WorldWideScience

Sample records for research recognition day

  1. 3 CFR 8360 - Proclamation 8360 of April 9, 2009. National Former Prisoner of War Recognition Day, 2009

    Science.gov (United States)

    2010-01-01

    ... of war, they endured the Bataan Death March, suffering starvation, torture, and unspeakable... Prisoner of War Recognition Day, 2009 8360 Proclamation 8360 Presidential Documents Proclamations Proclamation 8360 of April 9, 2009 Proc. 8360 National Former Prisoner of War Recognition Day, 2009By the...

  2. The recognition heuristic: A decade of research

    Directory of Open Access Journals (Sweden)

    Gerd Gigerenzer

    2011-02-01

    Full Text Available The recognition heuristic exploits the basic psychological capacity for recognition in order to make inferences about unknown quantities in the world. In this article, we review and clarify issues that emerged from our initial work (Goldstein and Gigerenzer, 1999, 2002, including the distinction between a recognition and an evaluation process. There is now considerable evidence that (i the recognition heuristic predicts the inferences of a substantial proportion of individuals consistently, even in the presence of one or more contradicting cues, (ii people are adaptive decision makers in that accordance increases with larger recognition validity and decreases in situations when the validity is low or wholly indeterminable, and (iii in the presence of contradicting cues, some individuals appear to select different strategies. Little is known about these individual differences, or how to precisely model the alternative strategies. Although some researchers have attributed judgments inconsistent with the use of the recognition heuristic to compensatory processing, little research on such compensatory models has been reported. We discuss extensions of the recognition model, open questions, unanticipated results, and the surprising predictive power of recognition in forecasting.

  3. Research on application of LADAR in ground vehicle recognition

    Science.gov (United States)

    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.

  4. Four challenges for cognitive research on the recognition heuristic and a call for a research strategy shift

    Directory of Open Access Journals (Sweden)

    Tracy Tomlinson

    2011-02-01

    Full Text Available The recognition heuristic assumes that people make inferences based on the output of recognition memory. While much work has been devoted to establishing the recognition heuristic as a viable description of how people make inferences, more work is needed to fully integrate research on the recognition heuristic with research from the broader cognitive psychology literature. In this article, we outline four challenges that should be met for this integration to take place, and close with a call to address these four challenges collectively, rather than piecemeal.

  5. [Research progress of multi-model medical image fusion and recognition].

    Science.gov (United States)

    Zhou, Tao; Lu, Huiling; Chen, Zhiqiang; Ma, Jingxian

    2013-10-01

    Medical image fusion and recognition has a wide range of applications, such as focal location, cancer staging and treatment effect assessment. Multi-model medical image fusion and recognition are analyzed and summarized in this paper. Firstly, the question of multi-model medical image fusion and recognition is discussed, and its advantage and key steps are discussed. Secondly, three fusion strategies are reviewed from the point of algorithm, and four fusion recognition structures are discussed. Thirdly, difficulties, challenges and possible future research direction are discussed.

  6. Image-based automatic recognition of larvae

    Science.gov (United States)

    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.

  7. Multi-Modal Activity Recognition Systems with Minimal Training Data and Unobtrusive Environmental Instrumentations

    OpenAIRE

    Bauer, Gerald

    2014-01-01

    The recognition of day-to-day activities is still a very challenging and important research topic. During recent years, a lot of research has gone into designing and realizing smart environ- ments in different application areas such as health care, maintenance, sports or smart homes. As a result, a large amount of sensor modalities were developed, different types of activity and context recognition services were implemented and the resulting systems were benchmarked using state-of-the-art eva...

  8. Student Poster Days Showcase Young Researchers | Poster

    Science.gov (United States)

    Student interns presented their research to the NCI at Frederick community during the annual Student Poster Days event, held in the Building 549 lobby and the Advanced Technology Research Facility (ATRF) atrium over two days.

  9. AN OPTICAL CHARACTER RECOGNITION RESEARCH AND DEMONSTRATION PROJECT.

    Science.gov (United States)

    1968

    RESEARCH AND DEVELOPMENT OF PROTOTYPE LIBRARY SYSTEMS WHICH UTILIZE OPTICAL CHARACTER RECOGNITION INPUT HAS CENTERED AROUND OPTICAL PAGE READERS AND DOCUMENT READERS. THE STATE-OF-THE-ART OF BOTH THESE OPTICAL SCANNERS IS SUCH THAT BOTH ARE ACCEPTABLE FOR LIBRARY INPUT PREPARATION. A DEMONSTRATION PROJECT UTILIZING THE TWO TYPES OF READERS, SINCE…

  10. VASIR: An Open-Source Research Platform for Advanced Iris Recognition Technologies.

    Science.gov (United States)

    Lee, Yooyoung; Micheals, Ross J; Filliben, James J; Phillips, P Jonathon

    2013-01-01

    The performance of iris recognition systems is frequently affected by input image quality, which in turn is vulnerable to less-than-optimal conditions due to illuminations, environments, and subject characteristics (e.g., distance, movement, face/body visibility, blinking, etc.). VASIR (Video-based Automatic System for Iris Recognition) is a state-of-the-art NIST-developed iris recognition software platform designed to systematically address these vulnerabilities. We developed VASIR as a research tool that will not only provide a reference (to assess the relative performance of alternative algorithms) for the biometrics community, but will also advance (via this new emerging iris recognition paradigm) NIST's measurement mission. VASIR is designed to accommodate both ideal (e.g., classical still images) and less-than-ideal images (e.g., face-visible videos). VASIR has three primary modules: 1) Image Acquisition 2) Video Processing, and 3) Iris Recognition. Each module consists of several sub-components that have been optimized by use of rigorous orthogonal experiment design and analysis techniques. We evaluated VASIR performance using the MBGC (Multiple Biometric Grand Challenge) NIR (Near-Infrared) face-visible video dataset and the ICE (Iris Challenge Evaluation) 2005 still-based dataset. The results showed that even though VASIR was primarily developed and optimized for the less-constrained video case, it still achieved high verification rates for the traditional still-image case. For this reason, VASIR may be used as an effective baseline for the biometrics community to evaluate their algorithm performance, and thus serves as a valuable research platform.

  11. Conducting spoken word recognition research online: Validation and a new timing method.

    Science.gov (United States)

    Slote, Joseph; Strand, Julia F

    2016-06-01

    Models of spoken word recognition typically make predictions that are then tested in the laboratory against the word recognition scores of human subjects (e.g., Luce & Pisoni Ear and Hearing, 19, 1-36, 1998). Unfortunately, laboratory collection of large sets of word recognition data can be costly and time-consuming. Due to the numerous advantages of online research in speed, cost, and participant diversity, some labs have begun to explore the use of online platforms such as Amazon's Mechanical Turk (AMT) to source participation and collect data (Buhrmester, Kwang, & Gosling Perspectives on Psychological Science, 6, 3-5, 2011). Many classic findings in cognitive psychology have been successfully replicated online, including the Stroop effect, task-switching costs, and Simon and flanker interference (Crump, McDonnell, & Gureckis PLoS ONE, 8, e57410, 2013). However, tasks requiring auditory stimulus delivery have not typically made use of AMT. In the present study, we evaluated the use of AMT for collecting spoken word identification and auditory lexical decision data. Although online users were faster and less accurate than participants in the lab, the results revealed strong correlations between the online and laboratory measures for both word identification accuracy and lexical decision speed. In addition, the scores obtained in the lab and online were equivalently correlated with factors that have been well established to predict word recognition, including word frequency and phonological neighborhood density. We also present and analyze a method for precise auditory reaction timing that is novel to behavioral research. Taken together, these findings suggest that AMT can be a viable alternative to the traditional laboratory setting as a source of participation for some spoken word recognition research.

  12. A pattern recognition methodology for evaluation of load profiles and typical days of large electricity customers

    International Nuclear Information System (INIS)

    Tsekouras, G.J.; Kotoulas, P.B.; Tsirekis, C.D.; Dialynas, E.N.; Hatziargyriou, N.D.

    2008-01-01

    This paper describes a pattern recognition methodology for the classification of the daily chronological load curves of each large electricity customer, in order to estimate his typical days and his respective representative daily load profiles. It is based on pattern recognition methods, such as k-means, self-organized maps (SOM), fuzzy k-means and hierarchical clustering, which are theoretically described and properly adapted. The parameters of each clustering method are properly selected by an optimization process, which is separately applied for each one of six adequacy measures. The results can be used for the short-term and mid-term load forecasting of each consumer, for the choice of the proper tariffs and the feasibility studies of demand side management programs. This methodology is analytically applied for one medium voltage industrial customer and synoptically for a set of medium voltage customers of the Greek power system. The results of the clustering methods are presented and discussed. (author)

  13. Research on Face Recognition Based on Embedded System

    Directory of Open Access Journals (Sweden)

    Hong Zhao

    2013-01-01

    Full Text Available Because a number of image feature data to store, complex calculation to execute during the face recognition, therefore the face recognition process was realized only by PCs with high performance. In this paper, the OpenCV facial Haar-like features were used to identify face region; the Principal Component Analysis (PCA was employed in quick extraction of face features and the Euclidean Distance was also adopted in face recognition; as thus, data amount and computational complexity would be reduced effectively in face recognition, and the face recognition could be carried out on embedded platform. Finally, based on Tiny6410 embedded platform, a set of embedded face recognition systems was constructed. The test results showed that the system has stable operation and high recognition rate can be used in portable and mobile identification and authentication.

  14. Problems with a False Recognition Paradigm for Developmental Memory Research

    Science.gov (United States)

    Lindauer, Barbara K.; Paris, Scott G.

    1976-01-01

    Developmental changes in memory organization based on synonym and antonym relationships were examined in three experiments. Subjects were 64 second graders and 64 sixth graders. Some inadequacies of a false recognition paradigm for developmental research are identified and some alternative analyses are proposed. (Author/JH)

  15. Re-thinking employee recognition: understanding employee experiences of recognition

    OpenAIRE

    Smith, Charlotte

    2013-01-01

    Despite widespread acceptance of the importance of employee recognition for both individuals and organisations and evidence of its increasing use in organisations, employee recognition has received relatively little focused attention from academic researchers. Particularly lacking is research exploring the lived experience of employee recognition and the interpretations and meanings which individuals give to these experiences. Drawing on qualitative interviews conducted as part of my PhD rese...

  16. Graphical symbol recognition

    OpenAIRE

    K.C. , Santosh; Wendling , Laurent

    2015-01-01

    International audience; The chapter focuses on one of the key issues in document image processing i.e., graphical symbol recognition. Graphical symbol recognition is a sub-field of a larger research domain: pattern recognition. The chapter covers several approaches (i.e., statistical, structural and syntactic) and specially designed symbol recognition techniques inspired by real-world industrial problems. It, in general, contains research problems, state-of-the-art methods that convey basic s...

  17. Radar automatic target recognition (ATR) and non-cooperative target recognition (NCTR)

    CERN Document Server

    Blacknell, David

    2013-01-01

    The ability to detect and locate targets by day or night, over wide areas, regardless of weather conditions has long made radar a key sensor in many military and civil applications. However, the ability to automatically and reliably distinguish different targets represents a difficult challenge. Radar Automatic Target Recognition (ATR) and Non-Cooperative Target Recognition (NCTR) captures material presented in the NATO SET-172 lecture series to provide an overview of the state-of-the-art and continuing challenges of radar target recognition. Topics covered include the problem as applied to th

  18. Students Share Their Research at Student Poster Day | Poster

    Science.gov (United States)

    Students Share Their Research at Student Poster Day  By Ashley DeVine, Staff Writer More than 50 Werner H. Kirsten student interns and college interns presented their research at Summer Student Poster Day on August 6 in the Building 549 lobby.  Joseph Bergman, a high school intern in the Center for Cancer Research Nanobiology Laboratory, participated in the event “for the

  19. Effects of repeated 9 and 30-day exposure to extremely low-frequency electromagnetic fields on social recognition behavior and estrogen receptors expression in olfactory bulb of Wistar female rats.

    Science.gov (United States)

    Bernal-Mondragón, C; Arriaga-Avila, V; Martínez-Abundis, E; Barrera-Mera, B; Mercado-Gómez, O; Guevara-Guzmán, R

    2017-02-01

    We investigated the short- and long-term effects of extremely low-frequency electromagnetic fields (EMF) on social recognition behavior and expression of α- and β-estrogen receptors (ER). Rats were exposed to 60-Hz electromagnetic fields for 9 or 30 days and tested for social recognition behavior. Immunohistochemistry and western blot assays were performed to evaluate α- and β-ER expression in the olfactory bulb of intact, ovariectomized (OVX), and ovariectomized+estradiol (E2) replacement (OVX+E2). Ovariectomization showed impairment of social recognition after 9 days of EMF exposure and a complete recovery after E2 replacement and so did those after 30 days. Short EMF exposure increased expression of β-ER in intact, but not in the others. Longer exposure produced a decrease in intact but an increase in OVX and OVX+E2. Our findings suggest a significant role for β-estrogen receptors and a lack of effect for α-estrogen receptors on a social recognition task. EMF: extremely low frequency electromagnetic fields; ERs: estrogen receptors; OB: olfactory bulb; OVX: ovariectomized; OVX + E 2 : ovariectomized + estradiol replacement; IEI: interexposure interval; β-ER: beta estrogen receptor; E 2 : replacement of estradiol; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; WB: Western blot; PBS: phosphate-buffer saline; PB: phosphate-buffer.

  20. Abstracts from the Fourteenth Rambam Research Day, December 7, 2017

    OpenAIRE

    Shraga Blazer (Editor); Ehud Klein (Editor)

    2018-01-01

    This Supplement of Rambam Maimonides Medical Journal presents the abstracts from the Fourteenth Annual Rambam Research Day. These abstracts represent the newest basic and clinical research coming out of Rambam Health Care Campus—research that is the oxygen for education and development of tomorrow’s generation of physicians. Hence, the research presented on Rambam Research Day is the foundation for understanding patient needs and improving treatment modalities. Bringing research from the benc...

  1. Infant visual attention and object recognition.

    Science.gov (United States)

    Reynolds, Greg D

    2015-05-15

    This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Changes in recognition memory over time: an ERP investigation into vocabulary learning.

    Directory of Open Access Journals (Sweden)

    Shekeila D Palmer

    Full Text Available Although it seems intuitive to assume that recognition memory fades over time when information is not reinforced, some aspects of word learning may benefit from a period of consolidation. In the present study, event-related potentials (ERP were used to examine changes in recognition memory responses to familiar and newly learned (novel words over time. Native English speakers were taught novel words associated with English translations, and subsequently performed a Recognition Memory task in which they made old/new decisions in response to both words (trained word vs. untrained word, and novel words (trained novel word vs. untrained novel word. The Recognition task was performed 45 minutes after training (Day 1 and then repeated the following day (Day 2 with no additional training session in between. For familiar words, the late parietal old/new effect distinguished old from new items on both Day 1 and Day 2, although response to trained items was significantly weaker on Day 2. For novel words, the LPC again distinguished old from new items on both days, but the effect became significantly larger on Day 2. These data suggest that while recognition memory for familiar items may fade over time, recognition of novel items, conscious recollection in particular may benefit from a period of consolidation.

  3. Research perspective: Time-of-day effects on noise annoyance

    Science.gov (United States)

    Fields, J. M.

    1980-01-01

    Some of the complications encountered in researching time-of-day effects on noise annoyance are reported. A conceptual framework for further research is provided. Some of the implications for the research methods that should be used are suggested.

  4. Research and Development of Target Recognition and Location Crawling Platform based on Binocular Vision

    Science.gov (United States)

    Xu, Weidong; Lei, Zhu; Yuan, Zhang; Gao, Zhenqing

    2018-03-01

    The application of visual recognition technology in industrial robot crawling and placing operation is one of the key tasks in the field of robot research. In order to improve the efficiency and intelligence of the material sorting in the production line, especially to realize the sorting of the scattered items, the robot target recognition and positioning crawling platform based on binocular vision is researched and developed. The images were collected by binocular camera, and the images were pretreated. Harris operator was used to identify the corners of the images. The Canny operator was used to identify the images. Hough-chain code recognition was used to identify the images. The target image in the image, obtain the coordinates of each vertex of the image, calculate the spatial position and posture of the target item, and determine the information needed to capture the movement and transmit it to the robot control crawling operation. Finally, In this paper, we use this method to experiment the wrapping problem in the express sorting process The experimental results show that the platform can effectively solve the problem of sorting of loose parts, so as to achieve the purpose of efficient and intelligent sorting.

  5. Statistical Pattern Recognition

    CERN Document Server

    Webb, Andrew R

    2011-01-01

    Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions.  It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields,

  6. Changes in Patient and Nurse Outcomes Associated with Magnet Hospital Recognition

    Science.gov (United States)

    Kutney-Lee, Ann; Stimpfel, Amy Witkoski; Sloane, Douglas M.; Cimiotti, Jeannie P.; Quinn, Lisa W.; Aiken, Linda H.

    2015-01-01

    Background Research has documented an association between Magnet hospitals and better outcomes for nurses and patients. However, little longitudinal evidence exists to support a causal link between Magnet recognition and outcomes. Objective To compare changes over time in surgical patient outcomes, nurse-reported quality, and nurse outcomes in a sample of hospitals that attained Magnet recognition between 1999 and 2007 with hospitals that remained non-Magnet. Research Design Retrospective, two-stage panel design using four secondary data sources. Subjects 136 Pennsylvania hospitals (11 “emerging” Magnets and 125 non-Magnets) Measures American Nurses Credentialing Center Magnet recognition; risk-adjusted rates of surgical 30-day mortality and failure-to-rescue, nurse-reported quality measures, and nurse outcomes; the Practice Environment Scale of the Nursing Work Index Methods Fixed effects difference models were used to compare changes in outcomes between emerging Magnet hospitals and hospitals that remained non-Magnet. Results Emerging Magnet hospitals demonstrated markedly greater improvements in their work environments than other hospitals. On average, the changes in 30-day surgical mortality and failure-to-rescue rates over the study period were more pronounced in emerging Magnet hospitals than in non-Magnet hospitals, by 2.4 fewer deaths per 1000 patients (pMagnet hospitals and non-Magnet hospitals were observed in nurse-reported quality of care and nurse outcomes. Conclusions In general, Magnet recognition is associated with significant improvements over time in the quality of the work environment, and in patient and nurse outcomes that exceed those of non-Magnet hospitals. PMID:25906016

  7. Everyday practice and unnoticed professional competence in day care work

    DEFF Research Database (Denmark)

    Ahrenkiel, Annegrethe; Warring, Niels; Nielsen, Birger Steen

    In Denmark more than 9 out 10 children attend day care centers that are publicly funded and regulated. The main part of employees, the social educators, at day care centers have attended a 3½ years educational programme with both theoretical and practical elements. Nevertheless it has been hard...... for the social educators to get recognition for their professional competencies and the societal importance of their work. Neoliberal governance has imposed a lot of demands for documentation, evaluation etc., and a growing focus on children’s learning in day care centers has resulted in national goals...... hand it can tend to underestimate the value of a large part of their work which is embedded in what in the paper will be explored as unnoticed professional competences. Building on empirical results from research in day care centers based on mixed methods (observations, interviews and action research...

  8. Face Detection and Recognition

    National Research Council Canada - National Science Library

    Jain, Anil K

    2004-01-01

    This report describes research efforts towards developing algorithms for a robust face recognition system to overcome many of the limitations found in existing two-dimensional facial recognition systems...

  9. Sudden Event Recognition: A Survey

    Directory of Open Access Journals (Sweden)

    Mohd Asyraf Zulkifley

    2013-08-01

    Full Text Available Event recognition is one of the most active research areas in video surveillance fields. Advancement in event recognition systems mainly aims to provide convenience, safety and an efficient lifestyle for humanity. A precise, accurate and robust approach is necessary to enable event recognition systems to respond to sudden changes in various uncontrolled environments, such as the case of an emergency, physical threat and a fire or bomb alert. The performance of sudden event recognition systems depends heavily on the accuracy of low level processing, like detection, recognition, tracking and machine learning algorithms. This survey aims to detect and characterize a sudden event, which is a subset of an abnormal event in several video surveillance applications. This paper discusses the following in detail: (1 the importance of a sudden event over a general anomalous event; (2 frameworks used in sudden event recognition; (3 the requirements and comparative studies of a sudden event recognition system and (4 various decision-making approaches for sudden event recognition. The advantages and drawbacks of using 3D images from multiple cameras for real-time application are also discussed. The paper concludes with suggestions for future research directions in sudden event recognition.

  10. PKMzeta maintains 1-day- and 6-day-old long-term object location but not object identity memory in dorsal hippocampus.

    Science.gov (United States)

    Hardt, Oliver; Migues, Paola V; Hastings, Margaret; Wong, Jacinda; Nader, Karim

    2010-06-01

    Continuous activity of the atypical protein kinase C isoform M zeta (PKMzeta) is necessary for maintaining long-term memory acquired in aversively or appetitively motivated associative learning tasks, such as active avoidance, aversive taste conditioning, auditory and contextual fear conditioning, radial arm maze, and watermaze. Whether unreinforced, nonassociative memory will also require PKMzeta for long-term maintenance is not known. Using recognition memory for object location and object identity, we found that inactivating PKMzeta in dorsal hippocampus abolishes 1-day and 6-day-old long-term recognition memory for object location, while recognition memory for object identity was not affected by this treatment. Memory for object location persisted for no more than 35 days after training. These results suggest that the dorsal hippocampus mediates long-term memory for where, but not what things have been encountered, and that PKMzeta maintains this type of spatial knowledge as long as the memory exists.

