accurate word decoding and automatic word recognition. However, LaBerge and…

Face Averages Enhance User Recognition for Smartphone Security

PubMed Central

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

2015-01-01

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

Object Occlusion Detection Using Automatic Camera Calibration for a Wide-Area Video Surveillance System

PubMed Central

Jung, Jaehoon; Yoon, Inhye; Paik, Joonki

2016-01-01

This paper presents an object occlusion detection algorithm using object depth information that is estimated by automatic camera calibration. The object occlusion problem is a major factor to degrade the performance of object tracking and recognition. To detect an object occlusion, the proposed algorithm consists of three steps: (i) automatic camera calibration using both moving objects and a background structure; (ii) object depth estimation; and (iii) detection of occluded regions. The proposed algorithm estimates the depth of the object without extra sensors but with a generic red, green and blue (RGB) camera. As a result, the proposed algorithm can be applied to improve the performance of object tracking and object recognition algorithms for video surveillance systems. PMID:27347978

Photonic correlator pattern recognition: Application to autonomous docking

NASA Technical Reports Server (NTRS)

Sjolander, Gary W.

1991-01-01

Optical correlators for real-time automatic pattern recognition applications have recently become feasible due to advances in high speed devices and filter formulation concepts. The devices are discussed in the context of their use in autonomous docking.

Measurement Marker Recognition In A Time Sequence Of Infrared Images For Biomedical Applications

NASA Astrophysics Data System (ADS)

Fiorini, A. R.; Fumero, R.; Marchesi, R.

1986-03-01

In thermographic measurements, quantitative surface temperature evaluation is often uncertain. The main reason is in the lack of available reference points in transient conditions. Reflective markers were used for automatic marker recognition and pixel coordinate computations. An algorithm selects marker icons to match marker references where particular luminance conditions are satisfied. Automatic marker recognition allows luminance compensation and temperature calibration of recorded infrared images. A biomedical application is presented: the dynamic behaviour of the surface temperature distributions is investigated in order to study the performance of two different pumping systems for extracorporeal circulation. Sequences of images are compared and results are discussed. Finally, the algorithm allows to monitor the experimental environment and to alert for the presence of unusual experimental conditions.

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Does time-lapse imaging have favorable results for embryo incubation and selection compared with conventional methods in clinical in vitro fertilization? A meta-analysis and systematic review of randomized controlled trials

PubMed Central

Yuan, Jing; Liu, Fenghua

2017-01-01

Objective The present study aimed to undertake a review of available evidence assessing whether time-lapse imaging (TLI) has favorable outcomes for embryo incubation and selection compared with conventional methods in clinical in vitro fertilization (IVF). Methods Using PubMed, EMBASE, Cochrane library and ClinicalTrial.gov up to February 2017 to search for randomized controlled trials (RCTs) comparing TLI versus conventional methods. Both studies randomized women and oocytes were included. For studies randomized women, the primary outcomes were live birth and ongoing pregnancy, the secondary outcomes were clinical pregnancy and miscarriage; for studies randomized oocytes, the primary outcome was blastocyst rate, the secondary outcome was good quality embryo on Day 2/3. Subgroup analysis was conducted based on different incubation and embryo selection between groups. Results Ten RCTs were included, four randomized oocytes and six randomized women. For oocyte-based review, the pool-analysis observed no significant difference between TLI group and control group for blastocyst rate [relative risk (RR) 1.08, 95% CI 0.94–1.25, I2 = 0%, two studies, including 1154 embryos]. The quality of evidence was moderate for all outcomes in oocyte-based review. For woman-based review, only one study provided live birth rate (RR 1,23, 95% CI 1.06–1.44,I2 N/A, one study, including 842 women), the pooled result showed no significant difference in ongoing pregnancy rate (RR 1.04, 95% CI 0.80–1.36, I2 = 59%, four studies, including 1403 women) between two groups. The quality of the evidence was low or very low for all outcomes in woman-based review. Conclusions Currently there is insufficient evidence to support that TLI is superior to conventional methods for human embryo incubation and selection. In consideration of the limitations and flaws of included studies, more well designed RCTs are still in need to comprehensively evaluate the effectiveness of clinical TLI use. PMID:28570713

Thai Automatic Speech Recognition

DTIC Science & Technology

2005-01-01

used in an external DARPA evaluation involving medical scenarios between an American Doctor and a naïve monolingual Thai patient. 2. Thai Language... dictionary generation more challenging, and (3) the lack of word segmentation, which calls for automatic segmentation approaches to make n-gram language...requires a dictionary and provides various segmentation algorithms to automatically select suitable segmentations. Here we used a maximal matching

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.

Method for automatic detection of wheezing in lung sounds.

PubMed

Riella, R J; Nohama, P; Maia, J M

2009-07-01

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

A multilingual gold-standard corpus for biomedical concept recognition: the Mantra GSC.

PubMed

Kors, Jan A; Clematide, Simon; Akhondi, Saber A; van Mulligen, Erik M; Rebholz-Schuhmann, Dietrich

2015-09-01

To create a multilingual gold-standard corpus for biomedical concept recognition. We selected text units from different parallel corpora (Medline abstract titles, drug labels, biomedical patent claims) in English, French, German, Spanish, and Dutch. Three annotators per language independently annotated the biomedical concepts, based on a subset of the Unified Medical Language System and covering a wide range of semantic groups. To reduce the annotation workload, automatically generated preannotations were provided. Individual annotations were automatically harmonized and then adjudicated, and cross-language consistency checks were carried out to arrive at the final annotations. The number of final annotations was 5530. Inter-annotator agreement scores indicate good agreement (median F-score 0.79), and are similar to those between individual annotators and the gold standard. The automatically generated harmonized annotation set for each language performed equally well as the best annotator for that language. The use of automatic preannotations, harmonized annotations, and parallel corpora helped to keep the manual annotation efforts manageable. The inter-annotator agreement scores provide a reference standard for gauging the performance of automatic annotation techniques. To our knowledge, this is the first gold-standard corpus for biomedical concept recognition in languages other than English. Other distinguishing features are the wide variety of semantic groups that are being covered, and the diversity of text genres that were annotated. © The Author 2015. Published by Oxford University Press on behalf of the American Medical Informatics Association.

[Research on automatic external defibrillator based on DSP].

PubMed

Jing, Jun; Ding, Jingyan; Zhang, Wei; Hong, Wenxue

2012-10-01

Electrical defibrillation is the most effective way to treat the ventricular tachycardia (VT) and ventricular fibrillation (VF). An automatic external defibrillator based on DSP is introduced in this paper. The whole design consists of the signal collection module, the microprocessor controlingl module, the display module, the defibrillation module and the automatic recognition algorithm for VF and non VF, etc. This automatic external defibrillator has achieved goals such as ECG signal real-time acquisition, ECG wave synchronous display, data delivering to U disk and automatic defibrillate when shockable rhythm appears, etc.

A System for Mailpiece ZIP Code Assignment through Contextual Analysis. Phase 2

DTIC Science & Technology

1991-03-01

Segmentation Address Block Interpretation Automatic Feature Generation Word Recognition Feature Detection Word Verification Optical Character Recognition Directory...in the Phase III effort. 1.1 Motivation The United States Postal Service (USPS) deploys large numbers of optical character recognition (OCR) machines...4):208-218, November 1986. [2] Gronmeyer, L. K., Ruffin, B. W., Lybanon, M. A., Neely, P. L., and Pierce, S. E. An Overview of Optical Character Recognition (OCR

Fully automatic segmentation of the femur from 3D-CT images using primitive shape recognition and statistical shape models.

PubMed

Ben Younes, Lassad; Nakajima, Yoshikazu; Saito, Toki

2014-03-01

Femur segmentation is well established and widely used in computer-assisted orthopedic surgery. However, most of the robust segmentation methods such as statistical shape models (SSM) require human intervention to provide an initial position for the SSM. In this paper, we propose to overcome this problem and provide a fully automatic femur segmentation method for CT images based on primitive shape recognition and SSM. Femur segmentation in CT scans was performed using primitive shape recognition based on a robust algorithm such as the Hough transform and RANdom SAmple Consensus. The proposed method is divided into 3 steps: (1) detection of the femoral head as sphere and the femoral shaft as cylinder in the SSM and the CT images, (2) rigid registration between primitives of SSM and CT image to initialize the SSM into the CT image, and (3) fitting of the SSM to the CT image edge using an affine transformation followed by a nonlinear fitting. The automated method provided good results even with a high number of outliers. The difference of segmentation error between the proposed automatic initialization method and a manual initialization method is less than 1 mm. The proposed method detects primitive shape position to initialize the SSM into the target image. Based on primitive shapes, this method overcomes the problem of inter-patient variability. Moreover, the results demonstrate that our method of primitive shape recognition can be used for 3D SSM initialization to achieve fully automatic segmentation of the femur.

Automatic recognition of lactating sow behaviors through depth image processing

USDA-ARS?s Scientific Manuscript database

Manual observation and classification of animal behaviors is laborious, time-consuming, and of limited ability to process large amount of data. A computer vision-based system was developed that automatically recognizes sow behaviors (lying, sitting, standing, kneeling, feeding, drinking, and shiftin...

Three-dimensional model-based object recognition and segmentation in cluttered scenes.

PubMed

Mian, Ajmal S; Bennamoun, Mohammed; Owens, Robyn

2006-10-01

Viewpoint independent recognition of free-form objects and their segmentation in the presence of clutter and occlusions is a challenging task. We present a novel 3D model-based algorithm which performs this task automatically and efficiently. A 3D model of an object is automatically constructed offline from its multiple unordered range images (views). These views are converted into multidimensional table representations (which we refer to as tensors). Correspondences are automatically established between these views by simultaneously matching the tensors of a view with those of the remaining views using a hash table-based voting scheme. This results in a graph of relative transformations used to register the views before they are integrated into a seamless 3D model. These models and their tensor representations constitute the model library. During online recognition, a tensor from the scene is simultaneously matched with those in the library by casting votes. Similarity measures are calculated for the model tensors which receive the most votes. The model with the highest similarity is transformed to the scene and, if it aligns accurately with an object in the scene, that object is declared as recognized and is segmented. This process is repeated until the scene is completely segmented. Experiments were performed on real and synthetic data comprised of 55 models and 610 scenes and an overall recognition rate of 95 percent was achieved. Comparison with the spin images revealed that our algorithm is superior in terms of recognition rate and efficiency.

Optimal Terminal Descent Guidance Logic to Achieve a Soft Lunar Touchdown

NASA Technical Reports Server (NTRS)

Lee, Allan Y.

2011-01-01

Altair Lunar Lander is the linchpin in the Constellation Program for human return to the Moon. In the 2010design reference mission, Altair is to be delivered to low Earth orbit by the Ares V heavy lift launch vehicle, and after subsequent docking with Orion in LEO, the Altair/Orion stack is delivered through trans-lunar injection (TLI). The Altair/Orion stack separates from the Ares V Earth departure stage shortly after TLI and continues the flight to the Moon as a single stack. Fig. 1 depicts one version of the Altair lunar lander.

iFER: facial expression recognition using automatically selected geometric eye and eyebrow features

NASA Astrophysics Data System (ADS)

Oztel, Ismail; Yolcu, Gozde; Oz, Cemil; Kazan, Serap; Bunyak, Filiz

2018-03-01

Facial expressions have an important role in interpersonal communications and estimation of emotional states or intentions. Automatic recognition of facial expressions has led to many practical applications and became one of the important topics in computer vision. We present a facial expression recognition system that relies on geometry-based features extracted from eye and eyebrow regions of the face. The proposed system detects keypoints on frontal face images and forms a feature set using geometric relationships among groups of detected keypoints. Obtained feature set is refined and reduced using the sequential forward selection (SFS) algorithm and fed to a support vector machine classifier to recognize five facial expression classes. The proposed system, iFER (eye-eyebrow only facial expression recognition), is robust to lower face occlusions that may be caused by beards, mustaches, scarves, etc. and lower face motion during speech production. Preliminary experiments on benchmark datasets produced promising results outperforming previous facial expression recognition studies using partial face features, and comparable results to studies using whole face information, only slightly lower by ˜ 2.5 % compared to the best whole face facial recognition system while using only ˜ 1 / 3 of the facial region.

The Automatic Recognition of the Abnormal Sky-subtraction Spectra Based on Hadoop

NASA Astrophysics Data System (ADS)

An, An; Pan, Jingchang

2017-10-01

The skylines, superimposing on the target spectrum as a main noise, If the spectrum still contains a large number of high strength skylight residuals after sky-subtraction processing, it will not be conducive to the follow-up analysis of the target spectrum. At the same time, the LAMOST can observe a quantity of spectroscopic data in every night. We need an efficient platform to proceed the recognition of the larger numbers of abnormal sky-subtraction spectra quickly. Hadoop, as a distributed parallel data computing platform, can deal with large amounts of data effectively. In this paper, we conduct the continuum normalization firstly and then a simple and effective method will be presented to automatic recognize the abnormal sky-subtraction spectra based on Hadoop platform. Obtain through the experiment, the Hadoop platform can implement the recognition with more speed and efficiency, and the simple method can recognize the abnormal sky-subtraction spectra and find the abnormal skyline positions of different residual strength effectively, can be applied to the automatic detection of abnormal sky-subtraction of large number of spectra.

Automatic detection and recognition of signs from natural scenes.

PubMed

Chen, Xilin; Yang, Jie; Zhang, Jing; Waibel, Alex

2004-01-01

In this paper, we present an approach to automatic detection and recognition of signs from natural scenes, and its application to a sign translation task. The proposed approach embeds multiresolution and multiscale edge detection, adaptive searching, color analysis, and affine rectification in a hierarchical framework for sign detection, with different emphases at each phase to handle the text in different sizes, orientations, color distributions and backgrounds. We use affine rectification to recover deformation of the text regions caused by an inappropriate camera view angle. The procedure can significantly improve text detection rate and optical character recognition (OCR) accuracy. Instead of using binary information for OCR, we extract features from an intensity image directly. We propose a local intensity normalization method to effectively handle lighting variations, followed by a Gabor transform to obtain local features, and finally a linear discriminant analysis (LDA) method for feature selection. We have applied the approach in developing a Chinese sign translation system, which can automatically detect and recognize Chinese signs as input from a camera, and translate the recognized text into English.

Neural-network classifiers for automatic real-world aerial image recognition

NASA Astrophysics Data System (ADS)

Greenberg, Shlomo; Guterman, Hugo

1996-08-01

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Neural-network classifiers for automatic real-world aerial image recognition.

PubMed

Greenberg, S; Guterman, H

1996-08-10

We describe the application of the multilayer perceptron (MLP) network and a version of the adaptive resonance theory version 2-A (ART 2-A) network to the problem of automatic aerial image recognition (AAIR). The classification of aerial images, independent of their positions and orientations, is required for automatic tracking and target recognition. Invariance is achieved by the use of different invariant feature spaces in combination with supervised and unsupervised neural networks. The performance of neural-network-based classifiers in conjunction with several types of invariant AAIR global features, such as the Fourier-transform space, Zernike moments, central moments, and polar transforms, are examined. The advantages of this approach are discussed. The performance of the MLP network is compared with that of a classical correlator. The MLP neural-network correlator outperformed the binary phase-only filter (BPOF) correlator. It was found that the ART 2-A distinguished itself with its speed and its low number of required training vectors. However, only the MLP classifier was able to deal with a combination of shift and rotation geometric distortions.

Automatic recognition of light source from color negative films using sorting classification techniques

NASA Astrophysics Data System (ADS)

Sanger, Demas S.; Haneishi, Hideaki; Miyake, Yoichi

1995-08-01

This paper proposed a simple and automatic method for recognizing the light sources from various color negative film brands by means of digital image processing. First, we stretched the image obtained from a negative based on the standardized scaling factors, then extracted the dominant color component among red, green, and blue components of the stretched image. The dominant color component became the discriminator for the recognition. The experimental results verified that any one of the three techniques could recognize the light source from negatives of any film brands and all brands greater than 93.2 and 96.6% correct recognitions, respectively. This method is significant for the automation of color quality control in color reproduction from color negative film in mass processing and printing machine.

Fashioning the Face: Sensorimotor Simulation Contributes to Facial Expression Recognition.

PubMed

Wood, Adrienne; Rychlowska, Magdalena; Korb, Sebastian; Niedenthal, Paula

2016-03-01

When we observe a facial expression of emotion, we often mimic it. This automatic mimicry reflects underlying sensorimotor simulation that supports accurate emotion recognition. Why this is so is becoming more obvious: emotions are patterns of expressive, behavioral, physiological, and subjective feeling responses. Activation of one component can therefore automatically activate other components. When people simulate a perceived facial expression, they partially activate the corresponding emotional state in themselves, which provides a basis for inferring the underlying emotion of the expresser. We integrate recent evidence in favor of a role for sensorimotor simulation in emotion recognition. We then connect this account to a domain-general understanding of how sensory information from multiple modalities is integrated to generate perceptual predictions in the brain. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Hybrid neuro-fuzzy approach for automatic vehicle license plate recognition

NASA Astrophysics Data System (ADS)

Lee, Hsi-Chieh; Jong, Chung-Shi

1998-03-01

Most currently available vehicle identification systems use techniques such as R.F., microwave, or infrared to help identifying the vehicle. Transponders are usually installed in the vehicle in order to transmit the corresponding information to the sensory system. It is considered expensive to install a transponder in each vehicle and the malfunction of the transponder will result in the failure of the vehicle identification system. In this study, novel hybrid approach is proposed for automatic vehicle license plate recognition. A system prototype is built which can be used independently or cooperating with current vehicle identification system in identifying a vehicle. The prototype consists of four major modules including the module for license plate region identification, the module for character extraction from the license plate, the module for character recognition, and the module for the SimNet neuro-fuzzy system. To test the performance of the proposed system, three hundred and eighty vehicle image samples are taken by a digital camera. The license plate recognition success rate of the prototype is approximately 91% while the character recognition success rate of the prototype is approximately 97%.

RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

NASA Astrophysics Data System (ADS)

El Bekri, Nadia; Angele, Susanne; Ruckhäberle, Martin; Peinsipp-Byma, Elisabeth; Haelke, Bruno

2015-10-01

This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The infrastructure analysis mode pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system offers him the possibility to assign the image signatures to features given by sample images. The underlying data set contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible solution set of objects is automatically reduced and matches only the objects that contain the selected features. Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly automated methods to extract image signatures and assign them to the corresponding features. This research includes methods for automatically determining the orientation of an object and geometric features like width and length of the object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive recognition assistance system.

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

Neural networks: Alternatives to conventional techniques for automatic docking

NASA Technical Reports Server (NTRS)

Vinz, Bradley L.

1994-01-01

Automatic docking of orbiting spacecraft is a crucial operation involving the identification of vehicle orientation as well as complex approach dynamics. The chaser spacecraft must be able to recognize the target spacecraft within a scene and achieve accurate closing maneuvers. In a video-based system, a target scene must be captured and transformed into a pattern of pixels. Successful recognition lies in the interpretation of this pattern. Due to their powerful pattern recognition capabilities, artificial neural networks offer a potential role in interpretation and automatic docking processes. Neural networks can reduce the computational time required by existing image processing and control software. In addition, neural networks are capable of recognizing and adapting to changes in their dynamic environment, enabling enhanced performance, redundancy, and fault tolerance. Most neural networks are robust to failure, capable of continued operation with a slight degradation in performance after minor failures. This paper discusses the particular automatic docking tasks neural networks can perform as viable alternatives to conventional techniques.

Automatic recognition of fundamental tissues on histology images of the human cardiovascular system.

PubMed

Mazo, Claudia; Trujillo, Maria; Alegre, Enrique; Salazar, Liliana

2016-10-01

Cardiovascular disease is the leading cause of death worldwide. Therefore, techniques for improving diagnosis and treatment in this field have become key areas for research. In particular, approaches for tissue image processing may support education system and medical practice. In this paper, an approach to automatic recognition and classification of fundamental tissues, using morphological information is presented. Taking a 40× or 10× histological image as input, three clusters are created with the k-means algorithm using a structural tensor and the red and the green channels. Loose connective tissue, light regions and cell nuclei are recognised on 40× images. Then, the cell nuclei's features - shape and spatial projection - and light regions are used to recognise and classify epithelial cells and tissue into flat, cubic and cylindrical. In a similar way, light regions, loose connective and muscle tissues are recognised on 10× images. Finally, the tissue's function and composition are used to refine muscle tissue recognition. Experimental validation is then carried out by histologist following expert criteria, along with manually annotated images that are used as a ground-truth. The results revealed that the proposed approach classified the fundamental tissues in a similar way to the conventional method employed by histologists. The proposed automatic recognition approach provides for epithelial tissues a sensitivity of 0.79 for cubic, 0.85 for cylindrical and 0.91 for flat. Furthermore, the experts gave our method an average score of 4.85 out of 5 in the recognition of loose connective tissue and 4.82 out of 5 for muscle tissue recognition. Copyright © 2016 Elsevier Ltd. All rights reserved.

Parametric Representation of the Speaker's Lips for Multimodal Sign Language and Speech Recognition

NASA Astrophysics Data System (ADS)

Ryumin, D.; Karpov, A. A.

2017-05-01

In this article, we propose a new method for parametric representation of human's lips region. The functional diagram of the method is described and implementation details with the explanation of its key stages and features are given. The results of automatic detection of the regions of interest are illustrated. A speed of the method work using several computers with different performances is reported. This universal method allows applying parametrical representation of the speaker's lipsfor the tasks of biometrics, computer vision, machine learning, and automatic recognition of face, elements of sign languages, and audio-visual speech, including lip-reading.

Terrain type recognition using ERTS-1 MSS images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N.

1973-01-01

For the automatic recognition of earth resources from ERTS-1 digital tapes, both multispectral and spatial pattern recognition techniques are important. Recognition of terrain types is based on spatial signatures that become evident by processing small portions of an image through selected algorithms. An investigation of spatial signatures that are applicable to ERTS-1 MSS images is described. Artifacts in the spatial signatures seem to be related to the multispectral scanner. A method for suppressing such artifacts is presented. Finally, results of terrain type recognition for one ERTS-1 image are presented.

Automatic anatomy recognition via multiobject oriented active shape models.

PubMed

Chen, Xinjian; Udupa, Jayaram K; Alavi, Abass; Torigian, Drew A

2010-12-01

This paper studies the feasibility of developing an automatic anatomy recognition (AAR) system in clinical radiology and demonstrates its operation on clinical 2D images. The anatomy recognition method described here consists of two main components: (a) multiobject generalization of OASM and (b) object recognition strategies. The OASM algorithm is generalized to multiple objects by including a model for each object and assigning a cost structure specific to each object in the spirit of live wire. The delineation of multiobject boundaries is done in MOASM via a three level dynamic programming algorithm, wherein the first level is at pixel level which aims to find optimal oriented boundary segments between successive landmarks, the second level is at landmark level which aims to find optimal location for the landmarks, and the third level is at the object level which aims to find optimal arrangement of object boundaries over all objects. The object recognition strategy attempts to find that pose vector (consisting of translation, rotation, and scale component) for the multiobject model that yields the smallest total boundary cost for all objects. The delineation and recognition accuracies were evaluated separately utilizing routine clinical chest CT, abdominal CT, and foot MRI data sets. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF and FPVF). The recognition accuracy was assessed (1) in terms of the size of the space of the pose vectors for the model assembly that yielded high delineation accuracy, (2) as a function of the number of objects and objects' distribution and size in the model, (3) in terms of the interdependence between delineation and recognition, and (4) in terms of the closeness of the optimum recognition result to the global optimum. When multiple objects are included in the model, the delineation accuracy in terms of TPVF can be improved to 97%-98% with a low FPVF of 0.1%-0.2%. Typically, a recognition accuracy of > or = 90% yielded a TPVF > or = 95% and FPVF < or = 0.5%. Over the three data sets and over all tested objects, in 97% of the cases, the optimal solutions found by the proposed method constituted the true global optimum. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy recognition system. Increasing the number of objects in the model can significantly improve both recognition and delineation accuracy. More spread out arrangement of objects in the model can lead to improved recognition and delineation accuracy. Including larger objects in the model also improved recognition and delineation. The proposed method almost always finds globally optimum solutions.

Randomized controlled trial comparing embryo culture in two incubator systems: G185 K-System versus EmbryoScope.

PubMed

Barberet, Julie; Chammas, Jérémy; Bruno, Céline; Valot, Elodie; Vuillemin, Clarisse; Jonval, Lysiane; Choux, Cécile; Sagot, Paul; Soudry, Agnès; Fauque, Patricia

2018-02-01

To study whether the closed culture system, as compared with a benchtop incubator with similar culture conditions, has a positive impact on intracytoplasmic sperm injection (ICSI) outcomes. Randomized controlled trial. University hospital. A total of 386 patients undergoing ICSI cycles with at least six mature oocytes were randomized. Of these patients, 195 were assigned to the group with culture in a time-lapse imaging (TLI) system (EmbryoScope) and 191 to the group with culture in the G185 K-System (G185). Rate of implantation (primary endpoint) and embryo morphology grade. No significant differences were found in the implantation rates. The proportion of high-grade embryos on day 2 was significantly higher in the TLI group compared with the G185 group (40.4% vs. 35.2%). The impact of the incubator on embryo morphology remained significant in multivariate analysis, which took into account the woman's age, the rank of attempt, and the smoking status (TLI vs. G185: odds ratio = 1.27; 95% confidence interval, [1.04-1.55]). No difference was found in the mean number of frozen embryos, even though the total proportion of frozen embryos was significantly higher in the TLI group than in the G185 group (29.5% vs. 24.8%). No difference in implantation rate was found between the two incubators for fresh cycles. It remains to be determined whether the observed differences in embryo morphology and the total number of embryos cryopreserved would translate into higher cumulative outcomes with subsequent frozen embryo transfers. NCT02722252. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Puzzle test: A tool for non-analytical clinical reasoning assessment.

PubMed

Monajemi, Alireza; Yaghmaei, Minoo

2016-01-01

Most contemporary clinical reasoning tests typically assess non-automatic thinking. Therefore, a test is needed to measure automatic reasoning or pattern recognition, which has been largely neglected in clinical reasoning tests. The Puzzle Test (PT) is dedicated to assess automatic clinical reasoning in routine situations. This test has been introduced first in 2009 by Monajemi et al in the Olympiad for Medical Sciences Students.PT is an item format that has gained acceptance in medical education, but no detailed guidelines exist for this test's format, construction and scoring. In this article, a format is described and the steps to prepare and administer valid and reliable PTs are presented. PT examines a specific clinical reasoning task: Pattern recognition. PT does not replace other clinical reasoning assessment tools. However, it complements them in strategies for assessing comprehensive clinical reasoning.

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.

Salient Feature Identification and Analysis using Kernel-Based Classification Techniques for Synthetic Aperture Radar Automatic Target Recognition

DTIC Science & Technology

2014-03-27

and machine learning for a range of research including such topics as medical imaging [10] and handwriting recognition [11]. The type of feature...1989. [11] C. Bahlmann, B. Haasdonk, and H. Burkhardt, “Online handwriting recognition with support vector machines-a kernel approach,” in Eighth...International Workshop on Frontiers in Handwriting Recognition, pp. 49–54, IEEE, 2002. [12] C. Cortes and V. Vapnik, “Support-vector networks,” Machine

Assessing the impact of graphical quality on automatic text recognition in digital maps

NASA Astrophysics Data System (ADS)

Chiang, Yao-Yi; Leyk, Stefan; Honarvar Nazari, Narges; Moghaddam, Sima; Tan, Tian Xiang

2016-08-01

Converting geographic features (e.g., place names) in map images into a vector format is the first step for incorporating cartographic information into a geographic information system (GIS). With the advancement in computational power and algorithm design, map processing systems have been considerably improved over the last decade. However, the fundamental map processing techniques such as color image segmentation, (map) layer separation, and object recognition are sensitive to minor variations in graphical properties of the input image (e.g., scanning resolution). As a result, most map processing results would not meet user expectations if the user does not "properly" scan the map of interest, pre-process the map image (e.g., using compression or not), and train the processing system, accordingly. These issues could slow down the further advancement of map processing techniques as such unsuccessful attempts create a discouraged user community, and less sophisticated tools would be perceived as more viable solutions. Thus, it is important to understand what kinds of maps are suitable for automatic map processing and what types of results and process-related errors can be expected. In this paper, we shed light on these questions by using a typical map processing task, text recognition, to discuss a number of map instances that vary in suitability for automatic processing. We also present an extensive experiment on a diverse set of scanned historical maps to provide measures of baseline performance of a standard text recognition tool under varying map conditions (graphical quality) and text representations (that can vary even within the same map sheet). Our experimental results help the user understand what to expect when a fully or semi-automatic map processing system is used to process a scanned map with certain (varying) graphical properties and complexities in map content.

Human Activity Recognition in AAL Environments Using Random Projections.

PubMed

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented.

Integrated approach for automatic target recognition using a network of collaborative sensors.

PubMed

Mahalanobis, Abhijit; Van Nevel, Alan

2006-10-01

We introduce what is believed to be a novel concept by which several sensors with automatic target recognition (ATR) capability collaborate to recognize objects. Such an approach would be suitable for netted systems in which the sensors and platforms can coordinate to optimize end-to-end performance. We use correlation filtering techniques to facilitate the development of the concept, although other ATR algorithms may be easily substituted. Essentially, a self-configuring geometry of netted platforms is proposed that positions the sensors optimally with respect to each other, and takes into account the interactions among the sensor, the recognition algorithms, and the classes of the objects to be recognized. We show how such a paradigm optimizes overall performance, and illustrate the collaborative ATR scheme for recognizing targets in synthetic aperture radar imagery by using viewing position as a sensor parameter.

Human Activity Recognition in AAL Environments Using Random Projections

PubMed Central

Damaševičius, Robertas; Vasiljevas, Mindaugas; Šalkevičius, Justas; Woźniak, Marcin

2016-01-01

Automatic human activity recognition systems aim to capture the state of the user and its environment by exploiting heterogeneous sensors attached to the subject's body and permit continuous monitoring of numerous physiological signals reflecting the state of human actions. Successful identification of human activities can be immensely useful in healthcare applications for Ambient Assisted Living (AAL), for automatic and intelligent activity monitoring systems developed for elderly and disabled people. In this paper, we propose the method for activity recognition and subject identification based on random projections from high-dimensional feature space to low-dimensional projection space, where the classes are separated using the Jaccard distance between probability density functions of projected data. Two HAR domain tasks are considered: activity identification and subject identification. The experimental results using the proposed method with Human Activity Dataset (HAD) data are presented. PMID:27413392

A beat-to-beat calculator for the diastolic pressure time index and the tension time index.

PubMed

Nose, Y; Tajimi, T; Watanabe, Y; Yokota, M; Akazawa, K; Nakamura, M

1987-01-01

We have developed a beat-to-beat calculator which can calculate in real-time the ratio of the diastolic pressure time index (DPTI), and the tension time index (TTI) as an index of the myocardial oxygen supply/demand balance. Physicians set up presumed value for the left ventricular endodiastolic pressure, a search area for the dicrotic notch, a threshold for the onset of the up-slope and the corresponding value of the calibration signal on the digital switches of the calculator. Next, the arterial pressure analog signal is input into the calculator. The calculator searches automatically for both the onset of the up-slope and the dicrotic notch. The arterial pressure curve is displayed beat-to-beat with the recognized onset and the dicrotic notch on the CRT to be confirmed by physicians. When physicians do not agree with the automatic recognition they can fit the automatic recognition to the observation. If the recognition of the onset is inadequate, the threshold can be re-adjusted to trigger the onset. If recognition of the dicrotic notch is inadequate, the physician can adjust the search-area. Therefore, physicians who operate the calculator can rely on the calculated DPTI/TTI. This calculator can continuously monitor the myocardial oxygen supply/demand balance in patients with acute myocardial infarction or just after open-heart surgery.

Electrophysiological Evidence of Automatic Early Semantic Processing

ERIC Educational Resources Information Center

Hinojosa, Jose A.; Martin-Loeches, Manuel; Munoz, Francisco; Casado, Pilar; Pozo, Miguel A.

2004-01-01

This study investigates the automatic-controlled nature of early semantic processing by means of the Recognition Potential (RP), an event-related potential response that reflects lexical selection processes. For this purpose tasks differing in their processing requirements were used. Half of the participants performed a physical task involving a…

Thallium release from acid mine drainages: Speciation in river and tap water from Valdicastello mining district (northwest Tuscany).

PubMed

Campanella, Beatrice; Casiot, Corinne; Onor, Massimo; Perotti, Martina; Petrini, Riccardo; Bramanti, Emilia

2017-08-15

In this work we present an advantageous method for the simultaneous separation and detection of Tl(I) and Tl(III) species through ion chromatography coupled with on-line inductively coupled plasma - mass spectrometry. Chromatographic separation between Tl(III) and Tl(I) was achieved in less than two minutes. The method was validated by recovery experiments on real samples, and by comparing the sum of the concentrations of individual Tl species with total thallium values obtained from continuous flow ICP-MS. The experimental procedure offers an accurate, sensitive and interference-free method for Tl speciation at trace levels in environmental samples. This allowed us to investigate the Tl speciation in acid mine drainages (AMD), surface waters and springs in a mining catchment in Valdicastello Carducci (Tuscany, Italy), where severe Tl contamination ad been evidenced previously. This study shows for the first time that Tl(III), in addition to Tl(I), is present in considerable amounts in water samples affected by acid mining outflow, raising the question of the origin of this thermodynamically unstable species. Copyright © 2017 Elsevier B.V. All rights reserved.

Flexible poly(ethylene carbonate)/garnet composite solid electrolyte reinforced by poly(vinylidene fluoride-hexafluoropropylene) for lithium metal batteries

NASA Astrophysics Data System (ADS)

He, Zijian; Chen, Long; Zhang, Bochen; Liu, Yongchang; Fan, Li-Zhen

2018-07-01

Solid-state electrolytes with high ionic conductivities, great flexibility, and easy processability are needed for high-performance solid-state rechargeable lithium batteries. In this work, we synthesize nanosized cubic Li6.25Al0.25La3Zr2O12 (LLZO) by solution combustion method and develop a flexible garnet-based composite solid electrolyte composed of LLZO, poly(ethylene carbonate) (PEC), poly(vinylidene fluoride-hexafluoropropylene) (P(VdF-HFP) and lithium bis(fluorosulfonyl)imide (LiFSI)). In the flexible composite solid electrolytes, LLZO nanoparticles, as ceramic matrix, have a positive effect on ionic conductivities and lithium ion transference number (tLi+). PEC, as a fast ion-conducting polymer, possesses high tLi+ inherently. P(VdF-HFP), as a binder, can strengthen mechanical properties. Consequently, the as-prepared composite solid electrolyte demonstrates high tLi+ (0.82) and superb thermal stability (remaining LLZO matrix after burning). All-solid-state LiFePO4|Li cells assembled with the flexible composite solid electrolyte deliver a high initial discharge specific capacity of 121.4 mAh g-1 and good cycling stability at 55 °C.

Material recognition based on thermal cues: Mechanisms and applications.

PubMed

Ho, Hsin-Ni

2018-01-01

Some materials feel colder to the touch than others, and we can use this difference in perceived coldness for material recognition. This review focuses on the mechanisms underlying material recognition based on thermal cues. It provides an overview of the physical, perceptual, and cognitive processes involved in material recognition. It also describes engineering domains in which material recognition based on thermal cues have been applied. This includes haptic interfaces that seek to reproduce the sensations associated with contact in virtual environments and tactile sensors aim for automatic material recognition. The review concludes by considering the contributions of this line of research in both science and engineering.

Material recognition based on thermal cues: Mechanisms and applications

PubMed Central

Ho, Hsin-Ni

2018-01-01

ABSTRACT Some materials feel colder to the touch than others, and we can use this difference in perceived coldness for material recognition. This review focuses on the mechanisms underlying material recognition based on thermal cues. It provides an overview of the physical, perceptual, and cognitive processes involved in material recognition. It also describes engineering domains in which material recognition based on thermal cues have been applied. This includes haptic interfaces that seek to reproduce the sensations associated with contact in virtual environments and tactile sensors aim for automatic material recognition. The review concludes by considering the contributions of this line of research in both science and engineering. PMID:29687043

Automatically Log Off Upon Disappearance of Facial Image

DTIC Science & Technology

2005-03-01

log off a PC when the user’s face disappears for an adjustable time interval. Among the fundamental technologies of biometrics, facial recognition is... facial recognition products. In this report, a brief overview of face detection technologies is provided. The particular neural network-based face...ensure that the user logging onto the system is the same person. Among the fundamental technologies of biometrics, facial recognition is the only

Signal recognition and parameter estimation of BPSK-LFM combined modulation

NASA Astrophysics Data System (ADS)

Long, Chao; Zhang, Lin; Liu, Yu

2015-07-01

Intra-pulse analysis plays an important role in electronic warfare. Intra-pulse feature abstraction focuses on primary parameters such as instantaneous frequency, modulation, and symbol rate. In this paper, automatic modulation recognition and feature extraction for combined BPSK-LFM modulation signals based on decision theoretic approach is studied. The simulation results show good recognition effect and high estimation precision, and the system is easy to be realized.

Adaptive method of recognition of signals for one and two-frequency signal system in the telephony on the background of speech

NASA Astrophysics Data System (ADS)

Kuznetsov, Michael V.

2006-05-01

For reliable teamwork of various systems of automatic telecommunication including transferring systems of optical communication networks it is necessary authentic recognition of signals for one- or two-frequency service signal system. The analysis of time parameters of an accepted signal allows increasing reliability of detection and recognition of the service signal system on a background of speech.

Speech Recognition as a Support Service for Deaf and Hard of Hearing Students: Adaptation and Evaluation. Final Report to Spencer Foundation.

ERIC Educational Resources Information Center

Stinson, Michael; Elliot, Lisa; McKee, Barbara; Coyne, Gina

This report discusses a project that adapted new automatic speech recognition (ASR) technology to provide real-time speech-to-text transcription as a support service for students who are deaf and hard of hearing (D/HH). In this system, as the teacher speaks, a hearing intermediary, or captionist, dictates into the speech recognition system in a…

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts

PubMed Central

Chun, Hong-Woo; Tsuruoka, Yoshimasa; Kim, Jin-Dong; Shiba, Rie; Nagata, Naoki; Hishiki, Teruyoshi; Tsujii, Jun'ichi

2006-01-01

Background Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. Methods We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Results Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. Conclusion A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques. PMID:17134477

Automatic recognition of topic-classified relations between prostate cancer and genes using MEDLINE abstracts.