  11. A recognition method research based on the heart sound texture map

    Directory of Open Access Journals (Sweden)

    Huizhong Cheng

    2016-06-01

    Full Text Available In order to improve the Heart Sound recognition rate and reduce the recognition time, in this paper, we introduces a new method for Heart Sound pattern recognition by using Heart Sound Texture Map. Based on the Heart Sound model, we give the Heart Sound time-frequency diagram and the Heart Sound Texture Map definition, we study the structure of the Heart Sound Window Function principle and realization method, and then discusses how to use the Heart Sound Window Function and the Short-time Fourier Transform to obtain two-dimensional Heart Sound time-frequency diagram, propose corner correlation recognition algorithm based on the Heart Sound Texture Map according to the characteristics of Heart Sound. The simulation results show that the Heart Sound Window Function compared with the traditional window function makes the first (S1 and the second (S2 Heart Sound texture clearer. And the corner correlation recognition algorithm based on the Heart Sound Texture Map can significantly improve the recognition rate and reduce the expense, which is an effective Heart Sound recognition method.

  12. Kernel learning algorithms for face recognition

    CERN Document Server

    Li, Jun-Bao; Pan, Jeng-Shyang

    2013-01-01

    Kernel Learning Algorithms for Face Recognition covers the framework of kernel based face recognition. This book discusses the advanced kernel learning algorithms and its application on face recognition. This book also focuses on the theoretical deviation, the system framework and experiments involving kernel based face recognition. Included within are algorithms of kernel based face recognition, and also the feasibility of the kernel based face recognition method. This book provides researchers in pattern recognition and machine learning area with advanced face recognition methods and its new

  13. World Food Day 2015 | IDRC - International Development Research ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    2016-05-25

    May 25, 2016 ... World Food Day 2015 ... Canadian Lorne Babiuk, a global leader in vaccine research, Killam Prize and Gairdner Wightman Award laureate, ... Sign up now for IDRC news and views sent directly to your inbox each month.

  14. Long-term memory for odors: influences of familiarity and identification across 64 days.

    Science.gov (United States)

    Cornell Kärnekull, Stina; Jönsson, Fredrik U; Willander, Johan; Sikström, Sverker; Larsson, Maria

    2015-05-01

    Few studies have investigated long-term odor recognition memory, although some early observations suggested that the forgetting rate of olfactory representations is slower than for other sensory modalities. This study investigated recognition memory across 64 days for high and low familiar odors and faces. Memory was assessed in 83 young participants at 4 occasions; immediate, 4, 16, and 64 days after encoding. The results indicated significant forgetting for odors and faces across the 64 days. The forgetting functions for the 2 modalities were not fundamentally different. Moreover, high familiar odors and faces were better remembered than low familiar ones, indicating an important role of semantic knowledge on recognition proficiency for both modalities. Although odor recognition was significantly better than chance at the 64 days testing, memory for the low familiar odors was relatively poor. Also, the results indicated that odor identification consistency across sessions, irrespective of accuracy, was positively related to successful recognition. © The Author 2015. Published by Oxford University Press.

  15. [Research of electroencephalography representational emotion recognition based on deep belief networks].

    Science.gov (United States)

    Yang, Hao; Zhang, Junran; Jiang, Xiaomei; Liu, Fei

    2018-04-01

    In recent years, with the rapid development of machine learning techniques,the deep learning algorithm has been widely used in one-dimensional physiological signal processing. In this paper we used electroencephalography (EEG) signals based on deep belief network (DBN) model in open source frameworks of deep learning to identify emotional state (positive, negative and neutrals), then the results of DBN were compared with support vector machine (SVM). The EEG signals were collected from the subjects who were under different emotional stimuli, and DBN and SVM were adopted to identify the EEG signals with changes of different characteristics and different frequency bands. We found that the average accuracy of differential entropy (DE) feature by DBN is 89.12%±6.54%, which has a better performance than previous research based on the same data set. At the same time, the classification effects of DBN are better than the results from traditional SVM (the average classification accuracy of 84.2%±9.24%) and its accuracy and stability have a better trend. In three experiments with different time points, single subject can achieve the consistent results of classification by using DBN (the mean standard deviation is1.44%), and the experimental results show that the system has steady performance and good repeatability. According to our research, the characteristic of DE has a better classification result than other characteristics. Furthermore, the Beta band and the Gamma band in the emotional recognition model have higher classification accuracy. To sum up, the performances of classifiers have a promotion by using the deep learning algorithm, which has a reference for establishing a more accurate system of emotional recognition. Meanwhile, we can trace through the results of recognition to find out the brain regions and frequency band that are related to the emotions, which can help us to understand the emotional mechanism better. This study has a high academic value and

  16. Research of convolutional neural networks for traffic sign recognition

    OpenAIRE

    Stadalnikas, Kasparas

    2017-01-01

    In this thesis the convolutional neural networks application for traffic sign recognition is analyzed. Thesis describes the basic operations, techniques that are commonly used to apply in the image classification using convolutional neural networks. Also, this paper describes the data sets used for traffic sign recognition, their problems affecting the final training results. The paper reviews most popular existing technologies – frameworks for developing the solution for traffic sign recogni...

  17. Nicotine enhances the reconsolidation of novel object recognition memory in rats.

    Science.gov (United States)

    Tian, Shaowen; Pan, Si; You, Yong

    2015-02-01

    There is increasing evidence that nicotine is involved in learning and memory. However, there are only few studies that have evaluated the relationship between nicotine and memory reconsolidation. In this study, we investigated the effects of nicotine on the reconsolidation of novel object recognition memory in rats. Behavior procedure involved four training phases: habituation (Days 1 and 2), sample (Day 3), reactivation (Day 4) and test (Day 6). Rats were injected with saline or nicotine (0.1, 0.2 and 0.4 mg/kg) immediately or 6h after reactivation. The discrimination index was used to assess memory performance and calculated as the difference in time exploring on the novel and familiar objects. Results showed that nicotine administration immediately but not 6 h after reactivation significantly enhanced memory performance of rats. Further results showed that the enhancing effect of nicotine on memory performance was dependent on memory reactivation, and was not attributed to the changes of the nonspecific responses (locomotor activity and anxiety level) 48 h after nicotine administration. The results suggest that post-reactivation nicotine administration enhances the reconsolidation of novel object recognition memory. Our present finding extends previous research on the nicotinic effects on learning and memory. Copyright © 2014 Elsevier Inc. All rights reserved.

  18. Sign language perception research for improving automatic sign language recognition

    NARCIS (Netherlands)

    Ten Holt, G.A.; Arendsen, J.; De Ridder, H.; Van Doorn, A.J.; Reinders, M.J.T.; Hendriks, E.A.

    2009-01-01

    Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of

  19. Markov Models for Handwriting Recognition

    CERN Document Server

    Plotz, Thomas

    2011-01-01

    Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden

  20. Human activity recognition and prediction

    CERN Document Server

    2016-01-01

    This book provides a unique view of human activity recognition, especially fine-grained human activity structure learning, human-interaction recognition, RGB-D data based action recognition, temporal decomposition, and causality learning in unconstrained human activity videos. The techniques discussed give readers tools that provide a significant improvement over existing methodologies of video content understanding by taking advantage of activity recognition. It links multiple popular research fields in computer vision, machine learning, human-centered computing, human-computer interaction, image classification, and pattern recognition. In addition, the book includes several key chapters covering multiple emerging topics in the field. Contributed by top experts and practitioners, the chapters present key topics from different angles and blend both methodology and application, composing a solid overview of the human activity recognition techniques. .

  1. Transfer-Appropriate Processing in Recognition Memory: Perceptual and Conceptual Effects on Recognition Memory Depend on Task Demands

    Science.gov (United States)

    Parks, Colleen M.

    2013-01-01

    Research examining the importance of surface-level information to familiarity in recognition memory tasks is mixed: Sometimes it affects recognition and sometimes it does not. One potential explanation of the inconsistent findings comes from the ideas of dual process theory of recognition and the transfer-appropriate processing framework, which…

  2. Violent video game players and non-players differ on facial emotion recognition.

    Science.gov (United States)

    Diaz, Ruth L; Wong, Ulric; Hodgins, David C; Chiu, Carina G; Goghari, Vina M

    2016-01-01

    Violent video game playing has been associated with both positive and negative effects on cognition. We examined whether playing two or more hours of violent video games a day, compared to not playing video games, was associated with a different pattern of recognition of five facial emotions, while controlling for general perceptual and cognitive differences that might also occur. Undergraduate students were categorized as violent video game players (n = 83) or non-gamers (n = 69) and completed a facial recognition task, consisting of an emotion recognition condition and a control condition of gender recognition. Additionally, participants completed questionnaires assessing their video game and media consumption, aggression, and mood. Violent video game players recognized fearful faces both more accurately and quickly and disgusted faces less accurately than non-gamers. Desensitization to violence, constant exposure to fear and anxiety during game playing, and the habituation to unpleasant stimuli, are possible mechanisms that could explain these results. Future research should evaluate the effects of violent video game playing on emotion processing and social cognition more broadly. © 2015 Wiley Periodicals, Inc.

  3. Automatic modulation recognition of communication signals

    CERN Document Server

    Azzouz, Elsayed Elsayed

    1996-01-01

    Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a cr...

  4. Non Audio-Video gesture recognition system

    DEFF Research Database (Denmark)

    Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis

    2016-01-01

    Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...

  5. Partnering for Success (OIT Customer Day Partner Recognition)

    Energy Technology Data Exchange (ETDEWEB)

    2002-04-01

    Office of Industrial Technologies document produced for 2002 Customer Day event, which features industry partners who have worked with OIT to achieve outstanding energy efficiency achievements from January 2001 to the present.

  6. Online recognition of Chinese characters: the state-of-the-art.

    Science.gov (United States)

    Liu, Cheng-Lin; Jaeger, Stefan; Nakagawa, Masaki

    2004-02-01

    Online handwriting recognition is gaining renewed interest owing to the increase of pen computing applications and new pen input devices. The recognition of Chinese characters is different from western handwriting recognition and poses a special challenge. To provide an overview of the technical status and inspire future research, this paper reviews the advances in online Chinese character recognition (OLCCR), with emphasis on the research works from the 1990s. Compared to the research in the 1980s, the research efforts in the 1990s aimed to further relax the constraints of handwriting, namely, the adherence to standard stroke orders and stroke numbers and the restriction of recognition to isolated characters only. The target of recognition has shifted from regular script to fluent script in order to better meet the requirements of practical applications. The research works are reviewed in terms of pattern representation, character classification, learning/adaptation, and contextual processing. We compare important results and discuss possible directions of future research.

  7. On-device mobile speech recognition

    OpenAIRE

    Mustafa, MK

    2016-01-01

    Despite many years of research, Speech Recognition remains an active area of research in Artificial Intelligence. Currently, the most common commercial application of this technology on mobile devices uses a wireless client – server approach to meet the computational and memory demands of the speech recognition process. Unfortunately, such an approach is unlikely to remain viable when fully applied over the approximately 7.22 Billion mobile phones currently in circulation. In this thesis we p...

  8. Familiar Person Recognition: Is Autonoetic Consciousness More Likely to Accompany Face Recognition Than Voice Recognition?

    Science.gov (United States)

    Barsics, Catherine; Brédart, Serge

    2010-11-01

    Autonoetic consciousness is a fundamental property of human memory, enabling us to experience mental time travel, to recollect past events with a feeling of self-involvement, and to project ourselves in the future. Autonoetic consciousness is a characteristic of episodic memory. By contrast, awareness of the past associated with a mere feeling of familiarity or knowing relies on noetic consciousness, depending on semantic memory integrity. Present research was aimed at evaluating whether conscious recollection of episodic memories is more likely to occur following the recognition of a familiar face than following the recognition of a familiar voice. Recall of semantic information (biographical information) was also assessed. Previous studies that investigated the recall of biographical information following person recognition used faces and voices of famous people as stimuli. In this study, the participants were presented with personally familiar people's voices and faces, thus avoiding the presence of identity cues in the spoken extracts and allowing a stricter control of frequency exposure with both types of stimuli (voices and faces). In the present study, the rate of retrieved episodic memories, associated with autonoetic awareness, was significantly higher from familiar faces than familiar voices even though the level of overall recognition was similar for both these stimuli domains. The same pattern was observed regarding semantic information retrieval. These results and their implications for current Interactive Activation and Competition person recognition models are discussed.

  9. [Face recognition in patients with autism spectrum disorders].

    Science.gov (United States)

    Kita, Yosuke; Inagaki, Masumi

    2012-07-01

    The present study aimed to review previous research conducted on face recognition in patients with autism spectrum disorders (ASD). Face recognition is a key question in the ASD research field because it can provide clues for elucidating the neural substrates responsible for the social impairment of these patients. Historically, behavioral studies have reported low performance and/or unique strategies of face recognition among ASD patients. However, the performance and strategy of ASD patients is comparable to those of the control group, depending on the experimental situation or developmental stage, suggesting that face recognition of ASD patients is not entirely impaired. Recent brain function studies, including event-related potential and functional magnetic resonance imaging studies, have investigated the cognitive process of face recognition in ASD patients, and revealed impaired function in the brain's neural network comprising the fusiform gyrus and amygdala. This impaired function is potentially involved in the diminished preference for faces, and in the atypical development of face recognition, eliciting symptoms of unstable behavioral characteristics in these patients. Additionally, face recognition in ASD patients is examined from a different perspective, namely self-face recognition, and facial emotion recognition. While the former topic is intimately linked to basic social abilities such as self-other discrimination, the latter is closely associated with mentalizing. Further research on face recognition in ASD patients should investigate the connection between behavioral and neurological specifics in these patients, by considering developmental changes and the spectrum clinical condition of ASD.

  10. NCR-days 2004; research for managing rivers: present and future issues

    NARCIS (Netherlands)

    Makaske, B.; Os, van A.G.

    2005-01-01

    These proceedings are the product of the NCR days 2004, held 46 November 2004 in Wageningen.The NCR days are a yearly conference at which mainly young scientists present their ongoing research on a wide variety of fluvial subjects. The 46 contributions (oral presentations and posters) to the

  11. Research on Interaction-oriented Gesture Recognition

    Directory of Open Access Journals (Sweden)

    Lu Huang

    2014-01-01

    Full Text Available This thesis designs a series of gesture interaction with the features of the natural human-machine interaction; besides, it utilizes the 3D acceleration sensors as interactive input. Afterwards, it builds the Discrete Hidden Markov Model to make gesture recognition by introducing the collection proposal of gesture interaction based on the acceleration sensors and pre-handling the gesture acceleration signal obtained in the collection. In the end, the thesis proofs the design proposal workable and effective according to the experiments.

  12. IDRC Bulletin — International Women's Day 2018 | IDRC ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    In this issue we celebrate women worldwide. Women wearing traditional Darfuri outfits participate at the parade. International Women's Day 2018. Empowering women. In recognition of International Women's Day, we invite you to celebrate women's accomplishments and to learn more about the challenges they face ...

  13. L2 Word Recognition: Influence of L1 Orthography on Multi-Syllabic Word Recognition

    Science.gov (United States)

    Hamada, Megumi

    2017-01-01

    L2 reading research suggests that L1 orthographic experience influences L2 word recognition. Nevertheless, the findings on multi-syllabic words in English are still limited despite the fact that a vast majority of words are multi-syllabic. The study investigated whether L1 orthography influences the recognition of multi-syllabic words, focusing on…

  14. Image preprocessing study on KPCA-based face recognition

    Science.gov (United States)

    Li, Xuan; Li, Dehua

    2015-12-01

    Face recognition as an important biometric identification method, with its friendly, natural, convenient advantages, has obtained more and more attention. This paper intends to research a face recognition system including face detection, feature extraction and face recognition, mainly through researching on related theory and the key technology of various preprocessing methods in face detection process, using KPCA method, focuses on the different recognition results in different preprocessing methods. In this paper, we choose YCbCr color space for skin segmentation and choose integral projection for face location. We use erosion and dilation of the opening and closing operation and illumination compensation method to preprocess face images, and then use the face recognition method based on kernel principal component analysis method for analysis and research, and the experiments were carried out using the typical face database. The algorithms experiment on MATLAB platform. Experimental results show that integration of the kernel method based on PCA algorithm under certain conditions make the extracted features represent the original image information better for using nonlinear feature extraction method, which can obtain higher recognition rate. In the image preprocessing stage, we found that images under various operations may appear different results, so as to obtain different recognition rate in recognition stage. At the same time, in the process of the kernel principal component analysis, the value of the power of the polynomial function can affect the recognition result.

  15. Features fusion based approach for handwritten Gujarati character recognition

    Directory of Open Access Journals (Sweden)

    Ankit Sharma

    2017-02-01

    Full Text Available Handwritten character recognition is a challenging area of research. Lots of research activities in the area of character recognition are already done for Indian languages such as Hindi, Bangla, Kannada, Tamil and Telugu. Literature review on handwritten character recognition indicates that in comparison with other Indian scripts research activities on Gujarati handwritten character recognition are very less.  This paper aims to bring Gujarati character recognition in attention. Recognition of isolated Gujarati handwritten characters is proposed using three different kinds of features and their fusion. Chain code based, zone based and projection profiles based features are utilized as individual features. One of the significant contribution of proposed work is towards the generation of large and representative dataset of 88,000 handwritten Gujarati characters. Experiments are carried out on this developed dataset. Artificial Neural Network (ANN, Support Vector Machine (SVM and Naive Bayes (NB classifier based methods are implemented for handwritten Gujarati character recognition. Experimental results show substantial enhancement over state-of-the-art and authenticate our proposals.

  16. Robustness-related issues in speaker recognition

    CERN Document Server

    Zheng, Thomas Fang

    2017-01-01

    This book presents an overview of speaker recognition technologies with an emphasis on dealing with robustness issues. Firstly, the book gives an overview of speaker recognition, such as the basic system framework, categories under different criteria, performance evaluation and its development history. Secondly, with regard to robustness issues, the book presents three categories, including environment-related issues, speaker-related issues and application-oriented issues. For each category, the book describes the current hot topics, existing technologies, and potential research focuses in the future. The book is a useful reference book and self-learning guide for early researchers working in the field of robust speech recognition.

  17. Development of a System for Automatic Recognition of Speech

    Directory of Open Access Journals (Sweden)

    Roman Jarina

    2003-01-01

    Full Text Available The article gives a review of a research on processing and automatic recognition of speech signals (ARR at the Department of Telecommunications of the Faculty of Electrical Engineering, University of iilina. On-going research is oriented to speech parametrization using 2-dimensional cepstral analysis, and to an application of HMMs and neural networks for speech recognition in Slovak language. The article summarizes achieved results and outlines future orientation of our research in automatic speech recognition.

  18. Pedestrian recognition using automotive radar sensors

    OpenAIRE

    A. Bartsch; F. Fitzek; R. H. Rasshofer

    2012-01-01

    The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insight...

  19. A New Database for Speaker Recognition

    DEFF Research Database (Denmark)

    Feng, Ling; Hansen, Lars Kai

    2005-01-01

    In this paper we discuss properties of speech databases used for speaker recognition research and evaluation, and we characterize some popular standard databases. The paper presents a new database called ELSDSR dedicated to speaker recognition applications. The main characteristics of this database...

  20. The recognition heuristic: a review of theory and tests.

    Science.gov (United States)

    Pachur, Thorsten; Todd, Peter M; Gigerenzer, Gerd; Schooler, Lael J; Goldstein, Daniel G

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect - the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference).

  1. The Recognition Heuristic: A Review of Theory and Tests

    Directory of Open Access Journals (Sweden)

    Thorsten ePachur

    2011-07-01

    Full Text Available The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a that recognition is often an ecologically valid cue; (b that people often follow recognition when making inferences; (c that recognition supersedes further cue knowledge; (d that its use can produce the less-is-more effect—the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference.

  2. The Recognition Heuristic: A Review of Theory and Tests

    Science.gov (United States)

    Pachur, Thorsten; Todd, Peter M.; Gigerenzer, Gerd; Schooler, Lael J.; Goldstein, Daniel G.

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) that recognition supersedes further cue knowledge; (d) that its use can produce the less-is-more effect – the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference). PMID:21779266

  3. Type-2 fuzzy graphical models for pattern recognition

    CERN Document Server

    Zeng, Jia

    2015-01-01

    This book discusses how to combine type-2 fuzzy sets and graphical models to solve a range of real-world pattern recognition problems such as speech recognition, handwritten Chinese character recognition, topic modeling as well as human action recognition. It covers these recent developments while also providing a comprehensive introduction to the fields of type-2 fuzzy sets and graphical models. Though primarily intended for graduate students, researchers and practitioners in fuzzy logic and pattern recognition, the book can also serve as a valuable reference work for researchers without any previous knowledge of these fields. Dr. Jia Zeng is a Professor at the School of Computer Science and Technology, Soochow University, China. Dr. Zhi-Qiang Liu is a Professor at the School of Creative Media, City University of Hong Kong, China.

  4. 8th International Conference on Computer Recognition Systems

    CERN Document Server

    Jackowski, Konrad; Kurzynski, Marek; Wozniak, Michał; Zolnierek, Andrzej

    2013-01-01

    The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 86 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Biometrics Data Stream Classification and Big Data Analytics  Features, learning, and classifiers Image processing and computer vision Medical applications Miscellaneous applications Pattern recognition and image processing in robotics  Speech and word recognition This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.

  5. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  6. Acoustic modeling for emotion recognition

    CERN Document Server

    Anne, Koteswara Rao; Vankayalapati, Hima Deepthi

    2015-01-01

     This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.

  7. Data structures, computer graphics, and pattern recognition

    CERN Document Server

    Klinger, A; Kunii, T L

    1977-01-01

    Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr

  8. The beauty of simple models: Themes in recognition heuristic research

    Directory of Open Access Journals (Sweden)

    Daniel G. Goldstein

    2011-07-01

    Full Text Available The advantage of models that do not use flexible parameters is that one can precisely show to what degree they predict behavior, and in what situations. In three issues of this journal, the recognition heuristic has been examined carefully from many points of view. We comment here on four themes, the use of optimization models to understand the rationality of heuristics, the generalization of the recognition input beyond a binary judgment, new conditions for less-is-more effects, and the importance of specifying boundary conditions for cognitive heuristics.