PubMed

Chun, Hong-Woo; Tsuruoka, Yoshimasa; Kim, Jin-Dong; Shiba, Rie; Nagata, Naoki; Hishiki, Teruyoshi; Tsujii, Jun'ichi

2006-11-24

Automatic recognition of relations between a specific disease term and its relevant genes or protein terms is an important practice of bioinformatics. Considering the utility of the results of this approach, we identified prostate cancer and gene terms with the ID tags of public biomedical databases. Moreover, considering that genetics experts will use our results, we classified them based on six topics that can be used to analyze the type of prostate cancers, genes, and their relations. We developed a maximum entropy-based named entity recognizer and a relation recognizer and applied them to a corpus-based approach. We collected prostate cancer-related abstracts from MEDLINE, and constructed an annotated corpus of gene and prostate cancer relations based on six topics by biologists. We used it to train the maximum entropy-based named entity recognizer and relation recognizer. Topic-classified relation recognition achieved 92.1% precision for the relation (an increase of 11.0% from that obtained in a baseline experiment). For all topics, the precision was between 67.6 and 88.1%. A series of experimental results revealed two important findings: a carefully designed relation recognition system using named entity recognition can improve the performance of relation recognition, and topic-classified relation recognition can be effectively addressed through a corpus-based approach using manual annotation and machine learning techniques.

A model of traffic signs recognition with convolutional neural network

NASA Astrophysics Data System (ADS)

Hu, Haihe; Li, Yujian; Zhang, Ting; Huo, Yi; Kuang, Wenqing

2016-10-01

In real traffic scenes, the quality of captured images are generally low due to some factors such as lighting conditions, and occlusion on. All of these factors are challengeable for automated recognition algorithms of traffic signs. Deep learning has provided a new way to solve this kind of problems recently. The deep network can automatically learn features from a large number of data samples and obtain an excellent recognition performance. We therefore approach this task of recognition of traffic signs as a general vision problem, with few assumptions related to road signs. We propose a model of Convolutional Neural Network (CNN) and apply the model to the task of traffic signs recognition. The proposed model adopts deep CNN as the supervised learning model, directly takes the collected traffic signs image as the input, alternates the convolutional layer and subsampling layer, and automatically extracts the features for the recognition of the traffic signs images. The proposed model includes an input layer, three convolutional layers, three subsampling layers, a fully-connected layer, and an output layer. To validate the proposed model, the experiments are implemented using the public dataset of China competition of fuzzy image processing. Experimental results show that the proposed model produces a recognition accuracy of 99.01 % on the training dataset, and yield a record of 92% on the preliminary contest within the fourth best.

Automatic anatomy recognition on CT images with pathology

NASA Astrophysics Data System (ADS)

Huang, Lidong; Udupa, Jayaram K.; Tong, Yubing; Odhner, Dewey; Torigian, Drew A.

2016-03-01

Body-wide anatomy recognition on CT images with pathology becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem because various diseases result in various abnormalities of objects such as shape and intensity patterns. We previously developed an automatic anatomy recognition (AAR) system [1] whose applicability was demonstrated on near normal diagnostic CT images in different body regions on 35 organs. The aim of this paper is to investigate strategies for adapting the previous AAR system to diagnostic CT images of patients with various pathologies as a first step toward automated body-wide disease quantification. The AAR approach consists of three main steps - model building, object recognition, and object delineation. In this paper, within the broader AAR framework, we describe a new strategy for object recognition to handle abnormal images. In the model building stage an optimal threshold interval is learned from near-normal training images for each object. This threshold is optimally tuned to the pathological manifestation of the object in the test image. Recognition is performed following a hierarchical representation of the objects. Experimental results for the abdominal body region based on 50 near-normal images used for model building and 20 abnormal images used for object recognition show that object localization accuracy within 2 voxels for liver and spleen and 3 voxels for kidney can be achieved with the new strategy.

Automaticity of Basic-Level Categorization Accounts for Labeling Effects in Visual Recognition Memory

ERIC Educational Resources Information Center

Richler, Jennifer J.; Gauthier, Isabel; Palmeri, Thomas J.

2011-01-01

Are there consequences of calling objects by their names? Lupyan (2008) suggested that overtly labeling objects impairs subsequent recognition memory because labeling shifts stored memory representations of objects toward the category prototype (representational shift hypothesis). In Experiment 1, we show that processing objects at the basic…

Variogram-based feature extraction for neural network recognition of logos

NASA Astrophysics Data System (ADS)

Pham, Tuan D.

2003-03-01

This paper presents a new approach for extracting spatial features of images based on the theory of regionalized variables. These features can be effectively used for automatic recognition of logo images using neural networks. Experimental results on a public-domain logo database show the effectiveness of the proposed approach.

Separating Speed from Accuracy in Beginning Reading Development

ERIC Educational Resources Information Center

Juul, Holger; Poulsen, Mads; Elbro, Carsten

2014-01-01

Phoneme awareness, letter knowledge, and rapid automatized naming (RAN) are well-known kindergarten predictors of later word recognition skills, but it is not clear whether they predict developments in accuracy or speed, or both. The present longitudinal study of 172 Danish beginning readers found that speed of word recognition mainly developed…

Model-based vision using geometric hashing

NASA Astrophysics Data System (ADS)

Akerman, Alexander, III; Patton, Ronald

1991-04-01

The Geometric Hashing technique developed by the NYU Courant Institute has been applied to various automatic target recognition applications. In particular, I-MATH has extended the hashing algorithm to perform automatic target recognition ofsynthetic aperture radar (SAR) imagery. For this application, the hashing is performed upon the geometric locations of dominant scatterers. In addition to being a robust model-based matching algorithm -- invariant under translation, scale, and 3D rotations of the target -- hashing is of particular utility because it can still perform effective matching when the target is partially obscured. Moreover, hashing is very amenable to a SIMD parallel processing architecture, and thus potentially realtime implementable.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping.

PubMed

Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

2015-07-27

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work.

Automatic Speech Recognition in Air Traffic Control: a Human Factors Perspective

NASA Technical Reports Server (NTRS)

Karlsson, Joakim

1990-01-01

The introduction of Automatic Speech Recognition (ASR) technology into the Air Traffic Control (ATC) system has the potential to improve overall safety and efficiency. However, because ASR technology is inherently a part of the man-machine interface between the user and the system, the human factors issues involved must be addressed. Here, some of the human factors problems are identified and related methods of investigation are presented. Research at M.I.T.'s Flight Transportation Laboratory is being conducted from a human factors perspective, focusing on intelligent parser design, presentation of feedback, error correction strategy design, and optimal choice of input modalities.

Contour matching for a fish recognition and migration-monitoring system

NASA Astrophysics Data System (ADS)

Lee, Dah-Jye; Schoenberger, Robert B.; Shiozawa, Dennis; Xu, Xiaoqian; Zhan, Pengcheng

2004-12-01

Fish migration is being monitored year round to provide valuable information for the study of behavioral responses of fish to environmental variations. However, currently all monitoring is done by human observers. An automatic fish recognition and migration monitoring system is more efficient and can provide more accurate data. Such a system includes automatic fish image acquisition, contour extraction, fish categorization, and data storage. Shape is a very important characteristic and shape analysis and shape matching are studied for fish recognition. Previous work focused on finding critical landmark points on fish shape using curvature function analysis. Fish recognition based on landmark points has shown satisfying results. However, the main difficulty of this approach is that landmark points sometimes cannot be located very accurately. Whole shape matching is used for fish recognition in this paper. Several shape descriptors, such as Fourier descriptors, polygon approximation and line segments, are tested. A power cepstrum technique has been developed in order to improve the categorization speed using contours represented in tangent space with normalized length. Design and integration including image acquisition, contour extraction and fish categorization are discussed in this paper. Fish categorization results based on shape analysis and shape matching are also included.

Development of A Two-Stage Procedure for the Automatic Recognition of Dysfluencies in the Speech of Children Who Stutter: I. Psychometric Procedures Appropriate for Selection of Training Material for Lexical Dysfluency Classifiers

PubMed Central

Howell, Peter; Sackin, Stevie; Glenn, Kazan

2007-01-01

This program of work is intended to develop automatic recognition procedures to locate and assess stuttered dysfluencies. This and the following article together, develop and test recognizers for repetitions and prolongations. The automatic recognizers classify the speech in two stages: In the first, the speech is segmented and in the second the segments are categorized. The units that are segmented are words. Here assessments by human judges on the speech of 12 children who stutter are described using a corresponding procedure. The accuracy of word boundary placement across judges, categorization of the words as fluent, repetition or prolongation, and duration of the different fluency categories are reported. These measures allow reliable instances of repetitions and prolongations to be selected for training and assessing the recognizers in the subsequent paper. PMID:9328878

Fine grained recognition of masonry walls for built heritage assessment

NASA Astrophysics Data System (ADS)

Oses, N.; Dornaika, F.; Moujahid, A.

2015-01-01

This paper presents the ground work carried out to achieve automatic fine grained recognition of stone masonry. This is a necessary first step in the development of the analysis tool. The built heritage that will be assessed consists of stone masonry constructions and many of the features analysed can be characterized according to the geometry and arrangement of the stones. Much of the assessment is carried out through visual inspection. Thus, we apply image processing on digital images of the elements under inspection. The main contribution of the paper is the performance evaluation of the automatic categorization of masonry walls from a set of extracted straight line segments. The element chosen to perform this evaluation is the stone arrangement of masonry walls. The validity of the proposed framework is assessed on real images of masonry walls using machine learning paradigms. These include classifiers as well as automatic feature selection.

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Improved Techniques for Automatic Chord Recognition from Music Audio Signals

ERIC Educational Resources Information Center

Cho, Taemin

2014-01-01

This thesis is concerned with the development of techniques that facilitate the effective implementation of capable automatic chord transcription from music audio signals. Since chord transcriptions can capture many important aspects of music, they are useful for a wide variety of music applications and also useful for people who learn and perform…

Automatic Cataloguing and Searching for Retrospective Data by Use of OCR Text.

ERIC Educational Resources Information Center

Tseng, Yuen-Hsien

2001-01-01

Describes efforts in supporting information retrieval from OCR (optical character recognition) degraded text. Reports on approaches used in an automatic cataloging and searching contest for books in multiple languages, including a vector space retrieval model, an n-gram indexing method, and a weighting scheme; and discusses problems of Asian…

RFID: A Revolution in Automatic Data Recognition

ERIC Educational Resources Information Center

Deal, Walter F., III

2004-01-01

Radio frequency identification, or RFID, is a generic term for technologies that use radio waves to automatically identify people or objects. There are several methods of identification, but the most common is to store a serial number that identifies a person or object, and perhaps other information, on a microchip that is attached to an antenna…

38 CFR 51.31 - Automatic recognition.

Code of Federal Regulations, 2012 CFR

2012-07-01

...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will be...

38 CFR 51.31 - Automatic recognition.

Code of Federal Regulations, 2011 CFR

2011-07-01

...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will be...

38 CFR 51.31 - Automatic recognition.

Code of Federal Regulations, 2013 CFR

2013-07-01

...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will be...

38 CFR 51.31 - Automatic recognition.

Code of Federal Regulations, 2014 CFR

2014-07-01

...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will be...

38 CFR 51.31 - Automatic recognition.

Code of Federal Regulations, 2010 CFR

2010-07-01

...) PER DIEM FOR NURSING HOME CARE OF VETERANS IN STATE HOMES Obtaining Per Diem for Nursing Home Care in... that already is recognized by VA as a State home for nursing home care at the time this part becomes effective, automatically will continue to be recognized as a State home for nursing home care but will be...

Investigating Prompt Difficulty in an Automatically Scored Speaking Performance Assessment

ERIC Educational Resources Information Center

Cox, Troy L.

2013-01-01

Speaking assessments for second language learners have traditionally been expensive to administer because of the cost of rating the speech samples. To reduce the cost, many researchers are investigating the potential of using automatic speech recognition (ASR) as a means to score examinee responses to open-ended prompts. This study examined the…

Computer-Aided Authoring System (AUTHOR) User's Guide. Volume I. Final Report.

ERIC Educational Resources Information Center

Guitard, Charles R.

This user's guide for AUTHOR, an automatic authoring system which produces programmed texts for teaching symbol recognition, provides detailed instructions to help the user construct and enter the information needed to create the programmed text, run the AUTHOR program, and edit the automatically composed paper. Major sections describe steps in…

Psychopaths lack the automatic avoidance of social threat: relation to instrumental aggression.

PubMed

Louise von Borries, Anna Katinka; Volman, Inge; de Bruijn, Ellen Rosalia Aloïs; Bulten, Berend Hendrik; Verkes, Robbert Jan; Roelofs, Karin

2012-12-30

Psychopathy (PP) is associated with marked abnormalities in social emotional behaviour, such as high instrumental aggression (IA). A crucial but largely ignored question is whether automatic social approach-avoidance tendencies may underlie this condition. We tested whether offenders with PP show lack of automatic avoidance tendencies, usually activated when (healthy) individuals are confronted with social threat stimuli (angry faces). We applied a computerized approach-avoidance task (AAT), where participants pushed or pulled pictures of emotional faces using a joystick, upon which the faces decreased or increased in size, respectively. Furthermore, participants completed an emotion recognition task which was used to control for differences in recognition of facial emotions. In contrast to healthy controls (HC), PP patients showed total absence of avoidance tendencies towards angry faces. Interestingly, those responses were related to levels of instrumental aggression and the (in)ability to experience personal distress (PD). These findings suggest that social performance in psychopaths is disturbed on a basic level of automatic action tendencies. The lack of implicit threat avoidance tendencies may underlie their aggressive behaviour. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

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

PubMed

Higgins, Eleanor L; Raskind, Marshall H

2004-12-01

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

The Automaticity of Emotional Face-Context Integration

PubMed Central

Aviezer, Hillel; Dudarev, Veronica; Bentin, Shlomo; Hassin, Ran R.

2011-01-01

Recent studies have demonstrated that context can dramatically influence the recognition of basic facial expressions, yet the nature of this phenomenon is largely unknown. In the present paper we begin to characterize the underlying process of face-context integration. Specifically, we examine whether it is a relatively controlled or automatic process. In Experiment 1 participants were motivated and instructed to avoid using the context while categorizing contextualized facial expression, or they were led to believe that the context was irrelevant. Nevertheless, they were unable to disregard the context, which exerted a strong effect on their emotion recognition. In Experiment 2, participants categorized contextualized facial expressions while engaged in a concurrent working memory task. Despite the load, the context exerted a strong influence on their recognition of facial expressions. These results suggest that facial expressions and their body contexts are integrated in an unintentional, uncontrollable, and relatively effortless manner. PMID:21707150

ATR applications of minimax entropy models of texture and shape

NASA Astrophysics Data System (ADS)

Zhu, Song-Chun; Yuille, Alan L.; Lanterman, Aaron D.

2001-10-01

Concepts from information theory have recently found favor in both the mainstream computer vision community and the military automatic target recognition community. In the computer vision literature, the principles of minimax entropy learning theory have been used to generate rich probabilitistic models of texture and shape. In addition, the method of types and large deviation theory has permitted the difficulty of various texture and shape recognition tasks to be characterized by 'order parameters' that determine how fundamentally vexing a task is, independent of the particular algorithm used. These information-theoretic techniques have been demonstrated using traditional visual imagery in applications such as simulating cheetah skin textures and such as finding roads in aerial imagery. We discuss their application to problems in the specific application domain of automatic target recognition using infrared imagery. We also review recent theoretical and algorithmic developments which permit learning minimax entropy texture models for infrared textures in reasonable timeframes.

Blind equalization and automatic modulation classification based on subspace for subcarrier MPSK optical communications

NASA Astrophysics Data System (ADS)

Chen, Dan; Guo, Lin-yuan; Wang, Chen-hao; Ke, Xi-zheng

2017-07-01

Equalization can compensate channel distortion caused by channel multipath effects, and effectively improve convergent of modulation constellation diagram in optical wireless system. In this paper, the subspace blind equalization algorithm is used to preprocess M-ary phase shift keying (MPSK) subcarrier modulation signal in receiver. Mountain clustering is adopted to get the clustering centers of MPSK modulation constellation diagram, and the modulation order is automatically identified through the k-nearest neighbor (KNN) classifier. The experiment has been done under four different weather conditions. Experimental results show that the convergent of constellation diagram is improved effectively after using the subspace blind equalization algorithm, which means that the accuracy of modulation recognition is increased. The correct recognition rate of 16PSK can be up to 85% in any kind of weather condition which is mentioned in paper. Meanwhile, the correct recognition rate is the highest in cloudy and the lowest in heavy rain condition.

Personalization algorithm for real-time activity recognition using PDA, wireless motion bands, and binary decision tree.

PubMed

Pärkkä, Juha; Cluitmans, Luc; Ermes, Miikka

2010-09-01

Inactive and sedentary lifestyle is a major problem in many industrialized countries today. Automatic recognition of type of physical activity can be used to show the user the distribution of his daily activities and to motivate him into more active lifestyle. In this study, an automatic activity-recognition system consisting of wireless motion bands and a PDA is evaluated. The system classifies raw sensor data into activity types online. It uses a decision tree classifier, which has low computational cost and low battery consumption. The classifier parameters can be personalized online by performing a short bout of an activity and by telling the system which activity is being performed. Data were collected with seven volunteers during five everyday activities: lying, sitting/standing, walking, running, and cycling. The online system can detect these activities with overall 86.6% accuracy and with 94.0% accuracy after classifier personalization.

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.

Activity Recognition for Personal Time Management

NASA Astrophysics Data System (ADS)

Prekopcsák, Zoltán; Soha, Sugárka; Henk, Tamás; Gáspár-Papanek, Csaba

We describe an accelerometer based activity recognition system for mobile phones with a special focus on personal time management. We compare several data mining algorithms for the automatic recognition task in the case of single user and multiuser scenario, and improve accuracy with heuristics and advanced data mining methods. The results show that daily activities can be recognized with high accuracy and the integration with the RescueTime software can give good insights for personal time management.

Performance of a Working Face Recognition Machine using Cortical Thought Theory

DTIC Science & Technology

1984-12-04

been considered (2). Recommendations from Bledsoe’s study included research on facial - recognition systems that are "completely automatic (remove the...C. L. Location of some facial features . computer, Palo Alto: Panoramic Research, Aug 1966. 2. Bledsoe, W. W. Man-machine facial recognition : Is...34 image?" It would seem - that the location and size of the features left in this contrast-expanded image contain the essential information of facial

Facial recognition in education system

NASA Astrophysics Data System (ADS)

Krithika, L. B.; Venkatesh, K.; Rathore, S.; Kumar, M. Harish

2017-11-01

Human beings exploit emotions comprehensively for conveying messages and their resolution. Emotion detection and face recognition can provide an interface between the individuals and technologies. The most successful applications of recognition analysis are recognition of faces. Many different techniques have been used to recognize the facial expressions and emotion detection handle varying poses. In this paper, we approach an efficient method to recognize the facial expressions to track face points and distances. This can automatically identify observer face movements and face expression in image. This can capture different aspects of emotion and facial expressions.

[Study on the automatic parameters identification of water pipe network model].

PubMed

Jia, Hai-Feng; Zhao, Qi-Feng

2010-01-01

Based on the problems analysis on development and application of water pipe network model, the model parameters automatic identification is regarded as a kernel bottleneck of model's application in water supply enterprise. The methodology of water pipe network model parameters automatic identification based on GIS and SCADA database is proposed. Then the kernel algorithm of model parameters automatic identification is studied, RSA (Regionalized Sensitivity Analysis) is used for automatic recognition of sensitive parameters, and MCS (Monte-Carlo Sampling) is used for automatic identification of parameters, the detail technical route based on RSA and MCS is presented. The module of water pipe network model parameters automatic identification is developed. At last, selected a typical water pipe network as a case, the case study on water pipe network model parameters automatic identification is conducted and the satisfied results are achieved.

Ionic Conductivity of TlBr1-xIx(x = 0, 0.2, 1): Candidate Gamma Ray Detector

NASA Astrophysics Data System (ADS)

Bishop, S. R.; Ciampi, G.; Lee, C. D.; Kuhn, M.; Tuller, H. L.; Higgins, W.; Shah, K. S.

2012-10-01

The ionic conductivity of TlBr, TlI and their solid solutions, candidates for high energy radiation detection, was examined using impedance spectroscopy. The orthorhombic to cubic phase change in TlI was observed via a steep change in conductivity with increasing temperature, whereas the TlBr-TlI solid solution was cubic throughout the measured temperature range, in agreement with the literature. The intrinsic conductivity of the cubic phase of each material showed nearly identical behavior, indicating that I substitution for Br has little to no effect on the combined defect formation and transport parameters in the studied range. Additionally, optical transmission was correlated with I concentration.

Transcribe Your Class: Using Speech Recognition to Improve Access for At-Risk Students

ERIC Educational Resources Information Center

Bain, Keith; Lund-Lucas, Eunice; Stevens, Janice

2012-01-01

Through a project supported by Canada's Social Development Partnerships Program, a team of leading National Disability Organizations, universities, and industry partners are piloting a prototype Hosted Transcription Service that uses speech recognition to automatically create multimedia transcripts that can be used by students for study purposes.…

Speech Recognition Software for Language Learning: Toward an Evaluation of Validity and Student Perceptions

ERIC Educational Resources Information Center

Cordier, Deborah

2009-01-01

A renewed focus on foreign language (FL) learning and speech for communication has resulted in computer-assisted language learning (CALL) software developed with Automatic Speech Recognition (ASR). ASR features for FL pronunciation (Lafford, 2004) are functional components of CALL designs used for FL teaching and learning. The ASR features…

Cortical Reorganization in Dyslexic Children after Phonological Training: Evidence from Early Evoked Potentials

ERIC Educational Resources Information Center

Spironelli, Chiara; Penolazzi, Barbara; Vio, Claudio; Angrilli, Alessandro

2010-01-01

Brain plasticity was investigated in 14 Italian children affected by developmental dyslexia after 6 months of phonological training. The means used to measure language reorganization was the recognition potential, an early wave, also called N150, elicited by automatic word recognition. This component peaks over the left temporo-occipital cortex…

Higher-order neural network software for distortion invariant object recognition

NASA Technical Reports Server (NTRS)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

3D automatic anatomy recognition based on iterative graph-cut-ASM

NASA Astrophysics Data System (ADS)

Chen, Xinjian; Udupa, Jayaram K.; Bagci, Ulas; Alavi, Abass; Torigian, Drew A.

2010-02-01

We call the computerized assistive process of recognizing, delineating, and quantifying organs and tissue regions in medical imaging, occurring automatically during clinical image interpretation, automatic anatomy recognition (AAR). The AAR system we are developing includes five main parts: model building, object recognition, object delineation, pathology detection, and organ system quantification. In this paper, we focus on the delineation part. For the modeling part, we employ the active shape model (ASM) strategy. For recognition and delineation, we integrate several hybrid strategies of combining purely image based methods with ASM. In this paper, an iterative Graph-Cut ASM (IGCASM) method is proposed for object delineation. An algorithm called GC-ASM was presented at this symposium last year for object delineation in 2D images which attempted to combine synergistically ASM and GC. Here, we extend this method to 3D medical image delineation. The IGCASM method effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. We propose a new GC cost function, which effectively integrates the specific image information with the ASM shape model information. The proposed methods are tested on a clinical abdominal CT data set. The preliminary results show that: (a) it is feasible to explicitly bring prior 3D statistical shape information into the GC framework; (b) the 3D IGCASM delineation method improves on ASM and GC and can provide practical operational time on clinical images.

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

PubMed

Han, Guanghui; Liu, Xiabi; Zheng, Guangyuan; Wang, Murong; Huang, Shan

2018-06-06

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images. Graphical abstract We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

Early CMV Viremia Is Associated with Impaired Viral Control following Nonmyeloablative Hematopoietic Cell Transplantation with a Total Lymphoid Irradiation and Antithymocyte Globulin Preparative Regimen

PubMed Central

Schaenman, Joanna M.; Shashidhar, Sumana; Rhee, Chanu; Wong, Jonathan; Navato, Shelly; Wong, Ruby M.; Ho, Dora Y.; Arai, Sally; Johnston, Laura; Brown, Janice M.

2017-01-01

The reconstitution of immune function after hematopoietic cell transplant (HCT) plays an important role in the control of viral infections. Both donor and recipient cytomegalovirus (CMV) serostatus has been shown to contribute to effective immune function; however, the influence of a nonmyeloablative preparative (NMA) regimen using total lymphoid irradiation (TLI) and antithymocyte globulin (ATG) on antiviral immune reconstitution has not yet been described. In 117 recipients of NMA HCT patients following ATG and TLI, not unexpectedly, CMV viremia was seen in approximately 60% of the seropositive patients regardless of donor serostatus, and recipient seropositivity significantly increased the odds of CMV viremia after transplant in a multivariate analysis. The administration of ATG and TLI resulted in a strikingly earlier viremia in the posttransplant period when compared to the previously reported timing of viremia following myeloablative preparative regimens, especially for transplant recipients who were seropositive for CMV with seronegative donors. Furthermore, early viremia in the setting of a CMV naïve donor was associated with a delay in functional antiviral control. These observations demonstrate the dynamic nature of immunity in relation to CMV antigen exposure in the complex environment resulting from NMA conditions where both donor and residual recipient immune response affect viral control. PMID:20736077

Virulence gene profiles of Arcobacter species isolated from animals, foods of animal origin, and humans in Andhra Pradesh, India.

PubMed

Sekhar, M Soma; Tumati, S R; Chinnam, B K; Kothapalli, V S; Sharif, N Mohammad

2017-06-01

This study aimed to detect putative virulence genes in Arcobacter species of animal and human origin. A total of 41 Arcobacter isolates (16 Arcobacter butzleri , 13 Arcobacter cryaerophilus , and 12 Arcobacter skirrowii ) isolated from diverse sources such as fecal swabs of livestock (21), raw foods of animal origin (13), and human stool samples (7) were subjected to a set of six uniplex polymerase chain reaction assays targeting Arcobacter putative virulence genes ( ciaB , pldA , tlyA , mviN , cadF , and cj1349 ). All the six virulence genes were detected among all the 16 A. butzleri isolates. Among the 13 A. cryaerophilus isolates, cadF, ciaB , cj1349, mviN , pldA , and tlyA genes were detected in 61.5, 84.6, 76.9, 76.9, 61.5, and 61.5% of isolates, respectively. Among the 12 A. skirrowii isolates, cadF, ciaB , cj1349, mviN , pldA , and tlyA genes were detected in 50.0, 91.6, 83.3, 66.6, 50, and 50% of isolates, respectively. Putative virulence genes were detected in majority of the Arcobacter isolates examined. The results signify the potential of Arcobacter species as an emerging foodborne pathogen.

Over a Decade of recA and tly Gene Sequence Typing of the Skin Bacterium Propionibacterium acnes: What Have We Learnt?

PubMed Central

2017-01-01

The Gram-positive, anaerobic bacterium Propionibacterium acnes forms part of the normal microbiota on human skin and mucosal surfaces. While normally associated with skin health, P. acnes is also an opportunistic pathogen linked with a range of human infections and clinical conditions. Over the last decade, our knowledge of the intraspecies phylogenetics and taxonomy of this bacterium has increased tremendously due to the introduction of DNA typing schemes based on single and multiple gene loci, as well as whole genomes. Furthermore, this work has led to the identification of specific lineages associated with skin health and human disease. In this review we will look back at the introduction of DNA sequence typing of P. acnes based on recA and tly loci, and then describe how these methods provided a basic understanding of the population genetic structure of the bacterium, and even helped characterize the grapevine-associated lineage of P. acnes, known as P. acnes type Zappe, which appears to have undergone a host switch from humans-to-plants. Particular limitations of recA and tly sequence typing will also be presented, as well as a detailed discussion of more recent, higher resolution, DNA-based methods to type P. acnes and investigate its evolutionary history in greater detail. PMID:29267255

Automatic micropropagation of plants--the vision-system: graph rewriting as pattern recognition

NASA Astrophysics Data System (ADS)

Schwanke, Joerg; Megnet, Roland; Jensch, Peter F.

1993-03-01

The automation of plant-micropropagation is necessary to produce high amounts of biomass. Plants have to be dissected on particular cutting-points. A vision-system is needed for the recognition of the cutting-points on the plants. With this background, this contribution is directed to the underlying formalism to determine cutting-points on abstract-plant models. We show the usefulness of pattern recognition by graph-rewriting along with some examples in this context.

Dietary Assessment on a Mobile Phone Using Image Processing and Pattern Recognition Techniques: Algorithm Design and System Prototyping

PubMed Central

Probst, Yasmine; Nguyen, Duc Thanh; Tran, Minh Khoi; Li, Wanqing

2015-01-01

Dietary assessment, while traditionally based on pen-and-paper, is rapidly moving towards automatic approaches. This study describes an Australian automatic food record method and its prototype for dietary assessment via the use of a mobile phone and techniques of image processing and pattern recognition. Common visual features including scale invariant feature transformation (SIFT), local binary patterns (LBP), and colour are used for describing food images. The popular bag-of-words (BoW) model is employed for recognizing the images taken by a mobile phone for dietary assessment. Technical details are provided together with discussions on the issues and future work. PMID:26225994

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

SU-C-19A-06: A Robust and Affordable Table Indexing Approach for Total Lymphoid Irradiation Treatment

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

Yu, S; Fahimian, B; Kenyon, M

2014-06-15

Purpose: Total lymphoid irradiation (TLI) is conventionally delivered through the dosimetric matching of mantle, spleen, and pelvis fields, necessitating multiple isocenters delivered through a combination of couch shifts and sliding of patients relative to the couch rendering the technique susceptible to shifting errors. To address this challenge, a novel technique for the couch indexing of TLI treatments is developed and evaluated through a multi-patient pilot trial. Methods: An immobilization device was designed consisting of a movable indexed slide board with an Exact Lok-Bar drilled into it. A Timo headrests were used fixate the head of the patient relative to themore » slide board. For the Varian Exact Couch™, the immobilization board was connected to the H3 notch to avoid the metal infrastructure of the couch for the delivery of the mantle and spleen fields. For tall patients the required shift for the pelvis isocenter reaches the shifting limit and the board was slid from H3 to H4 (a fixed distance of 14 cm). A total 22 patients were stratified in two groups of 11, one consisting of the conventional setup, and one group with the proposed immobilization technique. Results: The standard deviations (SD) of the couch positions in lateral, longitudinal, and vertical directions for 10 fractions for each patient in both groups were calculated. In the non-indexed group, the positioning SD ranged from 0.9 to 4.7 cm. Using our device, the positioning SD was reduced to a range of 0.2 to 0.9 cm, with the longitudinal direction showing the largest improvement. Conclusion: Matched field TLI remains error prone to geometrical misses. The feasibility of full indexing TLI treatments was validated and shown to result in a significant reduction of positioning errors.« less

Occurrence of virulence-associated genes in Arcobacter butzleri and Arcobacter cryaerophilus isolates from foodstuff, water, and clinical samples within the Czech Republic.

PubMed

Šilha, David; Vacková, Barbora; Šilhová, Lucie

2018-06-24

Bacteria of the Arcobacter (A.) genus, originating mainly from food and water, are dreaded germs for humans as well as animals. However, the virulence of these bacteria has not been fully elucidated yet. This study looked at the occurrence of eight virulence-associated factors (ciaB, cj1349, pldA, irgA, hecA, tlyA, mviN, hecB) in a total of 80 isolates of Arcobacter butzleri and 22 isolates of A. cryaerophilus. The isolates were derived from food, water, and clinical samples. A polymerase chain reaction using specific primers was used to detect these virulence-associated genes. The presence of all genes in the isolates of A. butzleri (98.8% ciaB, 95.0% cj1349, 98.8% pldA, 22.5% irgA, 31.3% hecA, 95.0% tlyA, 97.5% mviN, 38.8% hecB) and A. cryaerophilus (95.5% ciaB, 0.0% cj1349, 9.1% pldA, 0.0% irgA, 0.0% hecA, 31.8% tlyA, 90.9% mviN, 0.0% hecB) was monitored. Among the tested isolates, there were 13 isolates (12.7%) of A. butzleri, in which the presence of all eight virulence-associated genes was recorded in the genome. In contrast, in one A. cryaerophilus strain, none of the observed genes were detected. The presence of ciaB and mviN genes was significantly more frequent in A. cryaerophilus isolates than other genes (P < 0.05). In general, more virulence-associated genes have been detected in A. butzleri isolates compared to A. cryaerophilus. The most common gene combination (ciaB, cj1349, pldA, tlyA, mviN) was detected in case of 39 isolates. In 50.0% of A. butzleri isolates derived from clinical samples, all eight virulence-associated genes were significantly more frequently detected (P < 0.05). The tlyA gene occurred significantly more frequent in A. butzleri isolates from meat and water samples and irgA and hecB genes in clinical samples. Therefore, our study provides information about occurrence of virulence-associated genes in genome of Arcobacter isolates. These findings could be hazardous to human health, because the presence of virulence-associated genes is the assumption for potential dangerousness of these bacteria. Our results indicate high incidence of virulence-associated genes in Arcobacter genomes and hence potentially pathogenic properties of the studied strains.

Recognition of plant parts with problem-specific algorithms

NASA Astrophysics Data System (ADS)

Schwanke, Joerg; Brendel, Thorsten; Jensch, Peter F.; Megnet, Roland

1994-06-01

Automatic micropropagation is necessary to produce cost-effective high amounts of biomass. Juvenile plants are dissected in clean- room environment on particular points on the stem or the leaves. A vision-system detects possible cutting points and controls a specialized robot. This contribution is directed to the pattern- recognition algorithms to detect structural parts of the plant.

User Experience of a Mobile Speaking Application with Automatic Speech Recognition for EFL Learning

ERIC Educational Resources Information Center

Ahn, Tae youn; Lee, Sangmin-Michelle

2016-01-01

With the spread of mobile devices, mobile phones have enormous potential regarding their pedagogical use in language education. The goal of this study is to analyse user experience of a mobile-based learning system that is enhanced by speech recognition technology for the improvement of EFL (English as a foreign language) learners' speaking…

Exploring the Effect of Illumination on Automatic Expression Recognition using the ICT-3DRFE Database

DTIC Science & Technology

2011-11-04

environmen- tal lighting conditions that one can actually come across. L7 and L8 are also cases of low illumination intensity. To produce our experimental...Graphics (Proceedings of ACM SIGGRAPH), 26(3). [9] Riklin- Raviv T., Shashua A., (1999). The quotient image: class based recognition and synthesis under

EduSpeak[R]: A Speech Recognition and Pronunciation Scoring Toolkit for Computer-Aided Language Learning Applications

ERIC Educational Resources Information Center

Franco, Horacio; Bratt, Harry; Rossier, Romain; Rao Gadde, Venkata; Shriberg, Elizabeth; Abrash, Victor; Precoda, Kristin

2010-01-01

SRI International's EduSpeak[R] system is a software development toolkit that enables developers of interactive language education software to use state-of-the-art speech recognition and pronunciation scoring technology. Automatic pronunciation scoring allows the computer to provide feedback on the overall quality of pronunciation and to point to…

Optimal pattern synthesis for speech recognition based on principal component analysis

NASA Astrophysics Data System (ADS)

Korsun, O. N.; Poliyev, A. V.

2018-02-01

The algorithm for building an optimal pattern for the purpose of automatic speech recognition, which increases the probability of correct recognition, is developed and presented in this work. The optimal pattern forming is based on the decomposition of an initial pattern to principal components, which enables to reduce the dimension of multi-parameter optimization problem. At the next step the training samples are introduced and the optimal estimates for principal components decomposition coefficients are obtained by a numeric parameter optimization algorithm. Finally, we consider the experiment results that show the improvement in speech recognition introduced by the proposed optimization algorithm.

Towards a smart glove: arousal recognition based on textile Electrodermal Response.

PubMed

Valenza, Gaetano; Lanata, Antonio; Scilingo, Enzo Pasquale; De Rossi, Danilo

2010-01-01

This paper investigates the possibility of using Electrodermal Response, acquired by a sensing fabric glove with embedded textile electrodes, as reliable means for emotion recognition. Here, all the essential steps for an automatic recognition system are described, from the recording of physiological data set to a feature-based multiclass classification. Data were collected from 35 healthy volunteers during arousal elicitation by means of International Affective Picture System (IAPS) pictures. Experimental results show high discrimination after twenty steps of cross validation.

Phoneme Awareness, Visual-Verbal Paired-Associate Learning, and Rapid Automatized Naming as Predictors of Individual Differences in Reading Ability

ERIC Educational Resources Information Center

Warmington, Meesha; Hulme, Charles

2012-01-01

This study examines the concurrent relationships between phoneme awareness, visual-verbal paired-associate learning, rapid automatized naming (RAN), and reading skills in 7- to 11-year-old children. Path analyses showed that visual-verbal paired-associate learning and RAN, but not phoneme awareness, were unique predictors of word recognition,…

Assessing Children's Home Language Environments Using Automatic Speech Recognition Technology

ERIC Educational Resources Information Center

Greenwood, Charles R.; Thiemann-Bourque, Kathy; Walker, Dale; Buzhardt, Jay; Gilkerson, Jill

2011-01-01

The purpose of this research was to replicate and extend some of the findings of Hart and Risley using automatic speech processing instead of human transcription of language samples. The long-term goal of this work is to make the current approach to speech processing possible by researchers and clinicians working on a daily basis with families and…

Neural Mechanisms of Context Effects on Face Recognition: Automatic Binding and Context Shift Decrements

PubMed Central

Hayes, Scott M.; Baena, Elsa; Truong, Trong-Kha; Cabeza, Roberto

2011-01-01

Although people do not normally try to remember associations between faces and physical contexts, these associations are established automatically, as indicated by the difficulty of recognizing familiar faces in different contexts (“butcher-on-the-bus” phenomenon). The present functional MRI (fMRI) study investigated the automatic binding of faces and scenes. In the Face-Face (F-F) condition, faces were presented alone during both encoding and retrieval, whereas in the Face/Scene-Face (FS-F) condition, they were presented overlaid on scenes during encoding but alone during retrieval (context change). Although participants were instructed to focus only on the faces during both encoding and retrieval, recognition performance was worse in the FS-F than the F-F condition (“context shift decrement”—CSD), confirming automatic face-scene binding during encoding. This binding was mediated by the hippocampus as indicated by greater subsequent memory effects (remembered > forgotten) in this region for the FS-F than the F-F condition. Scene memory was mediated by the right parahippocampal cortex, which was reactivated during successful retrieval when the faces were associated with a scene during encoding (FS-F condition). Analyses using the CSD as a regressor yielded a clear hemispheric asymmetry in medial temporal lobe activity during encoding: left hippocampal and parahippocampal activity was associated with a smaller CSD, indicating more flexible memory representations immune to context changes, whereas right hippocampal/rhinal activity was associated with a larger CSD, indicating less flexible representations sensitive to context change. Taken together, the results clarify the neural mechanisms of context effects on face recognition. PMID:19925208

A fast automatic recognition and location algorithm for fetal genital organs in ultrasound images.

PubMed

Tang, Sheng; Chen, Si-ping

2009-09-01

Severe sex ratio imbalance at birth is now becoming an important issue in several Asian countries. Its leading immediate cause is prenatal sex-selective abortion following illegal sex identification by ultrasound scanning. In this paper, a fast automatic recognition and location algorithm for fetal genital organs is proposed as an effective method to help prevent ultrasound technicians from unethically and illegally identifying the sex of the fetus. This automatic recognition algorithm can be divided into two stages. In the 'rough' stage, a few pixels in the image, which are likely to represent the genital organs, are automatically chosen as points of interest (POIs) according to certain salient characteristics of fetal genital organs. In the 'fine' stage, a specifically supervised learning framework, which fuses an effective feature data preprocessing mechanism into the multiple classifier architecture, is applied to every POI. The basic classifiers in the framework are selected from three widely used classifiers: radial basis function network, backpropagation network, and support vector machine. The classification results of all the POIs are then synthesized to determine whether the fetal genital organ is present in the image, and to locate the genital organ within the positive image. Experiments were designed and carried out based on an image dataset comprising 658 positive images (images with fetal genital organs) and 500 negative images (images without fetal genital organs). The experimental results showed true positive (TP) and true negative (TN) results from 80.5% (265 from 329) and 83.0% (415 from 500) of samples, respectively. The average computation time was 453 ms per image.

A Plane Target Detection Algorithm in Remote Sensing Images based on Deep Learning Network Technology

NASA Astrophysics Data System (ADS)

Shuxin, Li; Zhilong, Zhang; Biao, Li

2018-01-01

Plane is an important target category in remote sensing targets and it is of great value to detect the plane targets automatically. As remote imaging technology developing continuously, the resolution of the remote sensing image has been very high and we can get more detailed information for detecting the remote sensing targets automatically. Deep learning network technology is the most advanced technology in image target detection and recognition, which provided great performance improvement in the field of target detection and recognition in the everyday scenes. We combined the technology with the application in the remote sensing target detection and proposed an algorithm with end to end deep network, which can learn from the remote sensing images to detect the targets in the new images automatically and robustly. Our experiments shows that the algorithm can capture the feature information of the plane target and has better performance in target detection with the old methods.

Automatic Recognition of Indoor Navigation Elements from Kinect Point Clouds

NASA Astrophysics Data System (ADS)

Zeng, L.; Kang, Z.

2017-09-01

This paper realizes automatically the navigating elements defined by indoorGML data standard - door, stairway and wall. The data used is indoor 3D point cloud collected by Kinect v2 launched in 2011 through the means of ORB-SLAM. By contrast, it is cheaper and more convenient than lidar, but the point clouds also have the problem of noise, registration error and large data volume. Hence, we adopt a shape descriptor - histogram of distances between two randomly chosen points, proposed by Osada and merges with other descriptor - in conjunction with random forest classifier to recognize the navigation elements (door, stairway and wall) from Kinect point clouds. This research acquires navigation elements and their 3-d location information from each single data frame through segmentation of point clouds, boundary extraction, feature calculation and classification. Finally, this paper utilizes the acquired navigation elements and their information to generate the state data of the indoor navigation module automatically. The experimental results demonstrate a high recognition accuracy of the proposed method.

Research and Development of Fully Automatic Alien Smoke Stack and Packaging System

NASA Astrophysics Data System (ADS)

Yang, Xudong; Ge, Qingkuan; Peng, Tao; Zuo, Ping; Dong, Weifu

2017-12-01

The problem of low efficiency of manual sorting packaging for the current tobacco distribution center, which developed a set of safe efficient and automatic type of alien smoke stack and packaging system. The functions of fully automatic alien smoke stack and packaging system adopt PLC control technology, servo control technology, robot technology, image recognition technology and human-computer interaction technology. The characteristics, principles, control process and key technology of the system are discussed in detail. Through the installation and commissioning fully automatic alien smoke stack and packaging system has a good performance and has completed the requirements for shaped cigarette.

Automatic anatomy recognition in post-tonsillectomy MR images of obese children with OSAS

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, Jayaram K.; Odhner, Dewey; Sin, Sanghun; Arens, Raanan

2015-03-01

Automatic Anatomy Recognition (AAR) is a recently developed approach for the automatic whole body wide organ segmentation. We previously tested that methodology on image cases with some pathology where the organs were not distorted significantly. In this paper, we present an advancement of AAR to handle organs which may have been modified or resected by surgical intervention. We focus on MRI of the neck in pediatric Obstructive Sleep Apnea Syndrome (OSAS). The proposed method consists of an AAR step followed by support vector machine techniques to detect the presence/absence of organs. The AAR step employs a hierarchical organization of the organs for model building. For each organ, a fuzzy model over a population is built. The model of the body region is then described in terms of the fuzzy models and a host of other descriptors which include parent to offspring relationship estimated over the population. Organs are recognized following the organ hierarchy by using an optimal threshold based search. The SVM step subsequently checks for evidence of the presence of organs. Experimental results show that AAR techniques can be combined with machine learning strategies within the AAR recognition framework for good performance in recognizing missing organs, in our case missing tonsils in post-tonsillectomy images as well as in simulating tonsillectomy images. The previous recognition performance is maintained achieving an organ localization accuracy of within 1 voxel when the organ is actually not removed. To our knowledge, no methods have been reported to date for handling significantly deformed or missing organs, especially in neck MRI.

Face recognition for criminal identification: An implementation of principal component analysis for face recognition

NASA Astrophysics Data System (ADS)

Abdullah, Nurul Azma; Saidi, Md. Jamri; Rahman, Nurul Hidayah Ab; Wen, Chuah Chai; Hamid, Isredza Rahmi A.

2017-10-01

In practice, identification of criminal in Malaysia is done through thumbprint identification. However, this type of identification is constrained as most of criminal nowadays getting cleverer not to leave their thumbprint on the scene. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. However, because of limited software developed to automatically detect the similarity between photo in the footage and recorded photo of criminals, the law enforce thumbprint identification. In this paper, an automated facial recognition system for criminal database was proposed using known Principal Component Analysis approach. This system will be able to detect face and recognize face automatically. This will help the law enforcements to detect or recognize suspect of the case if no thumbprint present on the scene. The results show that about 80% of input photo can be matched with the template data.

Local Navon letter processing affects skilled behavior: a golf-putting experiment.

PubMed

Lewis, Michael B; Dawkins, Gemma

2015-04-01

Expert or skilled behaviors (for example, face recognition or sporting performance) are typically performed automatically and with little conscious awareness. Previous studies, in various domains of performance, have shown that activities immediately prior to a task demanding a learned skill can affect performance. In sport, describing the to-be-performed action is detrimental, whereas in face recognition, describing a face or reading local Navon letters is detrimental. Two golf-putting experiments are presented that compare the effects that these three tasks have on experienced and novice golfers. Experiment 1 found a Navon effect on golf performance for experienced players. Experiment 2 found, for experienced players only, that performance was impaired following the three tasks described above, when compared with reading or global Navon tasks. It is suggested that the three tasks affect skilled performance by provoking a shift from automatic behavior to a more analytic style. By demonstrating similarities between effects in face recognition and sporting behavior, it is hoped to better understand concepts in both fields.

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Automated phenotype pattern recognition of zebrafish for high-throughput screening.

PubMed

Schutera, Mark; Dickmeis, Thomas; Mione, Marina; Peravali, Ravindra; Marcato, Daniel; Reischl, Markus; Mikut, Ralf; Pylatiuk, Christian

2016-07-03

Over the last years, the zebrafish (Danio rerio) has become a key model organism in genetic and chemical screenings. A growing number of experiments and an expanding interest in zebrafish research makes it increasingly essential to automatize the distribution of embryos and larvae into standard microtiter plates or other sample holders for screening, often according to phenotypical features. Until now, such sorting processes have been carried out by manually handling the larvae and manual feature detection. Here, a prototype platform for image acquisition together with a classification software is presented. Zebrafish embryos and larvae and their features such as pigmentation are detected automatically from the image. Zebrafish of 4 different phenotypes can be classified through pattern recognition at 72 h post fertilization (hpf), allowing the software to classify an embryo into 2 distinct phenotypic classes: wild-type versus variant. The zebrafish phenotypes are classified with an accuracy of 79-99% without any user interaction. A description of the prototype platform and of the algorithms for image processing and pattern recognition is presented.

On the recognition of emotional vocal expressions: motivations for a holistic approach.

PubMed

Esposito, Anna; Esposito, Antonietta M

2012-10-01

Human beings seem to be able to recognize emotions from speech very well and information communication technology aims to implement machines and agents that can do the same. However, to be able to automatically recognize affective states from speech signals, it is necessary to solve two main technological problems. The former concerns the identification of effective and efficient processing algorithms capable of capturing emotional acoustic features from speech sentences. The latter focuses on finding computational models able to classify, with an approximation as good as human listeners, a given set of emotional states. This paper will survey these topics and provide some insights for a holistic approach to the automatic analysis, recognition and synthesis of affective states.

Automatic welding detection by an intelligent tool pipe inspection

NASA Astrophysics Data System (ADS)

Arizmendi, C. J.; Garcia, W. L.; Quintero, M. A.

2015-07-01

This work provide a model based on machine learning techniques in welds recognition, based on signals obtained through in-line inspection tool called “smart pig” in Oil and Gas pipelines. The model uses a signal noise reduction phase by means of pre-processing algorithms and attribute-selection techniques. The noise reduction techniques were selected after a literature review and testing with survey data. Subsequently, the model was trained using recognition and classification algorithms, specifically artificial neural networks and support vector machines. Finally, the trained model was validated with different data sets and the performance was measured with cross validation and ROC analysis. The results show that is possible to identify welding automatically with an efficiency between 90 and 98 percent.

Use of pattern recognition and neural networks for non-metric sex diagnosis from lateral shape of calvarium: an innovative model for computer-aided diagnosis in forensic and physical anthropology.

PubMed

Cavalli, Fabio; Lusnig, Luca; Trentin, Edmondo

2017-05-01

Sex determination on skeletal remains is one of the most important diagnosis in forensic cases and in demographic studies on ancient populations. Our purpose is to realize an automatic operator-independent method to determine the sex from the bone shape and to test an intelligent, automatic pattern recognition system in an anthropological domain. Our multiple-classifier system is based exclusively on the morphological variants of a curve that represents the sagittal profile of the calvarium, modeled via artificial neural networks, and yields an accuracy higher than 80 %. The application of this system to other bone profiles is expected to further improve the sensibility of the methodology.

Processing Strategy and PI Effects in Recognition Memory of Word Lists.

ERIC Educational Resources Information Center

Hodge, Milton H.; Britton, Bruce K.

Previous research by A. I. Schulman argued that an observed systematic decline in recognition memory in long word lists was due to the build-up of input and output proactive interference (PI). It also suggested that input PI resulted from process automatization; that is, each list item was processed or encoded in much the same way, producing a set…

Tree-structured sensor fusion architecture for distributed sensor networks

NASA Astrophysics Data System (ADS)

Iyengar, S. Sitharama; Kashyap, Rangasami L.; Madan, Rabinder N.; Thomas, Daryl D.

1990-10-01

An assessment of numerous activities in the field of multisensor target recognition reveals several trends and conditions which are cause for concern. .These concerns are analyzed in terms of their potential impact on the ultimate employment of automatic target recognition in military systems. Suggestions for additional investigation and guidance for current activities are presented with respect to some of the identified concerns.

26 CFR 1.338(h)(10)-1 - Deemed asset sale and liquidation.

Code of Federal Regulations, 2014 CFR

2014-04-01

...)(iii) of this section, K recognizes no gain or loss, and K's basis in its T stock remains at $5,000... section 338(h)(10) election for T are as follows: (1) P. P is automatically deemed to have made a gain recognition election for its nonrecently purchased T stock, if any. The effect of a gain recognition election...

26 CFR 1.338(h)(10)-1 - Deemed asset sale and liquidation.

Code of Federal Regulations, 2012 CFR

2012-04-01

...)(iii) of this section, K recognizes no gain or loss, and K's basis in its T stock remains at $5,000... section 338(h)(10) election for T are as follows: (1) P. P is automatically deemed to have made a gain recognition election for its nonrecently purchased T stock, if any. The effect of a gain recognition election...

26 CFR 1.338(h)(10)-1 - Deemed asset sale and liquidation.

Code of Federal Regulations, 2013 CFR

2013-04-01

...)(iii) of this section, K recognizes no gain or loss, and K's basis in its T stock remains at $5,000... section 338(h)(10) election for T are as follows: (1) P. P is automatically deemed to have made a gain recognition election for its nonrecently purchased T stock, if any. The effect of a gain recognition election...

Leaf Movements of Indoor Plants Monitored by Terrestrial LiDAR

PubMed Central

Herrero-Huerta, Mónica; Lindenbergh, Roderik; Gard, Wolfgang

2018-01-01

Plant leaf movement is induced by some combination of different external and internal stimuli. Detailed geometric characterization of such movement is expected to improve understanding of these mechanisms. A metric high-quality, non-invasive and innovative sensor system to analyze plant movement is Terrestrial LiDAR (TLiDAR). This technique has an active sensor and is, therefore, independent of light conditions, able to obtain accurate high spatial and temporal resolution point clouds. In this study, a movement parameterization approach of leaf plants based on TLiDAR is introduced. For this purpose, two Calathea roseopicta plants were scanned in an indoor environment during 2 full-days, 1 day in natural light conditions and the other in darkness. The methodology to estimate leaf movement is based on segmenting individual leaves using an octree-based 3D-grid and monitoring the changes in their orientation by Principal Component Analysis. Additionally, canopy variations of the plant as a whole were characterized by a convex-hull approach. As a result, 9 leaves in plant 1 and 11 leaves in plant 2 were automatically detected with a global accuracy of 93.57 and 87.34%, respectively, compared to a manual detection. Regarding plant 1, in natural light conditions, the displacement average of the leaves between 7.00 a.m. and 12.30 p.m. was 3.67 cm as estimated using so-called deviation maps. The maximum displacement was 7.92 cm. In addition, the orientation changes of each leaf within a day were analyzed. The maximum variation in the vertical angle was 69.6° from 12.30 to 6.00 p.m. In darkness, the displacements were smaller and showed a different orientation pattern. The canopy volume of plant 1 changed more in the morning (4.42 dm3) than in the afternoon (2.57 dm3). The results of plant 2 largely confirmed the results of the first plant and were added to check the robustness of the methodology. The results show how to quantify leaf orientation variation and leaf movements along a day at mm accuracy in different light conditions. This confirms the feasibility of the proposed methodology to robustly analyse leaf movements. PMID:29527217

Localized contourlet features in vehicle make and model recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, B. S.

2009-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic Number Plate Recognition (ANPR) systems. Several vehicle MMR systems have been proposed in literature. In parallel to this, the usefulness of multi-resolution based feature analysis techniques leading to efficient object classification algorithms have received close attention from the research community. To this effect, Contourlet transforms that can provide an efficient directional multi-resolution image representation has recently been introduced. Already an attempt has been made in literature to use Curvelet/Contourlet transforms in vehicle MMR. In this paper we propose a novel localized feature detection method in Contourlet transform domain that is capable of increasing the classification rates up to 4%, as compared to the previously proposed Contourlet based vehicle MMR approach in which the features are non-localized and thus results in sub-optimal classification. Further we show that the proposed algorithm can achieve the increased classification accuracy of 96% at significantly lower computational complexity due to the use of Two Dimensional Linear Discriminant Analysis (2DLDA) for dimensionality reduction by preserving the features with high between-class variance and low inter-class variance.

An automatic target recognition system based on SAR image

NASA Astrophysics Data System (ADS)

Li, Qinfu; Wang, Jinquan; Zhao, Bo; Luo, Furen; Xu, Xiaojian

2009-10-01

In this paper, an automatic target recognition (ATR) system based on synthetic aperture radar (SAR) is proposed. This ATR system can play an important role in the simulation of up-to-data battlefield environment and be used in ATR research. To establish an integral and available system, the processing of SAR image was divided into four main stages which are de-noise, detection, cluster-discrimination and segment-recognition, respectively. The first three stages are used for searching region of interest (ROI). Once the ROIs are extracted, the recognition stage will be taken to compute the similarity between the ROIs and the templates in the electromagnetic simulation software National Electromagnetic Scattering Code (NESC). Due to the lack of the SAR raw data, the electromagnetic simulated images are added to the measured SAR background to simulate the battlefield environment8. The purpose of the system is to find the ROIs which can be the artificial military targets such as tanks, armored cars and so on and to categorize the ROIs into the right classes according to the existing templates. From the results we can see that the proposed system achieves a satisfactory result.

Multiclassifier information fusion methods for microarray pattern recognition

NASA Astrophysics Data System (ADS)

Braun, Jerome J.; Glina, Yan; Judson, Nicholas; Herzig-Marx, Rachel

2004-04-01

This paper addresses automatic recognition of microarray patterns, a capability that could have a major significance for medical diagnostics, enabling development of diagnostic tools for automatic discrimination of specific diseases. The paper presents multiclassifier information fusion methods for microarray pattern recognition. The input space partitioning approach based on fitness measures that constitute an a-priori gauging of classification efficacy for each subspace is investigated. Methods for generation of fitness measures, generation of input subspaces and their use in the multiclassifier fusion architecture are presented. In particular, two-level quantification of fitness that accounts for the quality of each subspace as well as the quality of individual neighborhoods within the subspace is described. Individual-subspace classifiers are Support Vector Machine based. The decision fusion stage fuses the information from mulitple SVMs along with the multi-level fitness information. Final decision fusion stage techniques, including weighted fusion as well as Dempster-Shafer theory based fusion are investigated. It should be noted that while the above methods are discussed in the context of microarray pattern recognition, they are applicable to a broader range of discrimination problems, in particular to problems involving a large number of information sources irreducible to a low-dimensional feature space.

Automatic recognition of surface landmarks of anatomical structures of back and posture

NASA Astrophysics Data System (ADS)

Michoński, Jakub; Glinkowski, Wojciech; Witkowski, Marcin; Sitnik, Robert

2012-05-01

Faulty postures, scoliosis and sagittal plane deformities should be detected as early as possible to apply preventive and treatment measures against major clinical consequences. To support documentation of the severity of deformity and diminish x-ray exposures, several solutions utilizing analysis of back surface topography data were introduced. A novel approach to automatic recognition and localization of anatomical landmarks of the human back is presented that may provide more repeatable results and speed up the whole procedure. The algorithm was designed as a two-step process involving a statistical model built upon expert knowledge and analysis of three-dimensional back surface shape data. Voronoi diagram is used to connect mean geometric relations, which provide a first approximation of the positions, with surface curvature distribution, which further guides the recognition process and gives final locations of landmarks. Positions obtained using the developed algorithms are validated with respect to accuracy of manual landmark indication by experts. Preliminary validation proved that the landmarks were localized correctly, with accuracy depending mostly on the characteristics of a given structure. It was concluded that recognition should mainly take into account the shape of the back surface, putting as little emphasis on the statistical approximation as possible.

Two-dimensional statistical linear discriminant analysis for real-time robust vehicle-type recognition

NASA Astrophysics Data System (ADS)

Zafar, I.; Edirisinghe, E. A.; Acar, S.; Bez, H. E.

2007-02-01

Automatic vehicle Make and Model Recognition (MMR) systems provide useful performance enhancements to vehicle recognitions systems that are solely based on Automatic License Plate Recognition (ALPR) systems. Several car MMR systems have been proposed in literature. However these approaches are based on feature detection algorithms that can perform sub-optimally under adverse lighting and/or occlusion conditions. In this paper we propose a real time, appearance based, car MMR approach using Two Dimensional Linear Discriminant Analysis that is capable of addressing this limitation. We provide experimental results to analyse the proposed algorithm's robustness under varying illumination and occlusions conditions. We have shown that the best performance with the proposed 2D-LDA based car MMR approach is obtained when the eigenvectors of lower significance are ignored. For the given database of 200 car images of 25 different make-model classifications, a best accuracy of 91% was obtained with the 2D-LDA approach. We use a direct Principle Component Analysis (PCA) based approach as a benchmark to compare and contrast the performance of the proposed 2D-LDA approach to car MMR. We conclude that in general the 2D-LDA based algorithm supersedes the performance of the PCA based approach.

Terminologies for text-mining; an experiment in the lipoprotein metabolism domain

PubMed Central

Alexopoulou, Dimitra; Wächter, Thomas; Pickersgill, Laura; Eyre, Cecilia; Schroeder, Michael

2008-01-01

Background The engineering of ontologies, especially with a view to a text-mining use, is still a new research field. There does not yet exist a well-defined theory and technology for ontology construction. Many of the ontology design steps remain manual and are based on personal experience and intuition. However, there exist a few efforts on automatic construction of ontologies in the form of extracted lists of terms and relations between them. Results We share experience acquired during the manual development of a lipoprotein metabolism ontology (LMO) to be used for text-mining. We compare the manually created ontology terms with the automatically derived terminology from four different automatic term recognition (ATR) methods. The top 50 predicted terms contain up to 89% relevant terms. For the top 1000 terms the best method still generates 51% relevant terms. In a corpus of 3066 documents 53% of LMO terms are contained and 38% can be generated with one of the methods. Conclusions Given high precision, automatic methods can help decrease development time and provide significant support for the identification of domain-specific vocabulary. The coverage of the domain vocabulary depends strongly on the underlying documents. Ontology development for text mining should be performed in a semi-automatic way; taking ATR results as input and following the guidelines we described. Availability The TFIDF term recognition is available as Web Service, described at PMID:18460175

Semi-automatic mapping of cultural heritage from airborne laser scanning using deep learning

NASA Astrophysics Data System (ADS)

Due Trier, Øivind; Salberg, Arnt-Børre; Holger Pilø, Lars; Tonning, Christer; Marius Johansen, Hans; Aarsten, Dagrun

2016-04-01

This paper proposes to use deep learning to improve semi-automatic mapping of cultural heritage from airborne laser scanning (ALS) data. Automatic detection methods, based on traditional pattern recognition, have been applied in a number of cultural heritage mapping projects in Norway for the past five years. Automatic detection of pits and heaps have been combined with visual interpretation of the ALS data for the mapping of deer hunting systems, iron production sites, grave mounds and charcoal kilns. However, the performance of the automatic detection methods varies substantially between ALS datasets. For the mapping of deer hunting systems on flat gravel and sand sediment deposits, the automatic detection results were almost perfect. However, some false detections appeared in the terrain outside of the sediment deposits. These could be explained by other pit-like landscape features, like parts of river courses, spaces between boulders, and modern terrain modifications. However, these were easy to spot during visual interpretation, and the number of missed individual pitfall traps was still low. For the mapping of grave mounds, the automatic method produced a large number of false detections, reducing the usefulness of the semi-automatic approach. The mound structure is a very common natural terrain feature, and the grave mounds are less distinct in shape than the pitfall traps. Still, applying automatic mound detection on an entire municipality did lead to a new discovery of an Iron Age grave field with more than 15 individual mounds. Automatic mound detection also proved to be useful for a detailed re-mapping of Norway's largest Iron Age grave yard, which contains almost 1000 individual graves. Combined pit and mound detection has been applied to the mapping of more than 1000 charcoal kilns that were used by an iron work 350-200 years ago. The majority of charcoal kilns were indirectly detected as either pits on the circumference, a central mound, or both. However, kilns with a flat interior and a shallow ditch along the circumference were often missed by the automatic detection method. The successfulness of automatic detection seems to depend on two factors: (1) the density of ALS ground hits on the cultural heritage structures being sought, and (2) to what extent these structures stand out from natural terrain structures. The first factor may, to some extent, be improved by using a higher number of ALS pulses per square meter. The second factor is difficult to change, and also highlights another challenge: how to make a general automatic method that is applicable in all types of terrain within a country. The mixed experience with traditional pattern recognition for semi-automatic mapping of cultural heritage led us to consider deep learning as an alternative approach. The main principle is that a general feature detector has been trained on a large image database. The feature detector is then tailored to a specific task by using a modest number of images of true and false examples of the features being sought. Results of using deep learning are compared with previous results using traditional pattern recognition.

Image processing strategies based on saliency segmentation for object recognition under simulated prosthetic vision.

PubMed

Li, Heng; Su, Xiaofan; Wang, Jing; Kan, Han; Han, Tingting; Zeng, Yajie; Chai, Xinyu

2018-01-01

Current retinal prostheses can only generate low-resolution visual percepts constituted of limited phosphenes which are elicited by an electrode array and with uncontrollable color and restricted grayscale. Under this visual perception, prosthetic recipients can just complete some simple visual tasks, but more complex tasks like face identification/object recognition are extremely difficult. Therefore, it is necessary to investigate and apply image processing strategies for optimizing the visual perception of the recipients. This study focuses on recognition of the object of interest employing simulated prosthetic vision. We used a saliency segmentation method based on a biologically plausible graph-based visual saliency model and a grabCut-based self-adaptive-iterative optimization framework to automatically extract foreground objects. Based on this, two image processing strategies, Addition of Separate Pixelization and Background Pixel Shrink, were further utilized to enhance the extracted foreground objects. i) The results showed by verification of psychophysical experiments that under simulated prosthetic vision, both strategies had marked advantages over Direct Pixelization in terms of recognition accuracy and efficiency. ii) We also found that recognition performance under two strategies was tied to the segmentation results and was affected positively by the paired-interrelated objects in the scene. The use of the saliency segmentation method and image processing strategies can automatically extract and enhance foreground objects, and significantly improve object recognition performance towards recipients implanted a high-density implant. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated target recognition and tracking using an optical pattern recognition neural network

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Emotion and language: Valence and arousal affect word recognition

PubMed Central

Brysbaert, Marc; Warriner, Amy Beth

2014-01-01

Emotion influences most aspects of cognition and behavior, but emotional factors are conspicuously absent from current models of word recognition. The influence of emotion on word recognition has mostly been reported in prior studies on the automatic vigilance for negative stimuli, but the precise nature of this relationship is unclear. Various models of automatic vigilance have claimed that the effect of valence on response times is categorical, an inverted-U, or interactive with arousal. The present study used a sample of 12,658 words, and included many lexical and semantic control factors, to determine the precise nature of the effects of arousal and valence on word recognition. Converging empirical patterns observed in word-level and trial-level data from lexical decision and naming indicate that valence and arousal exert independent monotonic effects: Negative words are recognized more slowly than positive words, and arousing words are recognized more slowly than calming words. Valence explained about 2% of the variance in word recognition latencies, whereas the effect of arousal was smaller. Valence and arousal do not interact, but both interact with word frequency, such that valence and arousal exert larger effects among low-frequency words than among high-frequency words. These results necessitate a new model of affective word processing whereby the degree of negativity monotonically and independently predicts the speed of responding. This research also demonstrates that incorporating emotional factors, especially valence, improves the performance of models of word recognition. PMID:24490848

Impact of translation on named-entity recognition in radiology texts

PubMed Central

Pedro, Vasco

2017-01-01

Abstract Radiology reports describe the results of radiography procedures and have the potential of being a useful source of information which can bring benefits to health care systems around the world. One way to automatically extract information from the reports is by using Text Mining tools. The problem is that these tools are mostly developed for English and reports are usually written in the native language of the radiologist, which is not necessarily English. This creates an obstacle to the sharing of Radiology information between different communities. This work explores the solution of translating the reports to English before applying the Text Mining tools, probing the question of what translation approach should be used. We created MRRAD (Multilingual Radiology Research Articles Dataset), a parallel corpus of Portuguese research articles related to Radiology and a number of alternative translations (human, automatic and semi-automatic) to English. This is a novel corpus which can be used to move forward the research on this topic. Using MRRAD we studied which kind of automatic or semi-automatic translation approach is more effective on the Named-entity recognition task of finding RadLex terms in the English version of the articles. Considering the terms extracted from human translations as our gold standard, we calculated how similar to this standard were the terms extracted using other translations. We found that a completely automatic translation approach using Google leads to F-scores (between 0.861 and 0.868, depending on the extraction approach) similar to the ones obtained through a more expensive semi-automatic translation approach using Unbabel (between 0.862 and 0.870). To better understand the results we also performed a qualitative analysis of the type of errors found in the automatic and semi-automatic translations. Database URL: https://github.com/lasigeBioTM/MRRAD PMID:29220455

Age-related differences in brain electrical activity during extended continuous face recognition in younger children, older children and adults.

PubMed

Van Strien, Jan W; Glimmerveen, Johanna C; Franken, Ingmar H A; Martens, Vanessa E G; de Bruin, Eveline A

2011-09-01

To examine the development of recognition memory in primary-school children, 36 healthy younger children (8-9 years old) and 36 healthy older children (11-12 years old) participated in an ERP study with an extended continuous face recognition task (Study 1). Each face of a series of 30 faces was shown randomly six times interspersed with distracter faces. The children were required to make old vs. new decisions. Older children responded faster than younger children, but younger children exhibited a steeper decrease in latencies across the five repetitions. Older children exhibited better accuracy for new faces, but there were no age differences in recognition accuracy for repeated faces. For the N2, N400 and late positive complex (LPC), we analyzed the old/new effects (repetition 1 vs. new presentation) and the extended repetition effects (repetitions 1 through 5). Compared to older children, younger children exhibited larger frontocentral N2 and N400 old/new effects. For extended face repetitions, negativity of the N2 and N400 decreased in a linear fashion in both age groups. For the LPC, an ERP component thought to reflect recollection, no significant old/new or extended repetition effects were found. Employing the same face recognition paradigm in 20 adults (Study 2), we found a significant N400 old/new effect at lateral frontal sites and a significant LPC repetition effect at parietal sites, with LPC amplitudes increasing linearly with the number of repetitions. This study clearly demonstrates differential developmental courses for the N400 and LPC pertaining to recognition memory for faces. It is concluded that face recognition in children is mediated by early and probably more automatic than conscious recognition processes. In adults, the LPC extended repetition effect indicates that adult face recognition memory is related to a conscious and graded recollection process rather than to an automatic recognition process. © 2011 Blackwell Publishing Ltd.

Outcome of the acute glomerular injury in proliferative lupus nephritis

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

Chagnac, A.; Kiberd, B.A.; Farinas, M.C.

1989-09-01

Treatment with total lymphoid irradiation (TLI) and corticosteroids markedly reduced activity of systemic lupus erythematosis in 10 patients with diffuse proliferative lupus nephritis (DPLN) complicated by a nephrotic syndrome. Physiologic and morphometric techniques were used serially before, and 12 and 36 mo post-TLI to characterize the course of glomerular injury. Judged by a progressive reduction in the density of glomerular cells and immune deposits, glomerular inflammation subsided. A sustained reduction in the fractional clearance of albumin, IgG and uncharged dextrans of radius greater than 50 A, pointed to a parallel improvement in glomerular barrier size-selectivity. Corresponding changes in GFR weremore » modest, however. A trend towards higher GFR at 12 mo was associated with a marked increase in the fraction of glomerular tuft area occupied by patent capillary loops as inflammatory changes receded. A late trend toward declining GFR beyond 12 mo was associated with progressive glomerulosclerosis, which affected 57% of all glomeruli globally by 36 mo post-TLI. Judged by a parallel increase in volume by 59%, remaining, patent glomeruli had undergone a process of adaptive enlargement. We propose that an increasing fraction of glomeruli continues to undergo progressive sclerosis after DPLN has become quiescent, and that the prevailing GFR depends on the extent to which hypertrophied remnant glomeruli can compensate for the ensuing loss of filtration surface area.« less

Allogeneic hematopoietic cell transplantation after failed autologous transplant for lymphoma using TLI and anti-thymocyte globulin conditioning.

PubMed

Rezvani, A R; Kanate, A S; Efron, B; Chhabra, S; Kohrt, H E; Shizuru, J A; Laport, G G; Miklos, D B; Benjamin, J E; Johnston, L J; Arai, S; Weng, W-K; Negrin, R S; Strober, S; Lowsky, R

2015-10-01

We describe 47 patients with lymphoma and failed prior autologous hematopoietic cell transplantation (HCT) who received TLI-ATG (anti-thymocyte globulin) conditioning followed by allogeneic HCT. Thirty-two patients had non-Hodgkin lymphoma (NHL; diffuse large B-cell lymphoma (n=19), T-cell NHL (n=6), mantle cell lymphoma (n=4) or other B-cell subtypes (n=3)), and 15 had Hodgkin lymphoma. The median follow-up was 4.9 (range, 2.1-11.9) years. The cumulative incidence of grade II-IV acute GvHD at day +100 was 12%, and the cumulative incidence of extensive chronic GvHD at 1 year was 36%. The 3-year cumulative incidences of overall survival (OS), PFS and non-relapse mortality (NRM) were 81%, 44% and 7%, respectively. Fifteen patients died (relapse, n=10; NRM, n=5). Among the 25 patients with relapse after allogeneic HCT, 11 (44%) achieved durable (>1 year) CRs following donor lymphocyte infusion or chemoradiotherapy. The majority of surviving patients (75%; n=24) were able to discontinue all immunosuppression. For patients with relapsed lymphoma after autologous HCT, allogeneic HCT using TLI-ATG conditioning is a well-tolerated, predominantly outpatient therapy with low NRM (7% at 3 years), a low incidence of GvHD, durable disease control and excellent OS (81% at 3 years).

Automatic Picking of Foraminifera: Design of the Foraminifera Image Recognition and Sorting Tool (FIRST) Prototype and Results of the Image Classification Scheme

NASA Astrophysics Data System (ADS)

de Garidel-Thoron, T.; Marchant, R.; Soto, E.; Gally, Y.; Beaufort, L.; Bolton, C. T.; Bouslama, M.; Licari, L.; Mazur, J. C.; Brutti, J. M.; Norsa, F.

2017-12-01

Foraminifera tests are the main proxy carriers for paleoceanographic reconstructions. Both geochemical and taxonomical studies require large numbers of tests to achieve statistical relevance. To date, the extraction of foraminifera from the sediment coarse fraction is still done by hand and thus time-consuming. Moreover, the recognition of morphotypes, ecologically relevant, requires some taxonomical skills not easily taught. The automatic recognition and extraction of foraminifera would largely help paleoceanographers to overcome these issues. Recent advances in automatic image classification using machine learning opens the way to automatic extraction of foraminifera. Here we detail progress on the design of an automatic picking machine as part of the FIRST project. The machine handles 30 pre-sieved samples (100-1000µm), separating them into individual particles (including foraminifera) and imaging each in pseudo-3D. The particles are classified and specimens of interest are sorted either for Individual Foraminifera Analyses (44 per slide) and/or for classical multiple analyses (8 morphological classes per slide, up to 1000 individuals per hole). The classification is based on machine learning using Convolutional Neural Networks (CNNs), similar to the approach used in the coccolithophorid imaging system SYRACO. To prove its feasibility, we built two training image datasets of modern planktonic foraminifera containing approximately 2000 and 5000 images each, corresponding to 15 & 25 morphological classes. Using a CNN with a residual topology (ResNet) we achieve over 95% correct classification for each dataset. We tested the network on 160,000 images from 45 depths of a sediment core from the Pacific ocean, for which we have human counts. The current algorithm is able to reproduce the downcore variability in both Globigerinoides ruber and the fragmentation index (r2 = 0.58 and 0.88 respectively). The FIRST prototype yields some promising results for high-resolution paleoceanographic studies and evolutionary studies.

Automatic recognition of conceptualization zones in scientific articles and two life science applications.

PubMed

Liakata, Maria; Saha, Shyamasree; Dobnik, Simon; Batchelor, Colin; Rebholz-Schuhmann, Dietrich

2012-04-01

Scholarly biomedical publications report on the findings of a research investigation. Scientists use a well-established discourse structure to relate their work to the state of the art, express their own motivation and hypotheses and report on their methods, results and conclusions. In previous work, we have proposed ways to explicitly annotate the structure of scientific investigations in scholarly publications. Here we present the means to facilitate automatic access to the scientific discourse of articles by automating the recognition of 11 categories at the sentence level, which we call Core Scientific Concepts (CoreSCs). These include: Hypothesis, Motivation, Goal, Object, Background, Method, Experiment, Model, Observation, Result and Conclusion. CoreSCs provide the structure and context to all statements and relations within an article and their automatic recognition can greatly facilitate biomedical information extraction by characterizing the different types of facts, hypotheses and evidence available in a scientific publication. We have trained and compared machine learning classifiers (support vector machines and conditional random fields) on a corpus of 265 full articles in biochemistry and chemistry to automatically recognize CoreSCs. We have evaluated our automatic classifications against a manually annotated gold standard, and have achieved promising accuracies with 'Experiment', 'Background' and 'Model' being the categories with the highest F1-scores (76%, 62% and 53%, respectively). We have analysed the task of CoreSC annotation both from a sentence classification as well as sequence labelling perspective and we present a detailed feature evaluation. The most discriminative features are local sentence features such as unigrams, bigrams and grammatical dependencies while features encoding the document structure, such as section headings, also play an important role for some of the categories. We discuss the usefulness of automatically generated CoreSCs in two biomedical applications as well as work in progress. A web-based tool for the automatic annotation of articles with CoreSCs and corresponding documentation is available online at http://www.sapientaproject.com/software http://www.sapientaproject.com also contains detailed information pertaining to CoreSC annotation and links to annotation guidelines as well as a corpus of manually annotated articles, which served as our training data. liakata@ebi.ac.uk Supplementary data are available at Bioinformatics online.

Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

PubMed Central

Siddiqi, Muhammad Hameed; Lee, Sungyoung; Lee, Young-Koo; Khan, Adil Mehmood; Truc, Phan Tran Ho

2013-01-01

Over the last decade, human facial expressions recognition (FER) has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difficult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER) system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, finally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER. PMID:24316568

Automated night/day standoff detection, tracking, and identification of personnel for installation protection

NASA Astrophysics Data System (ADS)

Lemoff, Brian E.; Martin, Robert B.; Sluch, Mikhail; Kafka, Kristopher M.; McCormick, William; Ice, Robert

2013-06-01

The capability to positively and covertly identify people at a safe distance, 24-hours per day, could provide a valuable advantage in protecting installations, both domestically and in an asymmetric warfare environment. This capability would enable installation security officers to identify known bad actors from a safe distance, even if they are approaching under cover of darkness. We will describe an active-SWIR imaging system being developed to automatically detect, track, and identify people at long range using computer face recognition. The system illuminates the target with an eye-safe and invisible SWIR laser beam, to provide consistent high-resolution imagery night and day. SWIR facial imagery produced by the system is matched against a watch-list of mug shots using computer face recognition algorithms. The current system relies on an operator to point the camera and to review and interpret the face recognition results. Automation software is being developed that will allow the system to be cued to a location by an external system, automatically detect a person, track the person as they move, zoom in on the face, select good facial images, and process the face recognition results, producing alarms and sharing data with other systems when people are detected and identified. Progress on the automation of this system will be presented along with experimental night-time face recognition results at distance.