  9. Hypothetical Pattern Recognition Design Using Multi-Layer Perceptorn Neural Network For Supervised Learning

    Directory of Open Access Journals (Sweden)

    Md. Abdullah-al-mamun

    2015-08-01

    Full Text Available Abstract Humans are capable to identifying diverse shape in the different pattern in the real world as effortless fashion due to their intelligence is grow since born with facing several learning process. Same way we can prepared an machine using human like brain called Artificial Neural Network that can be recognize different pattern from the real world object. Although the various techniques is exists to implementation the pattern recognition but recently the artificial neural network approaches have been giving the significant attention. Because the approached of artificial neural network is like a human brain that is learn from different observation and give a decision the previously learning rule. Over the 50 years research now a days pattern recognition for machine learning using artificial neural network got a significant achievement. For this reason many real world problem can be solve by modeling the pattern recognition process. The objective of this paper is to present the theoretical concept for pattern recognition design using Multi-Layer Perceptorn neural networkin the algorithm of artificial Intelligence as the best possible way of utilizing available resources to make a decision that can be a human like performance.

  10. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Directory of Open Access Journals (Sweden)

    Muhammad Hameed Siddiqi

    2013-12-01

    Full Text Available 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.

  11. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems

    Science.gov (United States)

    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

  12. Research on recognition of ramp angle based on transducer

    Directory of Open Access Journals (Sweden)

    Wenhao GU

    2015-12-01

    Full Text Available Focusing on the recognition of ramp angle, the relationship between the signal of vehicle transducer and real ramp angle is studied. The force change of vehicle on the ramp, and the relationship between the body tilt angle and front and rear suspension scale is discussed. According to the suspension and tire deformation, error angle of the ramp angle is deduced. A mathematical model is established with Matlab/Simulink and used for simulation to generate error curve of ramp angle. The results show that the error angle increases with the increasing of the ramp angle, and the limit value can reach 6.5%, while the identification method can effectively eliminate this error, and enhance the accuracy of ramp angle recognition.

  13. A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

    Directory of Open Access Journals (Sweden)

    Chenchen Huang

    2014-01-01

    Full Text Available Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

  14. Face Recognition and Tracking in Videos

    Directory of Open Access Journals (Sweden)

    Swapnil Vitthal Tathe

    2017-07-01

    Full Text Available Advancement in computer vision technology and availability of video capturing devices such as surveillance cameras has evoked new video processing applications. The research in video face recognition is mostly biased towards law enforcement applications. Applications involves human recognition based on face and iris, human computer interaction, behavior analysis, video surveillance etc. This paper presents face tracking framework that is capable of face detection using Haar features, recognition using Gabor feature extraction, matching using correlation score and tracking using Kalman filter. The method has good recognition rate for real-life videos and robust performance to changes due to illumination, environmental factors, scale, pose and orientations.

  15. Misattribution, false recognition and the sins of memory.

    Science.gov (United States)

    Schacter, D L; Dodson, C S

    2001-09-29

    Memory is sometimes a troublemaker. Schacter has classified memory's transgressions into seven fundamental 'sins': transience, absent-mindedness, blocking, misattribution, suggestibility, bias and persistence. This paper focuses on one memory sin, misattribution, that is implicated in false or illusory recognition of episodes that never occurred. We present data from cognitive, neuropsychological and neuroimaging studies that illuminate aspects of misattribution and false recognition. We first discuss cognitive research examining possible mechanisms of misattribution associated with false recognition. We also consider ways in which false recognition can be reduced or avoided, focusing in particular on the role of distinctive information. We next turn to neuropsychological research concerning patients with amnesia and Alzheimer's disease that reveals conditions under which such patients are less susceptible to false recognition than are healthy controls, thus providing clues about the brain mechanisms that drive false recognition. We then consider neuroimaging studies concerned with the neural correlates of true and false recognition, examining when the two forms of recognition can and cannot be distinguished on the basis of brain activity. Finally, we argue that even though misattribution and other memory sins are annoying and even dangerous, they can also be viewed as by-products of adaptive features of memory.

  16. Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research

    Directory of Open Access Journals (Sweden)

    Laslo Dinges

    2016-03-01

    Full Text Available Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers—that we proposed earlier—improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

  17. Synthesis of Common Arabic Handwritings to Aid Optical Character Recognition Research.

    Science.gov (United States)

    Dinges, Laslo; Al-Hamadi, Ayoub; Elzobi, Moftah; El-Etriby, Sherif

    2016-03-11

    Document analysis tasks such as pattern recognition, word spotting or segmentation, require comprehensive databases for training and validation. Not only variations in writing style but also the used list of words is of importance in the case that training samples should reflect the input of a specific area of application. However, generation of training samples is expensive in the sense of manpower and time, particularly if complete text pages including complex ground truth are required. This is why there is a lack of such databases, especially for Arabic, the second most popular language. However, Arabic handwriting recognition involves different preprocessing, segmentation and recognition methods. Each requires particular ground truth or samples to enable optimal training and validation, which are often not covered by the currently available databases. To overcome this issue, we propose a system that synthesizes Arabic handwritten words and text pages and generates corresponding detailed ground truth. We use these syntheses to validate a new, segmentation based system that recognizes handwritten Arabic words. We found that a modification of an Active Shape Model based character classifiers-that we proposed earlier-improves the word recognition accuracy. Further improvements are achieved, by using a vocabulary of the 50,000 most common Arabic words for error correction.

  18. Gender Recognition from Unconstrained and Articulated Human Body

    OpenAIRE

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, ho...

  19. Evidence for view-invariant face recognition units in unfamiliar face learning.

    Science.gov (United States)

    Etchells, David B; Brooks, Joseph L; Johnston, Robert A

    2017-05-01

    Many models of face recognition incorporate the idea of a face recognition unit (FRU), an abstracted representation formed from each experience of a face which aids recognition under novel viewing conditions. Some previous studies have failed to find evidence of this FRU representation. Here, we report three experiments which investigated this theoretical construct by modifying the face learning procedure from that in previous work. During learning, one or two views of previously unfamiliar faces were shown to participants in a serial matching task. Later, participants attempted to recognize both seen and novel views of the learned faces (recognition phase). Experiment 1 tested participants' recognition of a novel view, a day after learning. Experiment 2 was identical, but tested participants on the same day as learning. Experiment 3 repeated Experiment 1, but tested participants on a novel view that was outside the rotation of those views learned. Results revealed a significant advantage, across all experiments, for recognizing a novel view when two views had been learned compared to single view learning. The observed view invariance supports the notion that an FRU representation is established during multi-view face learning under particular learning conditions.

  20. A Modified Back propagation Algorithm for Optical Character Recognition

    OpenAIRE

    Jitendra Shrivastav; Prof. Ravindra Kumar Gupta; Dr. Shailendra Singh

    2013-01-01

    Character Recognition (CR) has been an active area of research and due to its diverse applicable environment; it continues to be a challenging research topic. There is a clear need for optical character recognition in order to provide a fast and accurate method to search both existing images as well as large archives of existing paper documents. However, existing optical character recognition programs suffer from a flawed tradeoff between speed and accuracy, making it less attractive for larg...

  1. A Survey of Online Activity Recognition Using Mobile Phones

    Directory of Open Access Journals (Sweden)

    Muhammad Shoaib

    2015-01-01

    Full Text Available Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research.

  2. A Survey of Online Activity Recognition Using Mobile Phones

    Science.gov (United States)

    Shoaib, Muhammad; Bosch, Stephan; Incel, Ozlem Durmaz; Scholten, Hans; Havinga, Paul J.M.

    2015-01-01

    Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this purpose, since these ubiquitous devices are equipped with various sensors, ranging from accelerometers to magnetic field sensors. In most of the current studies, sensor data collected for activity recognition are analyzed offline using machine learning tools. However, there is now a trend towards implementing activity recognition systems on these devices in an online manner, since modern mobile phones have become more powerful in terms of available resources, such as CPU, memory and battery. The research on offline activity recognition has been reviewed in several earlier studies in detail. However, work done on online activity recognition is still in its infancy and is yet to be reviewed. In this paper, we review the studies done so far that implement activity recognition systems on mobile phones and use only their on-board sensors. We discuss various aspects of these studies. Moreover, we discuss their limitations and present various recommendations for future research. PMID:25608213

  3. Acoustic Pattern Recognition on Android Devices

    DEFF Research Database (Denmark)

    Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild

    2013-01-01

    an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....

  4. The recognition heuristic : A review of theory and tests

    OpenAIRE

    Pachur, T.; Todd, P.; Gigerenzer, G.; Schooler, L.; Goldstein, D.

    2011-01-01

    The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a) that recognition is often an ecologically valid cue; (b) that people often follow recognition when making inferences; (c) th...

  5. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System.

    Science.gov (United States)

    Partila, Pavol; Voznak, Miroslav; Tovarek, Jaromir

    2015-01-01

    The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

  6. Impaired Word and Face Recognition in Older Adults with Type 2 Diabetes.

    Science.gov (United States)

    Jones, Nicola; Riby, Leigh M; Smith, Michael A

    2016-07-01

    Older adults with type 2 diabetes mellitus (DM2) exhibit accelerated decline in some domains of cognition including verbal episodic memory. Few studies have investigated the influence of DM2 status in older adults on recognition memory for more complex stimuli such as faces. In the present study we sought to compare recognition memory performance for words, objects and faces under conditions of relatively low and high cognitive load. Healthy older adults with good glucoregulatory control (n = 13) and older adults with DM2 (n = 24) were administered recognition memory tasks in which stimuli (faces, objects and words) were presented under conditions of either i) low (stimulus presented without a background pattern) or ii) high (stimulus presented against a background pattern) cognitive load. In a subsequent recognition phase, the DM2 group recognized fewer faces than healthy controls. Further, the DM2 group exhibited word recognition deficits in the low cognitive load condition. The recognition memory impairment observed in patients with DM2 has clear implications for day-to-day functioning. Although these deficits were not amplified under conditions of increased cognitive load, the present study emphasizes that recognition memory impairment for both words and more complex stimuli such as face are a feature of DM2 in older adults. Copyright © 2016 IMSS. Published by Elsevier Inc. All rights reserved.

  7. Three-dimensional fingerprint recognition by using convolution neural network

    Science.gov (United States)

    Tian, Qianyu; Gao, Nan; Zhang, Zonghua

    2018-01-01

    With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

  8. Touchless palmprint recognition systems

    CERN Document Server

    Genovese, Angelo; Scotti, Fabio

    2014-01-01

    This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems.It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Const

  9. Song Recognition without Identification: When People Cannot "Name that Tune" but Can Recognize It as Familiar

    Science.gov (United States)

    Kostic, Bogdan; Cleary, Anne M.

    2009-01-01

    Recognition without identification (RWI) is a common day-to-day experience (as when recognizing a face or a tune as familiar without being able to identify the person or the song). It is also a well-established laboratory-based empirical phenomenon: When identification of recognition test items is prevented, participants can discriminate between…

  10. Gait recognition based on integral outline

    Science.gov (United States)

    Ming, Guan; Fang, Lv

    2017-02-01

    Biometric identification technology replaces traditional security technology, which has become a trend, and gait recognition also has become a hot spot of research because its feature is difficult to imitate and theft. This paper presents a gait recognition system based on integral outline of human body. The system has three important aspects: the preprocessing of gait image, feature extraction and classification. Finally, using a method of polling to evaluate the performance of the system, and summarizing the problems existing in the gait recognition and the direction of development in the future.

  11. Pattern recognition and classification an introduction

    CERN Document Server

    Dougherty, Geoff

    2012-01-01

    The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi

  12. Setting a world record in 3D face recognition

    NARCIS (Netherlands)

    Spreeuwers, Lieuwe Jan

    2015-01-01

    Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition,

  13. The Four Day School Week. Research Brief

    Science.gov (United States)

    Muir, Mike

    2013-01-01

    Can four-day school weeks help districts save money? How do districts overcome the barriers of moving to a four-day week? What is the effect of a four-day week on students, staff and the community? This paper enumerates the benefits for students and teachers of four-day school weeks. Recommendations for implementation of a four-day week are also…

  14. Object recognition memory: neurobiological mechanisms of encoding, consolidation and retrieval.

    Science.gov (United States)

    Winters, Boyer D; Saksida, Lisa M; Bussey, Timothy J

    2008-07-01

    Tests of object recognition memory, or the judgment of the prior occurrence of an object, have made substantial contributions to our understanding of the nature and neurobiological underpinnings of mammalian memory. Only in recent years, however, have researchers begun to elucidate the specific brain areas and neural processes involved in object recognition memory. The present review considers some of this recent research, with an emphasis on studies addressing the neural bases of perirhinal cortex-dependent object recognition memory processes. We first briefly discuss operational definitions of object recognition and the common behavioural tests used to measure it in non-human primates and rodents. We then consider research from the non-human primate and rat literature examining the anatomical basis of object recognition memory in the delayed nonmatching-to-sample (DNMS) and spontaneous object recognition (SOR) tasks, respectively. The results of these studies overwhelmingly favor the view that perirhinal cortex (PRh) is a critical region for object recognition memory. We then discuss the involvement of PRh in the different stages--encoding, consolidation, and retrieval--of object recognition memory. Specifically, recent work in rats has indicated that neural activity in PRh contributes to object memory encoding, consolidation, and retrieval processes. Finally, we consider the pharmacological, cellular, and molecular factors that might play a part in PRh-mediated object recognition memory. Recent studies in rodents have begun to indicate the remarkable complexity of the neural substrates underlying this seemingly simple aspect of declarative memory.

  15. Designing of Medium-Size Humanoid Robot with Face Recognition Features

    Directory of Open Access Journals (Sweden)

    Christian Tarunajaya

    2016-02-01

    Full Text Available owadays, there have been so many development of robot that can receive command and do speech recognition and face recognition. In this research, we develop a humanoid robot system with a controller that based on Raspberry Pi 2. The methods we used are based on Audio recognition and detection, and also face recognition using PCA (Principal Component Analysis with OpenCV and Python. PCA is one of the algorithms to do face detection by doing reduction to the number of dimension of the image possessed. The result of this reduction process is then known as eigenface to do face recognition process. In this research, we still find a false recognition. It can be caused by many things, like database condition, maybe the images are too dark or less varied, blur test image, etc. The accuracy from 3 tests on different people is about 93% (28 correct recognitions out of 30.

  16. 8 CFR 1292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

    Science.gov (United States)

    2010-01-01

    ...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 1292.2...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization...

  17. Threshold models of recognition and the recognition heuristic

    Directory of Open Access Journals (Sweden)

    Edgar Erdfelder

    2011-02-01

    Full Text Available According to the recognition heuristic (RH theory, decisions follow the recognition principle: Given a high validity of the recognition cue, people should prefer recognized choice options compared to unrecognized ones. Assuming that the memory strength of choice options is strongly correlated with both the choice criterion and recognition judgments, the RH is a reasonable strategy that approximates optimal decisions with a minimum of cognitive effort (Davis-Stober, Dana, and Budescu, 2010. However, theories of recognition memory are not generally compatible with this assumption. For example, some threshold models of recognition presume that recognition judgments can arise from two types of cognitive states: (1 certainty states in which judgments are almost perfectly correlated with memory strength and (2 uncertainty states in which recognition judgments reflect guessing rather than differences in memory strength. We report an experiment designed to test the prediction that the RH applies to certainty states only. Our results show that memory states rather than recognition judgments affect use of recognition information in binary decisions.

  18. Editorial Research recognition

    Directory of Open Access Journals (Sweden)

    Gabriel Jacobs

    1997-12-01

    Full Text Available Only partly tongue in cheek, I suggested in ALT-N (number 18, July 1997 that we should consider mounting a Campaign for the Acknowledgement of Research into Educational Technology (CARET. I was astonished by the large number of responses in my mailbox, not one of them dissenting from the views I expressed, and many offering examples of how excellent peer-reviewed publications in good journals, sometimes associated with very respectable research grants, had vanished into the ether when it came to the last Research Assessment Exercise (RAE. Outside education as a discipline (and even there . . . , RAE subject panels appear to consider that research into learning technology is not really worth counting. University teachers of languages, history, biology, engineering and so on may produce seminal papers on learning technology within their subject-areas, and for the purposes of their department's RAE rating they might as well not have bothered - indeed, they may even be reprimanded for not having aimed more directly between the RAE goalposts. Even within disciplines such as computer science or psychology, where one might imagine that much research into educational technology would comfortably fit, I know of colleagues who have been on the receiving end of such discouragement.

  19. Syllable Transposition Effects in Korean Word Recognition

    Science.gov (United States)

    Lee, Chang H.; Kwon, Youan; Kim, Kyungil; Rastle, Kathleen

    2015-01-01

    Research on the impact of letter transpositions in visual word recognition has yielded important clues about the nature of orthographic representations. This study investigated the impact of syllable transpositions on the recognition of Korean multisyllabic words. Results showed that rejection latencies in visual lexical decision for…

  20. Kazakh Traditional Dance Gesture Recognition

    Science.gov (United States)

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

    2014-04-01

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

  1. Evaluation of the research diagnostic criteria for temporomandibular disorders for the recognition of an anterior disc displacement with reduction

    NARCIS (Netherlands)

    Naeije, M.; Kalaykova, S.; Visscher, C.M.; Lobbezoo, F.

    2009-01-01

    The aim of this Focus Article is to review critically the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) for the recognition of an anterior disc displacement with reduction (ADDR) in the temporomandibular joint (TMJ). This evaluation is based upon the experience gained

  2. Pattern Recognition Methods and Features Selection for Speech Emotion Recognition System

    Directory of Open Access Journals (Sweden)

    Pavol Partila

    2015-01-01

    Full Text Available The impact of the classification method and features selection for the speech emotion recognition accuracy is discussed in this paper. Selecting the correct parameters in combination with the classifier is an important part of reducing the complexity of system computing. This step is necessary especially for systems that will be deployed in real-time applications. The reason for the development and improvement of speech emotion recognition systems is wide usability in nowadays automatic voice controlled systems. Berlin database of emotional recordings was used in this experiment. Classification accuracy of artificial neural networks, k-nearest neighbours, and Gaussian mixture model is measured considering the selection of prosodic, spectral, and voice quality features. The purpose was to find an optimal combination of methods and group of features for stress detection in human speech. The research contribution lies in the design of the speech emotion recognition system due to its accuracy and efficiency.

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

    Directory of Open Access Journals (Sweden)

    Joanne M Chung

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

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

    Science.gov (United States)

    Chung, Joanne M; Robins, Richard W

    2015-01-01

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

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

    Science.gov (United States)

    Chung, Joanne M.; Robins, Richard W.

    2015-01-01

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

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

    Science.gov (United States)

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

    2014-09-01

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

  7. Indoor navigation by image recognition

    Science.gov (United States)

    Choi, Io Teng; Leong, Chi Chong; Hong, Ka Wo; Pun, Chi-Man

    2017-07-01

    With the progress of smartphones hardware, it is simple on smartphone using image recognition technique such as face detection. In addition, indoor navigation system development is much slower than outdoor navigation system. Hence, this research proves a usage of image recognition technique for navigation in indoor environment. In this paper, we introduced an indoor navigation application that uses the indoor environment features to locate user's location and a route calculating algorithm to generate an appropriate path for user. The application is implemented on Android smartphone rather than iPhone. Yet, the application design can also be applied on iOS because the design is implemented without using special features only for Android. We found that digital navigation system provides better and clearer location information than paper map. Also, the indoor environment is ideal for Image recognition processing. Hence, the results motivate us to design an indoor navigation system using image recognition.

  8. Recognition memory probes affect what is remembered in schizophrenia.

    Science.gov (United States)

    Schwartz, Barbara L; Parker, Elizabeth S; Rosse, Richard B; Deutsch, Stephen I

    2009-05-15

    Cognitive psychology offers tools to localize the memory processes most vulnerable to disruption in schizophrenia and to identify how patients with schizophrenia best remember. In this research, we used the University of Southern California Repeatable Episodic Memory Test (USC-REMT; Parker, E.S., Landau, S.M., Whipple, S.C., Schwartz, B.L., 2004. Aging, recall, and recognition: A study on the sensitivity of the University of Southern California Repeatable Episodic Memory Test (USC-REMT). Journal of Clinical and Experimental Neuropsychology 26(3), 428-440.) to examine how two different recognition memory probes affect memory performance in patients with schizophrenia and matched controls. Patients with schizophrenia studied equivalent word lists and were tested by yes-no recognition and forced-choice recognition following identical encoding and storage conditions. Compared with controls, patients with schizophrenia were particularly impaired when tested by yes-no recognition relative to forced-choice recognition. Patients had greatest deficits on hits in yes-no recognition but did not exhibit elevated false alarms. The data point to the importance of retrieval processes in schizophrenia, and highlight the need for further research on ways to help patients with schizophrenia access what they have learned.

  9. A multimodal approach to emotion recognition ability in autism spectrum disorders

    NARCIS (Netherlands)

    Jones, C.R.G.; Pickles, A.; Falcaro, M.; Marsden, A.J.S.; Happé, F.; Scott, S.K.; Sauter, D.; Tregay, J.; Phillips, R.J.; Baird, G.; Simonoff, E.; Charman, T.

    2011-01-01

    Background:  Autism spectrum disorders (ASD) are characterised by social and communication difficulties in day-to-day life, including problems in recognising emotions. However, experimental investigations of emotion recognition ability in ASD have been equivocal, hampered by small sample sizes,

  10. Object recognition in images by human vision and computer vision

    NARCIS (Netherlands)

    Chen, Q.; Dijkstra, J.; Vries, de B.