Phylogenetic diversity, antimicrobial susceptibility and virulence gene profiles of Brachyspira hyodysenteriae isolates from pigs in Germany

PubMed Central

Joerling, Jessica; Barth, Stefanie A.; Schlez, Karen; Willems, Hermann

2018-01-01

Swine dysentery (SD) is an economically important diarrheal disease in pigs caused by different strongly hemolytic Brachyspira (B.) species, such as B. hyodysenteriae, B. suanatina and B. hampsonii. Possible associations of epidemiologic data, such as multilocus sequence types (STs) to virulence gene profiles and antimicrobial susceptibility are rather scarce, particularly for B. hyodysenteriae isolates from Germany. In this study, B. hyodysenteriae (n = 116) isolated from diarrheic pigs between 1990 and 2016 in Germany were investigated for their STs, susceptibility to the major drugs used for treatment of SD (tiamulin and valnemulin) and genes that were previously linked with virulence and encode for hemolysins (tlyA, tlyB, tlyC, hlyA, BHWA1_RS02885, BHWA1_RS09085, BHWA1_RS04705, and BHWA1_RS02195), outer membrane proteins (OMPs) (bhlp16, bhlp17.6, bhlp29.7, bhmp39f, and bhmp39h) as well as iron acquisition factors (ftnA and bitC). Multilocus sequence typing (MLST) revealed that 79.4% of the isolates belonged to only three STs, namely ST52 (41.4%), ST8 (12.1%), and ST112 (25.9%) which have been observed in other European countries before. Another 24 isolates belonged to twelve new STs (ST113-118, ST120-123, ST131, and ST193). The temporal distribution of STs revealed the presence of new STs as well as the regular presence of ST52 over three decades (1990s–2000s). The proportion of strains that showed resistance to both tiamulin und valnemulin (39.1%) varied considerably among the most frequent STs ranging from 0% (0/14 isolates resistant) in ST8 isolates to 46.7% (14/30), 52.1% (25/48), and 85.7% (6/7) in isolates belonging to ST112, ST52, and ST114, respectively. All hemolysin genes as well as the iron-related gene ftnA and the OMP gene bhlp29.7 were regularly present in the isolates, while the OMP genes bhlp17.6 and bhmp39h could not be detected. Sequence analysis of hemolysin genes of selected isolates revealed co-evolution of tlyB, BHWA1_RS02885, BHWA1_RS09085, and BHWA1_RS02195 with the core genome and suggested independent evolution of tlyA, tlyC, and hlyA. Our data indicate that in Germany, swine dysentery might be caused by a limited number of B. hyodysenteriae clonal groups. Major STs (ST8, ST52, and ST112) are shared with other countries in Europe suggesting a possible role of the European intra-Community trade of pigs in the dissemination of certain clones. The identification of several novel STs, some of which are single or double locus variants of ST52, may on the other hand hint towards an ongoing diversification of the pathogen in the studied area. The linkage of pleuromutilin susceptibility and sequence type of an isolate might reflect a clonal expansion of the underlying resistance mechanism, namely mutations in the ribosomal RNA genes. A linkage between single virulence-associated genes (VAGs) or even VAG patterns and the phylogenetic background of the isolates could not be established, since almost all VAGs were regularly present in the isolates. PMID:29324785

Background feature descriptor for offline handwritten numeral recognition

NASA Astrophysics Data System (ADS)

Ming, Delie; Wang, Hao; Tian, Tian; Jie, Feiran; Lei, Bo

2011-11-01

This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor (sixteen-value numerical background expression). Through encoding the background pixels in the image according to a certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then reflected the structural features of the digits. Through pattern language description of images by these features, automatic segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and conclusions are presented. The experimental results of recognizing datasets from various practical application fields reflect that with this method, a good recognition effect can be achieved.

Facial Emotions Recognition using Gabor Transform and Facial Animation Parameters with Neural Networks

NASA Astrophysics Data System (ADS)

Harit, Aditya; Joshi, J. C., Col; Gupta, K. K.

2018-03-01

The paper proposed an automatic facial emotion recognition algorithm which comprises of two main components: feature extraction and expression recognition. The algorithm uses a Gabor filter bank on fiducial points to find the facial expression features. The resulting magnitudes of Gabor transforms, along with 14 chosen FAPs (Facial Animation Parameters), compose the feature space. There are two stages: the training phase and the recognition phase. Firstly, for the present 6 different emotions, the system classifies all training expressions in 6 different classes (one for each emotion) in the training stage. In the recognition phase, it recognizes the emotion by applying the Gabor bank to a face image, then finds the fiducial points, and then feeds it to the trained neural architecture.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information.

PubMed

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Yan, Bin; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition.

Automatic Artifact Removal from Electroencephalogram Data Based on A Priori Artifact Information

PubMed Central

Zhang, Chi; Tong, Li; Zeng, Ying; Jiang, Jingfang; Bu, Haibing; Li, Jianxin

2015-01-01

Electroencephalogram (EEG) is susceptible to various nonneural physiological artifacts. Automatic artifact removal from EEG data remains a key challenge for extracting relevant information from brain activities. To adapt to variable subjects and EEG acquisition environments, this paper presents an automatic online artifact removal method based on a priori artifact information. The combination of discrete wavelet transform and independent component analysis (ICA), wavelet-ICA, was utilized to separate artifact components. The artifact components were then automatically identified using a priori artifact information, which was acquired in advance. Subsequently, signal reconstruction without artifact components was performed to obtain artifact-free signals. The results showed that, using this automatic online artifact removal method, there were statistical significant improvements of the classification accuracies in both two experiments, namely, motor imagery and emotion recognition. PMID:26380294

The application of automatic recognition techniques in the Apollo 9 SO-65 experiment

NASA Technical Reports Server (NTRS)

Macdonald, R. B.

1970-01-01

A synoptic feature analysis is reported on Apollo 9 remote earth surface photographs that uses the methods of statistical pattern recognition to classify density points and clusterings in digital conversion of optical data. A computer derived geological map of a geological test site indicates that geological features of the range are separable, but that specific rock types are not identifiable.

Foreign Language Analysis and Recognition (FLARE) Progress

DTIC Science & Technology

2015-02-01

Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil). AFRL- RH -WP-TR-2015-0007 HAS BEEN REVIEWED AND IS... retrieval (IR). 15. SUBJECT TERMS Automatic speech recognition (ASR), information retrieval (IR). 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF...to the Haystack Multilingual Multimedia Information Extraction and Retrieval (MMIER) system that was initially developed under a prior work unit

Field programmable gate arrays-based number plate binarization and adjustment for automatic number plate recognition systems

NASA Astrophysics Data System (ADS)

Zhai, Xiaojun; Bensaali, Faycal; Sotudeh, Reza

2013-01-01

Number plate (NP) binarization and adjustment are important preprocessing stages in automatic number plate recognition (ANPR) systems and are used to link the number plate localization (NPL) and character segmentation stages. Successfully linking these two stages will improve the performance of the entire ANPR system. We present two optimized low-complexity NP binarization and adjustment algorithms. Efficient area/speed architectures based on the proposed algorithms are also presented and have been successfully implemented and tested using the Mentor Graphics RC240 FPGA development board, which together require only 9% of the available on-chip resources of a Virtex-4 FPGA, run with a maximum frequency of 95.8 MHz and are capable of processing one image in 0.07 to 0.17 ms.

Mutual information-based facial expression recognition

NASA Astrophysics Data System (ADS)

Hazar, Mliki; Hammami, Mohamed; Hanêne, Ben-Abdallah

2013-12-01

This paper introduces a novel low-computation discriminative regions representation for expression analysis task. The proposed approach relies on interesting studies in psychology which show that most of the descriptive and responsible regions for facial expression are located around some face parts. The contributions of this work lie in the proposition of new approach which supports automatic facial expression recognition based on automatic regions selection. The regions selection step aims to select the descriptive regions responsible or facial expression and was performed using Mutual Information (MI) technique. For facial feature extraction, we have applied Local Binary Patterns Pattern (LBP) on Gradient image to encode salient micro-patterns of facial expressions. Experimental studies have shown that using discriminative regions provide better results than using the whole face regions whilst reducing features vector dimension.

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.

Automatic recognition of falls in gait-slip training: Harness load cell based criteria.

PubMed

Yang, Feng; Pai, Yi-Chung

2011-08-11

Over-head-harness systems, equipped with load cell sensors, are essential to the participants' safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force, and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects' trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects' data revealed that the peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. Copyright © 2011 Elsevier Ltd. All rights reserved.

AUTOMATIC RECOGNITION OF FALLS IN GAIT-SLIP: A HARNESS LOAD CELL BASED CRITERION

PubMed Central

Yang, Feng; Pai, Yi-Chung

2012-01-01

Over-head-harness systems, equipped with load cell sensors, are essential to the participants’ safety and to the outcome assessment in perturbation training. The purpose of this study was to first develop an automatic outcome recognition criterion among young adults for gait-slip training and then verify such criterion among older adults. Each of 39 young and 71 older subjects, all protected by safety harness, experienced 8 unannounced, repeated slips, while walking on a 7-m walkway. Each trial was monitored with a motion capture system, bilateral ground reaction force (GRF), harness force and video recording. The fall trials were first unambiguously indentified with careful visual inspection of all video records. The recoveries without balance loss (in which subjects’ trailing foot landed anteriorly to the slipping foot) were also first fully recognized from motion and GRF analyses. These analyses then set the gold standard for the outcome recognition with load cell measurements. Logistic regression analyses based on young subjects’ data revealed that peak load cell force was the best predictor of falls (with 100% accuracy) at the threshold of 30% body weight. On the other hand, the peak moving average force of load cell across 1-s period, was the best predictor (with 100% accuracy) separating recoveries with backward balance loss (in which the recovery step landed posterior to slipping foot) from harness assistance at the threshold of 4.5% body weight. These threshold values were fully verified using the data from older adults (100% accuracy in recognizing falls). Because of the increasing popularity in the perturbation training coupling with the protective over-head-harness system, this new criterion could have far reaching implications in automatic outcome recognition during the movement therapy. PMID:21696744

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Female voice communications in high level aircraft cockpit noises--part II: vocoder and automatic speech recognition systems.

PubMed

Nixon, C; Anderson, T; Morris, L; McCavitt, A; McKinley, R; Yeager, D; McDaniel, M

1998-11-01

The intelligibility of female and male speech is equivalent under most ordinary living conditions. However, due to small differences between their acoustic speech signals, called speech spectra, one can be more or less intelligible than the other in certain situations such as high levels of noise. Anecdotal information, supported by some empirical observations, suggests that some of the high intensity noise spectra of military aircraft cockpits may degrade the intelligibility of female speech more than that of male speech. In an applied research study, the intelligibility of female and male speech was measured in several high level aircraft cockpit noise conditions experienced in military aviation. In Part I, (Nixon CW, et al. Aviat Space Environ Med 1998; 69:675-83) female speech intelligibility measured in the spectra and levels of aircraft cockpit noises and with noise-canceling microphones was lower than that of the male speech in all conditions. However, the differences were small and only those at some of the highest noise levels were significant. Although speech intelligibility of both genders was acceptable during normal cruise noises, improvements are required in most of the highest levels of noise created during maximum aircraft operating conditions. These results are discussed in a Part I technical report. This Part II report examines the intelligibility in the same aircraft cockpit noises of vocoded female and male speech and the accuracy with which female and male speech in some of the cockpit noises were understood by automatic speech recognition systems. The intelligibility of vocoded female speech was generally the same as that of vocoded male speech. No significant differences were measured between the recognition accuracy of male and female speech by the automatic speech recognition systems. The intelligibility of female and male speech was equivalent for these conditions.

The Effects of Using Flashcards to Develop Automaticity with Key Vocabulary Words for Students with and without Learning Disabilities Enrolled in a High School Spanish Course

ERIC Educational Resources Information Center

Stager, Phillip A.

2010-01-01

The purpose of this study was to investigate the effects of using flashcards to develop automaticity (rapid word recognition) with key vocabulary words and phrases in order to improve fluency and reading comprehension skills for participants with and without diagnosed learning disabilities enrolled in a high school Spanish course. Eighty-seven…

Ball-scale based hierarchical multi-object recognition in 3D medical images

NASA Astrophysics Data System (ADS)

Bağci, Ulas; Udupa, Jayaram K.; Chen, Xinjian

2010-03-01

This paper investigates, using prior shape models and the concept of ball scale (b-scale), ways of automatically recognizing objects in 3D images without performing elaborate searches or optimization. That is, the goal is to place the model in a single shot close to the right pose (position, orientation, and scale) in a given image so that the model boundaries fall in the close vicinity of object boundaries in the image. This is achieved via the following set of key ideas: (a) A semi-automatic way of constructing a multi-object shape model assembly. (b) A novel strategy of encoding, via b-scale, the pose relationship between objects in the training images and their intensity patterns captured in b-scale images. (c) A hierarchical mechanism of positioning the model, in a one-shot way, in a given image from a knowledge of the learnt pose relationship and the b-scale image of the given image to be segmented. The evaluation results on a set of 20 routine clinical abdominal female and male CT data sets indicate the following: (1) Incorporating a large number of objects improves the recognition accuracy dramatically. (2) The recognition algorithm can be thought as a hierarchical framework such that quick replacement of the model assembly is defined as coarse recognition and delineation itself is known as finest recognition. (3) Scale yields useful information about the relationship between the model assembly and any given image such that the recognition results in a placement of the model close to the actual pose without doing any elaborate searches or optimization. (4) Effective object recognition can make delineation most accurate.

Human abdomen recognition using camera and force sensor in medical robot system for automatic ultrasound scan.

PubMed

Bin Mustafa, Ammar Safwan; Ishii, Takashi; Matsunaga, Yoshiki; Nakadate, Ryu; Ishii, Hiroyuki; Ogawa, Kouji; Saito, Akiko; Sugawara, Motoaki; Niki, Kiyomi; Takanishi, Atsuo

2013-01-01

Physicians use ultrasound scans to obtain real-time images of internal organs, because such scans are safe and inexpensive. However, people in remote areas face difficulties to be scanned due to aging society and physician's shortage. Hence, it is important to develop an autonomous robotic system to perform remote ultrasound scans. Previously, we developed a robotic system for automatic ultrasound scan focusing on human's liver. In order to make it a completely autonomous system, we present in this paper a way to autonomously localize the epigastric region as the starting position for the automatic ultrasound scan. An image processing algorithm marks the umbilicus and mammary papillae on a digital photograph of the patient's abdomen. Then, we made estimation for the location of the epigastric region using the distances between these landmarks. A supporting algorithm distinguishes rib position from epigastrium using the relationship between force and displacement. We implemented these algorithms with the automatic scanning system into an apparatus: a Mitsubishi Electric's MELFA RV-1 six axis manipulator. Tests on 14 healthy male subjects showed the apparatus located the epigastric region with a success rate of 94%. The results suggest that image recognition was effective in localizing a human body part.

Automatic recognition of seismic intensity based on RS and GIS: a case study in Wenchuan Ms8.0 earthquake of China.

PubMed

Zhang, Qiuwen; Zhang, Yan; Yang, Xiaohong; Su, Bin

2014-01-01

In recent years, earthquakes have frequently occurred all over the world, which caused huge casualties and economic losses. It is very necessary and urgent to obtain the seismic intensity map timely so as to master the distribution of the disaster and provide supports for quick earthquake relief. Compared with traditional methods of drawing seismic intensity map, which require many investigations in the field of earthquake area or are too dependent on the empirical formulas, spatial information technologies such as Remote Sensing (RS) and Geographical Information System (GIS) can provide fast and economical way to automatically recognize the seismic intensity. With the integrated application of RS and GIS, this paper proposes a RS/GIS-based approach for automatic recognition of seismic intensity, in which RS is used to retrieve and extract the information on damages caused by earthquake, and GIS is applied to manage and display the data of seismic intensity. The case study in Wenchuan Ms8.0 earthquake in China shows that the information on seismic intensity can be automatically extracted from remotely sensed images as quickly as possible after earthquake occurrence, and the Digital Intensity Model (DIM) can be used to visually query and display the distribution of seismic intensity.

Development of an automated ultrasonic testing system

NASA Astrophysics Data System (ADS)

Shuxiang, Jiao; Wong, Brian Stephen

2005-04-01

Non-Destructive Testing is necessary in areas where defects in structures emerge over time due to wear and tear and structural integrity is necessary to maintain its usability. However, manual testing results in many limitations: high training cost, long training procedure, and worse, the inconsistent test results. A prime objective of this project is to develop an automatic Non-Destructive testing system for a shaft of the wheel axle of a railway carriage. Various methods, such as the neural network, pattern recognition methods and knowledge-based system are used for the artificial intelligence problem. In this paper, a statistical pattern recognition approach, Classification Tree is applied. Before feature selection, a thorough study on the ultrasonic signals produced was carried out. Based on the analysis of the ultrasonic signals, three signal processing methods were developed to enhance the ultrasonic signals: Cross-Correlation, Zero-Phase filter and Averaging. The target of this step is to reduce the noise and make the signal character more distinguishable. Four features: 1. The Auto Regressive Model Coefficients. 2. Standard Deviation. 3. Pearson Correlation 4. Dispersion Uniformity Degree are selected. And then a Classification Tree is created and applied to recognize the peak positions and amplitudes. Searching local maximum is carried out before feature computing. This procedure reduces much computation time in the real-time testing. Based on this algorithm, a software package called SOFRA was developed to recognize the peaks, calibrate automatically and test a simulated shaft automatically. The automatic calibration procedure and the automatic shaft testing procedure are developed.

Open Dataset for the Automatic Recognition of Sedentary Behaviors.

PubMed

Possos, William; Cruz, Robinson; Cerón, Jesús D; López, Diego M; Sierra-Torres, Carlos H

2017-01-01

Sedentarism is associated with the development of noncommunicable diseases (NCD) such as cardiovascular diseases (CVD), type 2 diabetes, and cancer. Therefore, the identification of specific sedentary behaviors (TV viewing, sitting at work, driving, relaxing, etc.) is especially relevant for planning personalized prevention programs. To build and evaluate a public a dataset for the automatic recognition (classification) of sedentary behaviors. The dataset included data from 30 subjects, who performed 23 sedentary behaviors while wearing a commercial wearable on the wrist, a smartphone on the hip and another in the thigh. Bluetooth Low Energy (BLE) beacons were used in order to improve the automatic classification of different sedentary behaviors. The study also compared six well know data mining classification techniques in order to identify the more precise method of solving the classification problem of the 23 defined behaviors. A better classification accuracy was obtained using the Random Forest algorithm and when data were collected from the phone on the hip. Furthermore, the use of beacons as a reference for obtaining the symbolic location of the individual improved the precision of the classification.

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

PubMed

Niioka, Hirohiko; Asatani, Satoshi; Yoshimura, Aina; Ohigashi, Hironori; Tagawa, Seiichi; Miyake, Jun

2018-01-01

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

Sequential injection ionic liquid dispersive liquid-liquid microextraction for thallium preconcentration and determination with flame atomic absorption spectrometry.

PubMed

Anthemidis, Aristidis N; Ioannou, Kallirroy-Ioanna G

2012-08-01

A novel, automatic on-line sequential injection dispersive liquid-liquid microextraction (SI-DLLME) method, based on 1-hexyl-3-methylimidazolium hexafluorophosphate ([Hmim][PF(6)]) ionic liquid as an extractant solvent was developed and demonstrated for trace thallium determination by flame atomic absorption spectrometry. The ionic liquid was on-line fully dispersed into the aqueous solution in a continuous flow format while the TlBr(4)(-) complex was easily migrated into the fine droplets of the extractant due to the huge contact area of them with the aqueous phase. Furthermore, the extractant was simply retained onto the surface of polyurethane foam packed into a microcolumn. No specific conditions like low temperature are required for extractant isolation. All analytical parameters of the proposed method were investigated and optimized. For 15 mL of sample solution, an enhancement factor of 290, a detection limit of 0.86 μg L(-1) and a precision (RSD) of 2.7% at 20.0 μg L(-1) Tl(I) concentration level, was obtained. The developed method was evaluated by analyzing certified reference materials while good recoveries from environmental and biological samples proved that present method was competitive in practical applications.

Segmentation and Recognition of Continuous Human Activity

DTIC Science & Technology

2001-01-01

This paper presents a methodology for automatic segmentation and recognition of continuous human activity . We segment a continuous human activity into...commencement or termination. We use single action sequences for the training data set. The test sequences, on the other hand, are continuous sequences of human ... activity that consist of three or more actions in succession. The system has been tested on continuous activity sequences containing actions such as

Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Yang, Liumao; Li, Shutao; Rabbani, Hossein; Liu, Zhimin; Peng, Qinghua; Chen, Xiangdong

2017-06-01

Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.

Divided attention enhances the recognition of emotional stimuli: evidence from the attentional boost effect.

PubMed

Rossi-Arnaud, Clelia; Spataro, Pietro; Costanzi, Marco; Saraulli, Daniele; Cestari, Vincenzo

2018-01-01

The present study examined predictions of the early-phase-elevated-attention hypothesis of the attentional boost effect (ABE), which suggests that transient increases in attention at encoding, as instantiated in the ABE paradigm, should enhance the recognition of neutral and positive items (whose encoding is mostly based on controlled processes), while having small or null effects on the recognition of negative items (whose encoding is primarily based on automatic processes). Participants were presented a sequence of negative, neutral and positive stimuli (pictures in Experiment 1, words in Experiment 2) associated to target (red) squares, distractor (green) squares or no squares (baseline condition). They were told to attend to the pictures/words and simultaneously press the spacebar of the computer when a red square appeared. In a later recognition task, stimuli associated to target squares were recognised better than stimuli associated to distractor squares, replicating the standard ABE. More importantly, we also found that: (a) the memory enhancement following target detection occurred with all types of stimuli (neutral, negative and positive) and (b) the advantage of negative stimuli over neutral stimuli was intact in the DA condition. These findings suggest that the encoding of negative stimuli depends on both controlled (attention-dependent) and automatic (attention-independent) processes.

Human Activity Recognition from Smart-Phone Sensor Data using a Multi-Class Ensemble Learning in Home Monitoring.

PubMed

Ghose, Soumya; Mitra, Jhimli; Karunanithi, Mohan; Dowling, Jason

2015-01-01

Home monitoring of chronically ill or elderly patient can reduce frequent hospitalisations and hence provide improved quality of care at a reduced cost to the community, therefore reducing the burden on the healthcare system. Activity recognition of such patients is of high importance in such a design. In this work, a system for automatic human physical activity recognition from smart-phone inertial sensors data is proposed. An ensemble of decision trees framework is adopted to train and predict the multi-class human activity system. A comparison of our proposed method with a multi-class traditional support vector machine shows significant improvement in activity recognition accuracies.

Robot Command Interface Using an Audio-Visual Speech Recognition System

NASA Astrophysics Data System (ADS)

Ceballos, Alexánder; Gómez, Juan; Prieto, Flavio; Redarce, Tanneguy

In recent years audio-visual speech recognition has emerged as an active field of research thanks to advances in pattern recognition, signal processing and machine vision. Its ultimate goal is to allow human-computer communication using voice, taking into account the visual information contained in the audio-visual speech signal. This document presents a command's automatic recognition system using audio-visual information. The system is expected to control the laparoscopic robot da Vinci. The audio signal is treated using the Mel Frequency Cepstral Coefficients parametrization method. Besides, features based on the points that define the mouth's outer contour according to the MPEG-4 standard are used in order to extract the visual speech information.

A Model for Prediction Bird and Ice Impact Loads on Structures.

DTIC Science & Technology

1982-05-01

F33615-77-C-5221 UNCLASSIFIED UDR-TR79_5 AFAAL-TR-82-20,6 NL AUFaL4flm-82-2046 A !m . ?Pm PmIc1! iO Bnom ’cc WPM I~ WADS CisTvmEw L. 1. BORHPN A. QIAU...Formation of an Element From Four Mid-Node Points Tlx X3 - X1 Tly a Y3 - ¥l Tz " Z3 - z1 T2x = X4 - X2 T2y = Y4 - Y2 T2 z ’ Z4 - Z2 The cross product...own length, i.e., Stl Tlx t a (29) Tlz tlz " -T7 where T1 - Tlx2 + Tly2 + Tlz 2 (30) The vector t 2 is defined by t 2 - ’ x t, so that its components

A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

PubMed Central

Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

2014-01-01

This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

A novel thermal face recognition approach using face pattern words

NASA Astrophysics Data System (ADS)

Zheng, Yufeng

2010-04-01

A reliable thermal face recognition system can enhance the national security applications such as prevention against terrorism, surveillance, monitoring and tracking, especially at nighttime. The system can be applied at airports, customs or high-alert facilities (e.g., nuclear power plant) for 24 hours a day. In this paper, we propose a novel face recognition approach utilizing thermal (long wave infrared) face images that can automatically identify a subject at both daytime and nighttime. With a properly acquired thermal image (as a query image) in monitoring zone, the following processes will be employed: normalization and denoising, face detection, face alignment, face masking, Gabor wavelet transform, face pattern words (FPWs) creation, face identification by similarity measure (Hamming distance). If eyeglasses are present on a subject's face, an eyeglasses mask will be automatically extracted from the querying face image, and then masked with all comparing FPWs (no more transforms). A high identification rate (97.44% with Top-1 match) has been achieved upon our preliminary face dataset (of 39 subjects) from the proposed approach regardless operating time and glasses-wearing condition.e

Image simulation for automatic license plate recognition

NASA Astrophysics Data System (ADS)

Bala, Raja; Zhao, Yonghui; Burry, Aaron; Kozitsky, Vladimir; Fillion, Claude; Saunders, Craig; Rodríguez-Serrano, José

2012-01-01

Automatic license plate recognition (ALPR) is an important capability for traffic surveillance applications, including toll monitoring and detection of different types of traffic violations. ALPR is a multi-stage process comprising plate localization, character segmentation, optical character recognition (OCR), and identification of originating jurisdiction (i.e. state or province). Training of an ALPR system for a new jurisdiction typically involves gathering vast amounts of license plate images and associated ground truth data, followed by iterative tuning and optimization of the ALPR algorithms. The substantial time and effort required to train and optimize the ALPR system can result in excessive operational cost and overhead. In this paper we propose a framework to create an artificial set of license plate images for accelerated training and optimization of ALPR algorithms. The framework comprises two steps: the synthesis of license plate images according to the design and layout for a jurisdiction of interest; and the modeling of imaging transformations and distortions typically encountered in the image capture process. Distortion parameters are estimated by measurements of real plate images. The simulation methodology is successfully demonstrated for training of OCR.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System.

PubMed

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-10-20

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias.

Arrhythmia Classification Based on Multi-Domain Feature Extraction for an ECG Recognition System

PubMed Central

Li, Hongqiang; Yuan, Danyang; Wang, Youxi; Cui, Dianyin; Cao, Lu

2016-01-01

Automatic recognition of arrhythmias is particularly important in the diagnosis of heart diseases. This study presents an electrocardiogram (ECG) recognition system based on multi-domain feature extraction to classify ECG beats. An improved wavelet threshold method for ECG signal pre-processing is applied to remove noise interference. A novel multi-domain feature extraction method is proposed; this method employs kernel-independent component analysis in nonlinear feature extraction and uses discrete wavelet transform to extract frequency domain features. The proposed system utilises a support vector machine classifier optimized with a genetic algorithm to recognize different types of heartbeats. An ECG acquisition experimental platform, in which ECG beats are collected as ECG data for classification, is constructed to demonstrate the effectiveness of the system in ECG beat classification. The presented system, when applied to the MIT-BIH arrhythmia database, achieves a high classification accuracy of 98.8%. Experimental results based on the ECG acquisition experimental platform show that the system obtains a satisfactory classification accuracy of 97.3% and is able to classify ECG beats efficiently for the automatic identification of cardiac arrhythmias. PMID:27775596

Spatial and temporal variability of the atmospheric turbidity in Tunisia

NASA Astrophysics Data System (ADS)

Saad, Mohamed; Trabelsi, Amel; Masmoudi, Mohamed; Alfaro, Stephane C.

2016-11-01

Atmospheric turbidity is an important parameter in meteorology, climatology and for providing hindsight on particulate air pollution in local areas. In this work we exploit 1260 direct solar radiation measurements performed in Sfax (Center Tunisia), from March 2015 to February 2016. These measurements were made with a pyrheliometer only when clouds did not obstruct the solar disk. The atmospheric turbidity is quantified by the means of both the Linke's turbidity factor (TLI) and Angström's coefficient (β). Over the year, values of TLI and β are found to vary in the ranges 1-15 and 0-0.7, with the most probable values around 3.5 and 0.05, respectively. However, a marked seasonal pattern is observed for the two turbidity parameters. They achieve their maximum in the spring and summer months, their minimum in winter and autumn appears as a transitional period. The comparison of the results obtained in Sfax with those of three AERONET stations located in north (Carthage), central-north (Ben Salem), and south (Medenine) Tunisia, reveals that this seasonal pattern of the atmospheric turbidity is valid for all the Tunisian territory, and probably beyond. At shorter (hourly) time scales, the diurnal behavior of the turbidity in Sfax is different in the summer months from the one observed during the rest of the year. Indeed, an enhancement of TLI is observed during the day. This is assumedly attributed to the production of secondary aerosols by atmospheric photochemistry.

Relation among HPA and HPG neuroendocrine systems, transmissible risk and neighborhood quality on development of substance use disorder: results of a 10-year prospective study.

PubMed

Tarter, Ralph E; Kirisci, Levent; Kirillova, Galina; Reynolds, Maureen; Gavaler, Judy; Ridenour, Ty; Horner, Michelle; Clark, Duncan; Vanyukov, Michael

2013-01-01

Research has shown involvement of hormones of the hypothalamic pituitary adrenal (HPA) axis and hypothalamic pituitary gonadal (HPG) axis in the regulation of behaviors that contribute to SUD risk and its intergenerational transmission. Neighborhood environment has also been shown to relate to hormones of these two neuroendocrine systems and behaviors associated with SUD liability. Accordingly, it was hypothesized that (1) parental SUD severity and neighborhood quality correlate with activity of the HPG axis (testosterone level) and HPA axis (cortisol stability), and (2) transmissible risk during childhood mediates these hormone variables on development of SUD measured in adulthood. Transmissible risk for SUD measured by the transmissible liability index (TLI; Vanyukov et al., 2009) along with saliva cortisol and plasma testosterone were prospectively measured in boys at ages 10-12 and 16. Neighborhood quality was measured using a composite score encompassing indicators of residential instability and economic disadvantage. SUD was assessed at age 22. Neither hormone variable cross-sectionally correlated with transmissible risk measured at ages 10-12 and 16. However, the TLI at age 10-12 predicted testosterone level and cortisol stability at age 16. Moreover, testosterone level, correlated with cortisol stability at age 16, predicted SUD at age 22. HPA and HPG axes activity do not underlie variation in TLI, however, high transmissible risk in childhood predicts neuroendocrine system activity presaging development of SUD. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Use of EEG workload indices for diagnostic monitoring of vigilance decrement.

PubMed

Kamzanova, Altyngul T; Kustubayeva, Almira M; Matthews, Gerald

2014-09-01

A study was run to test which of five electroencephalographic (EEG) indices was most diagnostic of loss of vigilance at two levels of workload. EEG indices of alertness include conventional spectral power measures as well as indices combining measures from multiple frequency bands, such as the Task Load Index (TLI) and the Engagement Index (El). However, it is unclear which indices are optimal for early detection of loss of vigilance. Ninety-two participants were assigned to one of two experimental conditions, cued (lower workload) and uncued (higher workload), and then performed a 40-min visual vigilance task. Performance on this task is believed to be limited by attentional resource availability. EEG was recorded continuously. Performance, subjective state, and workload were also assessed. The task showed a vigilance decrement in performance; cuing improved performance and reduced subjective workload. Lower-frequency alpha (8 to 10.9 Hz) and TLI were most sensitive to the task parameters. The magnitude of temporal change was larger for lower-frequency alpha. Surprisingly, higher TLI was associated with superior performance. Frontal theta and El were influenced by task workload only in the final period of work. Correlational data also suggested that the indices are distinct from one another. Lower-frequency alpha appears to be the optimal index for monitoring vigilance on the task used here, but further work is needed to test how diagnosticity of EEG indices varies with task demands. Lower-frequency alpha may be used to diagnose loss of operator alertness on tasks requiring vigilance.

Task Versus Component Consistency in the Development of Automatic Processes: Consistent Attending Versus Consistent Responding.

DTIC Science & Technology

1982-03-01

are two qualitatively different forms of human information processing (James, 1890; Hasher & Zacks, 1979; LaBerge , 1973, 1975; Logan, 1978, 1979...Kristofferson, M. W. When item recognition and visual search functions are similar. Perception & Psychophysics, 1972, 12, 379-384. LaBerge , D. Attention and...the measurement of perceptual learning. Hemory and3 Conition, 1973, 1, 263-276. LaBerge , D. Acquisition of automatic processing in purceptual and

Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

NASA Astrophysics Data System (ADS)

Yan, Fengxia; Udupa, Jayaram K.; Tong, Yubing; Xu, Guoping; Odhner, Dewey; Torigian, Drew A.

2018-03-01

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

Application of pattern recognition techniques to crime analysis

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

Bender, C.F.; Cox, L.A. Jr.; Chappell, G.A.

1976-08-15

The initial goal was to evaluate the capabilities of current pattern recognition techniques when applied to existing computerized crime data. Performance was to be evaluated both in terms of the system's capability to predict crimes and to optimize police manpower allocation. A relation was sought to predict the crime's susceptibility to solution, based on knowledge of the crime type, location, time, etc. The preliminary results of this work are discussed. They indicate that automatic crime analysis involving pattern recognition techniques is feasible, and that efforts to determine optimum variables and techniques are warranted. 47 figures (RWR)

DESIGN OF A PATTERN RECOGNITION DIGITAL COMPUTER WITH APPLICATION TO THE AUTOMATIC SCANNING OF BUBBLE CHAMBER NEGATIVES

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

McCormick, B.H.; Narasimhan, R.

1963-01-01

The overall computer system contains three main parts: an input device, a pattern recognition unit (PRU), and a control computer. The bubble chamber picture is divided into a grid of st run. Concent 1-mm squares on the film. It is then processed in parallel in a two-dimensional array of 1024 identical processing modules (stalactites) of the PRU. The array can function as a two- dimensional shift register in which results of successive shifting operations can be accumulated. The pattern recognition process is generally controlled by a conventional arithmetic computer. (A.G.W.)

Advanced miniature processing handware for ATR applications

NASA Technical Reports Server (NTRS)

Chao, Tien-Hsin (Inventor); Daud, Taher (Inventor); Thakoor, Anikumar (Inventor)

2003-01-01

A Hybrid Optoelectronic Neural Object Recognition System (HONORS), is disclosed, comprising two major building blocks: (1) an advanced grayscale optical correlator (OC) and (2) a massively parallel three-dimensional neural-processor. The optical correlator, with its inherent advantages in parallel processing and shift invariance, is used for target of interest (TOI) detection and segmentation. The three-dimensional neural-processor, with its robust neural learning capability, is used for target classification and identification. The hybrid optoelectronic neural object recognition system, with its powerful combination of optical processing and neural networks, enables real-time, large frame, automatic target recognition (ATR).

Intelligent Image Analysis for Image-Guided Laser Hair Removal and Skin Therapy

NASA Technical Reports Server (NTRS)

Walker, Brian; Lu, Thomas; Chao, Tien-Hsin

2012-01-01

We present the development of advanced automatic target recognition (ATR) algorithms for the hair follicles identification in digital skin images to accurately direct the laser beam to remove the hair. The ATR system first performs a wavelet filtering to enhance the contrast of the hair features in the image. The system then extracts the unique features of the targets and sends the features to an Adaboost based classifier for training and recognition operations. The ATR system automatically classifies the hair, moles, or other skin lesion and provides the accurate coordinates of the intended hair follicle locations. The coordinates can be used to guide a scanning laser to focus energy only on the hair follicles. The intended benefit would be to protect the skin from unwanted laser exposure and to provide more effective skin therapy.

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications.

PubMed

Corneanu, Ciprian Adrian; Simon, Marc Oliu; Cohn, Jeffrey F; Guerrero, Sergio Escalera

2016-08-01

Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.

Word position affects stimulus recognition: evidence for early ERP short-term plastic modulation.

PubMed

Spironelli, Chiara; Galfano, Giovanni; Umiltà, Carlo; Angrilli, Alessandro

2011-12-01

The present study was aimed at investigating the short-term plastic changes that follow word learning at a neurophysiological level. The main hypothesis was that word position (left or right visual field, LVF/RH or RVF/LH) in the initial learning phase would leave a trace that affected, in the subsequent recognition phase, the Recognition Potential (i.e., the first negative component distinguishing words from other stimuli) elicited 220-240 ms after centrally presented stimuli. Forty-eight students were administered, in the learning phase, 125 words for 4s, randomly presented half in the left and half in the right visual field. In the recognition phase, participants were split into two equal groups, one was assigned to the Word task, the other to the Picture task (in which half of the 125 pictures were new, and half matched prior studied words). During the Word task, old RVF/LH words elicited significantly greater negativity in left posterior sites with respect to old LVF/RH words, which in turn showed the same pattern of activation evoked by new words. Therefore, correspondence between stimulus spatial position and hemisphere specialized in automatic word recognition created a robust prime for subsequent recognition. During the Picture task, pictures matching old RVF/LH words showed no differences compared with new pictures, but evoked significantly greater negativity than pictures matching old LVF/RH words. Thus, the priming effect vanished when the task required a switch from visual analysis to stored linguistic information, whereas the lack of correspondence between stimulus position and network specialized in automatic word recognition (i.e., when words were presented to the LVF/RH) revealed the implicit costs for recognition. Results support the view that short-term plastic changes occurring in a linguistic learning task interact with both stimulus position and modality (written word vs. picture representation). Copyright © 2011 Elsevier B.V. All rights reserved.

Intelligent form removal with character stroke preservation

NASA Astrophysics Data System (ADS)

Garris, Michael D.

1996-03-01

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

Automatic identification of species with neural networks.

PubMed

Hernández-Serna, Andrés; Jiménez-Segura, Luz Fernanda

2014-01-01

A new automatic identification system using photographic images has been designed to recognize fish, plant, and butterfly species from Europe and South America. The automatic classification system integrates multiple image processing tools to extract the geometry, morphology, and texture of the images. Artificial neural networks (ANNs) were used as the pattern recognition method. We tested a data set that included 740 species and 11,198 individuals. Our results show that the system performed with high accuracy, reaching 91.65% of true positive fish identifications, 92.87% of plants and 93.25% of butterflies. Our results highlight how the neural networks are complementary to species identification.

Automatic translation among spoken languages

NASA Technical Reports Server (NTRS)

Walter, Sharon M.; Costigan, Kelly

1994-01-01

The Machine Aided Voice Translation (MAVT) system was developed in response to the shortage of experienced military field interrogators with both foreign language proficiency and interrogation skills. Combining speech recognition, machine translation, and speech generation technologies, the MAVT accepts an interrogator's spoken English question and translates it into spoken Spanish. The spoken Spanish response of the potential informant can then be translated into spoken English. Potential military and civilian applications for automatic spoken language translation technology are discussed in this paper.

A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech

PubMed Central

Tóth, László; Hoffmann, Ildikó; Gosztolya, Gábor; Vincze, Veronika; Szatlóczki, Gréta; Bánréti, Zoltán; Pákáski, Magdolna; Kálmán, János

2018-01-01

Background: Even today the reliable diagnosis of the prodromal stages of Alzheimer’s disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive de-cline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Methods: Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech sig-nals, first manually (using the Praat software), and then automatically, with an automatic speech recogni-tion (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. Results: The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process – that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. Conclusion: The temporal analysis of spontaneous speech can be exploited in implementing a new, auto-matic detection-based tool for screening MCI for the community. PMID:29165085

A Speech Recognition-based Solution for the Automatic Detection of Mild Cognitive Impairment from Spontaneous Speech.

PubMed

Toth, Laszlo; Hoffmann, Ildiko; Gosztolya, Gabor; Vincze, Veronika; Szatloczki, Greta; Banreti, Zoltan; Pakaski, Magdolna; Kalman, Janos

2018-01-01

Even today the reliable diagnosis of the prodromal stages of Alzheimer's disease (AD) remains a great challenge. Our research focuses on the earliest detectable indicators of cognitive decline in mild cognitive impairment (MCI). Since the presence of language impairment has been reported even in the mild stage of AD, the aim of this study is to develop a sensitive neuropsychological screening method which is based on the analysis of spontaneous speech production during performing a memory task. In the future, this can form the basis of an Internet-based interactive screening software for the recognition of MCI. Participants were 38 healthy controls and 48 clinically diagnosed MCI patients. The provoked spontaneous speech by asking the patients to recall the content of 2 short black and white films (one direct, one delayed), and by answering one question. Acoustic parameters (hesitation ratio, speech tempo, length and number of silent and filled pauses, length of utterance) were extracted from the recorded speech signals, first manually (using the Praat software), and then automatically, with an automatic speech recognition (ASR) based tool. First, the extracted parameters were statistically analyzed. Then we applied machine learning algorithms to see whether the MCI and the control group can be discriminated automatically based on the acoustic features. The statistical analysis showed significant differences for most of the acoustic parameters (speech tempo, articulation rate, silent pause, hesitation ratio, length of utterance, pause-per-utterance ratio). The most significant differences between the two groups were found in the speech tempo in the delayed recall task, and in the number of pauses for the question-answering task. The fully automated version of the analysis process - that is, using the ASR-based features in combination with machine learning - was able to separate the two classes with an F1-score of 78.8%. The temporal analysis of spontaneous speech can be exploited in implementing a new, automatic detection-based tool for screening MCI for the community. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

SigVox - A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Menenti, Massimo

2017-06-01

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.

Automatic 2.5-D Facial Landmarking and Emotion Annotation for Social Interaction Assistance.

PubMed

Zhao, Xi; Zou, Jianhua; Li, Huibin; Dellandrea, Emmanuel; Kakadiaris, Ioannis A; Chen, Liming

2016-09-01

People with low vision, Alzheimer's disease, and autism spectrum disorder experience difficulties in perceiving or interpreting facial expression of emotion in their social lives. Though automatic facial expression recognition (FER) methods on 2-D videos have been extensively investigated, their performance was constrained by challenges in head pose and lighting conditions. The shape information in 3-D facial data can reduce or even overcome these challenges. However, high expenses of 3-D cameras prevent their widespread use. Fortunately, 2.5-D facial data from emerging portable RGB-D cameras provide a good balance for this dilemma. In this paper, we propose an automatic emotion annotation solution on 2.5-D facial data collected from RGB-D cameras. The solution consists of a facial landmarking method and a FER method. Specifically, we propose building a deformable partial face model and fit the model to a 2.5-D face for localizing facial landmarks automatically. In FER, a novel action unit (AU) space-based FER method has been proposed. Facial features are extracted using landmarks and further represented as coordinates in the AU space, which are classified into facial expressions. Evaluated on three publicly accessible facial databases, namely EURECOM, FRGC, and Bosphorus databases, the proposed facial landmarking and expression recognition methods have achieved satisfactory results. Possible real-world applications using our algorithms have also been discussed.

License Plate Recognition System for Indian Vehicles

NASA Astrophysics Data System (ADS)

Sanap, P. R.; Narote, S. P.

2010-11-01

We consider the task of recognition of Indian vehicle number plates (also called license plates or registration plates in other countries). A system for Indian number plate recognition must cope with wide variations in the appearance of the plates. Each state uses its own range of designs with font variations between the designs. Also, vehicle owners may place the plates inside glass covered frames or use plates made of nonstandard materials. These issues compound the complexity of automatic number plate recognition, making existing approaches inadequate. We have developed a system that incorporates a novel combination of image processing and artificial neural network technologies to successfully locate and read Indian vehicle number plates in digital images. Commercial application of the system is envisaged.

Face Recognition in Humans and Machines

NASA Astrophysics Data System (ADS)

O'Toole, Alice; Tistarelli, Massimo

The study of human face recognition by psychologists and neuroscientists has run parallel to the development of automatic face recognition technologies by computer scientists and engineers. In both cases, there are analogous steps of data acquisition, image processing, and the formation of representations that can support the complex and diverse tasks we accomplish with faces. These processes can be understood and compared in the context of their neural and computational implementations. In this chapter, we present the essential elements of face recognition by humans and machines, taking a perspective that spans psychological, neural, and computational approaches. From the human side, we overview the methods and techniques used in the neurobiology of face recognition, the underlying neural architecture of the system, the role of visual attention, and the nature of the representations that emerges. From the computational side, we discuss face recognition technologies and the strategies they use to overcome challenges to robust operation over viewing parameters. Finally, we conclude the chapter with a look at some recent studies that compare human and machine performances at face recognition.

[Creating language model of the forensic medicine domain for developing a autopsy recording system by automatic speech recognition].

PubMed

Niijima, H; Ito, N; Ogino, S; Takatori, T; Iwase, H; Kobayashi, M

2000-11-01

For the purpose of practical use of speech recognition technology for recording of forensic autopsy, a language model of the speech recording system, specialized for the forensic autopsy, was developed. The language model for the forensic autopsy by applying 3-gram model was created, and an acoustic model for Japanese speech recognition by Hidden Markov Model in addition to the above were utilized to customize the speech recognition engine for forensic autopsy. A forensic vocabulary set of over 10,000 words was compiled and some 300,000 sentence patterns were made to create the forensic language model, then properly mixing with a general language model to attain high exactitude. When tried by dictating autopsy findings, this speech recognition system was proved to be about 95% of recognition rate that seems to have reached to the practical usability in view of speech recognition software, though there remains rooms for improving its hardware and application-layer software.

Enumeration of Enterobacter cloacae after chloramine exposure.

PubMed Central

Watters, S K; Pyle, B H; LeChevallier, M W; McFeters, G A

1989-01-01

Growth of Enterobacter cloacae on various media was compared after disinfection. This was done to examine the effects of monochloramine and chlorine on the enumeration of coliforms. The media used were TLY (nonselective; 5.5% tryptic soy broth, 0.3% yeast extract, 1.0% lactose, and 1.5% Bacto-Agar), m-T7 (selective; developed to recover injured coliforms), m-Endo (selective; contains sodium sulfite), TLYS (TLY with sodium sulfite), and m-T7S (m-T7 with sodium sulfite). Sodium sulfite in any medium improved the recovery of chloramine-treated E. cloacae. However, sodium sulfite in TLYS and m-T7S did not significantly improve the detection of chlorine-treated E. cloacae, and m-Endo was the least effective medium for recovering chlorinated bacteria. Differences in recovery of chlorine- and chloramine-treated E. cloacae are consistent with mechanistic differences between the disinfectants. PMID:2619309

The Development of the Speaker Independent ARM Continuous Speech Recognition System

DTIC Science & Technology

1992-01-01

spokeTi airborne reconnaissance reports u-ing a speech recognition system based on phoneme-level hidden Markov models (HMMs). Previous versions of the ARM...will involve automatic selection from multiple model sets, corresponding to different speaker types, and that the most rudimen- tary partition of a...The vocabulary size for the ARM task is 497 words. These words are related to the phoneme-level symbols corresponding to the models in the model set

Stochastic Modeling as a Means of Automatic Speech Recognition

DTIC Science & Technology

1975-04-01

companng ihc features of different speech recognition systems, attention is often focused on thc control structures and the methods o’ communication...with no need to use secondary storage . Note that we go from a group of separate knowledge sources to an integrated network representation in...exhaust the available lime or storage . - - - . . 1- .-.-.. mmm^~ i — ■ ■ ’ ■ C haplcr I - IN I ROÜliCl ION Page 13 On the other hand

Real-time speech gisting for ATC applications

NASA Astrophysics Data System (ADS)

Dunkelberger, Kirk A.

1995-06-01

Command and control within the ATC environment remains primarily voice-based. Hence, automatic real time, speaker independent, continuous speech recognition (CSR) has many obvious applications and implied benefits to the ATC community: automated target tagging, aircraft compliance monitoring, controller training, automatic alarm disabling, display management, and many others. However, while current state-of-the-art CSR systems provide upwards of 98% word accuracy in laboratory environments, recent low-intrusion experiments in the ATCT environments demonstrated less than 70% word accuracy in spite of significant investments in recognizer tuning. Acoustic channel irregularities and controller/pilot grammar verities impact current CSR algorithms at their weakest points. It will be shown herein, however, that real time context- and environment-sensitive gisting can provide key command phrase recognition rates of greater than 95% using the same low-intrusion approach. The combination of real time inexact syntactic pattern recognition techniques and a tight integration of CSR, gisting, and ATC database accessor system components is the key to these high phase recognition rates. A system concept for real time gisting in the ATC context is presented herein. After establishing an application context, discussion presents a minimal CSR technology context then focuses on the gisting mechanism, desirable interfaces into the ATCT database environment, and data and control flow within the prototype system. Results of recent tests for a subset of the functionality are presented together with suggestions for further research.

Can soft biometric traits assist user recognition?

NASA Astrophysics Data System (ADS)

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

2004-08-01

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

Road Signs Detection and Recognition Utilizing Images and 3d Point Cloud Acquired by Mobile Mapping System

NASA Astrophysics Data System (ADS)

Li, Y. H.; Shinohara, T.; Satoh, T.; Tachibana, K.

2016-06-01

High-definition and highly accurate road maps are necessary for the realization of automated driving, and road signs are among the most important element in the road map. Therefore, a technique is necessary which can acquire information about all kinds of road signs automatically and efficiently. Due to the continuous technical advancement of Mobile Mapping System (MMS), it has become possible to acquire large number of images and 3d point cloud efficiently with highly precise position information. In this paper, we present an automatic road sign detection and recognition approach utilizing both images and 3D point cloud acquired by MMS. The proposed approach consists of three stages: 1) detection of road signs from images based on their color and shape features using object based image analysis method, 2) filtering out of over detected candidates utilizing size and position information estimated from 3D point cloud, region of candidates and camera information, and 3) road sign recognition using template matching method after shape normalization. The effectiveness of proposed approach was evaluated by testing dataset, acquired from more than 180 km of different types of roads in Japan. The results show a very high success in detection and recognition of road signs, even under the challenging conditions such as discoloration, deformation and in spite of partial occlusions.

Measuring burden in dementia caregivers: Confirmatory factor analysis for short forms of the Zarit Burden Interview.

PubMed

Lin, Chung-Ying; Wang, Jung-Der; Pai, Ming-Chyi; Ku, Li-Jung Elizabeth

To examine the psychometric properties of different short versions of the Zarit Burden Interview (ZBI), and to find an efficient and valid short version for clinical use among dementia caregivers. A total of 270 Taiwanese dementia caregivers filled out the full form of the ZBI, which contains 22 items. Using the 22-item ZBI, we used confirmatory factor analysis (CFA) to calculate the fit indices of all proposed short versions with various items to determine useful short versions. Additional associations between each useful short version and informal care hours, as well as subjective financial situations, were examined to understand their concurrent validity. Based on the CFA results, three short versions of the ZBI, performed excellently (4-item version: comparative fit index [CFI]=1.000, Tucker-Lewis index [TLI]=1.035, standardized root mean square residual [SRMR]=0.019, and root mean square error of approximation [RMSEA]=0.000; 8-item version: CFI=0.970, TLI=0.958, SRMR=0.045, and RMSEA=0.065; 12-item version: CFI=0.959, TLI=0.950, SRMR=0.053, and RMSEA=0.075). In addition, the 12-item ZBI, as compared with other versions, had a higher correlation with the number of informal care hours. The 12-item ZBI was also highly correlated with the original 22-item ZBI (r=0.952). We found the 12-item ZBI to be a promising measure for healthcare providers to assess the burden of dementia caregivers quickly and efficiently. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Application of a Three-Dimensional Water Quality Model as a Decision Support Tool for the Management of Land-Use Changes in the Catchment of an Oligotrophic Lake

NASA Astrophysics Data System (ADS)

Trolle, Dennis; Spigel, Bob; Hamilton, David P.; Norton, Ned; Sutherland, Donna; Plew, David; Allan, Mathew G.

2014-09-01

While expansion of agricultural land area and intensification of agricultural practices through irrigation and fertilizer use can bring many benefits to communities, intensifying land use also causes more contaminants, such as nutrients and pesticides, to enter rivers, lakes, and groundwater. For lakes such as Benmore in the Waitaki catchment, South Island, New Zealand, an area which is currently undergoing agricultural intensification, this could potentially lead to marked degradation of water clarity as well as effects on ecological, recreational, commercial, and tourism values. We undertook a modeling study to demonstrate science-based options for consideration of agricultural intensification in the catchment of Lake Benmore. Based on model simulations of a range of potential future nutrient loadings, it is clear that different areas within Lake Benmore may respond differently to increased nutrient loadings. A western arm (Ahuriri) could be most severely affected by land-use changes and associated increases in nutrient loadings. Lake-wide annual averages of an eutrophication indicator, the trophic level index (TLI) were derived from simulated chlorophyll a, total nitrogen, and total phosphorus concentrations. Results suggest that the lake will shift from oligotrophic (TLI = 2-3) to eutrophic (TLI = 4-5) as external loadings are increased eightfold over current baseline loads, corresponding to the potential land-use intensification in the catchment. This study provides a basis for use of model results in a decision-making process by outlining the environmental consequences of a series of land-use management options, and quantifying nutrient load limits needed to achieve defined trophic state objectives.

The proliferation of normal human breast tissue implanted into athymic nude mice is stimulated by estrogen but not progesterone.

PubMed

Laidlaw, I J; Clarke, R B; Howell, A; Owen, A W; Potten, C S; Anderson, E

1995-01-01

In order to resolve the question of which ovarian steroid stimulates normal human mammary epithelial cell proliferation, we have implanted pieces of normal human breast tissue subcutaneously into athymic nude mice. These mice were then treated with slow-release pellets containing estradiol (E2) or progesterone (P) such that serum levels of E2 and P were increased to those seen in normal women. The proliferative activity of the tissue implants was assessed by uptake of tritiated thymidine and steroid receptor expression was measured immunocytochemically. Insertion of a 2 mg E2 pellet 14 days after tissue implantation increased the thymidine labeling index (TLI) from a median of 0.4% (n = 34) to a median of 2.1% after 7 days (n = 43; P < 0.001 by Mann Whitney U test). In contrast, treatment with a P pellet (4 mg) had no effect upon the TLI whereas P (4 mg) in combination with E2 (2 mg) had no effect over and above that of E2 alone. There was a significant correlation between the increase in TLI and either the E2 content of the pellets (P < 0.001 by linear regression) or the serum E2 levels achieved (P < 0.001). Expression of the P receptor was increased 15- to 20-fold by E2 treatment. We conclude that E2 is sufficient to stimulate human breast epithelial cell proliferation at physiologically relevant concentrations and that P does not affect proliferation either alone or after E2 priming.

Automated aural classification used for inter-species discrimination of cetaceans.

PubMed

Binder, Carolyn M; Hines, Paul C

2014-04-01

Passive acoustic methods are in widespread use to detect and classify cetacean species; however, passive acoustic systems often suffer from large false detection rates resulting from numerous transient sources. To reduce the acoustic analyst workload, automatic recognition methods may be implemented in a two-stage process. First, a general automatic detector is implemented that produces many detections to ensure cetacean presence is noted. Then an automatic classifier is used to significantly reduce the number of false detections and classify the cetacean species. This process requires development of a robust classifier capable of performing inter-species classification. Because human analysts can aurally discriminate species, an automated aural classifier that uses perceptual signal features was tested on a cetacean data set. The classifier successfully discriminated between four species of cetaceans-bowhead, humpback, North Atlantic right, and sperm whales-with 85% accuracy. It also performed well (100% accuracy) for discriminating sperm whale clicks from right whale gunshots. An accuracy of 92% and area under the receiver operating characteristic curve of 0.97 were obtained for the relatively challenging bowhead and humpback recognition case. These results demonstrated that the perceptual features employed by the aural classifier provided powerful discrimination cues for inter-species classification of cetaceans.

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery.

PubMed

Tian, Shu; Yin, Xu-Cheng; Wang, Zhi-Bin; Zhou, Fang; Hao, Hong-Wei

2015-01-01

The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness.

A VidEo-Based Intelligent Recognition and Decision System for the Phacoemulsification Cataract Surgery

PubMed Central

Yin, Xu-Cheng; Wang, Zhi-Bin; Zhou, Fang; Hao, Hong-Wei

2015-01-01

The phacoemulsification surgery is one of the most advanced surgeries to treat cataract. However, the conventional surgeries are always with low automatic level of operation and over reliance on the ability of surgeons. Alternatively, one imaginative scene is to use video processing and pattern recognition technologies to automatically detect the cataract grade and intelligently control the release of the ultrasonic energy while operating. Unlike cataract grading in the diagnosis system with static images, complicated background, unexpected noise, and varied information are always introduced in dynamic videos of the surgery. Here we develop a Video-Based Intelligent Recognitionand Decision (VeBIRD) system, which breaks new ground by providing a generic framework for automatically tracking the operation process and classifying the cataract grade in microscope videos of the phacoemulsification cataract surgery. VeBIRD comprises a robust eye (iris) detector with randomized Hough transform to precisely locate the eye in the noise background, an effective probe tracker with Tracking-Learning-Detection to thereafter track the operation probe in the dynamic process, and an intelligent decider with discriminative learning to finally recognize the cataract grade in the complicated video. Experiments with a variety of real microscope videos of phacoemulsification verify VeBIRD's effectiveness. PMID:26693249

Image processing and machine learning in the morphological analysis of blood cells.

PubMed

Rodellar, J; Alférez, S; Acevedo, A; Molina, A; Merino, A

2018-05-01

This review focuses on how image processing and machine learning can be useful for the morphological characterization and automatic recognition of cell images captured from peripheral blood smears. The basics of the 3 core elements (segmentation, quantitative features, and classification) are outlined, and recent literature is discussed. Although red blood cells are a significant part of this context, this study focuses on malignant lymphoid cells and blast cells. There is no doubt that these technologies may help the cytologist to perform efficient, objective, and fast morphological analysis of blood cells. They may also help in the interpretation of some morphological features and may serve as learning and survey tools. Although research is still needed, it is important to define screening strategies to exploit the potential of image-based automatic recognition systems integrated in the daily routine of laboratories along with other analysis methodologies. © 2018 John Wiley & Sons Ltd.

[Design and implementation of mobile terminal data acquisition for Chinese materia medica resources survey].

PubMed

Qi, Yuan-Hua; Wang, Hui; Zhang, Xiao-Bo; Jin, Yan; Ge, Xiao-Guang; Jing, Zhi-Xian; Wang, Ling; Zhao, Yu-Ping; Guo, Lan-Ping; Huang, Lu-Qi

2017-11-01

In this paper, a data acquisition system based on mobile terminal combining GPS, offset correction, automatic speech recognition and database networking technology was designed implemented with the function of locating the latitude and elevation information fast, taking conveniently various types of Chinese herbal plant photos, photos, samples habitat photos and so on. The mobile system realizes automatic association with Chinese medicine source information, through the voice recognition function it records the information of plant characteristics and environmental characteristics, and record relevant plant specimen information. The data processing platform based on Chinese medicine resources survey data reporting client can effectively assists in indoor data processing, derives the mobile terminal data to computer terminal. The established data acquisition system provides strong technical support for the fourth national survey of the Chinese materia medica resources (CMMR). Copyright© by the Chinese Pharmaceutical Association.

User acceptance of intelligent avionics: A study of automatic-aided target recognition

NASA Technical Reports Server (NTRS)

Becker, Curtis A.; Hayes, Brian C.; Gorman, Patrick C.

1991-01-01

User acceptance of new support systems typically was evaluated after the systems were specified, designed, and built. The current study attempts to assess user acceptance of an Automatic-Aided Target Recognition (ATR) system using an emulation of such a proposed system. The detection accuracy and false alarm level of the ATR system were varied systematically, and subjects rated the tactical value of systems exhibiting different performance levels. Both detection accuracy and false alarm level affected the subjects' ratings. The data from two experiments suggest a cut-off point in ATR performance below which the subjects saw little tactical value in the system. An ATR system seems to have obvious tactical value only if it functions at a correct detection rate of 0.7 or better with a false alarm level of 0.167 false alarms per square degree or fewer.

Convolutional neural networks with balanced batches for facial expressions recognition

NASA Astrophysics Data System (ADS)

Battini Sönmez, Elena; Cangelosi, Angelo

2017-03-01

This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

Evidence of automatic processing in sequence learning using process-dissociation

PubMed Central

Mong, Heather M.; McCabe, David P.; Clegg, Benjamin A.

2012-01-01

This paper proposes a way to apply process-dissociation to sequence learning in addition and extension to the approach used by Destrebecqz and Cleeremans (2001). Participants were trained on two sequences separated from each other by a short break. Following training, participants self-reported their knowledge of the sequences. A recognition test was then performed which required discrimination of two trained sequences, either under the instructions to call any sequence encountered in the experiment “old” (the inclusion condition), or only sequence fragments from one half of the experiment “old” (the exclusion condition). The recognition test elicited automatic and controlled process estimates using the process dissociation procedure, and suggested both processes were involved. Examining the underlying processes supporting performance may provide more information on the fundamental aspects of the implicit and explicit constructs than has been attainable through awareness testing. PMID:22679465

Results from the Crop Identification Technology Assessment for Remote Sensing (CITARS) project

NASA Technical Reports Server (NTRS)

Bauer, M. E. (Principal Investigator); Davis, B. J.; Bizzell, R. M.; Hall, F. G.; Feiveson, A. H.; Malila, W. A.; Rice, D. P.

1976-01-01

The author has identified the following significant results. It was found that several factors had a significant effect on crop identification performance: (1) crop maturity and site characteristics, (2) which of several different single date automatic data processing procedures was used for local recognition, (3) nonlocal recognition, both with and without preprocessing for the extension of recognition signatures, and (4) use of multidate data. It also was found that classification accuracy for field center pixels was not a reliable indicator of proportion estimation performance for whole areas, that bias was present in proportion estimates, and that training data and procedures strongly influenced crop identification performance.

Full-body gestures and movements recognition: user descriptive and unsupervised learning approaches in GDL classifier

NASA Astrophysics Data System (ADS)

Hachaj, Tomasz; Ogiela, Marek R.

2014-09-01

Gesture Description Language (GDL) is a classifier that enables syntactic description and real time recognition of full-body gestures and movements. Gestures are described in dedicated computer language named Gesture Description Language script (GDLs). In this paper we will introduce new GDLs formalisms that enable recognition of selected classes of movement trajectories. The second novelty is new unsupervised learning method with which it is possible to automatically generate GDLs descriptions. We have initially evaluated both proposed extensions of GDL and we have obtained very promising results. Both the novel methodology and evaluation results will be described in this paper.

A Robust and Affordable Table Indexing Approach for Multi-isocenter Dosimetrically Matched Fields.

PubMed

Yu, Amy; Fahimian, Benjamin; Million, Lynn; Hsu, Annie

2017-05-23

Purpose  Radiotherapy treatment planning of extended volume typically necessitates the utilization of multiple field isocenters and abutting dosimetrically matched fields in order to enable coverage beyond the field size limits. A common example includes total lymphoid irradiation (TLI) treatments, which are conventionally planned using dosimetric matching of the mantle, para-aortic/spleen, and pelvic fields. Due to the large irradiated volume and system limitations, such as field size and couch extension, a combination of couch shifts and sliding of patients are necessary to be correctly executed for accurate delivery of the plan. However, shifting of patients presents a substantial safety issue and has been shown to be prone to errors ranging from minor deviations to geometrical misses warranting a medical event. To address this complex setup and mitigate the safety issues relating to delivery, a practical technique for couch indexing of TLI treatments has been developed and evaluated through a retrospective analysis of couch position. Methods The indexing technique is based on the modification of the commonly available slide board to enable indexing of the patient position. Modifications include notching to enable coupling with indexing bars, and the addition of a headrest used to fixate the head of the patient relative to the slide board. For the clinical setup, a Varian Exact Couch TM (Varian Medical Systems, Inc, Palo Alto, CA) was utilized. Two groups of patients were treated: 20 patients with table indexing and 10 patients without. The standard deviations (SDs) of the couch positions in longitudinal, lateral, and vertical directions through the entire treatment cycle for each patient were calculated and differences in both groups were analyzed with Student's t-test. Results The longitudinal direction showed the largest improvement. In the non-indexed group, the positioning SD ranged from 2.0 to 7.9 cm. With the indexing device, the positioning SD was reduced to a range of 0.4 to 1.3 cm (p < 0.05 with 95% confidence level). The lateral positioning was slightly improved (p < 0.05 with 95% confidence level), while no improvement was observed in the vertical direction. Conclusions The conventional matched field TLI treatment is error-prone to geometrical setup error. The feasibility of full indexing TLI treatments was validated and shown to result in a significant reduction of positioning and shifting errors.

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.

Unconstrained face detection and recognition based on RGB-D camera for the visually impaired

NASA Astrophysics Data System (ADS)

Zhao, Xiangdong; Wang, Kaiwei; Yang, Kailun; Hu, Weijian

2017-02-01

It is highly important for visually impaired people (VIP) to be aware of human beings around themselves, so correctly recognizing people in VIP assisting apparatus provide great convenience. However, in classical face recognition technology, faces used in training and prediction procedures are usually frontal, and the procedures of acquiring face images require subjects to get close to the camera so that frontal face and illumination guaranteed. Meanwhile, labels of faces are defined manually rather than automatically. Most of the time, labels belonging to different classes need to be input one by one. It prevents assisting application for VIP with these constraints in practice. In this article, a face recognition system under unconstrained environment is proposed. Specifically, it doesn't require frontal pose or uniform illumination as required by previous algorithms. The attributes of this work lie in three aspects. First, a real time frontal-face synthesizing enhancement is implemented, and frontal faces help to increase recognition rate, which is proved with experiment results. Secondly, RGB-D camera plays a significant role in our system, from which both color and depth information are utilized to achieve real time face tracking which not only raises the detection rate but also gives an access to label faces automatically. Finally, we propose to use neural networks to train a face recognition system, and Principal Component Analysis (PCA) is applied to pre-refine the input data. This system is expected to provide convenient help for VIP to get familiar with others, and make an access for them to recognize people when the system is trained enough.

Automatic Recognition of Road Signs

NASA Astrophysics Data System (ADS)

Inoue, Yasuo; Kohashi, Yuuichirou; Ishikawa, Naoto; Nakajima, Masato

2002-11-01

The increase in traffic accidents is becoming a serious social problem with the recent rapid traffic increase. In many cases, the driver"s carelessness is the primary factor of traffic accidents, and the driver assistance system is demanded for supporting driver"s safety. In this research, we propose the new method of automatic detection and recognition of road signs by image processing. The purpose of this research is to prevent accidents caused by driver"s carelessness, and call attention to a driver when the driver violates traffic a regulation. In this research, high accuracy and the efficient sign detecting method are realized by removing unnecessary information except for a road sign from an image, and detect a road sign using shape features. At first, the color information that is not used in road signs is removed from an image. Next, edges except for circular and triangle ones are removed to choose sign shape. In the recognition process, normalized cross correlation operation is carried out to the two-dimensional differentiation pattern of a sign, and the accurate and efficient method for detecting the road sign is realized. Moreover, the real-time operation in a software base was realized by holding down calculation cost, maintaining highly precise sign detection and recognition. Specifically, it becomes specifically possible to process by 0.1 sec(s)/frame using a general-purpose PC (CPU: Pentium4 1.7GHz). As a result of in-vehicle experimentation, our system could process on real time and has confirmed that detection and recognition of a sign could be performed correctly.

Gimli: open source and high-performance biomedical name recognition

PubMed Central

2013-01-01

Background Automatic recognition of biomedical names is an essential task in biomedical information extraction, presenting several complex and unsolved challenges. In recent years, various solutions have been implemented to tackle this problem. However, limitations regarding system characteristics, customization and usability still hinder their wider application outside text mining research. Results We present Gimli, an open-source, state-of-the-art tool for automatic recognition of biomedical names. Gimli includes an extended set of implemented and user-selectable features, such as orthographic, morphological, linguistic-based, conjunctions and dictionary-based. A simple and fast method to combine different trained models is also provided. Gimli achieves an F-measure of 87.17% on GENETAG and 72.23% on JNLPBA corpus, significantly outperforming existing open-source solutions. Conclusions Gimli is an off-the-shelf, ready to use tool for named-entity recognition, providing trained and optimized models for recognition of biomedical entities from scientific text. It can be used as a command line tool, offering full functionality, including training of new models and customization of the feature set and model parameters through a configuration file. Advanced users can integrate Gimli in their text mining workflows through the provided library, and extend or adapt its functionalities. Based on the underlying system characteristics and functionality, both for final users and developers, and on the reported performance results, we believe that Gimli is a state-of-the-art solution for biomedical NER, contributing to faster and better research in the field. Gimli is freely available at http://bioinformatics.ua.pt/gimli. PMID:23413997

A Vocal-Based Analytical Method for Goose Behaviour Recognition

PubMed Central

Steen, Kim Arild; Therkildsen, Ole Roland; Karstoft, Henrik; Green, Ole

2012-01-01

Since human-wildlife conflicts are increasing, the development of cost-effective methods for reducing damage or conflict levels is important in wildlife management. A wide range of devices to detect and deter animals causing conflict are used for this purpose, although their effectiveness is often highly variable, due to habituation to disruptive or disturbing stimuli. Automated recognition of behaviours could form a critical component of a system capable of altering the disruptive stimuli to avoid this. In this paper we present a novel method to automatically recognise goose behaviour based on vocalisations from flocks of free-living barnacle geese (Branta leucopsis). The geese were observed and recorded in a natural environment, using a shielded shotgun microphone. The classification used Support Vector Machines (SVMs), which had been trained with labeled data. Greenwood Function Cepstral Coefficients (GFCC) were used as features for the pattern recognition algorithm, as they can be adjusted to the hearing capabilities of different species. Three behaviours are classified based in this approach, and the method achieves a good recognition of foraging behaviour (86–97% sensitivity, 89–98% precision) and a reasonable recognition of flushing (79–86%, 66–80%) and landing behaviour(73–91%, 79–92%). The Support Vector Machine has proven to be a robust classifier for this kind of classification, as generality and non-linear capabilities are important. We conclude that vocalisations can be used to automatically detect behaviour of conflict wildlife species, and as such, may be used as an integrated part of a wildlife management system. PMID:22737037

Computer vision system: a tool for evaluating the quality of wheat in a grain tank

NASA Astrophysics Data System (ADS)

Minkin, Uryi Igorevish; Panchenko, Aleksei Vladimirovich; Shkanaev, Aleksandr Yurievich; Konovalenko, Ivan Andreevich; Putintsev, Dmitry Nikolaevich; Sadekov, Rinat Nailevish

2018-04-01

The paper describes a technology that allows for automatizing the process of evaluating the grain quality in a grain tank of a combine harvester. Special recognition algorithm analyzes photographic images taken by the camera, and that provides automatic estimates of the total mass fraction of broken grains and the presence of non-grains. The paper also presents the operating details of the tank prototype as well as it defines the accuracy of the algorithms designed.

Neural network for intelligent query of an FBI forensic database

NASA Astrophysics Data System (ADS)

Uvanni, Lee A.; Rainey, Timothy G.; Balasubramanian, Uma; Brettle, Dean W.; Weingard, Fred; Sibert, Robert W.; Birnbaum, Eric

1997-02-01

Examiner is an automated fired cartridge case identification system utilizing a dual-use neural network pattern recognition technology, called the statistical-multiple object detection and location system (S-MODALS) developed by Booz(DOT)Allen & Hamilton, Inc. in conjunction with Rome Laboratory. S-MODALS was originally designed for automatic target recognition (ATR) of tactical and strategic military targets using multisensor fusion [electro-optical (EO), infrared (IR), and synthetic aperture radar (SAR)] sensors. Since S-MODALS is a learning system readily adaptable to problem domains other than automatic target recognition, the pattern matching problem of microscopic marks for firearms evidence was analyzed using S-MODALS. The physics; phenomenology; discrimination and search strategies; robustness requirements; error level and confidence level propagation that apply to the pattern matching problem of military targets were found to be applicable to the ballistic domain as well. The Examiner system uses S-MODALS to rank a set of queried cartridge case images from the most similar to the least similar image in reference to an investigative fired cartridge case image. The paper presents three independent tests and evaluation studies of the Examiner system utilizing the S-MODALS technology for the Federal Bureau of Investigation.

Automatic speech recognition (ASR) based approach for speech therapy of aphasic patients: A review

NASA Astrophysics Data System (ADS)

Jamal, Norezmi; Shanta, Shahnoor; Mahmud, Farhanahani; Sha'abani, MNAH

2017-09-01

This paper reviews the state-of-the-art an automatic speech recognition (ASR) based approach for speech therapy of aphasic patients. Aphasia is a condition in which the affected person suffers from speech and language disorder resulting from a stroke or brain injury. Since there is a growing body of evidence indicating the possibility of improving the symptoms at an early stage, ASR based solutions are increasingly being researched for speech and language therapy. ASR is a technology that transfers human speech into transcript text by matching with the system's library. This is particularly useful in speech rehabilitation therapies as they provide accurate, real-time evaluation for speech input from an individual with speech disorder. ASR based approaches for speech therapy recognize the speech input from the aphasic patient and provide real-time feedback response to their mistakes. However, the accuracy of ASR is dependent on many factors such as, phoneme recognition, speech continuity, speaker and environmental differences as well as our depth of knowledge on human language understanding. Hence, the review examines recent development of ASR technologies and its performance for individuals with speech and language disorders.

a Two-Step Classification Approach to Distinguishing Similar Objects in Mobile LIDAR Point Clouds

NASA Astrophysics Data System (ADS)

He, H.; Khoshelham, K.; Fraser, C.

2017-09-01

Nowadays, lidar is widely used in cultural heritage documentation, urban modeling, and driverless car technology for its fast and accurate 3D scanning ability. However, full exploitation of the potential of point cloud data for efficient and automatic object recognition remains elusive. Recently, feature-based methods have become very popular in object recognition on account of their good performance in capturing object details. Compared with global features describing the whole shape of the object, local features recording the fractional details are more discriminative and are applicable for object classes with considerable similarity. In this paper, we propose a two-step classification approach based on point feature histograms and the bag-of-features method for automatic recognition of similar objects in mobile lidar point clouds. Lamp post, street light and traffic sign are grouped as one category in the first-step classification for their inter similarity compared with tree and vehicle. A finer classification of the lamp post, street light and traffic sign based on the result of the first-step classification is implemented in the second step. The proposed two-step classification approach is shown to yield a considerable improvement over the conventional one-step classification approach.

Exploiting range imagery: techniques and applications

NASA Astrophysics Data System (ADS)

Armbruster, Walter

2009-07-01

Practically no applications exist for which automatic processing of 2D intensity imagery can equal human visual perception. This is not the case for range imagery. The paper gives examples of 3D laser radar applications, for which automatic data processing can exceed human visual cognition capabilities and describes basic processing techniques for attaining these results. The examples are drawn from the fields of helicopter obstacle avoidance, object detection in surveillance applications, object recognition at high range, multi-object-tracking, and object re-identification in range image sequences. Processing times and recognition performances are summarized. The techniques used exploit the bijective continuity of the imaging process as well as its independence of object reflectivity, emissivity and illumination. This allows precise formulations of the probability distributions involved in figure-ground segmentation, feature-based object classification and model based object recognition. The probabilistic approach guarantees optimal solutions for single images and enables Bayesian learning in range image sequences. Finally, due to recent results in 3D-surface completion, no prior model libraries are required for recognizing and re-identifying objects of quite general object categories, opening the way to unsupervised learning and fully autonomous cognitive systems.