    2010-01-01

    Object recognition plays a major role in human behaviour research in the built environment. Computer based object recognition techniques using images as input are challenging, but not an adequate representation of human vision. This paper reports on the differences in object shape recognition

  11. Computational intelligence in multi-feature visual pattern recognition hand posture and face recognition using biologically inspired approaches

    CERN Document Server

    Pisharady, Pramod Kumar; Poh, Loh Ai

    2014-01-01

    This book presents a collection of computational intelligence algorithms that addresses issues in visual pattern recognition such as high computational complexity, abundance of pattern features, sensitivity to size and shape variations and poor performance against complex backgrounds. The book has 3 parts. Part 1 describes various research issues in the field with a survey of the related literature. Part 2 presents computational intelligence based algorithms for feature selection and classification. The algorithms are discriminative and fast. The main application area considered is hand posture recognition. The book also discusses utility of these algorithms in other visual as well as non-visual pattern recognition tasks including face recognition, general object recognition and cancer / tumor classification. Part 3 presents biologically inspired algorithms for feature extraction. The visual cortex model based features discussed have invariance with respect to appearance and size of the hand, and provide good...

  12. Mirror self-recognition: a review and critique of attempts to promote and engineer self-recognition in primates.

    Science.gov (United States)

    Anderson, James R; Gallup, Gordon G

    2015-10-01

    We review research on reactions to mirrors and self-recognition in nonhuman primates, focusing on methodological issues. Starting with the initial demonstration in chimpanzees in 1970 and subsequent attempts to extend this to other species, self-recognition in great apes is discussed with emphasis on spontaneous manifestations of mirror-guided self-exploration as well as spontaneous use of the mirror to investigate foreign marks on otherwise nonvisible body parts-the mark test. Attempts to show self-recognition in other primates are examined with particular reference to the lack of convincing examples of spontaneous mirror-guided self-exploration, and efforts to engineer positive mark test responses by modifying the test or using conditioning techniques. Despite intensive efforts to demonstrate self-recognition in other primates, we conclude that to date there is no compelling evidence that prosimians, monkeys, or lesser apes-gibbons and siamangs-are capable of mirror self-recognition.

  13. Defining a Roadmap Towards Comparative Research in Online Activity Recognition on Mobile Phones

    NARCIS (Netherlands)

    Shoaib, M.; Bosch, S.; Durmaz, O.; Scholten, Johan; Havinga, Paul J.M.

    2015-01-01

    Many context-aware applications based on activity recognition are currently using mobile phones. Most of this work is done in an offline way. However, there is a shift towards an online approach in recent studies, where activity recognition systems are implemented on mobile phones. Unfortunately,

  14. Nueva investigacion sobre kindergarten de dia completo (Recent Research on All-Day Kindergarten). ERIC Digest.

    Science.gov (United States)

    Clark, Patricia

    Noting that much of the early research on the effects of all-day kindergarten had serious problems with internal and external validity due to inadequate methodological standards, this Spanish-language digest reviews research conducted in the 1990s. The digest discusses the academic, social, and behavioral effects of all-day kindergarten, as well…

  15. Research on Attribute Reduction in Hoisting Motor State Recognition of Quayside Container Crane

    Science.gov (United States)

    Li, F.; Tang, G.; Hu, X.

    2017-07-01

    In view of too many attributes in hoisting motor state recognition of quayside container crane. Attribute reduction method based on discernibility matrix is introduced to attribute reduction of lifting motor state information table. A method of attribute reduction based on the combination of rough set and genetic algorithm is proposed to deal with the hoisting motor state decision table. Under the condition that the information system's decision-making ability is unchanged, the redundant attribute is deleted. Which reduces the complexity and computation of the recognition process of the hoisting motor. It is possible to realize the fast state recognition.

  16. 78 FR 58865 - National POW/MIA Recognition Day, 2013

    Science.gov (United States)

    2013-09-25

    ... Forces. They represent the very best of the human spirit, stand tall for the values and freedoms we... are held captive as prisoners of war. We will never forget their sacrifice, nor will we ever abandon... my hand this nineteenth day of September, in the year of our Lord two thousand thirteen, and of the...

  17. Family Day Care in Australia: A Systematic Review of Research (1996-2010)

    Science.gov (United States)

    Bohanna, India; Davis, Elise; Corr, Lara; Priest, Naomi; Tan, Huong

    2012-01-01

    Family Day Care (FDC) is a distinctive form of child care chosen by many Australian families. However, there appears to be little empirical research on FDC conducted in Australia. The aim of this study was to systematically review the recent published literature on FDC research in Australia, assess its quality, and identify pertinent topics for…

  18. A Survey of Face Recognition Technique | Omidiora | Journal of ...

    African Journals Online (AJOL)

    A review of face recognition techniques has been carried out. Face recognition has been an attractive field in the society of both biological and computer vision of research. It exhibits the characteristics of being natural and low-intrusive. In this paper, an updated survey of techniques for face recognition is made. Methods of ...

  19. A Survey of 2D Face Recognition Techniques

    Directory of Open Access Journals (Sweden)

    Mejda Chihaoui

    2016-09-01

    Full Text Available Despite the existence of various biometric techniques, like fingerprints, iris scan, as well as hand geometry, the most efficient and more widely-used one is face recognition. This is because it is inexpensive, non-intrusive and natural. Therefore, researchers have developed dozens of face recognition techniques over the last few years. These techniques can generally be divided into three categories, based on the face data processing methodology. There are methods that use the entire face as input data for the proposed recognition system, methods that do not consider the whole face, but only some features or areas of the face and methods that use global and local face characteristics simultaneously. In this paper, we present an overview of some well-known methods in each of these categories. First, we expose the benefits of, as well as the challenges to the use of face recognition as a biometric tool. Then, we present a detailed survey of the well-known methods by expressing each method’s principle. After that, a comparison between the three categories of face recognition techniques is provided. Furthermore, the databases used in face recognition are mentioned, and some results of the applications of these methods on face recognition databases are presented. Finally, we highlight some new promising research directions that have recently appeared.

  20. Stereotypes and prejudice affect the recognition of emotional body postures.

    Science.gov (United States)

    Bijlstra, Gijsbert; Holland, Rob W; Dotsch, Ron; Wigboldus, Daniel H J

    2018-03-26

    Most research on emotion recognition focuses on facial expressions. However, people communicate emotional information through bodily cues as well. Prior research on facial expressions has demonstrated that emotion recognition is modulated by top-down processes. Here, we tested whether this top-down modulation generalizes to the recognition of emotions from body postures. We report three studies demonstrating that stereotypes and prejudice about men and women may affect how fast people classify various emotional body postures. Our results suggest that gender cues activate gender associations, which affect the recognition of emotions from body postures in a top-down fashion. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

  1. Evidences of the role of the rodent hippocampus in the non-spatial recognition memory.

    Science.gov (United States)

    Yi, Jee Hyun; Park, Hye Jin; Kim, Byeong C; Kim, Dong Hyun; Ryu, Jong Hoon

    2016-01-15

    The hippocampus is a key region responsible for processing spatial information. However, the role of the hippocampus in non-spatial recognition memory is still controversial. In the present study, we performed hippocampal lesioning to address this controversy. The hippocampi of mice were disrupted with bilateral cytotoxic lesions, and standard object recognition (non-spatial) and object location recognition (spatial) were tested. In the habituation period, mice with hippocampal lesions needed a significantly longer time to fully habituate to the test box. Interestingly, after 4 days of habituation (insufficient habituation), the recognition index was similar in the sham and hippocampal lesion groups. However, exploration time was significantly shorter in mice with hippocampal lesions compared with that in control mice. Interestingly, if mice were subjected to a 10-days-long period of habituation (full habituation), the recognition index was significantly lower in mice with hippocampal lesions compared with that in control mice; however, total exploration time was similar in both groups. Furthermore, the object recognition test after full habituation occluded hippocampal long-term potentiation, a cellular model of memory. These results indicate that sufficient habituation is required to observe the effects of hippocampal lesions on object recognition memory. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Flexible Piezoelectric Sensor-Based Gait Recognition

    Directory of Open Access Journals (Sweden)

    Youngsu Cha

    2018-02-01

    Full Text Available Most motion recognition research has required tight-fitting suits for precise sensing. However, tight-suit systems have difficulty adapting to real applications, because people normally wear loose clothes. In this paper, we propose a gait recognition system with flexible piezoelectric sensors in loose clothing. The gait recognition system does not directly sense lower-body angles. It does, however, detect the transition between standing and walking. Specifically, we use the signals from the flexible sensors attached to the knee and hip parts on loose pants. We detect the periodic motion component using the discrete time Fourier series from the signal during walking. We adapt the gait detection method to a real-time patient motion and posture monitoring system. In the monitoring system, the gait recognition operates well. Finally, we test the gait recognition system with 10 subjects, for which the proposed system successfully detects walking with a success rate over 93 %.

  3. Recognition Number of The Vehicle Plate Using Otsu Method and K-Nearest Neighbour Classification

    Directory of Open Access Journals (Sweden)

    Maulidia Rahmah Hidayah

    2017-05-01

    Full Text Available The current topic that is interesting as a solution of the impact of public service improvement toward vehicle is License Plate Recognition (LPR, but it still needs to develop the research of LPR method. Some of the previous researchs showed that K-Nearest Neighbour (KNN succeed in car license plate recognition. The Objectives of this research was to determine the implementation and accuracy of Otsu Method toward license plate recognition. The method of this research was Otsu method to extract the characteristics and image of the plate into binary image and KNN as recognition classification method of each character. The development of the license plate recognition program by using Otsu method and classification of KNN is following the steps of pattern recognition, such as input and sensing, pre-processing, extraction feature Otsu method binary, segmentation, KNN classification method and post-processing by calculating the level of accuracy. The study showed that this program can recognize by 82% from 100 test plate with 93,75% of number recognition accuracy and 91,92% of letter recognition accuracy. 

  4. Gender recognition from unconstrained and articulated human body.

    Science.gov (United States)

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition.

  5. Gender Recognition from Unconstrained and Articulated Human Body

    Science.gov (United States)

    Wu, Qin; Guo, Guodong

    2014-01-01

    Gender recognition has many useful applications, ranging from business intelligence to image search and social activity analysis. Traditional research on gender recognition focuses on face images in a constrained environment. This paper proposes a method for gender recognition in articulated human body images acquired from an unconstrained environment in the real world. A systematic study of some critical issues in body-based gender recognition, such as which body parts are informative, how many body parts are needed to combine together, and what representations are good for articulated body-based gender recognition, is also presented. This paper also pursues data fusion schemes and efficient feature dimensionality reduction based on the partial least squares estimation. Extensive experiments are performed on two unconstrained databases which have not been explored before for gender recognition. PMID:24977203

  6. Employee recognition: a key to motivation.

    Science.gov (United States)

    Magnus, M

    1981-02-01

    Productivity--why it's low and how to enhance it--is on everyone's mind these days. A major component of productivity is employee satisfaction. If an employee is dissatisfied, feels unappreciated or under-compensated, that employee will not perform to the best of his or her ability. How is the personnel administrator to address this pressing problem? One answer that emerges is employee recognition programs. In many cases, properly run recognition programs can boost awareness of the organization, build employee pride, raise morale and, ultimately, increase productivity. As some of our respondents observed, higher salary is not the best answer. While a larger paycheck is always appreciated, everyone's pride is boosted by a public demonstration of appreciation.

  7. Invariant Face recognition Using Infrared Images

    International Nuclear Information System (INIS)

    Zahran, E.G.

    2012-01-01

    Over the past few decades, face recognition has become a rapidly growing research topic due to the increasing demands in many applications of our daily life such as airport surveillance, personal identification in law enforcement, surveillance systems, information safety, securing financial transactions, and computer security. The objective of this thesis is to develop a face recognition system capable of recognizing persons with a high recognition capability, low processing time, and under different illumination conditions, and different facial expressions. The thesis presents a study for the performance of the face recognition system using two techniques; the Principal Component Analysis (PCA), and the Zernike Moments (ZM). The performance of the recognition system is evaluated according to several aspects including the recognition rate, and the processing time. Face recognition systems that use visual images are sensitive to variations in the lighting conditions and facial expressions. The performance of these systems may be degraded under poor illumination conditions or for subjects of various skin colors. Several solutions have been proposed to overcome these limitations. One of these solutions is to work in the Infrared (IR) spectrum. IR images have been suggested as an alternative source of information for detection and recognition of faces, when there is little or no control over lighting conditions. This arises from the fact that these images are formed due to thermal emissions from skin, which is an intrinsic property because these emissions depend on the distribution of blood vessels under the skin. On the other hand IR face recognition systems still have limitations with temperature variations and recognition of persons wearing eye glasses. In this thesis we will fuse IR images with visible images to enhance the performance of face recognition systems. Images are fused using the wavelet transform. Simulation results show that the fusion of visible and

  8. Pattern recognition and string matching

    CERN Document Server

    Cheng, Xiuzhen

    2002-01-01

    The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica­ tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro­ vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes student...

  9. WORD LEVEL DISCRIMINATIVE TRAINING FOR HANDWRITTEN WORD RECOGNITION

    NARCIS (Netherlands)

    Chen, W.; Gader, P.

    2004-01-01

    Word level training refers to the process of learning the parameters of a word recognition system based on word level criteria functions. Previously, researchers trained lexicon­driven handwritten word recognition systems at the character level individually. These systems generally use statistical

  10. Inertial Sensor-Based Gait Recognition: A Review

    Science.gov (United States)

    Sprager, Sebastijan; Juric, Matjaz B.

    2015-01-01

    With the recent development of microelectromechanical systems (MEMS), inertial sensors have become widely used in the research of wearable gait analysis due to several factors, such as being easy-to-use and low-cost. Considering the fact that each individual has a unique way of walking, inertial sensors can be applied to the problem of gait recognition where assessed gait can be interpreted as a biometric trait. Thus, inertial sensor-based gait recognition has a great potential to play an important role in many security-related applications. Since inertial sensors are included in smart devices that are nowadays present at every step, inertial sensor-based gait recognition has become very attractive and emerging field of research that has provided many interesting discoveries recently. This paper provides a thorough and systematic review of current state-of-the-art in this field of research. Review procedure has revealed that the latest advanced inertial sensor-based gait recognition approaches are able to sufficiently recognise the users when relying on inertial data obtained during gait by single commercially available smart device in controlled circumstances, including fixed placement and small variations in gait. Furthermore, these approaches have also revealed considerable breakthrough by realistic use in uncontrolled circumstances, showing great potential for their further development and wide applicability. PMID:26340634

  11. Improving entrepreneurial opportunity recognition through web content analytics

    Science.gov (United States)

    Bakar, Muhamad Shahbani Abu; Azmi, Azwiyati

    2017-10-01

    The ability to recognize and develop an opportunity into a venture defines an entrepreneur. Research in opportunity recognition has been robust and focuses more on explaining the processes involved in opportunity recognition. Factors such as prior knowledge, cognitive and creative capabilities are shown to affect opportunity recognition in entrepreneurs. Prior knowledge in areas such as customer problems, ways to serve the market, and technology has been shows in various studies to be a factor that facilitates entrepreneurs to identify and recognize opportunities. Findings from research also shows that experienced entrepreneurs search and scan for information to discover opportunities. Searching and scanning for information has also been shown to help novice entrepreneurs who lack prior knowledge to narrow this gap and enable them to better identify and recognize opportunities. There is less focus in research on finding empirically proven techniques and methods to develop and enhance opportunity recognition in student entrepreneurs. This is important as the country pushes for more graduate entrepreneurs that can drive the economy. This paper aims to discuss Opportunity Recognition Support System (ORSS), an information support system to help especially student entrepreneurs in identifying and recognizing business opportunities. The ORSS aims to provide the necessary knowledge to student entrepreneurs to be able to better identify and recognize opportunities. Applying design research, theories in opportunity recognition are applied to identify the requirements for the support system and the requirements in turn dictate the design of the support system. The paper proposes the use of web content mining and analytics as two core components and techniques for the support system. Web content mining can mine the vast knowledge repositories available on the internet and analytics can provide entrepreneurs with further insights into the information needed to recognize

  12. Using Interdisciplinary Research Methods to Revise and Strengthen the NWS TsunamiReadyTM Community Recognition Program

    Science.gov (United States)

    Scott, C.; Gregg, C. E.; Ritchie, L.; Stephen, M.; Farnham, C.; Fraser, S. A.; Gill, D.; Horan, J.; Houghton, B. F.; Johnson, V.; Johnston, D.

    2013-12-01

    The National Tsunami Hazard Mitigation Program (NTHMP) partnered with the National Weather Service (NWS) in early 2000 to create the TsunamiReadyTM Community Recognition program. TsunamiReadyTM, modeled after the older NWS StormReadyTM program, is designed to help cities, towns, counties, universities and other large sites in coastal areas reduce the potential for disastrous tsunami-related consequences. To achieve TsunamiReadyTM recognition, communities must meet certain criteria aimed at better preparing a community for tsunami, including specific actions within the following categories: communications and coordination, tsunami warning reception, local warning dissemination, community preparedness, and administration. Using multidisciplinary research methods and strategies from Public Health; Psychology; Political, Social and Physical Sciences and Evaluation, our research team is working directly with a purposive sample of community stakeholders in collaboration and feedback focus group sessions. Invitation to participate is based on a variety of factors including but not limited to an individual's role as a formal or informal community leader (e.g., in business, government, civic organizations), or their organization or agency affiliation to emergency management and response. Community organizing and qualitative research methods are being used to elicit discussion regarding TsunamiReadyTM requirements and the division of requirements based on some aspect of tsunami hazard, vulnerability and risk, such as proximity to active or passive plate margins or subduction zone generated tsunamis versus earthquake-landslide generated tsunamis . The primary aim of this research is to use social science to revise and refine the NWS TsunamiReadyTM Guidelines in an effort to better prepare communities to reduce risk to tsunamis.

  13. Pattern recognition

    CERN Document Server

    Theodoridis, Sergios

    2003-01-01

    Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to ""learn"" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10

  14. Image recognition on raw and processed potato detection: a review

    Science.gov (United States)

    Qi, Yan-nan; Lü, Cheng-xu; Zhang, Jun-ning; Li, Ya-shuo; Zeng, Zhen; Mao, Wen-hua; Jiang, Han-lu; Yang, Bing-nan

    2018-02-01

    Objective: Chinese potato staple food strategy clearly pointed out the need to improve potato processing, while the bottleneck of this strategy is technology and equipment of selection of appropriate raw and processed potato. The purpose of this paper is to summarize the advanced raw and processed potato detection methods. Method: According to consult research literatures in the field of image recognition based potato quality detection, including the shape, weight, mechanical damage, germination, greening, black heart, scab potato etc., the development and direction of this field were summarized in this paper. Result: In order to obtain whole potato surface information, the hardware was built by the synchronous of image sensor and conveyor belt to achieve multi-angle images of a single potato. Researches on image recognition of potato shape are popular and mature, including qualitative discrimination on abnormal and sound potato, and even round and oval potato, with the recognition accuracy of more than 83%. Weight is an important indicator for potato grading, and the image classification accuracy presents more than 93%. The image recognition of potato mechanical damage focuses on qualitative identification, with the main affecting factors of damage shape and damage time. The image recognition of potato germination usually uses potato surface image and edge germination point. Both of the qualitative and quantitative detection of green potato have been researched, currently scab and blackheart image recognition need to be operated using the stable detection environment or specific device. The image recognition of processed potato mainly focuses on potato chips, slices and fries, etc. Conclusion: image recognition as a food rapid detection tool have been widely researched on the area of raw and processed potato quality analyses, its technique and equipment have the potential for commercialization in short term, to meet to the strategy demand of development potato as

  15. Deep Complementary Bottleneck Features for Visual Speech Recognition

    NARCIS (Netherlands)

    Petridis, Stavros; Pantic, Maja

    Deep bottleneck features (DBNFs) have been used successfully in the past for acoustic speech recognition from audio. However, research on extracting DBNFs for visual speech recognition is very limited. In this work, we present an approach to extract deep bottleneck visual features based on deep

  16. Long-Term Social Recognition Memory in Zebrafish.

    Science.gov (United States)

    Madeira, Natália; Oliveira, Rui F

    2017-08-01

    In species in which individuals live in stable social groups, individual recognition is expected to evolve to allow individuals to remember past interactions with different individuals and adjust future behavior toward them accordingly. Thus, social memory is expected to be a ubiquitous component of social cognition of social species. However, few studies have investigated the occurrence of social memory in non-mammals. Here we evaluated the ability of zebrafish (Danio rerio) to recognize different conspecifics and to retain this information in long lasting (i.e. 24 h) memories. We used a social discrimination paradigm, adapted from mouse studies, in which the focal individual meets two pairs of conspecifics in two consecutive days: one conspecific is the same in both days and the other is different between days 1 and 2. If animals have the ability to discriminate between different conspecifics, it is predicted that they will spend more time exploring the novel than the familiar (i.e. already seen in day 1) conspecific. In this study, zebrafish with access to both olfactory and visual conspecific cues exhibited consistent recognition of a previously encountered (familiar) conspecific after a 24 h delay. This result supports the hypothesis that long-term social memory, previously described in mammals, is also present in zebrafish, hence extending the evidence for the presence of this type of memory to teleost fish.

  17. Hybrid methodological approach to context-dependent speech recognition

    Directory of Open Access Journals (Sweden)

    Dragiša Mišković

    2017-01-01

    Full Text Available Although the importance of contextual information in speech recognition has been acknowledged for a long time now, it has remained clearly underutilized even in state-of-the-art speech recognition systems. This article introduces a novel, methodologically hybrid approach to the research question of context-dependent speech recognition in human–machine interaction. To the extent that it is hybrid, the approach integrates aspects of both statistical and representational paradigms. We extend the standard statistical pattern-matching approach with a cognitively inspired and analytically tractable model with explanatory power. This methodological extension allows for accounting for contextual information which is otherwise unavailable in speech recognition systems, and using it to improve post-processing of recognition hypotheses. The article introduces an algorithm for evaluation of recognition hypotheses, illustrates it for concrete interaction domains, and discusses its implementation within two prototype conversational agents.