TU-C-17A-03: An Integrated Contour Evaluation Software Tool Using Supervised Pattern Recognition for Radiotherapy

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

Chen, H; Tan, J; Kavanaugh, J

Purpose: Radiotherapy (RT) contours delineated either manually or semiautomatically require verification before clinical usage. Manual evaluation is very time consuming. A new integrated software tool using supervised pattern contour recognition was thus developed to facilitate this process. Methods: The contouring tool was developed using an object-oriented programming language C# and application programming interfaces, e.g. visualization toolkit (VTK). The C# language served as the tool design basis. The Accord.Net scientific computing libraries were utilized for the required statistical data processing and pattern recognition, while the VTK was used to build and render 3-D mesh models from critical RT structures in real-timemore » and 360° visualization. Principal component analysis (PCA) was used for system self-updating geometry variations of normal structures based on physician-approved RT contours as a training dataset. The inhouse design of supervised PCA-based contour recognition method was used for automatically evaluating contour normality/abnormality. The function for reporting the contour evaluation results was implemented by using C# and Windows Form Designer. Results: The software input was RT simulation images and RT structures from commercial clinical treatment planning systems. Several abilities were demonstrated: automatic assessment of RT contours, file loading/saving of various modality medical images and RT contours, and generation/visualization of 3-D images and anatomical models. Moreover, it supported the 360° rendering of the RT structures in a multi-slice view, which allows physicians to visually check and edit abnormally contoured structures. Conclusion: This new software integrates the supervised learning framework with image processing and graphical visualization modules for RT contour verification. This tool has great potential for facilitating treatment planning with the assistance of an automatic contour evaluation module in avoiding unnecessary manual verification for physicians/dosimetrists. In addition, its nature as a compact and stand-alone tool allows for future extensibility to include additional functions for physicians’ clinical needs.« less

A Complete OCR System for Tamil Magazine Documents

NASA Astrophysics Data System (ADS)

Kokku, Aparna; Chakravarthy, Srinivasa

We present a complete optical character recognition (OCR) system for Tamil magazines/documents. All the standard elements of OCR process like de-skewing, preprocessing, segmentation, character recognition, and reconstruction are implemented. Experience with OCR problems teaches that for most subtasks of OCR, there is no single technique that gives perfect results for every type of document image. We exploit the ability of neural networks to learn from experience in solving the problems of segmentation and character recognition. Text segmentation of Tamil newsprint poses a new challenge owing to its italic-like font type; problems that arise in recognition of touching and close characters are discussed. Character recognition efficiency varied from 94 to 97% for this type of font. The grouping of blocks into logical units and the determination of reading order within each logical unit helped us in reconstructing automatically the document image in an editable format.

The fast iris image clarity evaluation based on Tenengrad and ROI selection

NASA Astrophysics Data System (ADS)

Gao, Shuqin; Han, Min; Cheng, Xu

2018-04-01

In iris recognition system, the clarity of iris image is an important factor that influences recognition effect. In the process of recognition, the blurred image may possibly be rejected by the automatic iris recognition system, which will lead to the failure of identification. Therefore it is necessary to evaluate the iris image definition before recognition. Considered the existing evaluation methods on iris image definition, we proposed a fast algorithm to evaluate the definition of iris image in this paper. In our algorithm, firstly ROI (Region of Interest) is extracted based on the reference point which is determined by using the feature of the light spots within the pupil, then Tenengrad operator is used to evaluate the iris image's definition. Experiment results show that, the iris image definition algorithm proposed in this paper could accurately distinguish the iris images of different clarity, and the algorithm has the merit of low computational complexity and more effectiveness.

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

PubMed

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

2012-11-01

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

DRR is a teenager

NASA Astrophysics Data System (ADS)

Nagy, George

2008-01-01

The fifteenth anniversary of the first SPIE symposium (titled Character Recognition Technologies) on Document Recognition and Retrieval provides an opportunity to examine DRR's contributions to the development of document technologies. Many of the tools taken for granted today, including workable general purpose OCR, large-scale, semi-automatic forms processing, inter-format table conversion, and text mining, followed research presented at this venue. This occasion also affords an opportunity to offer tribute to the conference organizers and proceedings editors and to the coterie of professionals who regularly participate in DRR.

Robust Recognition of Loud and Lombard speech in the Fighter Cockpit Environment

DTIC Science & Technology

1988-08-01

the latter as inter-speaker variability. According to Zue [Z85j, inter-speaker variabilities can be attributed to sociolinguistic background, dialect...34 Journal of the Acoustical Society of America , Vol 50, 1971. [At74I B. S. Atal, "Linear prediction for speaker identification," Journal of the Acoustical...Society of America , Vol 55, 1974. [B771 B. Beek, E. P. Neuberg, and D. C. Hodge, "An Assessment of the Technology of Automatic Speech Recognition for

Vigilante: Ultrafast Smart Sensor for Target Recognition and Precision Tracking in a Simulated CMD Scenario

NASA Technical Reports Server (NTRS)

Uldomkesmalee, Suraphol; Suddarth, Steven C.

1997-01-01

VIGILANTE is an ultrafast smart sensor testbed for generic Automatic Target Recognition (ATR) applications with a series of capability demonstration focussed on cruise missile defense (CMD). VIGILANTE's sensor/processor architecture is based on next-generation UV/visible/IR sensors and a tera-operations per second sugar-cube processor, as well as supporting airborne vehicle. Excellent results of efficient ATR methodologies that use an eigenvectors/neural network combination and feature-based precision tracking have been demonstrated in the laboratory environment.

System integration of pattern recognition, adaptive aided, upper limb prostheses

NASA Technical Reports Server (NTRS)

Lyman, J.; Freedy, A.; Solomonow, M.

1975-01-01

The requirements for successful integration of a computer aided control system for multi degree of freedom artificial arms are discussed. Specifications are established for a system which shares control between a human amputee and an automatic control subsystem. The approach integrates the following subsystems: (1) myoelectric pattern recognition, (2) adaptive computer aiding; (3) local reflex control; (4) prosthetic sensory feedback; and (5) externally energized arm with the functions of prehension, wrist rotation, elbow extension and flexion and humeral rotation.

Automatic classification of fish germ cells through optimum-path forest.

PubMed

Papa, João P; Gutierrez, Mario E M; Nakamura, Rodrigo Y M; Papa, Luciene P; Vicentini, Irene B F; Vicentini, Carlos A

2011-01-01

The spermatogenesis is crucial to the species reproduction, and its monitoring may shed light over some important information of such process. Thus, the germ cells quantification can provide useful tools to improve the reproduction cycle. In this paper, we present the first work that address this problem in fishes with machine learning techniques. We show here how to obtain high recognition accuracies in order to identify fish germ cells with several state-of-the-art supervised pattern recognition techniques.

Retrieval, automaticity, vocabulary elaboration, orthography (RAVE-O): a comprehensive, fluency-based reading intervention program.

PubMed

Wolf, M; Miller, L; Donnelly, K

2000-01-01

The most important implication of the double-deficit hypothesis (Wolf & Bowers, in this issue) concerns a new emphasis on fluency and automaticity in intervention for children with developmental reading disabilities. The RAVE-O (Retrieval, Automaticity, Vocabulary Elaboration, Orthography) program is an experimental, fluency-based approach to reading intervention that is designed to accompany a phonological analysis program. In an effort to address multiple possible sources of dysfluency in readers with disabilities, the program involves comprehensive emphases both on fluency in word attack, word identification, and comprehension and on automaticity in underlying componential processes (e.g., phonological, orthographic, semantic, and lexical retrieval skills). The goals, theoretical principles, and applied activities of the RAVE-O curriculum are described with particular stress on facilitating the development of rapid orthographic pattern recognition and on changing children's attitudes toward language.

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

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality

PubMed Central

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque

2018-01-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human–Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam. PMID:29389845

Facial Emotion Recognition: A Survey and Real-World User Experiences in Mixed Reality.

PubMed

Mehta, Dhwani; Siddiqui, Mohammad Faridul Haque; Javaid, Ahmad Y

2018-02-01

Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and facial expressions convey the feeling and the feedback to the user. This discipline of Human-Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.

Objective voice and speech analysis of persons with chronic hoarseness by prosodic analysis of speech samples.

PubMed

Haderlein, Tino; Döllinger, Michael; Matoušek, Václav; Nöth, Elmar

2016-10-01

Automatic voice assessment is often performed using sustained vowels. In contrast, speech analysis of read-out texts can be applied to voice and speech assessment. Automatic speech recognition and prosodic analysis were used to find regression formulae between automatic and perceptual assessment of four voice and four speech criteria. The regression was trained with 21 men and 62 women (average age 49.2 years) and tested with another set of 24 men and 49 women (48.3 years), all suffering from chronic hoarseness. They read the text 'Der Nordwind und die Sonne' ('The North Wind and the Sun'). Five voice and speech therapists evaluated the data on 5-point Likert scales. Ten prosodic and recognition accuracy measures (features) were identified which describe all the examined criteria. Inter-rater correlation within the expert group was between r = 0.63 for the criterion 'match of breath and sense units' and r = 0.87 for the overall voice quality. Human-machine correlation was between r = 0.40 for the match of breath and sense units and r = 0.82 for intelligibility. The perceptual ratings of different criteria were highly correlated with each other. Likewise, the feature sets modeling the criteria were very similar. The automatic method is suitable for assessing chronic hoarseness in general and for subgroups of functional and organic dysphonia. In its current version, it is almost as reliable as a randomly picked rater from a group of voice and speech therapists.

Practical vision based degraded text recognition system

NASA Astrophysics Data System (ADS)

Mohammad, Khader; Agaian, Sos; Saleh, Hani

2011-02-01

Rapid growth and progress in the medical, industrial, security and technology fields means more and more consideration for the use of camera based optical character recognition (OCR) Applying OCR to scanned documents is quite mature, and there are many commercial and research products available on this topic. These products achieve acceptable recognition accuracy and reasonable processing times especially with trained software, and constrained text characteristics. Even though the application space for OCR is huge, it is quite challenging to design a single system that is capable of performing automatic OCR for text embedded in an image irrespective of the application. Challenges for OCR systems include; images are taken under natural real world conditions, Surface curvature, text orientation, font, size, lighting conditions, and noise. These and many other conditions make it extremely difficult to achieve reasonable character recognition. Performance for conventional OCR systems drops dramatically as the degradation level of the text image quality increases. In this paper, a new recognition method is proposed to recognize solid or dotted line degraded characters. The degraded text string is localized and segmented using a new algorithm. The new method was implemented and tested using a development framework system that is capable of performing OCR on camera captured images. The framework allows parameter tuning of the image-processing algorithm based on a training set of camera-captured text images. Novel methods were used for enhancement, text localization and the segmentation algorithm which enables building a custom system that is capable of performing automatic OCR which can be used for different applications. The developed framework system includes: new image enhancement, filtering, and segmentation techniques which enabled higher recognition accuracies, faster processing time, and lower energy consumption, compared with the best state of the art published techniques. The system successfully produced impressive OCR accuracies (90% -to- 93%) using customized systems generated by our development framework in two industrial OCR applications: water bottle label text recognition and concrete slab plate text recognition. The system was also trained for the Arabic language alphabet, and demonstrated extremely high recognition accuracy (99%) for Arabic license name plate text recognition with processing times of 10 seconds. The accuracy and run times of the system were compared to conventional and many states of art methods, the proposed system shows excellent results.

Multilevel Analysis in Analyzing Speech Data

ERIC Educational Resources Information Center

Guddattu, Vasudeva; Krishna, Y.

2011-01-01

The speech produced by human vocal tract is a complex acoustic signal, with diverse applications in phonetics, speech synthesis, automatic speech recognition, speaker identification, communication aids, speech pathology, speech perception, machine translation, hearing research, rehabilitation and assessment of communication disorders and many…

AN AUTOMATIC DEVICE FOR READING TYPOGRAPHICAL TEXTS,

DTIC Science & Technology

permissible. The system represents an attempt to apply the methods of machines designed for typescript reading to machines reading printed texts...Some characteristics by which typescript and typographical material differ are presented. The basic aspects of the recognition algorithm are given. A

Sight-Word Practice in a Flash!

ERIC Educational Resources Information Center

Erwin, Robin W., Jr.

2016-01-01

For learners who need sight-word practice, including young students and struggling readers, digital flash cards may promote automatic word recognition when used as a supplemental activity to regular reading instruction. A novel use of common presentation software efficiently supports this practice strategy.

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures

PubMed Central

Pi, Yiming

2017-01-01

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar. PMID:29267249

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.

Dynamic Gesture Recognition with a Terahertz Radar Based on Range Profile Sequences and Doppler Signatures.

PubMed

Zhou, Zhi; Cao, Zongjie; Pi, Yiming

2017-12-21

The frequency of terahertz radar ranges from 0.1 THz to 10 THz, which is higher than that of microwaves. Multi-modal signals, including high-resolution range profile (HRRP) and Doppler signatures, can be acquired by the terahertz radar system. These two kinds of information are commonly used in automatic target recognition; however, dynamic gesture recognition is rarely discussed in the terahertz regime. In this paper, a dynamic gesture recognition system using a terahertz radar is proposed, based on multi-modal signals. The HRRP sequences and Doppler signatures were first achieved from the radar echoes. Considering the electromagnetic scattering characteristics, a feature extraction model is designed using location parameter estimation of scattering centers. Dynamic Time Warping (DTW) extended to multi-modal signals is used to accomplish the classifications. Ten types of gesture signals, collected from a terahertz radar, are applied to validate the analysis and the recognition system. The results of the experiment indicate that the recognition rate reaches more than 91%. This research verifies the potential applications of dynamic gesture recognition using a terahertz radar.

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EPA Pesticide Factsheets

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Automatic detection of confusion in elderly users of a web-based health instruction video.

PubMed

Postma-Nilsenová, Marie; Postma, Eric; Tates, Kiek

2015-06-01

Because of cognitive limitations and lower health literacy, many elderly patients have difficulty understanding verbal medical instructions. Automatic detection of facial movements provides a nonintrusive basis for building technological tools supporting confusion detection in healthcare delivery applications on the Internet. Twenty-four elderly participants (70-90 years old) were recorded while watching Web-based health instruction videos involving easy and complex medical terminology. Relevant fragments of the participants' facial expressions were rated by 40 medical students for perceived level of confusion and analyzed with automatic software for facial movement recognition. A computer classification of the automatically detected facial features performed more accurately and with a higher sensitivity than the human observers (automatic detection and classification, 64% accuracy, 0.64 sensitivity; human observers, 41% accuracy, 0.43 sensitivity). A drill-down analysis of cues to confusion indicated the importance of the eye and eyebrow region. Confusion caused by misunderstanding of medical terminology is signaled by facial cues that can be automatically detected with currently available facial expression detection technology. The findings are relevant for the development of Web-based services for healthcare consumers.

A new accurate pill recognition system using imprint information

NASA Astrophysics Data System (ADS)

Chen, Zhiyuan; Kamata, Sei-ichiro

2013-12-01

Great achievements in modern medicine benefit human beings. Also, it has brought about an explosive growth of pharmaceuticals that current in the market. In daily life, pharmaceuticals sometimes confuse people when they are found unlabeled. In this paper, we propose an automatic pill recognition technique to solve this problem. It functions mainly based on the imprint feature of the pills, which is extracted by proposed MSWT (modified stroke width transform) and described by WSC (weighted shape context). Experiments show that our proposed pill recognition method can reach an accurate rate up to 92.03% within top 5 ranks when trying to classify more than 10 thousand query pill images into around 2000 categories.

Research on gait-based human identification

NASA Astrophysics Data System (ADS)

Li, Youguo

Gait recognition refers to automatic identification of individual based on his/her style of walking. This paper proposes a gait recognition method based on Continuous Hidden Markov Model with Mixture of Gaussians(G-CHMM). First, we initialize a Gaussian mix model for training image sequence with K-means algorithm, then train the HMM parameters using a Baum-Welch algorithm. These gait feature sequences can be trained and obtain a Continuous HMM for every person, therefore, the 7 key frames and the obtained HMM can represent each person's gait sequence. Finally, the recognition is achieved by Front algorithm. The experiments made on CASIA gait databases obtain comparatively high correction identification ratio and comparatively strong robustness for variety of bodily angle.

From multidrug-resistant to extensively drug-resistant tuberculosis in Lisbon, Portugal: the stepwise mode of resistance acquisition.

PubMed

Perdigão, João; Macedo, Rita; Silva, Carla; Machado, Diana; Couto, Isabel; Viveiros, Miguel; Jordao, Luisa; Portugal, Isabel

2013-01-01

The development and transmission of extensively drug-resistant (XDR) tuberculosis (TB) constitutes a serious threat to the effective control of TB in several countries. Here, in an attempt to further elucidate the dynamics of the acquisition of resistance to second-line drugs and investigate an eventual role for eis promoter mutations in aminoglycoside resistance, we have studied a set of multidrug-resistant (MDR)/XDR-TB isolates circulating in Lisbon, Portugal. Forty-four MDR-TB or XDR-TB isolates were genotyped and screened for mutations in genes associated with second-line drug resistance, namely tlyA, gyrA, rrs and eis. The most prevalent mutations found in each gene were Ins755GT in tlyA, A1401G in rrs, G-10A in eis and S91P in gyrA. Additionally, two genetic clusters were found in this study: Lisboa3 and Q1. The characteristic mutational profile found among recent XDR-TB circulating in Lisbon was also found in MDR-TB strains isolated in the 1990s. Also investigated was the resistance level conferred by eis G-10A mutations, revealing that eis G-10A mutations may result in amikacin resistance undetectable by widely used phenotypic assays. The analysis of the distribution of the mutations found by genetic clustering showed that in the Q1 cluster, two mutations, gyrA D94A and rrs A1401G, were enough to ensure development of XDR-TB from an MDR strain. Moreover, in the Lisboa3 cluster it was possible to elaborate a model in which the development of low-level kanamycin resistance was at the origin of the emergence of XDR-TB strains that can be discriminated by tlyA mutations.

Evaluation of serum cobalamin concentrations in dogs of 164 dog breeds (2006-2010).

PubMed

Grützner, Niels; Cranford, Shannon M; Norby, Bo; Suchodolski, Jan S; Steiner, Jörg M

2012-11-01

Altered serum cobalamin concentrations have been observed in dogs with gastrointestinal disorders such as exocrine pancreatic insufficiency (EPI) or gastrointestinal inflammation. The aims of the current study were 1) to identify breeds with a higher proportion of dogs with a decreased serum cobalamin concentration, 2) to determine whether dogs with such decreased concentrations tend to have serum canine trypsin-like immunoreactivity (cTLI) concentrations diagnostic for EPI, and 3) to compare the number of submissions for serum cobalamin analysis by breed to the American Kennel Club (AKC) breed ranking list of 2009. In this retrospective study, results of 28,675 cobalamin tests were reviewed. Akitas, Chinese Shar-Peis, German Shepherd Dogs, Greyhounds, and Labrador Retrievers had increased proportions of serum cobalamin concentrations below the lower limit of the reference interval (<251 ng/l; all P < 0.0001). Akitas, Chinese Shar-Peis, German Shepherd Dogs, and Border Collies had increased proportions of serum cobalamin concentrations below the detection limit of the assay (<150 ng/l; all P < 0.0001). Akitas, Border Collies, and German Shepherd Dogs with serum cobalamin concentrations <150 ng/l were more likely to have a serum cTLI concentration considered diagnostic for EPI (≤2.5 µg/l; all P ≤ 0.001). The breed with the highest proportion of samples submitted for serum cobalamin analysis in comparison with the AKC ranking list was the Greyhound (odds ratio: 84.6; P < 0.0001). In Akitas and Border Collies, further investigations are warranted to clarify if a potentially breed-specific gastrointestinal disorder is responsible for the increased frequency of decreased serum cobalamin and cTLI concentrations.

Effective removal of trace thallium from surface water by nanosized manganese dioxide enhanced quartz sand filtration.

PubMed

Huangfu, Xiaoliu; Ma, Chengxue; Ma, Jun; He, Qiang; Yang, Chun; Zhou, Jian; Jiang, Jin; Wang, Yaan

2017-12-01

Thallium (Tl) has drawn wide concern due to its high toxicity even at extremely low concentrations, as well as its tendency for significant accumulation in the human body and other organisms. The need to develop effective strategies for trace Tl removal from drinking water is urgent. In this study, the removal of trace Tl (0.5 μg L -1 ) by conventional quartz sand filtration enhanced by nanosized manganese dioxide (nMnO 2 ) has been investigated using typical surface water obtained from northeast China. The results indicate that nMnO 2 enhanced quartz sand filtration could remove trace Tl(I) and Tl(III) efficiently through the adsorption of Tl onto nMnO 2 added to a water matrix and onto nMnO 2 attached on quartz sand surfaces. Tl(III)-HA complexes might be responsible for higher residual Tl(III) in the effluent compared to residual Tl(I). Competitive Ca 2+ cations inhibit Tl removal to a certain extent because the Ca 2+ ions will occupy the Tl adsorption site on nMnO 2 . Moreover, high concentrations of HA (10 mgTOC L -1 ), which notably complexes with and dissolves nMnO 2 (more than 78%), resulted in higher residual Tl(I) and Tl(III). Tl(III)-HA complexes might also enhance Tl(III) penetration to a certain extent. Additionally, a higher pH level could enhance the removal of trace Tl from surface water. Finally, a slight increase of residual Tl was observed after backwash, followed by the reduction of the Tl concentration in the effluent to a "steady" state again. The knowledge obtained here may provide a potential strategy for drinking water treatment plants threatened by trace Tl. Copyright © 2017. Published by Elsevier Ltd.

Occurrence of putative virulence genes on Arcobacter butzleri isolated from three different environmental sites throughout the dairy chain.

PubMed

Piva, S; Gariano, G R; Bonilauri, P; Giacometti, F; Decastelli, L; Florio, D; Massella, E; Serraino, A

2017-04-01

This comparative study investigated the occurrence of cadF, cj1349, ciaB, pldA, tlyA, hecA, hecB, mviN, irgA and IroE genes in 212 Arcobacter butzleri isolated from three different environmental sites linked to the dairy chain (farms, industrial and artisanal dairy plants) located in three Italian regions (Lombardy, Emilia-Romagna and Calabria). According to the presence of these genes, different pathotypes (P-types) were determined. The main genes detected were ciaB, mviN, tlyA, cj1349, pldA and cadF, while the least common genes were iroE, hecA, hecB and irgA. TlyA, irgA, hecA, hecB and iroE, which were significantly more frequent in isolates recovered in industrial dairy plants. Twelve P-types were detected. The occurrence of the most frequently detected P-types (P-types 1, 2, 3 and 5) differed significantly (P < 0·001) in relation to both the environmental site and geographical area of isolation. The highest diversity in P-types was observed in industrial dairy plants and in the Calabria region. The results of this study show a correlation between the occurrence of putative virulence genes and virulence genotype variability depending on the environmental site and geographical origin of the isolates. The present study provides insights into the similar distribution of putative virulence genes in a dairy chain and other sources' isolates and also into a geographical distribution of some P-types. We have shown that industrial dairy plants may represent an environmental site favouring a selection of the isolates with a higher pathogenetic pattern. © 2017 The Society for Applied Microbiology.

Blastocyst development in single medium with or without renewal on day 3: a prospective cohort study on sibling donor oocytes in a time-lapse incubator.

PubMed

Costa-Borges, Nuno; Bellés, Marta; Meseguer, Marcos; Galliano, Daniela; Ballesteros, Agustin; Calderón, Gloria

2016-03-01

To evaluate the efficiency of using a continuous (one-step) protocol with a single medium for the culture of human embryos in a time-lapse incubator (TLI). Prospective cohort study on sibling donor oocytes. University-affiliated in vitro fertilization (IVF) center. Embryos from 59 patients. Culture in a TLI in a single medium with or without renewal of the medium on day-3. Embryo morphology and morphokinetic parameters, clinical pregnancy, take-home baby rate, and perinatal outcomes. The blastocyst rates (68.3 vs. 66.8%) and the proportion of good-quality blastocysts (transferred plus frozen) obtained with the two-step (80.0%) protocol were statistically significantly similar to those obtained in the one-step protocol (72.2%). Similarly, morphokinetic events from early cleavage until late blastocyst stages were statistically significantly equivalent between both groups. No differences were found either in clinical pregnancy rates when comparing pure transfers performed with embryos selected from the two-step (75.0%), one-step (70.0%, respectively), and mixed (57.1%) groups. A total of 55 out of 91 embryos transferred implanted successfully (60.4%), resulting in a total of 37 newborns with a comparable birth weight mean among groups. Our findings support the idea that in a TLI with a controlled air purification system, human embryos can be successfully cultured continuously from day 0 onward in single medium with no need to renew it on day-3. This strategy does not affect embryo morphokinetics or development to term and offers more stable culture conditions for embryos as well as practical advantages and reduced costs for the IVF laboratory. Copyright © 2016 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

Accelerated Total Lymphoid Irradiation-containing Salvage Regimen for Patients With Refractory and Relapsed Hodgkin Lymphoma: 20 Years of Experience

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

Rimner, Andreas; Lovie, Shona; Hsu, Meier

Purpose: We report the long-term results of integrated accelerated involved field radiation therapy (IFRT) followed by total lymphoid irradiation (TLI) as part of the high-dose salvage regimen followed by autologous bone marrow transplantation or autologous stem cell transplantation in patients with relapsed or refractory Hodgkin lymphoma (HL). Methods and Materials: From November 1985 to July 2008, 186 previously unirradiated patients with relapsed or refractory HL underwent salvage therapy on 4 consecutive institutional review board–approved protocols. All patients had biopsy-proven primary refractory or relapsed HL. After standard-dose salvage chemotherapy (SC), accelerated IFRT (18-20 Gy) was given to relapsed or refractory sites, followedmore » by TLI (15-18 Gy) and high-dose chemotherapy. Overall survival (OS) and event-free survival (EFS) were analyzed by Cox analysis and disease-specific survival (DSS) by competing-risk regression. Results: With a median follow-up period of 57 months among survivors, 5- and 10-year OS rates were 68% and 56%, respectively; 5- and 10-year EFS rates were 62% and 56%, respectively; and 5- and 10-year cumulative incidences of HL-related deaths were 21% and 29%, respectively. On multivariate analysis, complete response to SC was independently associated with improved OS and EFS. Primary refractory disease and extranodal disease were independently associated with poor DSS. Eight patients had grade 3 or higher cardiac toxicity, with 3 deaths. Second malignancies developed in 10 patients, 5 of whom died. Conclusions: Accelerated IFRT followed by TLI and high-dose chemotherapy is an effective, feasible, and safe salvage strategy for patients with relapsed or refractory HL with excellent long-term OS, EFS, and DSS. Complete response to SC is the most important prognostic factor.« less

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.

Thoracic lymph node station recognition on CT images based on automatic anatomy recognition with an optimal parent strategy

NASA Astrophysics Data System (ADS)

Xu, Guoping; Udupa, Jayaram K.; Tong, Yubing; Cao, Hanqiang; Odhner, Dewey; Torigian, Drew A.; Wu, Xingyu

2018-03-01

Currently, there are many papers that have been published on the detection and segmentation of lymph nodes from medical images. However, it is still a challenging problem owing to low contrast with surrounding soft tissues and the variations of lymph node size and shape on computed tomography (CT) images. This is particularly very difficult on low-dose CT of PET/CT acquisitions. In this study, we utilize our previous automatic anatomy recognition (AAR) framework to recognize the thoracic-lymph node stations defined by the International Association for the Study of Lung Cancer (IASLC) lymph node map. The lymph node stations themselves are viewed as anatomic objects and are localized by using a one-shot method in the AAR framework. Two strategies have been taken in this paper for integration into AAR framework. The first is to combine some lymph node stations into composite lymph node stations according to their geometrical nearness. The other is to find the optimal parent (organ or union of organs) as an anchor for each lymph node station based on the recognition error and thereby find an overall optimal hierarchy to arrange anchor organs and lymph node stations. Based on 28 contrast-enhanced thoracic CT image data sets for model building, 12 independent data sets for testing, our results show that thoracic lymph node stations can be localized within 2-3 voxels compared to the ground truth.

Development of Portable Automatic Number Plate Recognition System on Android Mobile Phone

NASA Astrophysics Data System (ADS)

Mutholib, Abdul; Gunawan, Teddy S.; Chebil, Jalel; Kartiwi, Mira

2013-12-01

The Automatic Number Plate Recognition (ANPR) System has performed as the main role in various access control and security, such as: tracking of stolen vehicles, traffic violations (speed trap) and parking management system. In this paper, the portable ANPR implemented on android mobile phone is presented. The main challenges in mobile application are including higher coding efficiency, reduced computational complexity, and improved flexibility. Significance efforts are being explored to find suitable and adaptive algorithm for implementation of ANPR on mobile phone. ANPR system for mobile phone need to be optimize due to its limited CPU and memory resources, its ability for geo-tagging image captured using GPS coordinates and its ability to access online database to store the vehicle's information. In this paper, the design of portable ANPR on android mobile phone will be described as follows. First, the graphical user interface (GUI) for capturing image using built-in camera was developed to acquire vehicle plate number in Malaysia. Second, the preprocessing of raw image was done using contrast enhancement. Next, character segmentation using fixed pitch and an optical character recognition (OCR) using neural network were utilized to extract texts and numbers. Both character segmentation and OCR were using Tesseract library from Google Inc. The proposed portable ANPR algorithm was implemented and simulated using Android SDK on a computer. Based on the experimental results, the proposed system can effectively recognize the license plate number at 90.86%. The required processing time to recognize a license plate is only 2 seconds on average. The result is consider good in comparison with the results obtained from previous system that was processed in a desktop PC with the range of result from 91.59% to 98% recognition rate and 0.284 second to 1.5 seconds recognition time.

Towards automatic musical instrument timbre recognition

NASA Astrophysics Data System (ADS)

Park, Tae Hong

This dissertation is comprised of two parts---focus on issues concerning research and development of an artificial system for automatic musical instrument timbre recognition and musical compositions. The technical part of the essay includes a detailed record of developed and implemented algorithms for feature extraction and pattern recognition. A review of existing literature introducing historical aspects surrounding timbre research, problems associated with a number of timbre definitions, and highlights of selected research activities that have had significant impact in this field are also included. The developed timbre recognition system follows a bottom-up, data-driven model that includes a pre-processing module, feature extraction module, and a RBF/EBF (Radial/Elliptical Basis Function) neural network-based pattern recognition module. 829 monophonic samples from 12 instruments have been chosen from the Peter Siedlaczek library (Best Service) and other samples from the Internet and personal collections. Significant emphasis has been put on feature extraction development and testing to achieve robust and consistent feature vectors that are eventually passed to the neural network module. In order to avoid a garbage-in-garbage-out (GIGO) trap and improve generality, extra care was taken in designing and testing the developed algorithms using various dynamics, different playing techniques, and a variety of pitches for each instrument with inclusion of attack and steady-state portions of a signal. Most of the research and development was conducted in Matlab. The compositional part of the essay includes brief introductions to "A d'Ess Are ," "Aboji," "48 13 N, 16 20 O," and "pH-SQ." A general outline pertaining to the ideas and concepts behind the architectural designs of the pieces including formal structures, time structures, orchestration methods, and pitch structures are also presented.

Cherry recognition in natural environment based on the vision of picking robot

NASA Astrophysics Data System (ADS)

Zhang, Qirong; Chen, Shanxiong; Yu, Tingzhong; Wang, Yan

2017-04-01

In order to realize the automatic recognition of cherry in the natural environment, this paper designed a robot vision system recognition method. The first step of this method is to pre-process the cherry image by median filtering. The second step is to identify the colour of the cherry through the 0.9R-G colour difference formula, and then use the Otsu algorithm for threshold segmentation. The third step is to remove noise by using the area threshold. The fourth step is to remove the holes in the cherry image by morphological closed and open operation. The fifth step is to obtain the centroid and contour of cherry by using the smallest external rectangular and the Hough transform. Through this recognition process, we can successfully identify 96% of the cherry without blocking and adhesion.

Recognition of Emotions in Mexican Spanish Speech: An Approach Based on Acoustic Modelling of Emotion-Specific Vowels

PubMed Central

Caballero-Morales, Santiago-Omar

2013-01-01

An approach for the recognition of emotions in speech is presented. The target language is Mexican Spanish, and for this purpose a speech database was created. The approach consists in the phoneme acoustic modelling of emotion-specific vowels. For this, a standard phoneme-based Automatic Speech Recognition (ASR) system was built with Hidden Markov Models (HMMs), where different phoneme HMMs were built for the consonants and emotion-specific vowels associated with four emotional states (anger, happiness, neutral, sadness). Then, estimation of the emotional state from a spoken sentence is performed by counting the number of emotion-specific vowels found in the ASR's output for the sentence. With this approach, accuracy of 87–100% was achieved for the recognition of emotional state of Mexican Spanish speech. PMID:23935410

Accurate, fast, and secure biometric fingerprint recognition system utilizing sensor fusion of fingerprint patterns

NASA Astrophysics Data System (ADS)

El-Saba, Aed; Alsharif, Salim; Jagapathi, Rajendarreddy

2011-04-01

Fingerprint recognition is one of the first techniques used for automatically identifying people and today it is still one of the most popular and effective biometric techniques. With this increase in fingerprint biometric uses, issues related to accuracy, security and processing time are major challenges facing the fingerprint recognition systems. Previous work has shown that polarization enhancementencoding of fingerprint patterns increase the accuracy and security of fingerprint systems without burdening the processing time. This is mainly due to the fact that polarization enhancementencoding is inherently a hardware process and does not have detrimental time delay effect on the overall process. Unpolarized images, however, posses a high visual contrast and when fused (without digital enhancement) properly with polarized ones, is shown to increase the recognition accuracy and security of the biometric system without any significant processing time delay.

Image quality assessment for video stream recognition systems

NASA Astrophysics Data System (ADS)

Chernov, Timofey S.; Razumnuy, Nikita P.; Kozharinov, Alexander S.; Nikolaev, Dmitry P.; Arlazarov, Vladimir V.

2018-04-01

Recognition and machine vision systems have long been widely used in many disciplines to automate various processes of life and industry. Input images of optical recognition systems can be subjected to a large number of different distortions, especially in uncontrolled or natural shooting conditions, which leads to unpredictable results of recognition systems, making it impossible to assess their reliability. For this reason, it is necessary to perform quality control of the input data of recognition systems, which is facilitated by modern progress in the field of image quality evaluation. In this paper, we investigate the approach to designing optical recognition systems with built-in input image quality estimation modules and feedback, for which the necessary definitions are introduced and a model for describing such systems is constructed. The efficiency of this approach is illustrated by the example of solving the problem of selecting the best frames for recognition in a video stream for a system with limited resources. Experimental results are presented for the system for identity documents recognition, showing a significant increase in the accuracy and speed of the system under simulated conditions of automatic camera focusing, leading to blurring of frames.

Image Registration Workshop Proceedings

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline (Editor)

1997-01-01

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research.

Health smart home: towards an assistant tool for automatic assessment of the dependence of elders.

PubMed

Le, Xuan Hoa Binh; Di Mascolo, Maria; Gouin, Alexia; Noury, Norbert

2007-01-01

In order to help elders living alone to age in place independently and safely, it can be useful to have an assistant tool that can automatically assess their dependence and issue an alert if there is any loss of autonomy. The dependence can be assessed by the degree of performance, by the elders, of activities of daily living. This article presents an approach enabling the activity recognition for an elder living alone in a Health Smart Home equipped with noninvasive sensors.

Improved automatic adjustment of density and contrast in FCR system using neural network

NASA Astrophysics Data System (ADS)

Takeo, Hideya; Nakajima, Nobuyoshi; Ishida, Masamitsu; Kato, Hisatoyo

1994-05-01

FCR system has an automatic adjustment of image density and contrast by analyzing the histogram of image data in the radiation field. Advanced image recognition methods proposed in this paper can improve the automatic adjustment performance, in which neural network technology is used. There are two methods. Both methods are basically used 3-layer neural network with back propagation. The image data are directly input to the input-layer in one method and the histogram data is input in the other method. The former is effective to the imaging menu such as shoulder joint in which the position of interest region occupied on the histogram changes by difference of positioning and the latter is effective to the imaging menu such as chest-pediatrics in which the histogram shape changes by difference of positioning. We experimentally confirm the validity of these methods (about the automatic adjustment performance) as compared with the conventional histogram analysis methods.

Automatic textual annotation of video news based on semantic visual object extraction

NASA Astrophysics Data System (ADS)

Boujemaa, Nozha; Fleuret, Francois; Gouet, Valerie; Sahbi, Hichem

2003-12-01

In this paper, we present our work for automatic generation of textual metadata based on visual content analysis of video news. We present two methods for semantic object detection and recognition from a cross modal image-text thesaurus. These thesaurus represent a supervised association between models and semantic labels. This paper is concerned with two semantic objects: faces and Tv logos. In the first part, we present our work for efficient face detection and recogniton with automatic name generation. This method allows us also to suggest the textual annotation of shots close-up estimation. On the other hand, we were interested to automatically detect and recognize different Tv logos present on incoming different news from different Tv Channels. This work was done jointly with the French Tv Channel TF1 within the "MediaWorks" project that consists on an hybrid text-image indexing and retrieval plateform for video news.

Anatomical entity mention recognition at literature scale

PubMed Central

Pyysalo, Sampo; Ananiadou, Sophia

2014-01-01

Motivation: Anatomical entities ranging from subcellular structures to organ systems are central to biomedical science, and mentions of these entities are essential to understanding the scientific literature. Despite extensive efforts to automatically analyze various aspects of biomedical text, there have been only few studies focusing on anatomical entities, and no dedicated methods for learning to automatically recognize anatomical entity mentions in free-form text have been introduced. Results: We present AnatomyTagger, a machine learning-based system for anatomical entity mention recognition. The system incorporates a broad array of approaches proposed to benefit tagging, including the use of Unified Medical Language System (UMLS)- and Open Biomedical Ontologies (OBO)-based lexical resources, word representations induced from unlabeled text, statistical truecasing and non-local features. We train and evaluate the system on a newly introduced corpus that substantially extends on previously available resources, and apply the resulting tagger to automatically annotate the entire open access scientific domain literature. The resulting analyses have been applied to extend services provided by the Europe PubMed Central literature database. Availability and implementation: All tools and resources introduced in this work are available from http://nactem.ac.uk/anatomytagger. Contact: sophia.ananiadou@manchester.ac.uk Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:24162468

Automatic three-dimensional measurement of large-scale structure based on vision metrology.

PubMed

Zhu, Zhaokun; Guan, Banglei; Zhang, Xiaohu; Li, Daokui; Yu, Qifeng

2014-01-01

All relevant key techniques involved in photogrammetric vision metrology for fully automatic 3D measurement of large-scale structure are studied. A new kind of coded target consisting of circular retroreflective discs is designed, and corresponding detection and recognition algorithms based on blob detection and clustering are presented. Then a three-stage strategy starting with view clustering is proposed to achieve automatic network orientation. As for matching of noncoded targets, the concept of matching path is proposed, and matches for each noncoded target are found by determination of the optimal matching path, based on a novel voting strategy, among all possible ones. Experiments on a fixed keel of airship have been conducted to verify the effectiveness and measuring accuracy of the proposed methods.

Morphological feature extraction for the classification of digital images of cancerous tissues.

PubMed

Thiran, J P; Macq, B

1996-10-01

This paper presents a new method for automatic recognition of cancerous tissues from an image of a microscopic section. Based on the shape and the size analysis of the observed cells, this method provides the physician with nonsubjective numerical values for four criteria of malignancy. This automatic approach is based on mathematical morphology, and more specifically on the use of Geodesy. This technique is used first to remove the background noise from the image and then to operate a segmentation of the nuclei of the cells and an analysis of their shape, their size, and their texture. From the values of the extracted criteria, an automatic classification of the image (cancerous or not) is finally operated.

The CHEMDNER corpus of chemicals and drugs and its annotation principles.

PubMed

Krallinger, Martin; Rabal, Obdulia; Leitner, Florian; Vazquez, Miguel; Salgado, David; Lu, Zhiyong; Leaman, Robert; Lu, Yanan; Ji, Donghong; Lowe, Daniel M; Sayle, Roger A; Batista-Navarro, Riza Theresa; Rak, Rafal; Huber, Torsten; Rocktäschel, Tim; Matos, Sérgio; Campos, David; Tang, Buzhou; Xu, Hua; Munkhdalai, Tsendsuren; Ryu, Keun Ho; Ramanan, S V; Nathan, Senthil; Žitnik, Slavko; Bajec, Marko; Weber, Lutz; Irmer, Matthias; Akhondi, Saber A; Kors, Jan A; Xu, Shuo; An, Xin; Sikdar, Utpal Kumar; Ekbal, Asif; Yoshioka, Masaharu; Dieb, Thaer M; Choi, Miji; Verspoor, Karin; Khabsa, Madian; Giles, C Lee; Liu, Hongfang; Ravikumar, Komandur Elayavilli; Lamurias, Andre; Couto, Francisco M; Dai, Hong-Jie; Tsai, Richard Tzong-Han; Ata, Caglar; Can, Tolga; Usié, Anabel; Alves, Rui; Segura-Bedmar, Isabel; Martínez, Paloma; Oyarzabal, Julen; Valencia, Alfonso

2015-01-01

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.

Iris recognition and what is next? Iris diagnosis: a new challenging topic for machine vision from image acquisition to image interpretation

NASA Astrophysics Data System (ADS)

Perner, Petra

2017-03-01

Molecular image-based techniques are widely used in medicine to detect specific diseases. Look diagnosis is an important issue but also the analysis of the eye plays an important role in order to detect specific diseases. These topics are important topics in medicine and the standardization of these topics by an automatic system can be a new challenging field for machine vision. Compared to iris recognition has the iris diagnosis much more higher demands for the image acquisition and interpretation of the iris. One understands by iris diagnosis (Iridology) the investigation and analysis of the colored part of the eye, the iris, to discover factors, which play an important role for the prevention and treatment of illnesses, but also for the preservation of an optimum health. An automatic system would pave the way for a much wider use of the iris diagnosis for the diagnosis of illnesses and for the purpose of individual health protection. With this paper, we describe our work towards an automatic iris diagnosis system. We describe the image acquisition and the problems with it. Different ways are explained for image acquisition and image preprocessing. We describe the image analysis method for the detection of the iris. The meta-model for image interpretation is given. Based on this model we show the many tasks for image analysis that range from different image-object feature analysis, spatial image analysis to color image analysis. Our first results for the recognition of the iris are given. We describe how detecting the pupil and not wanted lamp spots. We explain how to recognize orange blue spots in the iris and match them against the topological map of the iris. Finally, we give an outlook for further work.

The CHEMDNER corpus of chemicals and drugs and its annotation principles

PubMed Central

2015-01-01

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/ PMID:25810773

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.

Social cognition in schizophrenia and healthy aging: differences and similarities.

PubMed

Silver, Henry; Bilker, Warren B

2014-12-01

Social cognition is impaired in schizophrenia but it is not clear whether this is specific for the illness and whether emotion perception is selectively affected. To study this we examined the perception of emotional and non-emotional clues in facial expressions, a key social cognitive skill, in schizophrenia patients and old healthy individuals using young healthy individuals as reference. Tests of object recognition, visual orientation, psychomotor speed, and working memory were included to allow multivariate analysis taking into account other cognitive functions Schizophrenia patients showed impairments in recognition of identity and emotional facial clues compared to young and old healthy groups. Severity was similar to that for object recognition and visuospatial processing. Older and younger healthy groups did not differ from each other on these tests. Schizophrenia patients and old healthy individuals were similarly impaired in the ability to automatically learn new faces during the testing procedure (measured by the CSTFAC index) compared to young healthy individuals. Social cognition is distinctly impaired in schizophrenia compared to healthy aging. Further study is needed to identify the mechanisms of automatic social cognitive learning impairment in schizophrenia patients and healthy aging individuals and determine whether similar neural systems are affected. Copyright © 2014 Elsevier B.V. All rights reserved.

Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

PubMed

Abbas, Qaisar; Fondon, Irene; Sarmiento, Auxiliadora; Jiménez, Soledad; Alemany, Pedro

2017-11-01

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge. In this article, a novel automatic recognition system for the five severity level of diabetic retinopathy (SLDR) is developed without performing any pre- and post-processing steps on retinal fundus images through learning of deep visual features (DVFs). These DVF features are extracted from each image by using color dense in scale-invariant and gradient location-orientation histogram techniques. To learn these DVF features, a semi-supervised multilayer deep-learning algorithm is utilized along with a new compressed layer and fine-tuning steps. This SLDR system was evaluated and compared with state-of-the-art techniques using the measures of sensitivity (SE), specificity (SP) and area under the receiving operating curves (AUC). On 750 fundus images (150 per category), the SE of 92.18%, SP of 94.50% and AUC of 0.924 values were obtained on average. These results demonstrate that the SLDR system is appropriate for early detection of DR and provide an effective treatment for prediction type of diabetes.

Retina vascular network recognition

NASA Astrophysics Data System (ADS)

Tascini, Guido; Passerini, Giorgio; Puliti, Paolo; Zingaretti, Primo

1993-09-01

The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.

Giro form reading machine

NASA Astrophysics Data System (ADS)

Minh Ha, Thien; Niggeler, Dieter; Bunke, Horst; Clarinval, Jose

1995-08-01

Although giro forms are used by many people in daily life for money remittance in Switzerland, the processing of these forms at banks and post offices is only partly automated. We describe an ongoing project for building an automatic system that is able to recognize various items printed or written on a giro form. The system comprises three main components, namely, an automatic form feeder, a camera system, and a computer. These components are connected in such a way that the system is able to process a bunch of forms without any human interactions. We present two real applications of our system in the field of payment services, which require the reading of both machine printed and handwritten information that may appear on a giro form. One particular feature of giro forms is their flexible layout, i.e., information items are located differently from one form to another, thus requiring an additional analysis step to localize them before recognition. A commercial optical character recognition software package is used for recognition of machine-printed information, whereas handwritten information is read by our own algorithms, the details of which are presented. The system is implemented by using a client/server architecture providing a high degree of flexibility to change. Preliminary results are reported supporting our claim that the system is usable in practice.

The software for automatic creation of the formal grammars used by speech recognition, computer vision, editable text conversion systems, and some new functions

NASA Astrophysics Data System (ADS)

Kardava, Irakli; Tadyszak, Krzysztof; Gulua, Nana; Jurga, Stefan

2017-02-01

For more flexibility of environmental perception by artificial intelligence it is needed to exist the supporting software modules, which will be able to automate the creation of specific language syntax and to make a further analysis for relevant decisions based on semantic functions. According of our proposed approach, of which implementation it is possible to create the couples of formal rules of given sentences (in case of natural languages) or statements (in case of special languages) by helping of computer vision, speech recognition or editable text conversion system for further automatic improvement. In other words, we have developed an approach, by which it can be achieved to significantly improve the training process automation of artificial intelligence, which as a result will give us a higher level of self-developing skills independently from us (from users). At the base of our approach we have developed a software demo version, which includes the algorithm and software code for the entire above mentioned component's implementation (computer vision, speech recognition and editable text conversion system). The program has the ability to work in a multi - stream mode and simultaneously create a syntax based on receiving information from several sources.

Automatic image orientation detection via confidence-based integration of low-level and semantic cues.

PubMed

Luo, Jiebo; Boutell, Matthew

2005-05-01

Automatic image orientation detection for natural images is a useful, yet challenging research topic. Humans use scene context and semantic object recognition to identify the correct image orientation. However, it is difficult for a computer to perform the task in the same way because current object recognition algorithms are extremely limited in their scope and robustness. As a result, existing orientation detection methods were built upon low-level vision features such as spatial distributions of color and texture. Discrepant detection rates have been reported for these methods in the literature. We have developed a probabilistic approach to image orientation detection via confidence-based integration of low-level and semantic cues within a Bayesian framework. Our current accuracy is 90 percent for unconstrained consumer photos, impressive given the findings of a psychophysical study conducted recently. The proposed framework is an attempt to bridge the gap between computer and human vision systems and is applicable to other problems involving semantic scene content understanding.

Semi-automatic recognition of marine debris on beaches

NASA Astrophysics Data System (ADS)

Ge, Zhenpeng; Shi, Huahong; Mei, Xuefei; Dai, Zhijun; Li, Daoji

2016-05-01

An increasing amount of anthropogenic marine debris is pervading the earth’s environmental systems, resulting in an enormous threat to living organisms. Additionally, the large amount of marine debris around the world has been investigated mostly through tedious manual methods. Therefore, we propose the use of a new technique, light detection and ranging (LIDAR), for the semi-automatic recognition of marine debris on a beach because of its substantially more efficient role in comparison with other more laborious methods. Our results revealed that LIDAR should be used for the classification of marine debris into plastic, paper, cloth and metal. Additionally, we reconstructed a 3-dimensional model of different types of debris on a beach with a high validity of debris revivification using LIDAR-based individual separation. These findings demonstrate that the availability of this new technique enables detailed observations to be made of debris on a large beach that was previously not possible. It is strongly suggested that LIDAR could be implemented as an appropriate monitoring tool for marine debris by global researchers and governments.

Automatic Surveying For Hazard Prevention On Glacier De GiÉtro, Switzerland

NASA Astrophysics Data System (ADS)

Bauder, A.; Funk, M.; Bösch, H.

Breaking off of large ice masses from the steep tongue of Glacier de Giétro may endanger a nearby reservoir. Such a falling ice mass could cause an oversplash over the dam at timeof a nearly filled lake. For this reason the glacier has been monitored intensively since the 1960's. An automatic theodolite was installed three years ago. It allows continuous displacement measurements of several targets on the glacier in order to detect short-term acceleration events. The installation includes a telemetric data transmission, which provides for immediate recognition of hazardous situations and early alarming. The obtained data were analysed in terms of precision and performance of the applied method. A high temporal resolution was gained. The comparison with traditional ob- servations shows clearly the potential of modern instruments to improve monitoring schems. We summarize the main results of this study and discuss the applicability of a modern motorized theodolite with target tracking and recognition ability for moni- toring purposes.

Memory loss versus memory distortion: the role of encoding and retrieval deficits in Korsakoff patients' false memories.

PubMed

Van Damme, Ilse; d'Ydewalle, Gery

2009-05-01

Recent studies with the Deese/Roediger-McDermott (DRM) paradigm have revealed that Korsakoff patients show reduced levels of false recognition and different patterns of false recall compared to controls. The present experiment examined whether this could be attributed to an encoding deficit, or rather to problems with explicitly retrieving thematic information at test. In a variation on the DRM paradigm, both patients and controls were presented with associative as well as categorised word lists, with the order of recall and recognition tests manipulated between-subjects. The results point to an important role for the automatic/controlled retrieval distinction: Korsakoff patients' false memory was only diminished compared to controls' when automatic or short-term memory processes could not be used to fulfil the task at hand. Hence, the patients' explicit retrieval deficit appears to be crucial in explaining past and present data. Results are discussed in terms of fuzzy-trace and activation-monitoring theories.

Hierarchical classification of dynamically varying radar pulse repetition interval modulation patterns.

PubMed

Kauppi, Jukka-Pekka; Martikainen, Kalle; Ruotsalainen, Ulla

2010-12-01

The central purpose of passive signal intercept receivers is to perform automatic categorization of unknown radar signals. Currently, there is an urgent need to develop intelligent classification algorithms for these devices due to emerging complexity of radar waveforms. Especially multifunction radars (MFRs) capable of performing several simultaneous tasks by utilizing complex, dynamically varying scheduled waveforms are a major challenge for automatic pattern classification systems. To assist recognition of complex radar emissions in modern intercept receivers, we have developed a novel method to recognize dynamically varying pulse repetition interval (PRI) modulation patterns emitted by MFRs. We use robust feature extraction and classifier design techniques to assist recognition in unpredictable real-world signal environments. We classify received pulse trains hierarchically which allows unambiguous detection of the subpatterns using a sliding window. Accuracy, robustness and reliability of the technique are demonstrated with extensive simulations using both static and dynamically varying PRI modulation patterns. Copyright © 2010 Elsevier Ltd. All rights reserved.

Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

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%.

Voice technology and BBN

NASA Technical Reports Server (NTRS)

Wolf, Jared J.

1977-01-01

The following research was discussed: (1) speech signal processing; (2) automatic speech recognition; (3) continuous speech understanding; (4) speaker recognition; (5) speech compression; (6) subjective and objective evaluation of speech communication system; (7) measurement of the intelligibility and quality of speech when degraded by noise or other masking stimuli; (8) speech synthesis; (9) instructional aids for second-language learning and for training of the deaf; and (10) investigation of speech correlates of psychological stress. Experimental psychology, control systems, and human factors engineering, which are often relevant to the proper design and operation of speech systems are described.

Face recognition in the thermal infrared domain

NASA Astrophysics Data System (ADS)

Kowalski, M.; Grudzień, A.; Palka, N.; Szustakowski, M.

2017-10-01

Biometrics refers to unique human characteristics. Each unique characteristic may be used to label and describe individuals and for automatic recognition of a person based on physiological or behavioural properties. One of the most natural and the most popular biometric trait is a face. The most common research methods on face recognition are based on visible light. State-of-the-art face recognition systems operating in the visible light spectrum achieve very high level of recognition accuracy under controlled environmental conditions. Thermal infrared imagery seems to be a promising alternative or complement to visible range imaging due to its relatively high resistance to illumination changes. A thermal infrared image of the human face presents its unique heat-signature and can be used for recognition. The characteristics of thermal images maintain advantages over visible light images, and can be used to improve algorithms of human face recognition in several aspects. Mid-wavelength or far-wavelength infrared also referred to as thermal infrared seems to be promising alternatives. We present the study on 1:1 recognition in thermal infrared domain. The two approaches we are considering are stand-off face verification of non-moving person as well as stop-less face verification on-the-move. The paper presents methodology of our studies and challenges for face recognition systems in the thermal infrared domain.

U.S. EPA, Pesticide Product Label, MORGRO EPTAM 2.3 GRANULAR, 08/11/1967

EPA Pesticide Factsheets

2011-04-13

... i_. '" v_"", -- fpc,;:;. Li..l~~ :x.- ~.fttH~ ~n:t tlrlilP Ju-,ln~ tli( fll ..... l:li jt:.:;. 1\\6 111 .n~ Ml'Clur,;~ ;-OW''':lti, '_:~es . .lhr~Jh~~ ar.~ ~r:OlllJ "-('v._r ..... ...

Guadalcanal Operation. Volume 2

DTIC Science & Technology

1943-01-01

latest being a large peep baltle sight and leaf , permitting quick sighting. The sling was of a lacquered tight web. A cleaning rod snapped into the...defense along the Lunga would be by battalions, First, Second and Third, The First Battalion crossed the Ilu River in the vicinity of tli;-: Papaya

[Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

PubMed

Hu, T H; Wan, L; Liu, T A; Wang, M W; Chen, T; Wang, Y H

2017-12-01

Deep learning and neural network models have been new research directions and hot issues in the fields of machine learning and artificial intelligence in recent years. Deep learning has made a breakthrough in the applications of image and speech recognitions, and also has been extensively used in the fields of face recognition and information retrieval because of its special superiority. Bone X-ray images express different variations in black-white-gray gradations, which have image features of black and white contrasts and level differences. Based on these advantages of deep learning in image recognition, we combine it with the research of bone age assessment to provide basic datum for constructing a forensic automatic system of bone age assessment. This paper reviews the basic concept and network architectures of deep learning, and describes its recent research progress on image recognition in different research fields at home and abroad, and explores its advantages and application prospects in bone age assessment. Copyright© by the Editorial Department of Journal of Forensic Medicine.

Constraints in distortion-invariant target recognition system simulation

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Razzaque, Md A.

2000-11-01

Automatic target recognition (ATR) is a mature but active research area. In an earlier paper, we proposed a novel ATR approach for recognition of targets varying in fine details, rotation, and translation using a Learning Vector Quantization (LVQ) Neural Network (NN). The proposed approach performed segmentation of multiple objects and the identification of the objects using LVQNN. In this current paper, we extend the previous approach for recognition of targets varying in rotation, translation, scale, and combination of all three distortions. We obtain the analytical results of the system level design to show that the approach performs well with some constraints. The first constraint determines the size of the input images and input filters. The second constraint shows the limits on amount of rotation, translation, and scale of input objects. We present the simulation verification of the constraints using DARPA's Moving and Stationary Target Recognition (MSTAR) images with different depression and pose angles. The simulation results using MSTAR images verify the analytical constraints of the system level design.

Pc-based car license plate reading

NASA Astrophysics Data System (ADS)

Tanabe, Katsuyoshi; Marubayashi, Eisaku; Kawashima, Harumi; Nakanishi, Tadashi; Shio, Akio

1994-03-01

A PC-based car license plate recognition system has been developed. The system recognizes Chinese characters and Japanese phonetic hiragana characters as well as six digits on Japanese license plates. The system consists of a CCD camera, vehicle sensors, a strobe unit, a monitoring center, and an i486-based PC. The PC includes in its extension slots: a vehicle detector board, a strobe emitter board, and an image grabber board. When a passing vehicle is detected by the vehicle sensors, the strobe emits a pulse of light. The light pulse is synchronized with the time the vehicle image is frozen on an image grabber board. The recognition process is composed of three steps: image thresholding, character region extraction, and matching-based character recognition. The recognition software can handle obscured characters. Experimental results for hundreds of outdoor images showed high recognition performance within relatively short performance times. The results confirmed that the system is applicable to a wide variety of applications such as automatic vehicle identification and travel time measurement.

Automatic speech recognition research at NASA-Ames Research Center

NASA Technical Reports Server (NTRS)

Coler, Clayton R.; Plummer, Robert P.; Huff, Edward M.; Hitchcock, Myron H.

1977-01-01

A trainable acoustic pattern recognizer manufactured by Scope Electronics is presented. The voice command system VCS encodes speech by sampling 16 bandpass filters with center frequencies in the range from 200 to 5000 Hz. Variations in speaking rate are compensated for by a compression algorithm that subdivides each utterance into eight subintervals in such a way that the amount of spectral change within each subinterval is the same. The recorded filter values within each subinterval are then reduced to a 15-bit representation, giving a 120-bit encoding for each utterance. The VCS incorporates a simple recognition algorithm that utilizes five training samples of each word in a vocabulary of up to 24 words. The recognition rate of approximately 85 percent correct for untrained speakers and 94 percent correct for trained speakers was not considered adequate for flight systems use. Therefore, the built-in recognition algorithm was disabled, and the VCS was modified to transmit 120-bit encodings to an external computer for recognition.

Research on application of LADAR in ground vehicle recognition

NASA Astrophysics Data System (ADS)

Lan, Jinhui; Shen, Zhuoxun

2009-11-01

For the requirement of many practical applications in the field of military, the research of 3D target recognition is active. The representation that captures the salient attributes of a 3D target independent of the viewing angle will be especially useful to the automatic 3D target recognition system. This paper presents a new approach of image generation based on Laser Detection and Ranging (LADAR) data. Range image of target is obtained by transformation of point cloud. In order to extract features of different ground vehicle targets and to recognize targets, zernike moment properties of typical ground vehicle targets are researched in this paper. A technique of support vector machine is applied to the classification and recognition of target. The new method of image generation and feature representation has been applied to the outdoor experiments. Through outdoor experiments, it can be proven that the method of image generation is stability, the moments are effective to be used as features for recognition, and the LADAR can be applied to the field of 3D target recognition.

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

TU-FG-209-12: Treatment Site and View Recognition in X-Ray Images with Hierarchical Multiclass Recognition Models

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

Chang, X; Mazur, T; Yang, D

Purpose: To investigate an approach of automatically recognizing anatomical sites and imaging views (the orientation of the image acquisition) in 2D X-ray images. Methods: A hierarchical (binary tree) multiclass recognition model was developed to recognize the treatment sites and views in x-ray images. From top to bottom of the tree, the treatment sites are grouped hierarchically from more general to more specific. Each node in the hierarchical model was designed to assign images to one of two categories of anatomical sites. The binary image classification function of each node in the hierarchical model is implemented by using a PCA transformationmore » and a support vector machine (SVM) model. The optimal PCA transformation matrices and SVM models are obtained by learning from a set of sample images. Alternatives of the hierarchical model were developed to support three scenarios of site recognition that may happen in radiotherapy clinics, including two or one X-ray images with or without view information. The performance of the approach was tested with images of 120 patients from six treatment sites – brain, head-neck, breast, lung, abdomen and pelvis – with 20 patients per site and two views (AP and RT) per patient. Results: Given two images in known orthogonal views (AP and RT), the hierarchical model achieved a 99% average F1 score to recognize the six sites. Site specific view recognition models have 100 percent accuracy. The computation time to process a new patient case (preprocessing, site and view recognition) is 0.02 seconds. Conclusion: The proposed hierarchical model of site and view recognition is effective and computationally efficient. It could be useful to automatically and independently confirm the treatment sites and views in daily setup x-ray 2D images. It could also be applied to guide subsequent image processing tasks, e.g. site and view dependent contrast enhancement and image registration. The senior author received research grants from ViewRay Inc. and Varian Medical System.« less

Cultural Differences in Perceptual Reorganization in US and Pirahã Adults

PubMed Central

Yoon, Jennifer M. D.; Witthoft, Nathan; Winawer, Jonathan; Frank, Michael C.; Everett, Daniel L.; Gibson, Edward

2014-01-01

Visual illusions and other perceptual phenomena can be used as tools to uncover the otherwise hidden constructive processes that give rise to perception. Although many perceptual processes are assumed to be universal, variable susceptibility to certain illusions and perceptual effects across populations suggests a role for factors that vary culturally. One striking phenomenon is seen with two-tone images—photos reduced to two tones: black and white. Deficient recognition is observed in young children under conditions that trigger automatic recognition in adults. Here we show a similar lack of cue-triggered perceptual reorganization in the Pirahã, a hunter-gatherer tribe with limited exposure to modern visual media, suggesting such recognition is experience- and culture-specific. PMID:25411970

Behavioral features recognition and oestrus detection based on fast approximate clustering algorithm in dairy cows

NASA Astrophysics Data System (ADS)

Tian, Fuyang; Cao, Dong; Dong, Xiaoning; Zhao, Xinqiang; Li, Fade; Wang, Zhonghua

2017-06-01

Behavioral features recognition was an important effect to detect oestrus and sickness in dairy herds and there is a need for heat detection aid. The detection method was based on the measure of the individual behavioural activity, standing time, and temperature of dairy using vibrational sensor and temperature sensor in this paper. The data of behavioural activity index, standing time, lying time and walking time were sent to computer by lower power consumption wireless communication system. The fast approximate K-means algorithm (FAKM) was proposed to deal the data of the sensor for behavioral features recognition. As a result of technical progress in monitoring cows using computers, automatic oestrus detection has become possible.

Automated Detection of Stereotypical Motor Movements

ERIC Educational Resources Information Center

Goodwin, Matthew S.; Intille, Stephen S.; Albinali, Fahd; Velicer, Wayne F.

2011-01-01

To overcome problems with traditional methods for measuring stereotypical motor movements in persons with Autism Spectrum Disorders (ASD), we evaluated the use of wireless three-axis accelerometers and pattern recognition algorithms to automatically detect body rocking and hand flapping in children with ASD. Findings revealed that, on average,…

Reading Fluency and College Readiness

ERIC Educational Resources Information Center

Rasinski, Timothy V.; Chang, Shu-Ching; Edmondson, Elizabeth; Nageldinger, James; Nigh, Jennifer; Remark, Linda; Kenney, Kristen Srsen; Walsh-Moorman, Elizabeth; Yildirim, Kasim; Nichols, William Dee; Paige, David D.; Rupley, William H.

2017-01-01

The Common Core State Standards suggest that an appropriate goal for secondary education is college and career readiness. Previous research has identified reading fluency as a critical component for proficient reading. One component of fluency is word recognition accuracy and automaticity. The present study attempted to determine the word…

Research Directory for Manpower, Personnel, Training, and Human Factors.

DTIC Science & Technology

1991-01-01

Enhance Automatic Recognition of Speech in Noisy, Highly Stressful Environments Cofod R* Lica Systems Inc 703-359-0996 Smart Contract Preparation...Lab 301-278-2946 Smart Contract Preparation Expediter Frezell T LTCOL Human Engineering Lab 301-278-5998 Impulse Noise Hazard Information Processing R&D

Automatic Recognition and Understanding of the Driving Environment for Driver Feedback

DOT National Transportation Integrated Search

2018-01-01

A smart driving system must consider two key elements to be able to generate recommendations and make driving decisions that are effective and accurate: The environment of the car and the behavior of the driver. Our long-term goal is to develop techn...

Automatic Target Recognition: Statistical Feature Selection of Non-Gaussian Distributed Target Classes

DTIC Science & Technology

2011-06-01

implementing, and evaluating many feature selection algorithms. Mucciardi and Gose compared seven different techniques for choosing subsets of pattern...122 THIS PAGE INTENTIONALLY LEFT BLANK 123 LIST OF REFERENCES [1] A. Mucciardi and E. Gose , “A comparison of seven techniques for

The Mucciardi-Gose Clustering Algorithm and Its Applications in Automatic Pattern Recognition.

DTIC Science & Technology

A procedure known as the Mucciardi- Gose clustering algorithm, CLUSTR, for determining the geometrical or statistical relationships among groups of N...discussion of clustering algorithms is given; the particular advantages of the Mucciardi- Gose procedure are described. The mathematical basis for, and the

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

PubMed

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

2017-01-01

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

How should a speech recognizer work?

PubMed

Scharenborg, Odette; Norris, Dennis; Bosch, Louis; McQueen, James M

2005-11-12

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input. 2005 Lawrence Erlbaum Associates, Inc.

CNN based approach for activity recognition using a wrist-worn accelerometer.

PubMed

Panwar, Madhuri; Dyuthi, S Ram; Chandra Prakash, K; Biswas, Dwaipayan; Acharyya, Amit; Maharatna, Koushik; Gautam, Arvind; Naik, Ganesh R

2017-07-01

In recent years, significant advancements have taken place in human activity recognition using various machine learning approaches. However, feature engineering have dominated conventional methods involving the difficult process of optimal feature selection. This problem has been mitigated by using a novel methodology based on deep learning framework which automatically extracts the useful features and reduces the computational cost. As a proof of concept, we have attempted to design a generalized model for recognition of three fundamental movements of the human forearm performed in daily life where data is collected from four different subjects using a single wrist worn accelerometer sensor. The validation of the proposed model is done with different pre-processing and noisy data condition which is evaluated using three possible methods. The results show that our proposed methodology achieves an average recognition rate of 99.8% as opposed to conventional methods based on K-means clustering, linear discriminant analysis and support vector machine.

Process dissociation of familiarity and recollection in children: response deadline affects recollection but not familiarity.

PubMed

Koenig, Laura; Wimmer, Marina C; Hollins, Timothy J

2015-03-01

According to dual-process theories, recollection (slow and associated with contextual details) and familiarity (fast and automatic) are two independent processes underlying recognition memory. An adapted version of the process dissociation paradigm was used to measure recognition memory in 5-, 7-, and 11-year-olds and adults. In Experiment 1, it was found that 5-year-olds already recollect details of items (i.e., number). Recollection increased particularly between 5 and 7 years. Familiarity differed between 5 years and adulthood. In Experiment 2, under limited response time during retrieval, recollection was eliminated in 5-year-olds and reduced across all ages, whereas familiarity was left unaffected. Together, these findings are consistent with dual-process theories of recognition memory and provide support for two processes underlying recognition memory from a developmental perspective. Copyright © 2014 Elsevier Inc. All rights reserved.

Fuzzy support vector machines for adaptive Morse code recognition.

PubMed

Yang, Cheng-Hong; Jin, Li-Cheng; Chuang, Li-Yeh

2006-11-01

Morse code is now being harnessed for use in rehabilitation applications of augmentative-alternative communication and assistive technology, facilitating mobility, environmental control and adapted worksite access. In this paper, Morse code is selected as a communication adaptive device for 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. Therefore, an adaptive automatic recognition method with a high recognition rate is needed. The proposed system uses both fuzzy support vector machines and the variable-degree variable-step-size least-mean-square algorithm to achieve these objectives. We apply fuzzy memberships to each point, and provide different contributions to the decision learning function for support vector machines. Statistical analyses demonstrated that the proposed method elicited a higher recognition rate than other algorithms in the literature.

Dynamic gesture recognition using neural networks: a fundament for advanced interaction construction

NASA Astrophysics Data System (ADS)

Boehm, Klaus; Broll, Wolfgang; Sokolewicz, Michael A.

1994-04-01

Interaction in virtual reality environments is still a challenging task. Static hand posture recognition is currently the most common and widely used method for interaction using glove input devices. In order to improve the naturalness of interaction, and thereby decrease the user-interface learning time, there is a need to be able to recognize dynamic gestures. In this paper we describe our approach to overcoming the difficulties of dynamic gesture recognition (DGR) using neural networks. Backpropagation neural networks have already proven themselves to be appropriate and efficient for posture recognition. However, the extensive amount of data involved in DGR requires a different approach. Because of features such as topology preservation and automatic-learning, Kohonen Feature Maps are particularly suitable for the reduction of the high dimensional data space that is the result of a dynamic gesture, and are thus implemented for this task.

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.

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.

Exogenous temporal cues enhance recognition memory in an object-based manner.

PubMed

Ohyama, Junji; Watanabe, Katsumi

2010-11-01

Exogenous attention enhances the perception of attended items in both a space-based and an object-based manner. Exogenous attention also improves recognition memory for attended items in the space-based mode. However, it has not been examined whether object-based exogenous attention enhances recognition memory. To address this issue, we examined whether a sudden visual change in a task-irrelevant stimulus (an exogenous cue) would affect participants' recognition memory for items that were serially presented around a cued time. The results showed that recognition accuracy for an item was strongly enhanced when the visual cue occurred at the same location and time as the item (Experiments 1 and 2). The memory enhancement effect occurred when the exogenous visual cue and an item belonged to the same object (Experiments 3 and 4) and even when the cue was counterpredictive of the timing of an item to be asked about (Experiment 5). The present study suggests that an exogenous temporal cue automatically enhances the recognition accuracy for an item that is presented at close temporal proximity to the cue and that recognition memory enhancement occurs in an object-based manner.

Modeling of electrodes and implantable pulse generator cases for the analysis of implant tip heating under MR imaging

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

Acikel, Volkan, E-mail: vacik@ee.bilkent.edu.tr; Atalar, Ergin; Uslubas, Ali

Purpose: The authors’ purpose is to model the case of an implantable pulse generator (IPG) and the electrode of an active implantable medical device using lumped circuit elements in order to analyze their effect on radio frequency induced tissue heating problem during a magnetic resonance imaging (MRI) examination. Methods: In this study, IPG case and electrode are modeled with a voltage source and impedance. Values of these parameters are found using the modified transmission line method (MoTLiM) and the method of moments (MoM) simulations. Once the parameter values of an electrode/IPG case model are determined, they can be connected tomore » any lead, and tip heating can be analyzed. To validate these models, both MoM simulations and MR experiments were used. The induced currents on the leads with the IPG case or electrode connections were solved using the proposed models and the MoTLiM. These results were compared with the MoM simulations. In addition, an electrode was connected to a lead via an inductor. The dissipated power on the electrode was calculated using the MoTLiM by changing the inductance and the results were compared with the specific absorption rate results that were obtained using MoM. Then, MRI experiments were conducted to test the IPG case and the electrode models. To test the IPG case, a bare lead was connected to the case and placed inside a uniform phantom. During a MRI scan, the temperature rise at the lead was measured by changing the lead length. The power at the lead tip for the same scenario was also calculated using the IPG case model and MoTLiM. Then, an electrode was connected to a lead via an inductor and placed inside a uniform phantom. During a MRI scan, the temperature rise at the electrode was measured by changing the inductance and compared with the dissipated power on the electrode resistance. Results: The induced currents on leads with the IPG case or electrode connection were solved for using the combination of the MoTLiM and the proposed lumped circuit models. These results were compared with those from the MoM simulations. The mean square error was less than 9%. During the MRI experiments, when the IPG case was introduced, the resonance lengths were calculated to have an error less than 13%. Also the change in tip temperature rise at resonance lengths was predicted with less than 4% error. For the electrode experiments, the value of the matching impedance was predicted with an error less than 1%. Conclusions: Electrical models for the IPG case and electrode are suggested, and the method is proposed to determine the parameter values. The concept of matching of the electrode to the lead is clarified using the defined electrode impedance and the lead Thevenin impedance. The effect of the IPG case and electrode on tip heating can be predicted using the proposed theory. With these models, understanding the tissue heating due to the implants becomes easier. Also, these models are beneficial for implant safety testers and designers. Using these models, worst case conditions can be determined and the corresponding implant test experiments can be planned.« less

SU-D-201-05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework

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

Santhanam, A; Min, Y; Beron, P

Purpose: Patient safety hazards such as a wrong patient/site getting treated can lead to catastrophic results. The purpose of this project is to automatically detect potential patient safety hazards during the radiotherapy setup and alert the therapist before the treatment is initiated. Methods: We employed a set of co-located and co-registered 3D cameras placed inside the treatment room. Each camera provided a point-cloud of fraxels (fragment pixels with 3D depth information). Each of the cameras were calibrated using a custom-built calibration target to provide 3D information with less than 2 mm error in the 500 mm neighborhood around the isocenter.more » To identify potential patient safety hazards, the treatment room components and the patient’s body needed to be identified and tracked in real-time. For feature recognition purposes, we used a graph-cut based feature recognition with principal component analysis (PCA) based feature-to-object correlation to segment the objects in real-time. Changes in the object’s position were tracked using the CamShift algorithm. The 3D object information was then stored for each classified object (e.g. gantry, couch). A deep learning framework was then used to analyze all the classified objects in both 2D and 3D and was then used to fine-tune a convolutional network for object recognition. The number of network layers were optimized to identify the tracked objects with >95% accuracy. Results: Our systematic analyses showed that, the system was effectively able to recognize wrong patient setups and wrong patient accessories. The combined usage of 2D camera information (color + depth) enabled a topology-preserving approach to verify patient safety hazards in an automatic manner and even in scenarios where the depth information is partially available. Conclusion: By utilizing the 3D cameras inside the treatment room and a deep learning based image classification, potential patient safety hazards can be effectively avoided.« less

Intelligent Automatic Right-Left Sign Lamp Based on Brain Signal Recognition System

NASA Astrophysics Data System (ADS)

Winda, A.; Sofyan; Sthevany; Vincent, R. S.

2017-12-01

Comfort as a part of the human factor, plays important roles in nowadays advanced automotive technology. Many of the current technologies go in the direction of automotive driver assistance features. However, many of the driver assistance features still require physical movement by human to enable the features. In this work, the proposed method is used in order to make certain feature to be functioning without any physical movement, instead human just need to think about it in their mind. In this work, brain signal is recorded and processed in order to be used as input to the recognition system. Right-Left sign lamp based on the brain signal recognition system can potentially replace the button or switch of the specific device in order to make the lamp work. The system then will decide whether the signal is ‘Right’ or ‘Left’. The decision of the Right-Left side of brain signal recognition will be sent to a processing board in order to activate the automotive relay, which will be used to activate the sign lamp. Furthermore, the intelligent system approach is used to develop authorized model based on the brain signal. Particularly Support Vector Machines (SVMs)-based classification system is used in the proposed system to recognize the Left-Right of the brain signal. Experimental results confirm the effectiveness of the proposed intelligent Automatic brain signal-based Right-Left sign lamp access control system. The signal is processed by Linear Prediction Coefficient (LPC) and Support Vector Machines (SVMs), and the resulting experiment shows the training and testing accuracy of 100% and 80%, respectively.

Body-wide anatomy recognition in PET/CT images

NASA Astrophysics Data System (ADS)

Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Zhao, Liming; Torigian, Drew A.

2015-03-01

With the rapid growth of positron emission tomography/computed tomography (PET/CT)-based medical applications, body-wide anatomy recognition on whole-body PET/CT images becomes crucial for quantifying body-wide disease burden. This, however, is a challenging problem and seldom studied due to unclear anatomy reference frame and low spatial resolution of PET images as well as low contrast and spatial resolution of the associated low-dose CT images. We previously developed an automatic anatomy recognition (AAR) system [15] whose applicability was demonstrated on diagnostic computed tomography (CT) and magnetic resonance (MR) images in different body regions on 35 objects. The aim of the present work is to investigate strategies for adapting the previous AAR system to low-dose CT and PET images toward automated body-wide disease quantification. Our adaptation of the previous AAR methodology to PET/CT images in this paper focuses on 16 objects in three body regions - thorax, abdomen, and pelvis - and consists of the following steps: collecting whole-body PET/CT images from existing patient image databases, delineating all objects in these images, modifying the previous hierarchical models built from diagnostic CT images to account for differences in appearance in low-dose CT and PET images, automatically locating objects in these images following object hierarchy, and evaluating performance. Our preliminary evaluations indicate that the performance of the AAR approach on low-dose CT images achieves object localization accuracy within about 2 voxels, which is comparable to the accuracies achieved on diagnostic contrast-enhanced CT images. Object recognition on low-dose CT images from PET/CT examinations without requiring diagnostic contrast-enhanced CT seems feasible.