  18. Use of digital speech recognition in diagnostics radiology

    International Nuclear Information System (INIS)

    Arndt, H.; Stockheim, D.; Mutze, S.; Petersein, J.; Gregor, P.; Hamm, B.

    1999-01-01

    Purpose: Applicability and benefits of digital speech recognition in diagnostic radiology were tested using the speech recognition system SP 6000. Methods: The speech recognition system SP 6000 was integrated into the network of the institute and connected to the existing Radiological Information System (RIS). Three subjects used this system for writing 2305 findings from dictation. After the recognition process the date, length of dictation, time required for checking/correction, kind of examination and error rate were recorded for every dictation. With the same subjects, a correlation was performed with 625 conventionally written finding. Results: After an 1-hour initial training the average error rates were 8.4 to 13.3%. The first adaptation of the speech recognition system (after nine days) decreased the average error rates to 2.4 to 10.7% due to the ability of the program to learn. The 2 nd and 3 rd adaptations resulted only in small changes of the error rate. An individual comparison of the error rate developments in the same kind of investigation showed the relative independence of the error rate on the individual user. Conclusion: The results show that the speech recognition system SP 6000 can be evaluated as an advantageous alternative for quickly recording radiological findings. A comparison between manually writing and dictating the findings verifies the individual differences of the writing speeds and shows the advantage of the application of voice recognition when faced with normal keyboard performance. (orig.) [de

  19. Weighted Feature Gaussian Kernel SVM for Emotion Recognition.

    Science.gov (United States)

    Wei, Wei; Jia, Qingxuan

    2016-01-01

    Emotion recognition with weighted feature based on facial expression is a challenging research topic and has attracted great attention in the past few years. This paper presents a novel method, utilizing subregion recognition rate to weight kernel function. First, we divide the facial expression image into some uniform subregions and calculate corresponding recognition rate and weight. Then, we get a weighted feature Gaussian kernel function and construct a classifier based on Support Vector Machine (SVM). At last, the experimental results suggest that the approach based on weighted feature Gaussian kernel function has good performance on the correct rate in emotion recognition. The experiments on the extended Cohn-Kanade (CK+) dataset show that our method has achieved encouraging recognition results compared to the state-of-the-art methods.

  20. Biometric Features in Person Recognition Systems

    Directory of Open Access Journals (Sweden)

    Edgaras Ivanovas

    2011-03-01

    Full Text Available Lately a lot of research effort is devoted for recognition of a human being using his biometric characteristics. Biometric recognition systems are used in various applications, e. g., identification for state border crossing or firearm, which allows only enrolled persons to use it. In this paper biometric characteristics and their properties are reviewed. Development of high accuracy system requires distinctive and permanent characteristics, whereas development of user friendly system requires collectable and acceptable characteristics. It is showed that properties of biometric characteristics do not influence research effort significantly. Properties of biometric characteristic features and their influence are discussed.Article in Lithuanian

  1. Comparison of the Reynell Developmental Language Scale II and the Galker test of word-recognition-in-noise in Danish day-care children

    DEFF Research Database (Denmark)

    Lous, Jørgen; Glenn Lauritsen, Maj Britt

    2018-01-01

    Objective: To search for predictive factors for language development measured by two receptive language tests for children, the Galker test (a word-recognition-in-noise test) testing hearing and vocabulary, and the Danish version of Reynell Developmental Language Scale (2nd revision, RDLS II) test...... in terms of the degree to which variables were able to predict test scores at the age of three to five years. Methods: All children aged three and five years attending 20 day-care centres for children without cognitive development issues from the Municipality of Hillerød, Denmark, were invited......, a language comprehension test. The study analysed if information about background variables and parents and pre-school teachers was predictive for test scores; if earlier middle ear disease, actual hearing loss and tympanometry was important for language development; and if the two receptive tests differed...

  2. Material recognition based on thermal cues: Mechanisms and applications.

    Science.gov (United States)

    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.

  3. Proceedings of the Conference on Industry and Day Care (Urban Research Corporation, Chicago, 1970).

    Science.gov (United States)

    Urban Research Corp., Chicago, IL.

    This booklet of conference proceedings reflects the efforts of the Urban Research Corporation to continue conversation between industry and day care specialists. A group of 175 industry representatives, early childhood specialists, community agency representatives, and day care operators and franchisers convened to discuss their mutual concerns.…

  4. 8 CFR 292.2 - Organizations qualified for recognition; requests for recognition; withdrawal of recognition...

    Science.gov (United States)

    2010-01-01

    ...; requests for recognition; withdrawal of recognition; accreditation of representatives; roster. 292.2...; withdrawal of recognition; accreditation of representatives; roster. (a) Qualifications of organizations. A non-profit religious, charitable, social service, or similar organization established in the United...

  5. Human body contour data based activity recognition.

    Science.gov (United States)

    Myagmarbayar, Nergui; Yuki, Yoshida; Imamoglu, Nevrez; Gonzalez, Jose; Otake, Mihoko; Yu, Wenwei

    2013-01-01

    This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effective, safe and easier at-home rehabilitation to most motor-function impaired patients (MIPs). In our previous research, a full framework was established towards this research goal. In this research, we aimed at improving the human activity recognition by using contour data of the tracked human subject extracted from the depth images as the signal source, instead of the lower limb joint angle data used in the previous research, which are more likely to be affected by the motion of the robot and human subjects. Several geometric parameters, such as, the ratio of height to weight of the tracked human subject, and distance (pixels) between centroid points of upper and lower parts of human body, were calculated from the contour data, and used as the features for the activity recognition. A Hidden Markov Model (HMM) is employed to classify different human activities from the features. Experimental results showed that the human activity recognition could be achieved with a high correct rate.

  6. Activity recognition from minimal distinguishing subsequence mining

    Science.gov (United States)

    Iqbal, Mohammad; Pao, Hsing-Kuo

    2017-08-01

    Human activity recognition is one of the most important research topics in the era of Internet of Things. To separate different activities given sensory data, we utilize a Minimal Distinguishing Subsequence (MDS) mining approach to efficiently find distinguishing patterns among different activities. We first transform the sensory data into a series of sensor triggering events and operate the MDS mining procedure afterwards. The gap constraints are also considered in the MDS mining. Given the multi-class nature of most activity recognition tasks, we modify the MDS mining approach from a binary case to a multi-class one to fit the need for multiple activity recognition. We also study how to select the best parameter set including the minimal and the maximal support thresholds in finding the MDSs for effective activity recognition. Overall, the prediction accuracy is 86.59% on the van Kasteren dataset which consists of four different activities for recognition.

  7. Real-time embedded face recognition for smart home

    NARCIS (Netherlands)

    Zuo, F.; With, de P.H.N.

    2005-01-01

    We propose a near real-time face recognition system for embedding in consumer applications. The system is embedded in a networked home environment and enables personalized services by automatic identification of users. The aim of our research is to design and build a face recognition system that is

  8. [Face recognition in patients with schizophrenia].

    Science.gov (United States)

    Doi, Hirokazu; Shinohara, Kazuyuki

    2012-07-01

    It is well known that patients with schizophrenia show severe deficiencies in social communication skills. These deficiencies are believed to be partly derived from abnormalities in face recognition. However, the exact nature of these abnormalities exhibited by schizophrenic patients with respect to face recognition has yet to be clarified. In the present paper, we review the main findings on face recognition deficiencies in patients with schizophrenia, particularly focusing on abnormalities in the recognition of facial expression and gaze direction, which are the primary sources of information of others' mental states. The existing studies reveal that the abnormal recognition of facial expression and gaze direction in schizophrenic patients is attributable to impairments in both perceptual processing of visual stimuli, and cognitive-emotional responses to social information. Furthermore, schizophrenic patients show malfunctions in distributed neural regions, ranging from the fusiform gyrus recruited in the structural encoding of facial stimuli, to the amygdala which plays a primary role in the detection of the emotional significance of stimuli. These findings were obtained from research in patient groups with heterogeneous characteristics. Because previous studies have indicated that impairments in face recognition in schizophrenic patients might vary according to the types of symptoms, it is of primary importance to compare the nature of face recognition deficiencies and the impairments of underlying neural functions across sub-groups of patients.

  9. Brand recognition in television advertising: The influence of brand presence and brand introduction

    Directory of Open Access Journals (Sweden)

    Charlene Gerber

    2014-05-01

    Problem investigated: Brand recognition and recall are established advertising effectiveness measurements to assess brand awareness. Of particular interest is whether encoding of brand information as measured by brand recognition is influenced by brand presence and brand introduction. Design/methodology/approach: A meta-analysis was performed on responses to 25 television advertisements, gathered from 50 000 respondents. Findings: The findings indicated a positive linear relationship between brand presence and brand recognition but a negative linear relationship between brand introduction and brand recognition, whilst brand introduction and brand presence predicted variance in brand recognition. Value of research: The researchers concluded that a brand should be present in an advertisement for about two-thirds of the time for optimum brand recognition.

  10. 9th International Conference on Computer Recognition Systems

    CERN Document Server

    Jackowski, Konrad; Kurzyński, Marek; Woźniak, Michał; Żołnierek, Andrzej

    2016-01-01

    The computer recognition systems are nowadays one of the most promising directions in artificial intelligence. This book is the most comprehensive study of this field. It contains a collection of 79 carefully selected articles contributed by experts of pattern recognition. It reports on current research with respect to both methodology and applications. In particular, it includes the following sections: Features, learning, and classifiers Biometrics Data Stream Classification and Big Data Analytics Image processing and computer vision Medical applications Applications RGB-D perception: recent developments and applications This book is a great reference tool for scientists who deal with the problems of designing computer pattern recognition systems. Its target readers can be the as well researchers as students of computer science, artificial intelligence or robotics.  .

  11. Writing and Speech Recognition : Observing Error Correction Strategies of Professional Writers

    NARCIS (Netherlands)

    Leijten, M.A.J.C.

    2007-01-01

    In this thesis we describe the organization of speech recognition based writing processes. Writing can be seen as a visual representation of spoken language: a combination that speech recognition takes full advantage of. In the field of writing research, speech recognition is a new writing

  12. Human activity recognition from wireless sensor network data: benchmark and software

    NARCIS (Netherlands)

    van Kasteren, T.L.M.; Englebienne, G.; Kröse, B.J.A.; Chen, L.; Nugent, C.; Biswas, J.; Hoey, J.

    2011-01-01

    Although activity recognition is an active area of research no common benchmark for evaluating the performance of activity recognition methods exists. In this chapter we present the state of the art probabilistic models used in activity recognition and show their performance on several real world

  13. Displaying a Poster, Unifying a Campus: Undergraduate Research Day at Penn State Wilkes-Barre

    Directory of Open Access Journals (Sweden)

    Jennie Levine Knies

    2015-11-01

    Full Text Available This article describes the first official Undergraduate Research Day at Penn State Wilkes-Barre, a small campus with approximately 550 undergraduate students and 8 four-year degree programs. In 2015, an informal planning committee, consisting of two librarians and two faculty members, embarked on a project to turn what had been an informal course assignment into a campus-wide research event.  By remaining flexible, engaged, and open to collaboration, the committee made Undergraduate Research Day in April 2015 a success, and plans are underway to hold this event in subsequent years.  The event energized and motivated students, faculty, and staff on campus and paved the way toward a unified organizational identity on campus.

  14. [Neurological disease and facial recognition].

    Science.gov (United States)

    Kawamura, Mitsuru; Sugimoto, Azusa; Kobayakawa, Mutsutaka; Tsuruya, Natsuko

    2012-07-01

    To discuss the neurological basis of facial recognition, we present our case reports of impaired recognition and a review of previous literature. First, we present a case of infarction and discuss prosopagnosia, which has had a large impact on face recognition research. From a study of patient symptoms, we assume that prosopagnosia may be caused by unilateral right occipitotemporal lesion and right cerebral dominance of facial recognition. Further, circumscribed lesion and degenerative disease may also cause progressive prosopagnosia. Apperceptive prosopagnosia is observed in patients with posterior cortical atrophy (PCA), pathologically considered as Alzheimer's disease, and associative prosopagnosia in frontotemporal lobar degeneration (FTLD). Second, we discuss face recognition as part of communication. Patients with Parkinson disease show social cognitive impairments, such as difficulty in facial expression recognition and deficits in theory of mind as detected by the reading the mind in the eyes test. Pathological and functional imaging studies indicate that social cognitive impairment in Parkinson disease is possibly related to damages in the amygdalae and surrounding limbic system. The social cognitive deficits can be observed in the early stages of Parkinson disease, and even in the prodromal stage, for example, patients with rapid eye movement (REM) sleep behavior disorder (RBD) show impairment in facial expression recognition. Further, patients with myotonic dystrophy type 1 (DM 1), which is a multisystem disease that mainly affects the muscles, show social cognitive impairment similar to that of Parkinson disease. Our previous study showed that facial expression recognition impairment of DM 1 patients is associated with lesion in the amygdalae and insulae. Our study results indicate that behaviors and personality traits in DM 1 patients, which are revealed by social cognitive impairment, are attributable to dysfunction of the limbic system.

  15. Blockade of intracellular Zn2+ signaling in the dentate gyrus erases recognition memory via impairment of maintained LTP.

    Science.gov (United States)

    Tamano, Haruna; Minamino, Tatsuya; Fujii, Hiroaki; Takada, Shunsuke; Nakamura, Masatoshi; Ando, Masaki; Takeda, Atsushi

    2015-08-01

    There is no evidence on the precise role of synaptic Zn2+ signaling on the retention and recall of recognition memory. On the basis of the findings that intracellular Zn2+ signaling in the dentate gyrus is required for object recognition, short-term memory, the present study deals with the effect of spatiotemporally blocking Zn2+ signaling in the dentate gyrus after LTP induction and learning. Three-day-maintained LTP was impaired 1 day after injection of clioquinol into the dentate gyrus, which transiently reduced intracellular Zn2+ signaling in the dentate gyrus. The irreversible impairment was rescued not only by co-injection of ZnCl2 , which ameliorated the loss of Zn2+ signaling, but also by pre-injection of Jasplakinolide, a stabilizer of F-actin, prior to clioquinol injection. Simultaneously, 3-day-old space recognition memory was impaired 1 day after injection of clioquinol into the dentate gyrus, but not by pre-injection of Jasplakinolide. Jasplakinolide also rescued both impairments of 3-day-maintained LTP and 3-day-old memory after injection of ZnAF-2DA into the dentate gyrus, which blocked intracellular Zn2+ signaling in the dentate gyrus. The present paper indicates that the blockade and/or loss of intracellular Zn2+ signaling in the dentate gyrus coincidently impair maintained LTP and recognition memory. The mechanism maintaining LTP via intracellular Zn2+ signaling in dentate granule cells, which may be involved in the formation of F-actin, may retain space recognition memory. © 2015 Wiley Periodicals, Inc.

  16. Predator recognition in rainbowfish, Melanotaenia duboulayi, embryos.

    Directory of Open Access Journals (Sweden)

    Lois Jane Oulton

    Full Text Available Exposure to olfactory cues during embryonic development can have long term impacts on birds and amphibians behaviour. Despite the vast literature on predator recognition and responses in fishes, few researchers have determined how fish embryos respond to predator cues. Here we exposed four-day-old rainbowfish (Melanotaenia duboulayi embryos to cues emanating from a novel predator, a native predator and injured conspecifics. Their response was assessed by monitoring heart rate and hatch time. Results showed that embryos have an innate capacity to differentiate between cues as illustrated by faster heart rates relative to controls. The greatest increase in heart rate occurred in response to native predator odour. While we found no significant change in the time taken for eggs to hatch, all treatments experienced slight delays as expected if embryos are attempting to reduce exposure to larval predators.

  17. Facial Expression Recognition Based on TensorFlow Platform

    Directory of Open Access Journals (Sweden)

    Xia Xiao-Ling

    2017-01-01

    Full Text Available Facial expression recognition have a wide range of applications in human-machine interaction, pattern recognition, image understanding, machine vision and other fields. Recent years, it has gradually become a hot research. However, different people have different ways of expressing their emotions, and under the influence of brightness, background and other factors, there are some difficulties in facial expression recognition. In this paper, based on the Inception-v3 model of TensorFlow platform, we use the transfer learning techniques to retrain facial expression dataset (The Extended Cohn-Kanade dataset, which can keep the accuracy of recognition and greatly reduce the training time.

  18. Emotion recognition a pattern analysis approach

    CERN Document Server

    Konar, Amit

    2014-01-01

    Offers both foundations and advances on emotion recognition in a single volumeProvides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domainsInspires young researchers to prepare themselves for their own researchDemonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.

  19. Cognitive object recognition system (CORS)

    Science.gov (United States)

    Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy

    2010-04-01

    We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.

  20. Influences on Facial Emotion Recognition in Deaf Children

    Science.gov (United States)

    Sidera, Francesc; Amadó, Anna; Martínez, Laura

    2017-01-01

    This exploratory research is aimed at studying facial emotion recognition abilities in deaf children and how they relate to linguistic skills and the characteristics of deafness. A total of 166 participants (75 deaf) aged 3-8 years were administered the following tasks: facial emotion recognition, naming vocabulary and cognitive ability. The…

  1. Research of Face Recognition with Fisher Linear Discriminant

    Science.gov (United States)

    Rahim, R.; Afriliansyah, T.; Winata, H.; Nofriansyah, D.; Ratnadewi; Aryza, S.

    2018-01-01

    Face identification systems are developing rapidly, and these developments drive the advancement of biometric-based identification systems that have high accuracy. However, to develop a good face recognition system and to have high accuracy is something that’s hard to find. Human faces have diverse expressions and attribute changes such as eyeglasses, mustache, beard and others. Fisher Linear Discriminant (FLD) is a class-specific method that distinguishes facial image images into classes and also creates distance between classes and intra classes so as to produce better classification.

  2. RESEARCH ON FOREST FLAME RECOGNITION ALGORITHM BASED ON IMAGE FEATURE

    Directory of Open Access Journals (Sweden)

    Z. Wang

    2017-09-01

    Full Text Available In recent years, fire recognition based on image features has become a hotspot in fire monitoring. However, due to the complexity of forest environment, the accuracy of forest fireworks recognition based on image features is low. Based on this, this paper proposes a feature extraction algorithm based on YCrCb color space and K-means clustering. Firstly, the paper prepares and analyzes the color characteristics of a large number of forest fire image samples. Using the K-means clustering algorithm, the forest flame model is obtained by comparing the two commonly used color spaces, and the suspected flame area is discriminated and extracted. The experimental results show that the extraction accuracy of flame area based on YCrCb color model is higher than that of HSI color model, which can be applied in different scene forest fire identification, and it is feasible in practice.

  3. Towards automatic musical instrument timbre recognition

    Science.gov (United States)

    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.

  4. Convolutional Neural Network-Based Finger-Vein Recognition Using NIR Image Sensors.

    Science.gov (United States)

    Hong, Hyung Gil; Lee, Min Beom; Park, Kang Ryoung

    2017-06-06

    Conventional finger-vein recognition systems perform recognition based on the finger-vein lines extracted from the input images or image enhancement, and texture feature extraction from the finger-vein images. In these cases, however, the inaccurate detection of finger-vein lines lowers the recognition accuracy. In the case of texture feature extraction, the developer must experimentally decide on a form of the optimal filter for extraction considering the characteristics of the image database. To address this problem, this research proposes a finger-vein recognition method that is robust to various database types and environmental changes based on the convolutional neural network (CNN). In the experiments using the two finger-vein databases constructed in this research and the SDUMLA-HMT finger-vein database, which is an open database, the method proposed in this research showed a better performance compared to the conventional methods.

  5. Who is the boss? Individual recognition memory and social hierarchy formation in crayfish.

    Science.gov (United States)

    Jiménez-Morales, Nayeli; Mendoza-Ángeles, Karina; Porras-Villalobos, Mercedes; Ibarra-Coronado, Elizabeth; Roldán-Roldán, Gabriel; Hernández-Falcón, Jesús

    2018-01-01

    Under laboratory conditions, crayfish establish hierarchical orders through agonistic encounters whose outcome defines the dominant one and one, or more, submissive animals. These agonistic encounters are ritualistic, based on threats, pushes, attacks, grabs, and avoidance behaviors that include retreats and escape responses. Agonistic behavior in a triad of unfamiliar, size-matched animals is intense on the first day of social interaction and the intensity fades on daily repetitions. The dominant animal keeps its status for long periods, and the submissive ones seem to remember 'who the boss is'. It has been assumed that animals remember and recognize their hierarchical status by urine signals, but the putative substance mediating this recognition has not been reported. The aim of this work was to characterize this hierarchical recognition memory. Triads of unfamiliar crayfish (male animals, size and weight-matched) were faced during standardized agonistic protocols for five consecutive days to analyze memory acquisition dynamics (Experiment 1). In Experiment 2, dominant crayfish were shifted among triads to disclose whether hierarchy depended upon individual recognition memory or recognition of status. The maintenance of the hierarchical structure without behavioral reinforcement was assessed by immobilizing the dominant animal during eleven daily agonistic encounters, and considering any shift in the dominance order (Experiment 3). Standard amnesic treatments (anisomycin, scopolamine or cold-anesthesia) were given to all members of the triads immediately after the first interaction session to prevent individual recognition memory consolidation and evaluate its effect on the hierarchical order (Experiment 4). Acquisition of hierarchical recognition occurs at the first agonistic encounter and agonistic behavior gradually diminishes in the following days; animals keep their hierarchical order despite the inability of the dominant crayfish to attack the submissive

  6. A Survey on Banknote Recognition Methods by Various Sensors

    Science.gov (United States)

    Lee, Ji Woo; Hong, Hyung Gil; Kim, Ki Wan; Park, Kang Ryoung

    2017-01-01

    Despite a decrease in the use of currency due to the recent growth in the use of electronic financial transactions, real money transactions remain very important in the global market. While performing transactions with real money, touching and counting notes by hand, is still a common practice in daily life, various types of automated machines, such as ATMs and banknote counters, are essential for large-scale and safe transactions. This paper presents studies that have been conducted in four major areas of research (banknote recognition, counterfeit banknote detection, serial number recognition, and fitness classification) in the accurate banknote recognition field by various sensors in such automated machines, and describes the advantages and drawbacks of the methods presented in those studies. While to a limited extent some surveys have been presented in previous studies in the areas of banknote recognition or counterfeit banknote recognition, this paper is the first of its kind to review all four areas. Techniques used in each of the four areas recognize banknote information (denomination, serial number, authenticity, and physical condition) based on image or sensor data, and are actually applied to banknote processing machines across the world. This study also describes the technological challenges faced by such banknote recognition techniques and presents future directions of research to overcome them. PMID:28208733

  7. The Neural Correlates of Everyday Recognition Memory

    Science.gov (United States)

    Milton, F.; Muhlert, N.; Butler, C. R.; Benattayallah, A.; Zeman, A. Z.