Multi-stream face recognition on dedicated mobile devices for crime-fighting

NASA Astrophysics Data System (ADS)

Jassim, Sabah A.; Sellahewa, Harin

2006-09-01

Automatic face recognition is a useful tool in the fight against crime and terrorism. Technological advance in mobile communication systems and multi-application mobile devices enable the creation of hybrid platforms for active and passive surveillance. A dedicated mobile device that incorporates audio-visual sensors would not only complement existing networks of fixed surveillance devices (e.g. CCTV) but could also provide wide geographical coverage in almost any situation and anywhere. Such a device can hold a small portion of a law-enforcing agency biometric database that consist of audio and/or visual data of a number of suspects/wanted or missing persons who are expected to be in a local geographical area. This will assist law-enforcing officers on the ground in identifying persons whose biometric templates are downloaded onto their devices. Biometric data on the device can be regularly updated which will reduce the number of faces an officer has to remember. Such a dedicated device would act as an active/passive mobile surveillance unit that incorporate automatic identification. This paper is concerned with the feasibility of using wavelet-based face recognition schemes on such devices. The proposed schemes extend our recently developed face verification scheme for implementation on a currently available PDA. In particular we will investigate the use of a combination of wavelet frequency channels for multi-stream face recognition. We shall present experimental results on the performance of our proposed schemes for a number of publicly available face databases including a new AV database of videos recorded on a PDA.

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

Target recognition of log-polar ladar range images using moment invariants

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jie; Yu, Haoyong

2017-01-01

The ladar range image has received considerable attentions in the automatic target recognition field. However, previous research does not cover target recognition using log-polar ladar range images. Therefore, we construct a target recognition system based on log-polar ladar range images in this paper. In this system combined moment invariants and backpropagation neural network are selected as shape descriptor and shape classifier, respectively. In order to fully analyze the effect of log-polar sampling pattern on recognition result, several comparative experiments based on simulated and real range images are carried out. Eventually, several important conclusions are drawn: (i) if combined moments are computed directly by log-polar range images, translation, rotation and scaling invariant properties of combined moments will be invalid (ii) when object is located in the center of field of view, recognition rate of log-polar range images is less sensitive to the changing of field of view (iii) as object position changes from center to edge of field of view, recognition performance of log-polar range images will decline dramatically (iv) log-polar range images has a better noise robustness than Cartesian range images. Finally, we give a suggestion that it is better to divide field of view into recognition area and searching area in the real application.

An image-based automatic recognition method for the flowering stage of maize

NASA Astrophysics Data System (ADS)

Yu, Zhenghong; Zhou, Huabing; Li, Cuina

2018-03-01

In this paper, we proposed an image-based approach for automatic recognizing the flowering stage of maize. A modified HOG/SVM detection framework is first adopted to detect the ears of maize. Then, we use low-rank matrix recovery technology to precisely extract the ears at pixel level. At last, a new feature called color gradient histogram, as an indicator, is proposed to determine the flowering stage. Comparing experiment has been carried out to testify the validity of our method and the results indicate that our method can meet the demand for practical observation.

Automatic ground control point recognition with parallel associative memory

NASA Technical Reports Server (NTRS)

Al-Tahir, Raid; Toth, Charles K.; Schenck, Anton F.

1990-01-01

The basic principle of the associative memory is to match the unknown input pattern against a stored training set, and responding with the 'closest match' and the corresponding label. Generally, an associative memory system requires two preparatory steps: selecting attributes of the pattern class, and training the system by associating patterns with labels. Experimental results gained from using Parallel Associative Memory are presented. The primary concern is an automatic search for ground control points in aerial photographs. Synthetic patterns are tested followed by real data. The results are encouraging as a relatively high level of correct matches is reached.

Automatic building identification under bomb damage conditions

NASA Astrophysics Data System (ADS)

Woodley, Robert; Noll, Warren; Barker, Joseph; Wunsch, Donald C., II

2009-05-01

Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.

Modeling the Perceptual Learning of Novel Dialect Features

ERIC Educational Resources Information Center

Tatman, Rachael

2017-01-01

All language use reflects the user's social identity in systematic ways. While humans can easily adapt to this sociolinguistic variation, automatic speech recognition (ASR) systems continue to struggle with it. This dissertation makes three main contributions. The first is to provide evidence that modern state-of-the-art commercial ASR systems…

Polarimetric Imaging System for Automatic Target Detection and Recognition

DTIC Science & Technology

2000-03-01

technique shown in Figure 4(b) can also be used to integrate polarizer arrays with other types of imaging sensors, such as LWIR cameras and uncooled...vertical stripe pattern in this φ image is caused by nonuniformities in the particular polarizer array used. 2. CIRCULAR POLARIZATION IMAGING USING

AstroCV: Astronomy computer vision library

NASA Astrophysics Data System (ADS)

González, Roberto E.; Muñoz, Roberto P.; Hernández, Cristian A.

2018-04-01

AstroCV processes and analyzes big astronomical datasets, and is intended to provide a community repository of high performance Python and C++ algorithms used for image processing and computer vision. The library offers methods for object recognition, segmentation and classification, with emphasis in the automatic detection and classification of galaxies.

Semi-automated identification of leopard frogs

USGS Publications Warehouse

Petrovska-Delacrétaz, Dijana; Edwards, Aaron; Chiasson, John; Chollet, Gérard; Pilliod, David S.

2014-01-01

Principal component analysis is used to implement a semi-automatic recognition system to identify recaptured northern leopard frogs (Lithobates pipiens). Results of both open set and closed set experiments are given. The presented algorithm is shown to provide accurate identification of 209 individual leopard frogs from a total set of 1386 images.

Morphosyntactic Neural Analysis for Generalized Lexical Normalization

ERIC Educational Resources Information Center

Leeman-Munk, Samuel Paul

2016-01-01

The phenomenal growth of social media, web forums, and online reviews has spurred a growing interest in automated analysis of user-generated text. At the same time, a proliferation of voice recordings and efforts to archive culture heritage documents are fueling demand for effective automatic speech recognition (ASR) and optical character…

Bayesian Methods and Confidence Intervals for Automatic Target Recognition of SAR Canonical Shapes

DTIC Science & Technology

2014-03-27

and DirectX [22]. The CUDA platform was developed by the NVIDIA Corporation to allow programmers access to the computational capabilities of the...were used for the intense repetitive computations. Developing CUDA software requires writing code for specialized compilers provided by NVIDIA and

Multilingual Videos for MOOCs and OER

ERIC Educational Resources Information Center

Valor Miró, Juan Daniel; Baquero-Arnal, Pau; Civera, Jorge; Turró, Carlos; Juan, Alfons

2018-01-01

Massive Open Online Courses (MOOCs) and Open Educational Resources (OER) are rapidly growing, but are not usually offered in multiple languages due to the lack of cost-effective solutions to translate the different objects comprising them and particularly videos. However, current state-of-the-art automatic speech recognition (ASR) and machine…

Towards Automatic Threat Recognition

DTIC Science & Technology

2006-12-01

York: Bantam. Forschungsinstitut für Kommunikation, Informationsverarbeitung und Ergonomie FGAN Informationstechnik und Führungssysteme KIE Towards...Informationsverarbeitung und Ergonomie FGAN Informationstechnik und Führungssysteme KIE Content Preliminaries about Information Fusion The System Ontology Unification...as Processing Principle Back to the Example Conclusion and Outlook Forschungsinstitut für Kommunikation, Informationsverarbeitung und Ergonomie FGAN

Use of Computer Speech Technologies To Enhance Learning.

ERIC Educational Resources Information Center

Ferrell, Joe

1999-01-01

Discusses the design of an innovative learning system that uses new technologies for the man-machine interface, incorporating a combination of Automatic Speech Recognition (ASR) and Text To Speech (TTS) synthesis. Highlights include using speech technologies to mimic the attributes of the ideal tutor and design features. (AEF)

An automatic speech recognition system with speaker-independent identification support

NASA Astrophysics Data System (ADS)

Caranica, Alexandru; Burileanu, Corneliu

2015-02-01

The novelty of this work relies on the application of an open source research software toolkit (CMU Sphinx) to train, build and evaluate a speech recognition system, with speaker-independent support, for voice-controlled hardware applications. Moreover, we propose to use the trained acoustic model to successfully decode offline voice commands on embedded hardware, such as an ARMv6 low-cost SoC, Raspberry PI. This type of single-board computer, mainly used for educational and research activities, can serve as a proof-of-concept software and hardware stack for low cost voice automation systems.

Infrared target recognition based on improved joint local ternary pattern

NASA Astrophysics Data System (ADS)

Sun, Junding; Wu, Xiaosheng

2016-05-01

This paper presents a simple, efficient, yet robust approach, named joint orthogonal combination of local ternary pattern, for automatic forward-looking infrared target recognition. It gives more advantages to describe the macroscopic textures and microscopic textures by fusing variety of scales than the traditional LBP-based methods. In addition, it can effectively reduce the feature dimensionality. Further, the rotation invariant and uniform scheme, the robust LTP, and soft concave-convex partition are introduced to enhance its discriminative power. Experimental results demonstrate that the proposed method can achieve competitive results compared with the state-of-the-art methods.

Auditory models for speech analysis

NASA Astrophysics Data System (ADS)

Maybury, Mark T.

This paper reviews the psychophysical basis for auditory models and discusses their application to automatic speech recognition. First an overview of the human auditory system is presented, followed by a review of current knowledge gleaned from neurological and psychoacoustic experimentation. Next, a general framework describes established peripheral auditory models which are based on well-understood properties of the peripheral auditory system. This is followed by a discussion of current enhancements to that models to include nonlinearities and synchrony information as well as other higher auditory functions. Finally, the initial performance of auditory models in the task of speech recognition is examined and additional applications are mentioned.

3D automatic anatomy segmentation based on iterative graph-cut-ASM.

PubMed

Chen, Xinjian; Bagci, Ulas

2011-08-01

This paper studies the feasibility of developing an automatic anatomy segmentation (AAS) system in clinical radiology and demonstrates its operation on clinical 3D images. The AAS system, the authors are developing consists of two main parts: object recognition and object delineation. As for recognition, a hierarchical 3D scale-based multiobject method is used for the multiobject recognition task, which incorporates intensity weighted ball-scale (b-scale) information into the active shape model (ASM). For object delineation, an iterative graph-cut-ASM (IGCASM) algorithm is proposed, which effectively combines the rich statistical shape information embodied in ASM with the globally optimal delineation capability of the GC method. The presented IGCASM algorithm is a 3D generalization of the 2D GC-ASM method that they proposed previously in Chen et al. [Proc. SPIE, 7259, 72590C1-72590C-8 (2009)]. The proposed methods are tested on two datasets comprised of images obtained from 20 patients (10 male and 10 female) of clinical abdominal CT scans, and 11 foot magnetic resonance imaging (MRI) scans. The test is for four organs (liver, left and right kidneys, and spleen) segmentation, five foot bones (calcaneus, tibia, cuboid, talus, and navicular). The recognition and delineation accuracies were evaluated separately. The recognition accuracy was evaluated in terms of translation, rotation, and scale (size) error. The delineation accuracy was evaluated in terms of true and false positive volume fractions (TPVF, FPVF). The efficiency of the delineation method was also evaluated on an Intel Pentium IV PC with a 3.4 GHZ CPU machine. The recognition accuracies in terms of translation, rotation, and scale error over all organs are about 8 mm, 10 degrees and 0.03, and over all foot bones are about 3.5709 mm, 0.35 degrees and 0.025, respectively. The accuracy of delineation over all organs for all subjects as expressed in TPVF and FPVF is 93.01% and 0.22%, and all foot bones for all subjects are 93.75% and 0.28%, respectively. While the delineations for the four organs can be accomplished quite rapidly with average of 78 s, the delineations for the five foot bones can be accomplished with average of 70 s. The experimental results showed the feasibility and efficacy of the proposed automatic anatomy segmentation system: (a) the incorporation of shape priors into the GC framework is feasible in 3D as demonstrated previously for 2D images; (b) our results in 3D confirm the accuracy behavior observed in 2D. The hybrid strategy IGCASM seems to be more robust and accurate than ASM and GC individually; and (c) delineations within body regions and foot bones of clinical importance can be accomplished quite rapidly within 1.5 min.

Automatic updating and 3D modeling of airport information from high resolution images using GIS and LIDAR data

NASA Astrophysics Data System (ADS)

Lv, Zheng; Sui, Haigang; Zhang, Xilin; Huang, Xianfeng

2007-11-01

As one of the most important geo-spatial objects and military establishment, airport is always a key target in fields of transportation and military affairs. Therefore, automatic recognition and extraction of airport from remote sensing images is very important and urgent for updating of civil aviation and military application. In this paper, a new multi-source data fusion approach on automatic airport information extraction, updating and 3D modeling is addressed. Corresponding key technologies including feature extraction of airport information based on a modified Ostu algorithm, automatic change detection based on new parallel lines-based buffer detection algorithm, 3D modeling based on gradual elimination of non-building points algorithm, 3D change detecting between old airport model and LIDAR data, typical CAD models imported and so on are discussed in detail. At last, based on these technologies, we develop a prototype system and the results show our method can achieve good effects.

Automatic digital image analysis for identification of mitotic cells in synchronous mammalian cell cultures.

PubMed

Eccles, B A; Klevecz, R R

1986-06-01

Mitotic frequency in a synchronous culture of mammalian cells was determined fully automatically and in real time using low-intensity phase-contrast microscopy and a newvicon video camera connected to an EyeCom III image processor. Image samples, at a frequency of one per minute for 50 hours, were analyzed by first extracting the high-frequency picture components, then thresholding and probing for annular objects indicative of putative mitotic cells. Both the extraction of high-frequency components and the recognition of rings of varying radii and discontinuities employed novel algorithms. Spatial and temporal relationships between annuli were examined to discern the occurrences of mitoses, and such events were recorded in a computer data file. At present, the automatic analysis is suited for random cell proliferation rate measurements or cell cycle studies. The automatic identification of mitotic cells as described here provides a measure of the average proliferative activity of the cell population as a whole and eliminates more than eight hours of manual review per time-lapse video recording.

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.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis.

PubMed

Cheng, Yezeng; Larin, Kirill V

2006-12-20

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

Artificial fingerprint recognition by using optical coherence tomography with autocorrelation analysis

NASA Astrophysics Data System (ADS)

Cheng, Yezeng; Larin, Kirill V.

2006-12-01

Fingerprint recognition is one of the most widely used methods of biometrics. This method relies on the surface topography of a finger and, thus, is potentially vulnerable for spoofing by artificial dummies with embedded fingerprints. In this study, we applied the optical coherence tomography (OCT) technique to distinguish artificial materials commonly used for spoofing fingerprint scanning systems from the real skin. Several artificial fingerprint dummies made from household cement and liquid silicone rubber were prepared and tested using a commercial fingerprint reader and an OCT system. While the artificial fingerprints easily spoofed the commercial fingerprint reader, OCT images revealed the presence of them at all times. We also demonstrated that an autocorrelation analysis of the OCT images could be potentially used in automatic recognition systems.

The automaticity of emotion recognition.

PubMed

Tracy, Jessica L; Robins, Richard W

2008-02-01

Evolutionary accounts of emotion typically assume that humans evolved to quickly and efficiently recognize emotion expressions because these expressions convey fitness-enhancing messages. The present research tested this assumption in 2 studies. Specifically, the authors examined (a) how quickly perceivers could recognize expressions of anger, contempt, disgust, embarrassment, fear, happiness, pride, sadness, shame, and surprise; (b) whether accuracy is improved when perceivers deliberate about each expression's meaning (vs. respond as quickly as possible); and (c) whether accurate recognition can occur under cognitive load. Across both studies, perceivers quickly and efficiently (i.e., under cognitive load) recognized most emotion expressions, including the self-conscious emotions of pride, embarrassment, and shame. Deliberation improved accuracy in some cases, but these improvements were relatively small. Discussion focuses on the implications of these findings for the cognitive processes underlying emotion recognition.

A probabilistic union model with automatic order selection for noisy speech recognition.

PubMed

Jancovic, P; Ming, J

2001-09-01

A critical issue in exploiting the potential of the sub-band-based approach to robust speech recognition is the method of combining the sub-band observations, for selecting the bands unaffected by noise. A new method for this purpose, i.e., the probabilistic union model, was recently introduced. This model has been shown to be capable of dealing with band-limited corruption, requiring no knowledge about the band position and statistical distribution of the noise. A parameter within the model, which we call its order, gives the best results when it equals the number of noisy bands. Since this information may not be available in practice, in this paper we introduce an automatic algorithm for selecting the order, based on the state duration pattern generated by the hidden Markov model (HMM). The algorithm has been tested on the TIDIGITS database corrupted by various types of additive band-limited noise with unknown noisy bands. The results have shown that the union model equipped with the new algorithm can achieve a recognition performance similar to that achieved when the number of noisy bands is known. The results show a very significant improvement over the traditional full-band model, without requiring prior information on either the position or the number of noisy bands. The principle of the algorithm for selecting the order based on state duration may also be applied to other sub-band combination methods.

Infrared Cephalic-Vein to Assist Blood Extraction Tasks: Automatic Projection and Recognition

NASA Astrophysics Data System (ADS)

Lagüela, S.; Gesto, M.; Riveiro, B.; González-Aguilera, D.

2017-05-01

Thermal infrared band is not commonly used in photogrammetric and computer vision algorithms, mainly due to the low spatial resolution of this type of imagery. However, this band captures sub-superficial information, increasing the capabilities of visible bands regarding applications. This fact is especially important in biomedicine and biometrics, allowing the geometric characterization of interior organs and pathologies with photogrammetric principles, as well as the automatic identification and labelling using computer vision algorithms. This paper presents advances of close-range photogrammetry and computer vision applied to thermal infrared imagery, with the final application of Augmented Reality in order to widen its application in the biomedical field. In this case, the thermal infrared image of the arm is acquired and simultaneously projected on the arm, together with the identification label of the cephalic-vein. This way, blood analysts are assisted in finding the vein for blood extraction, especially in those cases where the identification by the human eye is a complex task. Vein recognition is performed based on the Gaussian temperature distribution in the area of the vein, while the calibration between projector and thermographic camera is developed through feature extraction and pattern recognition. The method is validated through its application to a set of volunteers, with different ages and genres, in such way that different conditions of body temperature and vein depth are covered for the applicability and reproducibility of the method.

Early Visual Word Processing Is Flexible: Evidence from Spatiotemporal Brain Dynamics.

PubMed

Chen, Yuanyuan; Davis, Matthew H; Pulvermüller, Friedemann; Hauk, Olaf

2015-09-01

Visual word recognition is often described as automatic, but the functional locus of top-down effects is still a matter of debate. Do task demands modulate how information is retrieved, or only how it is used? We used EEG/MEG recordings to assess whether, when, and how task contexts modify early retrieval of specific psycholinguistic information in occipitotemporal cortex, an area likely to contribute to early stages of visual word processing. Using a parametric approach, we analyzed the spatiotemporal response patterns of occipitotemporal cortex for orthographic, lexical, and semantic variables in three psycholinguistic tasks: silent reading, lexical decision, and semantic decision. Task modulation of word frequency and imageability effects occurred simultaneously in ventral occipitotemporal regions-in the vicinity of the putative visual word form area-around 160 msec, following task effects on orthographic typicality around 100 msec. Frequency and typicality also produced task-independent effects in anterior temporal lobe regions after 200 msec. The early task modulation for several specific psycholinguistic variables indicates that occipitotemporal areas integrate perceptual input with prior knowledge in a task-dependent manner. Still, later task-independent effects in anterior temporal lobes suggest that word recognition eventually leads to retrieval of semantic information irrespective of task demands. We conclude that even a highly overlearned visual task like word recognition should be described as flexible rather than automatic.

Cultivating Teacher Leadership for School Improvement.

ERIC Educational Resources Information Center

Bauer, Scott C.; Haydel, Juanita; Cody, Caroline

The state of Louisiana has commissioned several pilot projects to design, implement, and evaluate a teacher leader curriculum leading to eligibility for the new teacher leader certificate. The purpose of this paper is to describe one of these pilots, the St. Charles Teacher Leader Institute (TLI), which was implemented as a partnership between the…

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Science.gov Websites

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NASA's Planned Return to the Moon: Global Access and Anytime Return Requirement Implications on the Lunar Orbit Insertion Burns

NASA Technical Reports Server (NTRS)

Garn, Michelle; Qu, Min; Chrone, Jonathan; Su, Philip; Karlgaard, Chris

2008-01-01

Lunar orbit insertion LOI is a critical maneuver for any mission going to the Moon. Optimizing the geometry of this maneuver is crucial to the success of the architecture designed to return humans to the Moon. LOI burns necessary to meet current NASA Exploration Constellation architecture requirements for the lunar sortie missions are driven mainly by the requirement for global access and "anytime" return from the lunar surface. This paper begins by describing the Earth-Moon geometry which creates the worst case (delta)V for both the LOI and the translunar injection (TLI) maneuvers over the full metonic cycle. The trajectory which optimizes the overall (delta)V performance of the mission is identified, trade studies results covering the entire lunar globe are mapped onto the contour plots, and the effects of loitering in low lunar orbit as a means of reducing the insertion (delta)V are described. Finally, the lighting conditions on the lunar surface are combined with the LOI and TLI analyses to identify geometries with ideal lighting conditions at sites of interest which minimize the mission (delta)V.

Augmenting tendon and ligament repair with platelet-rich plasma (PRP)

PubMed Central

Yuan, Ting; Zhang, Chang-Qing; Wang, James H-C.

2013-01-01

Summary Tendon and ligament injuries (TLI) commonly occur in athletes and non-athletes alike, and remarkably debilitate patients’ athletic and personal abilities. Current clinical treatments, such as reconstruction surgeries, do not adequately heal these injuries and often result in the formation of scar tissue that is prone to re-injury. Platelet-rich plasma (PRP) is a widely used alternative option that is also safe because of its autologous nature. PRP contains a number of growth factors that are responsible for its potential to heal TLIs effectively. In this review, we provide a comprehensive report on PRP. While basic science studies in general indicate the potential of PRP to treat TLIs effectively, a review of existing literature on the clinical use of PRP for the treatment of TLIs indicates a lack of consensus due to varied treatment outcomes. This suggests that current PRP treatment protocols for TLIs may not be optimal, and that not all TLIs may be effectively treated with PRP. Certainly, additional basic science studies are needed to develop optimal treatment protocols and determine those TLI conditions that can be treated effectively. PMID:24367773

Automatic localization of IASLC-defined mediastinal lymph node stations on CT images using fuzzy models

NASA Astrophysics Data System (ADS)

Matsumoto, Monica M. S.; Beig, Niha G.; Udupa, Jayaram K.; Archer, Steven; Torigian, Drew A.

2014-03-01

Lung cancer is associated with the highest cancer mortality rates among men and women in the United States. The accurate and precise identification of the lymph node stations on computed tomography (CT) images is important for staging disease and potentially for prognosticating outcome in patients with lung cancer, as well as for pretreatment planning and response assessment purposes. To facilitate a standard means of referring to lymph nodes, the International Association for the Study of Lung Cancer (IASLC) has recently proposed a definition of the different lymph node stations and zones in the thorax. However, nodal station identification is typically performed manually by visual assessment in clinical radiology. This approach leaves room for error due to the subjective and potentially ambiguous nature of visual interpretation, and is labor intensive. We present a method of automatically recognizing the mediastinal IASLC-defined lymph node stations by modifying a hierarchical fuzzy modeling approach previously developed for body-wide automatic anatomy recognition (AAR) in medical imagery. Our AAR-lymph node (AAR-LN) system follows the AAR methodology and consists of two steps. In the first step, the various lymph node stations are manually delineated on a set of CT images following the IASLC definitions. These delineations are then used to build a fuzzy hierarchical model of the nodal stations which are considered as 3D objects. In the second step, the stations are automatically located on any given CT image of the thorax by using the hierarchical fuzzy model and object recognition algorithms. Based on 23 data sets used for model building, 22 independent data sets for testing, and 10 lymph node stations, a mean localization accuracy of within 1-6 voxels has been achieved by the AAR-LN system.

Real-time Flare Detection in Ground-Based Hα Imaging at Kanzelhöhe Observatory

NASA Astrophysics Data System (ADS)

Pötzi, W.; Veronig, A. M.; Riegler, G.; Amerstorfer, U.; Pock, T.; Temmer, M.; Polanec, W.; Baumgartner, D. J.

2015-03-01

Kanzelhöhe Observatory (KSO) regularly performs high-cadence full-disk imaging of the solar chromosphere in the Hα and Ca ii K spectral lines as well as in the solar photosphere in white light. In the frame of ESA's (European Space Agency) Space Situational Awareness (SSA) program, a new system for real-time Hα data provision and automatic flare detection was developed at KSO. The data and events detected are published in near real-time at ESA's SSA Space Weather portal (http://swe.ssa.esa.int/web/guest/kso-federated). In this article, we describe the Hα instrument, the image-recognition algorithms we developed, and the implementation into the KSO Hα observing system. We also present the evaluation results of the real-time data provision and flare detection for a period of five months. The Hα data provision worked in 99.96 % of the images, with a mean time lag of four seconds between image recording and online provision. Within the given criteria for the automatic image-recognition system (at least three Hα images are needed for a positive detection), all flares with an area ≥ 50 micro-hemispheres that were located within 60° of the solar center and occurred during the KSO observing times were detected, a number of 87 events in total. The automatically determined flare importance and brightness classes were correct in ˜ 85 %. The mean flare positions in heliographic longitude and latitude were correct to within ˜ 1°. The median of the absolute differences for the flare start and peak times from the automatic detections in comparison with the official NOAA (and KSO) visual flare reports were 3 min (1 min).

A novel automatic method for monitoring Tourette motor tics through a wearable device.

PubMed

Bernabei, Michel; Preatoni, Ezio; Mendez, Martin; Piccini, Luca; Porta, Mauro; Andreoni, Giuseppe

2010-09-15

The aim of this study was to propose a novel automatic method for quantifying motor-tics caused by the Tourette Syndrome (TS). In this preliminary report, the feasibility of the monitoring process was tested over a series of standard clinical trials in a population of 12 subjects affected by TS. A wearable instrument with an embedded three-axial accelerometer was used to detect and classify motor tics during standing and walking activities. An algorithm was devised to analyze acceleration data by: eliminating noise; detecting peaks connected to pathological events; and classifying intensity and frequency of motor tics into quantitative scores. These indexes were compared with the video-based ones provided by expert clinicians, which were taken as the gold-standard. Sensitivity, specificity, and accuracy of tic detection were estimated, and an agreement analysis was performed through the least square regression and the Bland-Altman test. The tic recognition algorithm showed sensitivity = 80.8% ± 8.5% (mean ± SD), specificity = 75.8% ± 17.3%, and accuracy = 80.5% ± 12.2%. The agreement study showed that automatic detection tended to overestimate the number of tics occurred. Although, it appeared this may be a systematic error due to the different recognition principles of the wearable and video-based systems. Furthermore, there was substantial concurrency with the gold-standard in estimating the severity indexes. The proposed methodology gave promising performances in terms of automatic motor-tics detection and classification in a standard clinical context. The system may provide physicians with a quantitative aid for TS assessment. Further developments will focus on the extension of its application to everyday long-term monitoring out of clinical environments. © 2010 Movement Disorder Society.

System transfer modelling for automatic target recognizer evaluations

NASA Astrophysics Data System (ADS)

Clark, Lloyd G.

1991-11-01

Image processing to accomplish automatic recognition of military vehicles has promised increased weapons systems effectiveness and reduced timelines for a number of Department of Defense missions. Automatic Target Recognizers (ATR) are often claimed to be able to recognize many different ground vehicles as possible targets in military air-to- surface targeting applications. The targeting scenario conditions include different vehicle poses and histories as well as a variety of imaging geometries, intervening atmospheres, and background environments. Testing these ATR subsystems in most cases has been limited to a handful of the scenario conditions of interest, as is represented by imagery collected with the desired imaging sensor. The question naturally arises as to how robust the performance of the ATR is for all scenario conditions of interest, not just for the set of imagery upon which an algorithm was trained.

Electrophysiological evidence of automatic early semantic processing.

PubMed

Hinojosa, José A; Martín-Loeches, Manuel; Muñoz, Francisco; Casado, Pilar; Pozo, Miguel A

2004-01-01

This study investigates the automatic-controlled nature of early semantic processing by means of the Recognition Potential (RP), an event-related potential response that reflects lexical selection processes. For this purpose tasks differing in their processing requirements were used. Half of the participants performed a physical task involving a lower-upper case discrimination judgement (shallow processing requirements), whereas the other half carried out a semantic task, consisting in detecting animal names (deep processing requirements). Stimuli were identical in the two tasks. Reaction time measures revealed that the physical task was easier to perform than the semantic task. However, RP effects elicited by the physical and semantic tasks did not differ in either latency, amplitude, or topographic distribution. Thus, the results from the present study suggest that early semantic processing is automatically triggered whenever a linguistic stimulus enters the language processor.

Human Activity Recognition from Body Sensor Data using Deep Learning.

PubMed

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

2018-04-16

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

Toward End-to-End Face Recognition Through Alignment Learning

NASA Astrophysics Data System (ADS)

Zhong, Yuanyi; Chen, Jiansheng; Huang, Bo

2017-08-01

Plenty of effective methods have been proposed for face recognition during the past decade. Although these methods differ essentially in many aspects, a common practice of them is to specifically align the facial area based on the prior knowledge of human face structure before feature extraction. In most systems, the face alignment module is implemented independently. This has actually caused difficulties in the designing and training of end-to-end face recognition models. In this paper we study the possibility of alignment learning in end-to-end face recognition, in which neither prior knowledge on facial landmarks nor artificially defined geometric transformations are required. Specifically, spatial transformer layers are inserted in front of the feature extraction layers in a Convolutional Neural Network (CNN) for face recognition. Only human identity clues are used for driving the neural network to automatically learn the most suitable geometric transformation and the most appropriate facial area for the recognition task. To ensure reproducibility, our model is trained purely on the publicly available CASIA-WebFace dataset, and is tested on the Labeled Face in the Wild (LFW) dataset. We have achieved a verification accuracy of 99.08\\% which is comparable to state-of-the-art single model based methods.

Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

NASA Astrophysics Data System (ADS)

Sun, Kaioqiong; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.

2014-03-01

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

Recognition of upper airway and surrounding structures at MRI in pediatric PCOS and OSAS

NASA Astrophysics Data System (ADS)

Tong, Yubing; Udupa, J. K.; Odhner, D.; Sin, Sanghun; Arens, Raanan

2013-03-01

Obstructive Sleep Apnea Syndrome (OSAS) is common in obese children with risk being 4.5 fold compared to normal control subjects. Polycystic Ovary Syndrome (PCOS) has recently been shown to be associated with OSAS that may further lead to significant cardiovascular and neuro-cognitive deficits. We are investigating image-based biomarkers to understand the architectural and dynamic changes in the upper airway and the surrounding hard and soft tissue structures via MRI in obese teenage children to study OSAS. At the previous SPIE conferences, we presented methods underlying Fuzzy Object Models (FOMs) for Automatic Anatomy Recognition (AAR) based on CT images of the thorax and the abdomen. The purpose of this paper is to demonstrate that the AAR approach is applicable to a different body region and image modality combination, namely in the study of upper airway structures via MRI. FOMs were built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. FOMs encode the uncertainty and variability present in the form and relationships among the objects over a study population. Totally 11 basic objects (17 including composite) were modeled. Automatic recognition for the best pose of FOMs in a given image was implemented by using four methods - a one-shot method that does not require search, another three searching methods that include Fisher Linear Discriminate (FLD), a b-scale energy optimization strategy, and optimum threshold recognition method. In all, 30 multi-fold cross validation experiments based on 15 patient MRI data sets were carried out to assess the accuracy of recognition. The results indicate that the objects can be recognized with an average location error of less than 5 mm or 2-3 voxels. Then the iterative relative fuzzy connectedness (IRFC) algorithm was adopted for delineation of the target organs based on the recognized results. The delineation results showed an overall FP and TP volume fraction of 0.02 and 0.93.

Development and Validation of the Body Concealment Scale for Scleroderma.

PubMed

Jewett, Lisa R; Malcarne, Vanessa L; Kwakkenbos, Linda; Harcourt, Diana; Rumsey, Nichola; Körner, Annett; Steele, Russell J; Hudson, Marie; Baron, Murray; Haythornthwaite, Jennifer A; Heinberg, Leslie; Wigley, Fredrick M; Thombs, Brett D

2016-08-01

Body concealment is a component of social avoidance among people with visible differences from disfiguring conditions, including systemic sclerosis (SSc). The study objective was to develop a measure of body concealment related to avoidance behaviors in SSc. Initial items for the Body Concealment Scale for Scleroderma (BCSS) were selected using item analysis in a development sample of 93 American SSc patients. The factor structure of the BCSS was evaluated in 742 Canadian patients with single-factor, 2-factor, and bifactor confirmatory factor analysis models. Convergent and divergent validity were assessed by comparing the BCSS total score with the Brief-Satisfaction with Appearance Scale (Brief-SWAP) and measures of depressive symptoms and pain. A 2-factor model (Comparative Fit Index [CFI] 0.99, Tucker-Lewis Index [TLI] 0.98, Root Mean Square Error of Approximation [RMSEA] 0.08) fit substantially better than a 1-factor model (CFI 0.95, TLI 0.94, RMSEA 0.15) for the 9-item BCSS, but the Concealment with Clothing and Concealment of Hands factors were highly correlated (α = 0.79). The bifactor model (CFI 0.99, TLI 0.99, RMSEA 0.08) also fit well. In the bifactor model, the omega coefficient was high for the general factor (ω = 0.80), but low for the Concealment with Clothing (ω = 0.01) and Concealment of Hands (ω = 0.33) factors. The BCSS total score correlated more strongly with the Brief-SWAP Social Discomfort (r = 0.59) and Dissatisfaction with Appearance (r = 0.53) subscales than with measures of depressive symptoms and pain. The BCSS sum score is a valid indicator of body concealment in SSc that extends the concepts of body concealment and avoidance beyond the realms of body shape and weight to concerns of individuals with visible differences from SSc. © 2016, American College of Rheumatology.

Embodied Memory Judgments: A Case of Motor Fluency

ERIC Educational Resources Information Center

Yang, Shu-Ju; Gallo, David A.; Beilock, Sian L.

2009-01-01

It is well known that perceptual and conceptual fluency can influence episodic memory judgments. Here, the authors asked whether fluency arising from the motor system also impacts recognition memory. Past research has shown that the perception of letters automatically activates motor programs of typing actions in skilled typists. In this study,…

Actes des Journees de linguistique (Proceedings of the Linguistics Conference) (9th, 1995).

ERIC Educational Resources Information Center

Audette, Julie, Ed.; And Others

Papers (entirely in French) presented at the conference on linguistics include these topics: language used in the legislature of New Brunswick; cohesion in the text of Arabic-speaking language learners; automatic adverb recognition; logic of machine translation in teaching revision; expansion in physics texts; discourse analysis and the syntax of…

Training Letter and Orthographic Pattern Recognition in Children with Slow Naming Speed

ERIC Educational Resources Information Center

Conrad, Nicole J.; Levy, Betty Ann

2011-01-01

Although research has established that performance on a rapid automatized naming (RAN) task is related to reading, the nature of this relationship is unclear. Bowers (2001) proposed that processes underlying performance on the RAN task and orthographic knowledge make independent and additive contributions to reading performance. We examined the…

Data Intensive Systems (DIS) Benchmark Performance Summary

DTIC Science & Technology

2003-08-01

models assumed by today’s conventional architectures. Such applications include model- based Automatic Target Recognition (ATR), synthetic aperture...radar (SAR) codes, large scale dynamic databases/battlefield integration, dynamic sensor- based processing, high-speed cryptanalysis, high speed...distributed interactive and data intensive simulations, data-oriented problems characterized by pointer- based and other highly irregular data structures

Dismount Threat Recognition through Automatic Pose Identification

DTIC Science & Technology

2012-03-01

10 2.2.2 Enabling Technologies . . . . . . . . . . . . . . 11 2.2.3 Associative Memory Neural Networks . . . . . . 12 III. Methodology...20 3.2.3 Creating Separability . . . . . . . . . . . . . . . 23 3.3 Training the Associative Memory Neural Network... Effects of Parameter and Method Choices . . . . . . . . 30 4.3.1 Decimel versus Bipolar . . . . . . . . . . . . . . 30 4.3.2 Bipolar and Binary Values

Spoken Grammar Practice and Feedback in an ASR-Based CALL System

ERIC Educational Resources Information Center

de Vries, Bart Penning; Cucchiarini, Catia; Bodnar, Stephen; Strik, Helmer; van Hout, Roeland

2015-01-01

Speaking practice is important for learners of a second language. Computer assisted language learning (CALL) systems can provide attractive opportunities for speaking practice when combined with automatic speech recognition (ASR) technology. In this paper, we present a CALL system that offers spoken practice of word order, an important aspect of…

intelligentCAPTURE 1.0 Adds Tables of Content to Library Catalogues and Improves Retrieval.

ERIC Educational Resources Information Center

Hauer, Manfred; Simedy, Walton

2002-01-01

Describes an online library catalog that was developed for an Austrian scientific library that includes table of contents in addition to the standard bibliographic information in order to increase relevance for searchers. Discusses the technology involved, including OCR (Optical Character Recognition) and automatic indexing techniques; weighted…

The Promise of NLP and Speech Processing Technologies in Language Assessment

ERIC Educational Resources Information Center

Chapelle, Carol A.; Chung, Yoo-Ree

2010-01-01

Advances in natural language processing (NLP) and automatic speech recognition and processing technologies offer new opportunities for language testing. Despite their potential uses on a range of language test item types, relatively little work has been done in this area, and it is therefore not well understood by test developers, researchers or…

Validation of Automated Scoring of Oral Reading

ERIC Educational Resources Information Center

Balogh, Jennifer; Bernstein, Jared; Cheng, Jian; Van Moere, Alistair; Townshend, Brent; Suzuki, Masanori

2012-01-01

A two-part experiment is presented that validates a new measurement tool for scoring oral reading ability. Data collected by the U.S. government in a large-scale literacy assessment of adults were analyzed by a system called VersaReader that uses automatic speech recognition and speech processing technologies to score oral reading fluency. In the…

Reading in EFL: Facts and Fictions.

ERIC Educational Resources Information Center

Paran, Amos

1996-01-01

Examines the representation of the reading process in English as a Foreign Language (EFL) texts. The article argues that many of these representations are dated and based on a theory that was never a mainstream theory of first-language reading. Suggestions for exercises to strengthen automatic word recognition in EFL readers are provided. (33…

Voice Interactive Analysis System Study. Final Report, August 28, 1978 through March 23, 1979.

ERIC Educational Resources Information Center

Harry, D. P.; And Others

The Voice Interactive Analysis System study continued research and development of the LISTEN real-time, minicomputer based connected speech recognition system, within NAVTRAEQUIPCEN'S program of developing automatic speech technology in support of training. An attempt was made to identify the most effective features detected by the TTI-500 model…