    2011-01-01

    We used a novel automatic camera, SenseCam, to create a recognition memory test for real-life events. Adapting a "Remember/Know" paradigm, we asked healthy undergraduates, who wore SenseCam for 2 days, in their everyday environments, to classify images as strongly or weakly remembered, strongly or weakly familiar or novel, while brain activation…

  8. Face recognition in the thermal infrared domain

    Science.gov (United States)

    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.

  9. Annual State of Connecticut Obstetrics and Gynecology Resident Research Day.

    Science.gov (United States)

    Seagle, Brandon-Luke L; Ballard, Jennifer; Kakar, Freshta; Panarelli, Erin; Samuelson, Robert; Shahabi, Shohreh

    2015-01-01

    To increase opportunities for Obstetrics and Gynecology(Ob/Gyn) residents to present their research, an Annual State of Connecticut Ob/Gyn Resident Research Day (RRD) was created. At the first annual RRD, 33 residents, representing five of six Connecticut Ob/Gyn residency programs, presented 39 poster and eight oral presentations. RRD evaluators rated the overall symposium and the quality of resident oral and poster presentations as either "excellent" or "above average." Residency program directors reported that the symposium was "very helpful" for evidencing resident scholarship as required by the Accreditation Council for Graduate Medical Education (ACGME). Surveyed residents reported that the symposium promoted their research and was a valuable investment of their time. An annual specialty-specific, statewide RRD was created, experienced good participation, and was well evaluated. The annual, statewide Ob/Gyn RRD may serve as a model for development of other specialty-specific, statewide RRD events.

  10. ACOUSTIC SPEECH RECOGNITION FOR MARATHI LANGUAGE USING SPHINX

    Directory of Open Access Journals (Sweden)

    Aman Ankit

    2016-09-01

    Full Text Available Speech recognition or speech to text processing, is a process of recognizing human speech by the computer and converting into text. In speech recognition, transcripts are created by taking recordings of speech as audio and their text transcriptions. Speech based applications which include Natural Language Processing (NLP techniques are popular and an active area of research. Input to such applications is in natural language and output is obtained in natural language. Speech recognition mostly revolves around three approaches namely Acoustic phonetic approach, Pattern recognition approach and Artificial intelligence approach. Creation of acoustic model requires a large database of speech and training algorithms. The output of an ASR system is recognition and translation of spoken language into text by computers and computerized devices. ASR today finds enormous application in tasks that require human machine interfaces like, voice dialing, and etc. Our key contribution in this paper is to create corpora for Marathi language and explore the use of Sphinx engine for automatic speech recognition

  11. Face Age and Eye Gaze Influence Older Adults' Emotion Recognition.

    Science.gov (United States)

    Campbell, Anna; Murray, Janice E; Atkinson, Lianne; Ruffman, Ted

    2017-07-01

    Eye gaze has been shown to influence emotion recognition. In addition, older adults (over 65 years) are not as influenced by gaze direction cues as young adults (18-30 years). Nevertheless, these differences might stem from the use of young to middle-aged faces in emotion recognition research because older adults have an attention bias toward old-age faces. Therefore, using older face stimuli might allow older adults to process gaze direction cues to influence emotion recognition. To investigate this idea, young and older adults completed an emotion recognition task with young and older face stimuli displaying direct and averted gaze, assessing labeling accuracy for angry, disgusted, fearful, happy, and sad faces. Direct gaze rather than averted gaze improved young adults' recognition of emotions in young and older faces, but for older adults this was true only for older faces. The current study highlights the impact of stimulus face age and gaze direction on emotion recognition in young and older adults. The use of young face stimuli with direct gaze in most research might contribute to age-related emotion recognition differences. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  12. Textual emotion recognition for enhancing enterprise computing

    Science.gov (United States)

    Quan, Changqin; Ren, Fuji

    2016-05-01

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

  13. Research of Obstacle Recognition Technology in Cross-Country Environment for Unmanned Ground Vehicle

    Directory of Open Access Journals (Sweden)

    Zhao Yibing

    2014-01-01

    Full Text Available Being aimed at the obstacle recognition problem of unmanned ground vehicles in cross-country environment, this paper uses monocular vision sensor to realize the obstacle recognition of typical obstacles. Firstly, median filtering algorithm is applied during image preprocessing that can eliminate the noise. Secondly, image segmentation method based on the Fisher criterion function is used to segment the region of interest. Then, morphological method is used to process the segmented image, which is preparing for the subsequent analysis. The next step is to extract the color feature S, color feature a and edge feature “verticality” of image are extracted based on the HSI color space, the Lab color space, and two value images. Finally multifeature fusion algorithm based on Bayes classification theory is used for obstacle recognition. Test results show that the algorithm has good robustness and accuracy.

  14. Spoof Detection for Finger-Vein Recognition System Using NIR Camera

    Directory of Open Access Journals (Sweden)

    Dat Tien Nguyen

    2017-10-01

    Full Text Available Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD, is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor based on the observations of the researchers about the difference between real (live and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR camera-based finger-vein recognition system using convolutional neural network (CNN to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA for dimensionality reduction of feature space and support vector machine (SVM for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared

  15. Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

    Science.gov (United States)

    Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

    2017-10-01

    Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN

  16. Semi-Supervised Half-Quadratic Nonnegative Matrix Factorization for Face Recognition

    KAUST Repository

    Alghamdi, Masheal M.

    2014-01-01

    complications to the face recognition research. Many algorithms are devoted to solving the face recognition problem, among which the family of nonnegative matrix factorization (NMF) algorithms has been widely used as a compact data representation method

  17. Latin Letters Recognition Using Optical Character Recognition to Convert Printed Media Into Digital Format

    Directory of Open Access Journals (Sweden)

    Rio Anugrah

    2017-12-01

    Full Text Available Printed media is still popular now days society. Unfortunately, such media encountered several drawbacks. For example, this type of media consumes large storage that impact in high maintenance cost. To keep printed information more efficient and long-lasting, people usually convert it into digital format. In this paper, we built Optical Character Recognition (OCR system to enable automatic conversion the image containing the sentence in Latin characters into digital text-shaped information. This system consists of several interrelated stages including preprocessing, segmentation, feature extraction, classifier, model and recognition. In preprocessing, the median filter is used to clarify the image from noise and the Otsu’s function is used to binarize the image. It followed by character segmentation using connected component labeling. Artificial neural network (ANN is used for feature extraction to recognize the character. The result shows that this system enable to recognize the characters in the image whose success rate is influenced by the training of the system.

  18. EMPIRICAL STUDY OF CAR LICENSE PLATES RECOGNITION

    Directory of Open Access Journals (Sweden)

    Nasa Zata Dina

    2015-01-01

    Full Text Available The number of vehicles on the road has increased drastically in recent years. The license plate is an identity card for a vehicle. It can map to the owner and further information about vehicle. License plate information is useful to help traffic management systems. For example, traffic management systems can check for vehicles moving at speeds not permitted by law and can also be installed in parking areas to se-cure the entrance or exit way for vehicles. License plate recognition algorithms have been proposed by many researchers. License plate recognition requires license plate detection, segmentation, and charac-ters recognition. The algorithm detects the position of a license plate and extracts the characters. Various license plate recognition algorithms have been implemented, and each algorithm has its strengths and weaknesses. In this research, I implement three algorithms for detecting license plates, three algorithms for segmenting license plates, and two algorithms for recognizing license plate characters. I evaluate each of these algorithms on the same two datasets, one from Greece and one from Thailand. For detecting li-cense plates, the best result is obtained by a Haar cascade algorithm. After the best result of license plate detection is obtained, for the segmentation part a Laplacian based method has the highest accuracy. Last, the license plate recognition experiment shows that a neural network has better accuracy than other algo-rithm. I summarize and analyze the overall performance of each method for comparison.

  19. Does Employee Recognition Affect Positive Psychological Functioning and Well-Being?

    Science.gov (United States)

    Merino, M Dolores; Privado, Jesús

    2015-09-14

    Employee recognition is one of the typical characteristics of healthy organizations. The majority of research on recognition has studied the consequences of this variable on workers. But few investigations have focused on understanding what mechanisms mediate between recognition and its consequences. This work aims to understand whether the relationship between employee recognition and well-being, psychological resources mediate. To answer this question a sample of 1831 workers was used. The variables measured were: employee recognition, subjective well-being and positive psychological functioning (PPF), which consists of 11 psychological resources. In the analysis of data, structural equation models were applied. The results confirmed our hypothesis and showed that PPF mediate the relationship between recognition and well-being. The effect of recognition over PPF is two times greater (.39) with peer-recognition than with supervisor-recognition (.20), and, the effect of PPF over well-being is .59. This study highlights the importance of promoting employee recognition policies in organizations for the impact it has, not only on well-being, but also on the positive psychological functioning of the workers.

  20. Multithread Face Recognition in Cloud

    Directory of Open Access Journals (Sweden)

    Dakshina Ranjan Kisku

    2016-01-01

    Full Text Available Faces are highly challenging and dynamic objects that are employed as biometrics evidence in identity verification. Recently, biometrics systems have proven to be an essential security tools, in which bulk matching of enrolled people and watch lists is performed every day. To facilitate this process, organizations with large computing facilities need to maintain these facilities. To minimize the burden of maintaining these costly facilities for enrollment and recognition, multinational companies can transfer this responsibility to third-party vendors who can maintain cloud computing infrastructures for recognition. In this paper, we showcase cloud computing-enabled face recognition, which utilizes PCA-characterized face instances and reduces the number of invariant SIFT points that are extracted from each face. To achieve high interclass and low intraclass variances, a set of six PCA-characterized face instances is computed on columns of each face image by varying the number of principal components. Extracted SIFT keypoints are fused using sum and max fusion rules. A novel cohort selection technique is applied to increase the total performance. The proposed protomodel is tested on BioID and FEI face databases, and the efficacy of the system is proven based on the obtained results. We also compare the proposed method with other well-known methods.

  1. Speech and audio processing for coding, enhancement and recognition

    CERN Document Server

    Togneri, Roberto; Narasimha, Madihally

    2015-01-01

    This book describes the basic principles underlying the generation, coding, transmission and enhancement of speech and audio signals, including advanced statistical and machine learning techniques for speech and speaker recognition with an overview of the key innovations in these areas. Key research undertaken in speech coding, speech enhancement, speech recognition, emotion recognition and speaker diarization are also presented, along with recent advances and new paradigms in these areas. ·         Offers readers a single-source reference on the significant applications of speech and audio processing to speech coding, speech enhancement and speech/speaker recognition. Enables readers involved in algorithm development and implementation issues for speech coding to understand the historical development and future challenges in speech coding research; ·         Discusses speech coding methods yielding bit-streams that are multi-rate and scalable for Voice-over-IP (VoIP) Networks; ·     �...

  2. Good and bad gifts: the communicative function of employee recognition

    OpenAIRE

    Smith, Charlotte

    2015-01-01

    Purpose Through an interpretation of individuals’ accounts of their recognition experiences, this paper contributes to knowledge about employee recognition by offering insights into how individuals experience and understand recognition in the workplace and its possible social functions and implications. Design/methodology/approach In-depth interviews were conducted with individuals drawn from two research organisations, an insurance company and a local council, in order to underst...

  3. An Agent-mediated Ontology-based Approach for Composite Activity Recognition in Smart Homes

    OpenAIRE

    Okeyo, George; Chen, Liming; Wang, H.

    2013-01-01

    Activity recognition enables ambient assisted living applications to provide activity-aware services to users in smart homes. Despite significant progress being made in activity recognition research, the focus has been on simple activity recognition leaving composite activity recognition an open problem. For instance, knowledge-driven activity recognition has recently attracted increasing attention but mainly focused on simple activities. This paper extends previous work by introducing a know...

  4. Confirmation bias in studies of nestmate recognition: a cautionary note for research into the behaviour of animals.

    Science.gov (United States)

    van Wilgenburg, Ellen; Elgar, Mark A

    2013-01-01

    Confirmation bias is a tendency of people to interpret information in a way that confirms their expectations. A long recognized phenomenon in human psychology, confirmation bias can distort the results of a study and thus reduce its reliability. While confirmation bias can be avoided by conducting studies blind to treatment groups, this practice is not always used. Surprisingly, this is true of research in animal behaviour, and the extent to which confirmation bias influences research outcomes in this field is rarely investigated. Here we conducted a meta-analysis, using studies on nestmate recognition in ants, to compare the outcomes of studies that were conducted blind with those that were not. Nestmate recognition studies typically perform intra- and inter colony aggression assays, with the a priori expectation that there should be little or no aggression among nestmates. Aggressive interactions between ants can include subtle behaviours such as mandible flaring and recoil, which can be hard to quantify, making these types of assays prone to confirmation bias. Our survey revealed that only 29% of our sample of 79 studies were conducted blind. These studies were more likely to report aggression among nestmates if they were conducted blind (73%) than if they were not (21%). Moreover, we found that the effect size between nestmate and non-nestmate treatment means is significantly lower in experiments conducted blind than those in which colony identity is known (1.38 versus 2.76). We discuss the implications of the impact of confirmation bias for research that attempts to obtain quantitative synthesises of data from different studies.

  5. Confirmation bias in studies of nestmate recognition: a cautionary note for research into the behaviour of animals.

    Directory of Open Access Journals (Sweden)

    Ellen van Wilgenburg

    Full Text Available Confirmation bias is a tendency of people to interpret information in a way that confirms their expectations. A long recognized phenomenon in human psychology, confirmation bias can distort the results of a study and thus reduce its reliability. While confirmation bias can be avoided by conducting studies blind to treatment groups, this practice is not always used. Surprisingly, this is true of research in animal behaviour, and the extent to which confirmation bias influences research outcomes in this field is rarely investigated. Here we conducted a meta-analysis, using studies on nestmate recognition in ants, to compare the outcomes of studies that were conducted blind with those that were not. Nestmate recognition studies typically perform intra- and inter colony aggression assays, with the a priori expectation that there should be little or no aggression among nestmates. Aggressive interactions between ants can include subtle behaviours such as mandible flaring and recoil, which can be hard to quantify, making these types of assays prone to confirmation bias. Our survey revealed that only 29% of our sample of 79 studies were conducted blind. These studies were more likely to report aggression among nestmates if they were conducted blind (73% than if they were not (21%. Moreover, we found that the effect size between nestmate and non-nestmate treatment means is significantly lower in experiments conducted blind than those in which colony identity is known (1.38 versus 2.76. We discuss the implications of the impact of confirmation bias for research that attempts to obtain quantitative synthesises of data from different studies.

  6. Picture languages formal models for picture recognition

    CERN Document Server

    Rosenfeld, Azriel

    1979-01-01

    Computer Science and Applied Mathematics: Picture Languages: Formal Models for Picture Recognition treats pictorial pattern recognition from the formal standpoint of automata theory. This book emphasizes the capabilities and relative efficiencies of two types of automata-array automata and cellular array automata, with respect to various array recognition tasks. The array automata are simple processors that perform sequences of operations on arrays, while the cellular array automata are arrays of processors that operate on pictures in a highly parallel fashion, one processor per picture element. This compilation also reviews a collection of results on two-dimensional sequential and parallel array acceptors. Some of the analogous one-dimensional results and array grammars and their relation to acceptors are likewise covered in this text. This publication is suitable for researchers, professionals, and specialists interested in pattern recognition and automata theory.

  7. Effects of modality and repetition in a continuous recognition memory task: Repetition has no effect on auditory recognition memory.

    Science.gov (United States)

    Amir Kassim, Azlina; Rehman, Rehan; Price, Jessica M

    2018-04-01

    Previous research has shown that auditory recognition memory is poorer compared to visual and cross-modal (visual and auditory) recognition memory. The effect of repetition on memory has been robust in showing improved performance. It is not clear, however, how auditory recognition memory compares to visual and cross-modal recognition memory following repetition. Participants performed a recognition memory task, making old/new discriminations to new stimuli, stimuli repeated for the first time after 4-7 intervening items (R1), or repeated for the second time after 36-39 intervening items (R2). Depending on the condition, participants were either exposed to visual stimuli (2D line drawings), auditory stimuli (spoken words), or cross-modal stimuli (pairs of images and associated spoken words). Results showed that unlike participants in the visual and cross-modal conditions, participants in the auditory recognition did not show improvements in performance on R2 trials compared to R1 trials. These findings have implications for pedagogical techniques in education, as well as for interventions and exercises aimed at boosting memory performance. Copyright © 2018 Elsevier B.V. All rights reserved.

  8. Image processing and recognition for biological images.

    Science.gov (United States)

    Uchida, Seiichi

    2013-05-01

    This paper reviews image processing and pattern recognition techniques, which will be useful to analyze bioimages. Although this paper does not provide their technical details, it will be possible to grasp their main tasks and typical tools to handle the tasks. Image processing is a large research area to improve the visibility of an input image and acquire some valuable information from it. As the main tasks of image processing, this paper introduces gray-level transformation, binarization, image filtering, image segmentation, visual object tracking, optical flow and image registration. Image pattern recognition is the technique to classify an input image into one of the predefined classes and also has a large research area. This paper overviews its two main modules, that is, feature extraction module and classification module. Throughout the paper, it will be emphasized that bioimage is a very difficult target for even state-of-the-art image processing and pattern recognition techniques due to noises, deformations, etc. This paper is expected to be one tutorial guide to bridge biology and image processing researchers for their further collaboration to tackle such a difficult target. © 2013 The Author Development, Growth & Differentiation © 2013 Japanese Society of Developmental Biologists.

  9. L2 Word Recognition: Influence of L1 Orthography on Multi-syllabic Word Recognition.

    Science.gov (United States)

    Hamada, Megumi

    2017-10-01

    L2 reading research suggests that L1 orthographic experience influences L2 word recognition. Nevertheless, the findings on multi-syllabic words in English are still limited despite the fact that a vast majority of words are multi-syllabic. The study investigated whether L1 orthography influences the recognition of multi-syllabic words, focusing on the position of an embedded word. The participants were Arabic ESL learners, Chinese ESL learners, and native speakers of English. The task was a word search task, in which the participants identified a target word embedded in a pseudoword at the initial, middle, or final position. The search accuracy and speed indicated that all groups showed a strong preference for the initial position. The accuracy data further indicated group differences. The Arabic group showed higher accuracy in the final than middle, while the Chinese group showed the opposite and the native speakers showed no difference between the two positions. The findings suggest that L2 multi-syllabic word recognition involves unique processes.

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

    Science.gov (United States)

    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.

  11. Facial Affect Recognition and Social Anxiety in Preschool Children

    Science.gov (United States)

    Ale, Chelsea M.; Chorney, Daniel B.; Brice, Chad S.; Morris, Tracy L.

    2010-01-01

    Research relating anxiety and facial affect recognition has focused mostly on school-aged children and adults and has yielded mixed results. The current study sought to demonstrate an association among behavioural inhibition and parent-reported social anxiety, shyness, social withdrawal and facial affect recognition performance in 30 children,…

  12. Research on Recognition and Evaluation of Traffic Guide Sign

    OpenAIRE

    Li Yuan; Ming-jie Cai; Tang-yi Guo; Yu Jiang

    2015-01-01

    Traffic guide signs are effective only when they are clearly recognized by drivers. Three experiments were conducted in this study. In the first, the influence factors of guide sign recognition were studied. This study investigated 11 main factors with a convenience sample of drivers from Nanjing city in China. Weights of different influence factors were obtained through analytic hierarchy process (AHP). The results showed that the setting position, occlusion degree, and character size of gui...

  13. Multi-digit handwritten sindhi numerals recognition using som neural network

    International Nuclear Information System (INIS)

    Chandio, A.A.; Jalbani, A.H.; Awan, S.A.

    2017-01-01

    In this research paper a multi-digit Sindhi handwritten numerals recognition system using SOM Neural Network is presented. Handwritten digits recognition is one of the challenging tasks and a lot of research is being carried out since many years. A remarkable work has been done for recognition of isolated handwritten characters as well as digits in many languages like English, Arabic, Devanagari, Chinese, Urdu and Pashto. However, the literature reviewed does not show any remarkable work done for Sindhi numerals recognition. The recognition of Sindhi digits is a difficult task due to the various writing styles and different font sizes. Therefore, SOM (Self-Organizing Map), a NN (Neural Network) method is used which can recognize digits with various writing styles and different font sizes. Only one sample is required to train the network for each pair of multi-digit numerals. A database consisting of 4000 samples of multi-digits consisting only two digits from 10-50 and other matching numerals have been collected by 50 users and the experimental results of proposed method show that an accuracy of 86.89% is achieved. (author)

  14. Enriched environment effects on remote object recognition memory.

    Science.gov (United States)

    Melani, Riccardo; Chelini, Gabriele; Cenni, Maria Cristina; Berardi, Nicoletta

    2017-06-03

    Since Ebbinghaus' classical work on oblivion and saving effects, we know that declarative memories may become at first spontaneously irretrievable and only subsequently completely extinguished. Recently, this time-dependent path toward memory-trace loss has been shown to correlate with different patterns of brain activation. Environmental enrichment (EE) enhances learning and memory and affects system memory consolidation. However, there is no evidence on whether and how EE could affect the time-dependent path toward oblivion. We used Object Recognition Test (ORT) to assess in adult mice put in EE for 40days (EE mice) or left in standard condition (SC mice) memory retrieval of the familiar objects 9 and 21days after learning with or without a brief retraining performed the day before. We found that SC mice show preferential exploration of new object at day 9 only with retraining, while EE mice do it even without. At day 21 SC mice do not show preferential exploration of novel object, irrespective of the retraining, while EE mice are still capable to benefit from retraining, even if they were not able to spontaneously recover the trace. Analysis of c-fos expression 20days after learning shows a different pattern of active brain areas in response to the retraining session in EE and SC mice, with SC mice recruiting the same brain network as naïve SC or EE mice following de novo learning. This suggests that EE promotes formation of longer lasting object recognition memory, allowing a longer time window during which saving is present. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  15. Optical Pattern Recognition

    Science.gov (United States)

    Yu, Francis T. S.; Jutamulia, Suganda

    2008-10-01

    Contributors; Preface; 1. Pattern recognition with optics Francis T. S. Yu and Don A. Gregory; 2. Hybrid neural networks for nonlinear pattern recognition Taiwei Lu; 3. Wavelets, optics, and pattern recognition Yao Li and Yunglong Sheng; 4. Applications of the fractional Fourier transform to optical pattern recognition David Mendlovic, Zeev Zalesky and Haldum M. Oxaktas; 5. Optical implementation of mathematical morphology Tien-Hsin Chao; 6. Nonlinear optical correlators with improved discrimination capability for object location and recognition Leonid P. Yaroslavsky; 7. Distortion-invariant quadratic filters Gregory Gheen; 8. Composite filter synthesis as applied to pattern recognition Shizhou Yin and Guowen Lu; 9. Iterative procedures in electro-optical pattern recognition Joseph Shamir; 10. Optoelectronic hybrid system for three-dimensional object pattern recognition Guoguang Mu, Mingzhe Lu and Ying Sun; 11. Applications of photrefractive devices in optical pattern recognition Ziangyang Yang; 12. Optical pattern recognition with microlasers Eung-Gi Paek; 13. Optical properties and applications of bacteriorhodopsin Q. Wang Song and Yu-He Zhang; 14. Liquid-crystal spatial light modulators Aris Tanone and Suganda Jutamulia; 15. Representations of fully complex functions on real-time spatial light modulators Robert W. Cohn and Laurence G. Hassbrook; Index.

  16. A new selective developmental deficit: Impaired object recognition with normal face recognition.

    Science.gov (United States)

    Germine, Laura; Cashdollar, Nathan; Düzel, Emrah; Duchaine, Bradley

    2011-05-01

    Studies of developmental deficits in face recognition, or developmental prosopagnosia, have shown that individuals who have not suffered brain damage can show face recognition impairments coupled with normal object recognition (Duchaine and Nakayama, 2005; Duchaine et al., 2006; Nunn et al., 2001). However, no developmental cases with the opposite dissociation - normal face recognition with impaired object recognition - have been reported. The existence of a case of non-face developmental visual agnosia would indicate that the development of normal face recognition mechanisms does not rely on the development of normal object recognition mechanisms. To see whether a developmental variant of non-face visual object agnosia exists, we conducted a series of web-based object and face recognition tests to screen for individuals showing object recognition memory impairments but not face recognition impairments. Through this screening process, we identified AW, an otherwise normal 19-year-old female, who was then tested in the lab on face and object recognition tests. AW's performance was impaired in within-class visual recognition memory across six different visual categories (guns, horses, scenes, tools, doors, and cars). In contrast, she scored normally on seven tests of face recognition, tests of memory for two other object categories (houses and glasses), and tests of recall memory for visual shapes. Testing confirmed that her impairment was not related to a general deficit in lower-level perception, object perception, basic-level recognition, or memory. AW's results provide the first neuropsychological evidence that recognition memory for non-face visual object categories can be selectively impaired in individuals without brain damage or other memory impairment. These results indicate that the development of recognition memory for faces does not depend on intact object recognition memory and provide further evidence for category-specific dissociations in visual

  17. Research on the transfer learning of the vehicle logo recognition

    Science.gov (United States)

    Zhao, Wei

    2017-08-01

    The Convolutional Neural Network of Deep Learning has been a huge success in the field of image intelligent transportation system can effectively solve the traffic safety, congestion, vehicle management and other problems of traffic in the city. Vehicle identification is a vital part of intelligent transportation, and the effective information in vehicles is of great significance to vehicle identification. With the traffic system on the vehicle identification technology requirements are getting higher and higher, the vehicle as an important type of vehicle information, because it should not be removed, difficult to change and other features for vehicle identification provides an important method. The current vehicle identification recognition (VLR) is mostly used to extract the characteristics of the method of classification, which for complex classification of its generalization ability to be some constraints, if the use of depth learning technology, you need a lot of training samples. In this paper, the method of convolution neural network based on transfer learning can solve this problem effectively, and it has important practical application value in the task of vehicle mark recognition.

  18. Achillea millefolium Aqueous Extract does not Impair Recognition ...

    African Journals Online (AJOL)

    Purpose: To investigate the effect of the aqueous extract of Achillea millefolium on recognition memory in mice. Methods: Male mice (35) were used. The aqueous extract of A. millefolium was prepared using a Soxhlet apparatus and injected intraperitoneally in a dose of 50, 250, 500 or 1000 mg/kg daily for 20 days.

  19. Self-body recognition depends on implicit and explicit self-esteem.

    Science.gov (United States)

    Richetin, Juliette; Xaiz, Annalisa; Maravita, Angelo; Perugini, Marco

    2012-03-01

    The present contribution bridges research on body image, self-esteem, and body recognition. Recent work in neuroscience indicates a superiority in the processing of self relative to others' body parts. The present contribution shows that this ability is not universal but it is qualified by individual differences in implicit and explicit self-esteem. In fact, two studies (n₁ = 41 and n₂ = 35) using two different paradigms in body recognition and direct and indirect measures of self-esteem reveal that this advantage in recognizing one's own body parts is associated with one's level of self-esteem. Moreover, it appears that measures of implicit and explicit self-esteem provide different contributions to self-body recognition abilities and that these contributions depend on how self-body recognition is assessed. Implications of these results are discussed notably in the perspective of research on body image. Copyright © 2011 Elsevier Ltd. All rights reserved.

  20. Speech emotion recognition methods: A literature review

    Science.gov (United States)

    Basharirad, Babak; Moradhaseli, Mohammadreza

    2017-10-01

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

  1. Image recognition and consistency of response

    Science.gov (United States)

    Haygood, Tamara M.; Ryan, John; Liu, Qing Mary A.; Bassett, Roland; Brennan, Patrick C.

    2012-02-01

    Purpose: To investigate the connection between conscious recognition of an image previously encountered in an experimental setting and consistency of response to the experimental question. Materials and Methods: Twenty-four radiologists viewed 40 frontal chest radiographs and gave their opinion as to the position of a central venous catheter. One-to-three days later they again viewed 40 frontal chest radiographs and again gave their opinion as to the position of the central venous catheter. Half of the radiographs in the second set were repeated images from the first set and half were new. The radiologists were asked of each image whether it had been included in the first set. For this study, we are evaluating only the 20 repeated images. We used the Kruskal-Wallis test and Fisher's exact test to determine the relationship between conscious recognition of a previously interpreted image and consistency in interpretation of the image. Results. There was no significant correlation between recognition of the image and consistency in response regarding the position of the central venous catheter. In fact, there was a trend in the opposite direction, with radiologists being slightly more likely to give a consistent response with respect to images they did not recognize than with respect to those they did recognize. Conclusion: Radiologists' recognition of previously-encountered images in an observer-performance study does not noticeably color their interpretation on the second encounter.

  2. National convention on world homoeopathy day: Enhancing quality of research in homoeopathy

    Directory of Open Access Journals (Sweden)

    Bindu Sharma

    2017-01-01

    Full Text Available A national convention on World Homoeopathy Day was held to commemorate the 262nd birth anniversary of Dr. Samuel Hahnemann on 9th–10th April, 2017 at National Agricultural Science Complex, Pusa, New Delhi, India. The theme of the convention was ‘Enhancing Quality of Research in Homoeopathy’ inspired by the World Health Organization Traditional Medicine Strategy 2014–2023 for achieving Universal Health Coverage. Organised by the Central Council for Research in Homoeopathy, an autonomous research organisation of the Ministry of AYUSH, Government of India, the convention witnessed 11 oral presentations and focused group discussions held in parallel in the main and side hall, respectively. On this occasion, Awards of Excellence such as Lifetime Achievement Award, Best Teacher and Researcher Award, Young Scientist Award and Best Research Paper Award were also given.

  3. Hierarchical Context Modeling for Video Event Recognition.

    Science.gov (United States)

    Wang, Xiaoyang; Ji, Qiang

    2016-10-11

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

  4. Oxytocin improves emotion recognition for older males.

    Science.gov (United States)

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

    2014-10-01

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

  5. Does comorbid anxiety counteract emotion recognition deficits in conduct disorder?

    Science.gov (United States)

    Short, Roxanna M L; Sonuga-Barke, Edmund J S; Adams, Wendy J; Fairchild, Graeme

    2016-08-01

    Previous research has reported altered emotion recognition in both conduct disorder (CD) and anxiety disorders (ADs) - but these effects appear to be of different kinds. Adolescents with CD often show a generalised pattern of deficits, while those with ADs show hypersensitivity to specific negative emotions. Although these conditions often cooccur, little is known regarding emotion recognition performance in comorbid CD+ADs. Here, we test the hypothesis that in the comorbid case, anxiety-related emotion hypersensitivity counteracts the emotion recognition deficits typically observed in CD. We compared facial emotion recognition across four groups of adolescents aged 12-18 years: those with CD alone (n = 28), ADs alone (n = 23), cooccurring CD+ADs (n = 20) and typically developing controls (n = 28). The emotion recognition task we used systematically manipulated the emotional intensity of facial expressions as well as fixation location (eye, nose or mouth region). Conduct disorder was associated with a generalised impairment in emotion recognition; however, this may have been modulated by group differences in IQ. AD was associated with increased sensitivity to low-intensity happiness, disgust and sadness. In general, the comorbid CD+ADs group performed similarly to typically developing controls. Although CD alone was associated with emotion recognition impairments, ADs and comorbid CD+ADs were associated with normal or enhanced emotion recognition performance. The presence of comorbid ADs appeared to counteract the effects of CD, suggesting a potentially protective role, although future research should examine the contribution of IQ and gender to these effects. © 2016 Association for Child and Adolescent Mental Health.

  6. Prevalence of face recognition deficits in middle childhood.

    Science.gov (United States)

    Bennetts, Rachel J; Murray, Ebony; Boyce, Tian; Bate, Sarah

    2017-02-01

    Approximately 2-2.5% of the adult population is believed to show severe difficulties with face recognition, in the absence of any neurological injury-a condition known as developmental prosopagnosia (DP). However, to date no research has attempted to estimate the prevalence of face recognition deficits in children, possibly because there are very few child-friendly, well-validated tests of face recognition. In the current study, we examined face and object recognition in a group of primary school children (aged 5-11 years), to establish whether our tests were suitable for children and to provide an estimate of face recognition difficulties in children. In Experiment 1 (n = 184), children completed a pre-existing test of child face memory, the Cambridge Face Memory Test-Kids (CFMT-K), and a bicycle test with the same format. In Experiment 2 (n = 413), children completed three-alternative forced-choice matching tasks with faces and bicycles. All tests showed good psychometric properties. The face and bicycle tests were well matched for difficulty and showed a similar developmental trajectory. Neither the memory nor the matching tests were suitable to detect impairments in the youngest groups of children, but both tests appear suitable to screen for face recognition problems in middle childhood. In the current sample, 1.2-5.2% of children showed difficulties with face recognition; 1.2-4% showed face-specific difficulties-that is, poor face recognition with typical object recognition abilities. This is somewhat higher than previous adult estimates: It is possible that face matching tests overestimate the prevalence of face recognition difficulties in children; alternatively, some children may "outgrow" face recognition difficulties.

  7. Independent component analysis of edge information for face recognition

    CERN Document Server

    Karande, Kailash Jagannath

    2013-01-01

    The book presents research work on face recognition using edge information as features for face recognition with ICA algorithms. The independent components are extracted from edge information. These independent components are used with classifiers to match the facial images for recognition purpose. In their study, authors have explored Canny and LOG edge detectors as standard edge detection methods. Oriented Laplacian of Gaussian (OLOG) method is explored to extract the edge information with different orientations of Laplacian pyramid. Multiscale wavelet model for edge detection is also propos

  8. Differential effects of spaced vs. massed training in long-term object-identity and object-location recognition memory.

    Science.gov (United States)

    Bello-Medina, Paola C; Sánchez-Carrasco, Livia; González-Ornelas, Nadia R; Jeffery, Kathryn J; Ramírez-Amaya, Víctor

    2013-08-01

    Here we tested whether the well-known superiority of spaced training over massed training is equally evident in both object identity and object location recognition memory. We trained animals with objects placed in a variable or in a fixed location to produce a location-independent object identity memory or a location-dependent object representation. The training consisted of 5 trials that occurred either on one day (Massed) or over the course of 5 consecutive days (Spaced). The memory test was done in independent groups of animals either 24h or 7 days after the last training trial. In each test the animals were exposed to either a novel object, when trained with the objects in variable locations, or to a familiar object in a novel location, when trained with objects in fixed locations. The difference in time spent exploring the changed versus the familiar objects was used as a measure of recognition memory. For the object-identity-trained animals, spaced training produced clear evidence of recognition memory after both 24h and 7 days, but massed-training animals showed it only after 24h. In contrast, for the object-location-trained animals, recognition memory was evident after both retention intervals and with both training procedures. When objects were placed in variable locations for the two types of training and the test was done with a brand-new location, only the spaced-training animals showed recognition at 24h, but surprisingly, after 7 days, animals trained using both procedures were able to recognize the change, suggesting a post-training consolidation process. We suggest that the two training procedures trigger different neural mechanisms that may differ in the two segregated streams that process object information and that may consolidate differently. Copyright © 2013 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

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

    2013-02-01

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

  10. Subspace methods for pattern recognition in intelligent environment

    CERN Document Server

    Jain, Lakhmi

    2014-01-01

    This research book provides a comprehensive overview of the state-of-the-art subspace learning methods for pattern recognition in intelligent environment. With the fast development of internet and computer technologies, the amount of available data is rapidly increasing in our daily life. How to extract core information or useful features is an important issue. Subspace methods are widely used for dimension reduction and feature extraction in pattern recognition. They transform a high-dimensional data to a lower-dimensional space (subspace), where most information is retained. The book covers a broad spectrum of subspace methods including linear, nonlinear and multilinear subspace learning methods and applications. The applications include face alignment, face recognition, medical image analysis, remote sensing image classification, traffic sign recognition, image clustering, super resolution, edge detection, multi-view facial image synthesis.

  11. Entrance C - New Automatic Number Plate Recognition System

    CERN Multimedia

    2013-01-01

    Entrance C (Satigny) is now equipped with a latest-generation Automatic Number Plate Recognition (ANPR) system and a fast-action road gate.   During the month of August, Entrance C will be continuously open from 7.00 a.m. to 7.00 p.m. (working days only). The security guards will open the gate as usual from 7.00 a.m. to 9.00 a.m. and from 5.00 p.m. to 7.00 p.m. For the rest of the working day (9.00 a.m. to 5.00 p.m.) the gate will operate automatically. Please observe the following points:       Stop at the STOP sign on the ground     Position yourself next to the card reader for optimal recognition     Motorcyclists must use their CERN card     Cyclists may not activate the gate and should use the bicycle turnstile     Keep a safe distance from the vehicle in front of you   If access is denied, please check that your vehicle regist...

  12. Neural activity during emotion recognition after combined cognitive plus social-cognitive training in schizophrenia

    Science.gov (United States)

    Hooker, Christine I.; Bruce, Lori; Fisher, Melissa; Verosky, Sara C.; Miyakawa, Asako; Vinogradov, Sophia

    2012-01-01

    Cognitive remediation training has been shown to improve both cognitive and social-cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social-cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 hour (10-week) remediation intervention which included both cognitive and social-cognitive training would influence neural function in regions that support social-cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 minutes/day] plus social-cognition training (SCT) which was focused on emotion recognition [~5–15 minutes per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. FMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social-cognition training impacts neural mechanisms that support social-cognition skills. PMID:22695257

  13. Machine learning techniques for gait biometric recognition using the ground reaction force

    CERN Document Server

    Mason, James Eric; Woungang, Isaac

    2016-01-01

    This book focuses on how machine learning techniques can be used to analyze and make use of one particular category of behavioral biometrics known as the gait biometric. A comprehensive Ground Reaction Force (GRF)-based Gait Biometrics Recognition framework is proposed and validated by experiments. In addition, an in-depth analysis of existing recognition techniques that are best suited for performing footstep GRF-based person recognition is also proposed, as well as a comparison of feature extractors, normalizers, and classifiers configurations that were never directly compared with one another in any previous GRF recognition research. Finally, a detailed theoretical overview of many existing machine learning techniques is presented, leading to a proposal of two novel data processing techniques developed specifically for the purpose of gait biometric recognition using GRF. This book · introduces novel machine-learning-based temporal normalization techniques · bridges research gaps concerning the effect of ...

  14. Cough Recognition Based on Mel Frequency Cepstral Coefficients and Dynamic Time Warping

    Science.gov (United States)

    Zhu, Chunmei; Liu, Baojun; Li, Ping

    Cough recognition provides important clinical information for the treatment of many respiratory diseases, but the assessment of cough frequency over a long period of time remains unsatisfied for either clinical or research purpose. In this paper, according to the advantage of dynamic time warping (DTW) and the characteristic of cough recognition, an attempt is made to adapt DTW as the recognition algorithm for cough recognition. The process of cough recognition based on mel frequency cepstral coefficients (MFCC) and DTW is introduced. Experiment results of testing samples from 3 subjects show that acceptable performances of cough recognition are obtained by DTW with a small training set.

  15. Speech Recognition

    Directory of Open Access Journals (Sweden)

    Adrian Morariu

    2009-01-01

    Full Text Available This paper presents a method of speech recognition by pattern recognition techniques. Learning consists in determining the unique characteristics of a word (cepstral coefficients by eliminating those characteristics that are different from one word to another. For learning and recognition, the system will build a dictionary of words by determining the characteristics of each word to be used in the recognition. Determining the characteristics of an audio signal consists in the following steps: noise removal, sampling it, applying Hamming window, switching to frequency domain through Fourier transform, calculating the magnitude spectrum, filtering data, determining cepstral coefficients.

  16. The neural correlates of visual self-recognition.

    Science.gov (United States)

    Devue, Christel; Brédart, Serge

    2011-03-01

    This paper presents a review of studies that were aimed at determining which brain regions are recruited during visual self-recognition, with a particular focus on self-face recognition. A complex bilateral network, involving frontal, parietal and occipital areas, appears to be associated with self-face recognition, with a particularly high implication of the right hemisphere. Results indicate that it remains difficult to determine which specific cognitive operation is reflected by each recruited brain area, in part due to the variability of used control stimuli and experimental tasks. A synthesis of the interpretations provided by previous studies is presented. The relevance of using self-recognition as an indicator of self-awareness is discussed. We argue that a major aim of future research in the field should be to identify more clearly the cognitive operations induced by the perception of the self-face, and search for dissociations between neural correlates and cognitive components. Copyright © 2010 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

    Guo, Dongwei; Wang, Zhe

    2018-05-01

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

  18. Using eye movements as an index of implicit face recognition in autism spectrum disorder.

    Science.gov (United States)

    Hedley, Darren; Young, Robyn; Brewer, Neil

    2012-10-01

    Individuals with an autism spectrum disorder (ASD) typically show impairment on face recognition tasks. Performance has usually been assessed using overt, explicit recognition tasks. Here, a complementary method involving eye tracking was used to examine implicit face recognition in participants with ASD and in an intelligence quotient-matched non-ASD control group. Differences in eye movement indices between target and foil faces were used as an indicator of implicit face recognition. Explicit face recognition was assessed using old-new discrimination and reaction time measures. Stimuli were faces of studied (target) or unfamiliar (foil) persons. Target images at test were either identical to the images presented at study or altered by changing the lighting, pose, or by masking with visual noise. Participants with ASD performed worse than controls on the explicit recognition task. Eye movement-based measures, however, indicated that implicit recognition may not be affected to the same degree as explicit recognition. Autism Res 2012, 5: 363-379. © 2012 International Society for Autism Research, Wiley Periodicals, Inc. © 2012 International Society for Autism Research, Wiley Periodicals, Inc.

  19. Recognition and Toleration

    DEFF Research Database (Denmark)

    Lægaard, Sune

    2010-01-01

    Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms...... or interpretations of recognition and toleration are considered, confusing and problematic uses of the terms are noted, and the compatibility of toleration and recognition is discussed. The article argues that there is a range of legitimate and importantly different conceptions of both toleration and recognition...

  20. Developing a Credit Recognition System for Chinese Higher Education Institutions

    Science.gov (United States)

    Li, Fuhui

    2015-01-01

    In recent years, a credit recognition system has been developing in Chinese higher education institutions. Much research has been done on this development, but it has been concentrated on system building, barriers/issues and international practices. The relationship between credit recognition system reforms and democratisation of higher education…

  1. 78 FR 79703 - Submission for OMB Review; 30-Day Comment Request: Application Process for Clinical Research...

    Science.gov (United States)

    2013-12-31

    ...; 30-Day Comment Request: Application Process for Clinical Research Training and Medical Education at..., MD, Deputy Director, Office of Clinical Research Training and Medical Education, NIH Clinical Center... Clinical Research Training and Medical Education at the Clinical Center and its Impact on Course and...

  2. Pattern recognition and modelling of earthquake registrations with interactive computer support

    International Nuclear Information System (INIS)

    Manova, Katarina S.

    2004-01-01

    The object of the thesis is Pattern Recognition. Pattern recognition i.e. classification, is applied in many fields: speech recognition, hand printed character recognition, medical analysis, satellite and aerial-photo interpretations, biology, computer vision, information retrieval and so on. In this thesis is studied its applicability in seismology. Signal classification is an area of great importance in a wide variety of applications. This thesis deals with the problem of (automatic) classification of earthquake signals, which are non-stationary signals. Non-stationary signal classification is an area of active research in the signal and image processing community. The goal of the thesis is recognition of earthquake signals according to their epicentral zone. Source classification i.e. recognition is based on transformation of seismograms (earthquake registrations) to images, via time-frequency transformations, and applying image processing and pattern recognition techniques for feature extraction, classification and recognition. The tested data include local earthquakes from seismic regions in Macedonia. By using actual seismic data it is shown that proposed methods provide satisfactory results for classification and recognition.(Author)

  3. Recognition of Time Stamps on Full-Disk Hα Images Using Machine Learning Methods

    Science.gov (United States)

    Xu, Y.; Huang, N.; Jing, J.; Liu, C.; Wang, H.; Fu, G.

    2016-12-01

    Observation and understanding of the physics of the 11-year solar activity cycle and 22-year magnetic cycle are among the most important research topics in solar physics. The solar cycle is responsible for magnetic field and particle fluctuation in the near-earth environment that have been found increasingly important in affecting the living of human beings in the modern era. A systematic study of large-scale solar activities, as made possible by our rich data archive, will further help us to understand the global-scale magnetic fields that are closely related to solar cycles. The long-time-span data archive includes both full-disk and high-resolution Hα images. Prior to the widely use of CCD cameras in 1990s, 35-mm films were the major media to store images. The research group at NJIT recently finished the digitization of film data obtained by the National Solar Observatory (NSO) and Big Bear Solar Observatory (BBSO) covering the period of 1953 to 2000. The total volume of data exceeds 60 TB. To make this huge database scientific valuable, some processing and calibration are required. One of the most important steps is to read the time stamps on all of the 14 million images, which is almost impossible to be done manually. We implemented three different methods to recognize the time stamps automatically, including Optical Character Recognition (OCR), Classification Tree and TensorFlow. The latter two are known as machine learning algorithms which are very popular now a day in pattern recognition area. We will present some sample images and the results of clock recognition from all three methods.

  4. Tektite 2 habitability research program: Day-to-day life in the habitat

    Science.gov (United States)

    Nowlis, D. P.

    1972-01-01

    Because it is widely agreed that the field of environmental psychology is quite young, it was determined that a sample of recorded observations from a representative mission should be included in the report on Tektite to give the professional reader a better feeling of normal day-to-day life in the isolated habitat. Names of the crew members have been replaced with numbers and some off-color words have been replaced by more acceptable slang; some remarks have been omitted that might lead to easy identification of the subjects. Otherwise, the following pages are exactly as transcribed during the late afternoons and the evenings of the mission.

  5. Mother's Happiness with Cognitive - Executive Functions and Facial Emotional Recognition in School Children with Down Syndrome.

    Science.gov (United States)

    Malmir, Maryam; Seifenaraghi, Maryam; Farhud, Dariush D; Afrooz, G Ali; Khanahmadi, Mohammad

    2015-05-01

    According to the mother's key roles in bringing up emotional and cognitive abilities of mentally retarded children and respect to positive psychology in recent decades, this research is administered to assess the relation between mother's happiness level with cognitive- executive functions (i.e. attention, working memory, inhibition and planning) and facial emotional recognition ability as two factors in learning and adjustment skills in mentally retarded children with Down syndrome. This study was an applied research and data were analyzed by Pearson correlation procedure. Population is included all school children with Down syndrome (9-12 yr) that come from Tehran, Iran. Overall, 30 children were selected as an in access sample. After selection and agreement of parents, the Wechsler Intelligence Scale for Children-Revised (WISC-R) was performed to determine the student's IQ, and then mothers were invited to fill out the Oxford Happiness Inventory (OHI). Cognitive-executive functions were evaluated by tests as followed: Continues Performance Test (CPT), N-Back, Stroop test (day and night version) and Tower of London. Ekman emotion facial expression test was also accomplished for assessing facial emotional recognition in children with Down syndrome, individually. Mother's happiness level had a positive relation with cognitive-executive functions (attention, working memory, inhibition and planning) and facial emotional recognition in her children with Down syndrome, significantly. Parents' happiness (especially mothers) is a powerful predictor for cognitive and emotional abilities of their children.

  6. Familiarity Breeds Attempts: A Critical Review of Dual-Process Theories of Recognition.

    Science.gov (United States)

    Mandler, George

    2008-09-01

    Recognition memory and recall/recollection are the major divisions of the psychology of human memory. Theories of recognition have shifted from a "strength" approach to a dual-process view, which distinguishes between knowing that one has experienced an object before and knowing what it was. In this article, I discuss the history of this approach and the two processes of familiarity and recollection and locate their origin in pattern matching and organization. I evaluate various theories in terms of their basic requirements and their defining research and propose the extension of the original two process theory to domains such as pictorial recognition. Finally, I present the main phenomena that a dual-process theory of recognition must account for and discuss future needs and directions of research and development. © 2008 Association for Psychological Science.

  7. Wrong capital? Problems with recognition of knowledge presented by non-native students in international education

    DEFF Research Database (Denmark)

    Wilken, Lisanne

    This paper presents research on problems of knowledge recognition among students of various nationalities at an international organisation......This paper presents research on problems of knowledge recognition among students of various nationalities at an international organisation...

  8. Research on Three-dimensional Motion History Image Model and Extreme Learning Machine for Human Body Movement Trajectory Recognition

    Directory of Open Access Journals (Sweden)

    Zheng Chang

    2015-01-01

    Full Text Available Based on the traditional machine vision recognition technology and traditional artificial neural networks about body movement trajectory, this paper finds out the shortcomings of the traditional recognition technology. By combining the invariant moments of the three-dimensional motion history image (computed as the eigenvector of body movements and the extreme learning machine (constructed as the classification artificial neural network of body movements, the paper applies the method to the machine vision of the body movement trajectory. In detail, the paper gives a detailed introduction about the algorithm and realization scheme of the body movement trajectory recognition based on the three-dimensional motion history image and the extreme learning machine. Finally, by comparing with the results of the recognition experiments, it attempts to verify that the method of body movement trajectory recognition technology based on the three-dimensional motion history image and extreme learning machine has a more accurate recognition rate and better robustness.

  9. Selective attention meets spontaneous recognition memory: Evidence for effects at retrieval.

    Science.gov (United States)

    Moen, Katherine C; Miller, Jeremy K; Lloyd, Marianne E

    2017-03-01

    Previous research on the effects of Divided Attention on recognition memory have shown consistent impairments during encoding but more variable effects at retrieval. The present study explored whether effects of Selective Attention at retrieval and subsequent testing were parallel to those of Divided Attention. Participants studied a list of pictures and then had a recognition memory test that included both full attention and selective attention (the to be responded to object was overlaid atop a blue outlined object) trials. All participants then completed a second recognition memory test. The results of 2 experiments suggest that subsequent tests consistently show impacts of the status of the ignored stimulus, and that having an initial test changes performance on a later test. The results are discussed in relation to effect of attention on memory more generally as well as spontaneous recognition memory research. Copyright © 2017 Elsevier Inc. All rights reserved.

  10. Advances in oriental document analysis and recognition techniques

    CERN Document Server

    Lee, Seong-Whan

    1999-01-01

    In recent years, rapid progress has been made in computer processing of oriental languages, and the research developments in this area have resulted in tremendous changes in handwriting processing, printed oriental character recognition, document analysis and recognition, automatic input methodologies for oriental languages, etc. Advances in computer processing of oriental languages can also be seen in multimedia computing and the World Wide Web. Many of the results in those domains are presented in this book.

  11. Learned image representations for visual recognition

    DEFF Research Database (Denmark)

    Larsen, Anders Boesen Lindbo

    This thesis addresses the problem of extracting image structures for representing images effectively in order to solve visual recognition tasks. Problems from diverse research areas (medical imaging, material science and food processing) have motivated large parts of the methodological development...

  12. The Formulation of Extra-Territorial Recognition

    Czech Academy of Sciences Publication Activity Database

    Hrubec, Marek

    2010-01-01

    Roč. 1, č. 1 (2010), s. 65-72 ISSN 1674-1277 R&D Projects: GA MŠk(CZ) LC06013 Institutional research plan: CEZ:AV0Z90090514 Keywords : global justice * extra-territorial recognition Subject RIV: AA - Philosophy ; Religion

  13. A specific association between facial disgust recognition and estradiol levels in naturally cycling women.

    Directory of Open Access Journals (Sweden)

    Sunjeev K Kamboj

    Full Text Available Subtle changes in social cognition are associated with naturalistic fluctuations in estrogens and progesterone over the course of the menstrual cycle. Using a dynamic emotion recognition task we aimed to provide a comprehensive description of the association between ovarian hormone levels and emotion recognition performance using a variety of performance metrics. Naturally cycling, psychiatrically healthy women attended a single experimental session during a follicular (days 7-13; n = 16, early luteal (days 15-19; n = 14 or late luteal phase (days 22-27; n = 14 of their menstrual cycle. Correct responses and reaction times to dynamic facial expressions were recorded and a two-high threshold analysis was used to assess discrimination and response bias. Salivary progesterone and estradiol were assayed and subjective measures of premenstrual symptoms, anxiety and positive and negative affect assessed. There was no interaction between cycle phase (follicular, early luteal, late luteal and facial expression (sad, happy, fearful, angry, neutral and disgusted on any of the recognition performance metrics. However, across the sample as a whole, progesterone levels were positively correlated with reaction times to a variety of facial expressions (anger, happiness, sadness and neutral expressions. In contrast, estradiol levels were specifically correlated with disgust processing on three performance indices (correct responses, response bias and discrimination. Premenstrual symptoms, anxiety and positive and negative affect were not associated with emotion recognition indices or hormone levels. The study highlights the role of naturalistic variations in ovarian hormone levels in modulating emotion recognition. In particular, progesterone seems to have a general slowing effect on facial expression processing. Our findings also provide the first behavioural evidence of a specific role for estrogens in the processing of disgust in humans.

  14. Quadcopter Control Using Speech Recognition

    Science.gov (United States)

    Malik, H.; Darma, S.; Soekirno, S.

    2018-04-01

    This research reported a comparison from a success rate of speech recognition systems that used two types of databases they were existing databases and new databases, that were implemented into quadcopter as motion control. Speech recognition system was using Mel frequency cepstral coefficient method (MFCC) as feature extraction that was trained using recursive neural network method (RNN). MFCC method was one of the feature extraction methods that most used for speech recognition. This method has a success rate of 80% - 95%. Existing database was used to measure the success rate of RNN method. The new database was created using Indonesian language and then the success rate was compared with results from an existing database. Sound input from the microphone was processed on a DSP module with MFCC method to get the characteristic values. Then, the characteristic values were trained using the RNN which result was a command. The command became a control input to the single board computer (SBC) which result was the movement of the quadcopter. On SBC, we used robot operating system (ROS) as the kernel (Operating System).

  15. A real time mobile-based face recognition with fisherface methods

    Science.gov (United States)

    Arisandi, D.; Syahputra, M. F.; Putri, I. L.; Purnamawati, S.; Rahmat, R. F.; Sari, P. P.

    2018-03-01

    Face Recognition is a field research in Computer Vision that study about learning face and determine the identity of the face from a picture sent to the system. By utilizing this face recognition technology, learning process about people’s identity between students in a university will become simpler. With this technology, student won’t need to browse student directory in university’s server site and look for the person with certain face trait. To obtain this goal, face recognition application use image processing methods consist of two phase, pre-processing phase and recognition phase. In pre-processing phase, system will process input image into the best image for recognition phase. Purpose of this pre-processing phase is to reduce noise and increase signal in image. Next, to recognize face phase, we use Fisherface Methods. This methods is chosen because of its advantage that would help system of its limited data. Therefore from experiment the accuracy of face recognition using fisherface is 90%.

  16. Anticipatory coarticulation facilitates word recognition in toddlers.

    Science.gov (United States)

    Mahr, Tristan; McMillan, Brianna T M; Saffran, Jenny R; Ellis Weismer, Susan; Edwards, Jan

    2015-09-01

    Children learn from their environments and their caregivers. To capitalize on learning opportunities, young children have to recognize familiar words efficiently by integrating contextual cues across word boundaries. Previous research has shown that adults can use phonetic cues from anticipatory coarticulation during word recognition. We asked whether 18-24 month-olds (n=29) used coarticulatory cues on the word "the" when recognizing the following noun. We performed a looking-while-listening eyetracking experiment to examine word recognition in neutral vs. facilitating coarticulatory conditions. Participants looked to the target image significantly sooner when the determiner contained facilitating coarticulatory cues. These results provide the first evidence that novice word-learners can take advantage of anticipatory sub-phonemic cues during word recognition. Copyright © 2015 Elsevier B.V. All rights reserved.

  17. Recognition bias and the physical attractiveness stereotype.

    Science.gov (United States)

    Rohner, Jean-Christophe; Rasmussen, Anders

    2012-06-01

    Previous studies have found a recognition bias for information consistent with the physical attractiveness stereotype (PAS), in which participants believe that they remember that attractive individuals have positive qualities and that unattractive individuals have negative qualities, regardless of what information actually occurred. The purpose of this research was to examine whether recognition bias for PAS congruent information is replicable and invariant across a variety of conditions (i.e. generalizable). The effects of nine different moderator variables were examined in two experiments. With a few exceptions, the effect of PAS congruence on recognition bias was independent of the moderator variables. The results suggest that the tendency to believe that one remembers information consistent with the physical attractiveness stereotype is a robust phenomenon. © 2012 The Authors. Scandinavian Journal of Psychology © 2012 The Scandinavian Psychological Associations.

  18. Use of the recognition heuristic depends on the domain's recognition validity, not on the recognition validity of selected sets of objects.

    Science.gov (United States)

    Pohl, Rüdiger F; Michalkiewicz, Martha; Erdfelder, Edgar; Hilbig, Benjamin E

    2017-07-01

    According to the recognition-heuristic theory, decision makers solve paired comparisons in which one object is recognized and the other not by recognition alone, inferring that recognized objects have higher criterion values than unrecognized ones. However, success-and thus usefulness-of this heuristic depends on the validity of recognition as a cue, and adaptive decision making, in turn, requires that decision makers are sensitive to it. To this end, decision makers could base their evaluation of the recognition validity either on the selected set of objects (the set's recognition validity), or on the underlying domain from which the objects were drawn (the domain's recognition validity). In two experiments, we manipulated the recognition validity both in the selected set of objects and between domains from which the sets were drawn. The results clearly show that use of the recognition heuristic depends on the domain's recognition validity, not on the set's recognition validity. In other words, participants treat all sets as roughly representative of the underlying domain and adjust their decision strategy adaptively (only) with respect to the more general environment rather than the specific items they are faced with.

  19. Handwritten recognition of Tamil vowels using deep learning

    Science.gov (United States)

    Ram Prashanth, N.; Siddarth, B.; Ganesh, Anirudh; Naveen Kumar, Vaegae

    2017-11-01

    We come across a large volume of handwritten texts in our daily lives and handwritten character recognition has long been an important area of research in pattern recognition. The complexity of the task varies among different languages and it so happens largely due to the similarity between characters, distinct shapes and number of characters which are all language-specific properties. There have been numerous works on character recognition of English alphabets and with laudable success, but regional languages have not been dealt with very frequently and with similar accuracies. In this paper, we explored the performance of Deep Belief Networks in the classification of Handwritten Tamil vowels, and conclusively compared the results obtained. The proposed method has shown satisfactory recognition accuracy in light of difficulties faced with regional languages such as similarity between characters and minute nuances that differentiate them. We can further extend this to all the Tamil characters.

  20. Forensic Speaker Recognition Law Enforcement and Counter-Terrorism

    CERN Document Server

    Patil, Hemant

    2012-01-01

    Forensic Speaker Recognition: Law Enforcement and Counter-Terrorism is an anthology of the research findings of 35 speaker recognition experts from around the world. The volume provides a multidimensional view of the complex science involved in determining whether a suspect’s voice truly matches forensic speech samples, collected by law enforcement and counter-terrorism agencies, that are associated with the commission of a terrorist act or other crimes. While addressing such topics as the challenges of forensic case work, handling speech signal degradation, analyzing features of speaker recognition to optimize voice verification system performance, and designing voice applications that meet the practical needs of law enforcement and counter-terrorism agencies, this material all sounds a common theme: how the rigors of forensic utility are demanding new levels of excellence in all aspects of speaker recognition. The contributors are among the most eminent scientists in speech engineering and signal process...

  1. The Pandora software development kit for pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

    Marshall, J.S.; Thomson, M.A. [University of Cambridge, Cavendish Laboratory, Cambridge (United Kingdom)

    2015-09-15

    The development of automated solutions to pattern recognition problems is important in many areas of scientific research and human endeavour. This paper describes the implementation of the Pandora software development kit, which aids the process of designing, implementing and running pattern recognition algorithms. The Pandora Application Programming Interfaces ensure simple specification of the building-blocks defining a pattern recognition problem. The logic required to solve the problem is implemented in algorithms. The algorithms request operations to create or modify data structures and the operations are performed by the Pandora framework. This design promotes an approach using many decoupled algorithms, each addressing specific topologies. Details of algorithms addressing two pattern recognition problems in High Energy Physics are presented: reconstruction of events at a high-energy e{sup +}e{sup -} linear collider and reconstruction of cosmic ray or neutrino events in a liquid argon time projection chamber. (orig.)

  2. Static facial expression recognition with convolution neural networks

    Science.gov (United States)

    Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei

    2018-03-01

    Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

  3. Face Recognition Using Local Quantized Patterns and Gabor Filters

    Science.gov (United States)

    Khryashchev, V.; Priorov, A.; Stepanova, O.; Nikitin, A.

    2015-05-01

    The problem of face recognition in a natural or artificial environment has received a great deal of researchers' attention over the last few years. A lot of methods for accurate face recognition have been proposed. Nevertheless, these methods often fail to accurately recognize the person in difficult scenarios, e.g. low resolution, low contrast, pose variations, etc. We therefore propose an approach for accurate and robust face recognition by using local quantized patterns and Gabor filters. The estimation of the eye centers is used as a preprocessing stage. The evaluation of our algorithm on different samples from a standardized FERET database shows that our method is invariant to the general variations of lighting, expression, occlusion and aging. The proposed approach allows about 20% correct recognition accuracy increase compared with the known face recognition algorithms from the OpenCV library. The additional use of Gabor filters can significantly improve the robustness to changes in lighting conditions.

  4. Contribution to automatic handwritten characters recognition. Application to optical moving characters recognition

    International Nuclear Information System (INIS)

    Gokana, Denis

    1986-01-01

    This paper describes a research work on computer aided vision relating to the design of a vision system which can recognize isolated handwritten characters written on a mobile support. We use a technique which consists in analyzing information contained in the contours of the polygon circumscribed to the character's shape. These contours are segmented and labelled to give a new set of features constituted by: - right and left 'profiles', - topological and algebraic unvarying properties. A new method of character's recognition induced from this representation based on a multilevel hierarchical technique is then described. In the primary level, we use a fuzzy classification with dynamic programming technique using 'profiles'. The other levels adjust the recognition by using topological and algebraic unvarying properties. Several results are presented and an accuracy of 99 pc was reached for handwritten numeral characters, thereby attesting the robustness of our algorithm. (author) [fr

  5. Human Activity Recognition from Body Sensor Data using Deep Learning.

    Science.gov (United States)

    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.

  6. Support vector machine-based facial-expression recognition method combining shape and appearance

    Science.gov (United States)

    Han, Eun Jung; Kang, Byung Jun; Park, Kang Ryoung; Lee, Sangyoun

    2010-11-01

    Facial expression recognition can be widely used for various applications, such as emotion-based human-machine interaction, intelligent robot interfaces, face recognition robust to expression variation, etc. Previous studies have been classified as either shape- or appearance-based recognition. The shape-based method has the disadvantage that the individual variance of facial feature points exists irrespective of similar expressions, which can cause a reduction of the recognition accuracy. The appearance-based method has a limitation in that the textural information of the face is very sensitive to variations in illumination. To overcome these problems, a new facial-expression recognition method is proposed, which combines both shape and appearance information, based on the support vector machine (SVM). This research is novel in the following three ways as compared to previous works. First, the facial feature points are automatically detected by using an active appearance model. From these, the shape-based recognition is performed by using the ratios between the facial feature points based on the facial-action coding system. Second, the SVM, which is trained to recognize the same and different expression classes, is proposed to combine two matching scores obtained from the shape- and appearance-based recognitions. Finally, a single SVM is trained to discriminate four different expressions, such as neutral, a smile, anger, and a scream. By determining the expression of the input facial image whose SVM output is at a minimum, the accuracy of the expression recognition is much enhanced. The experimental results showed that the recognition accuracy of the proposed method was better than previous researches and other fusion methods.

  7. Neural activity during emotion recognition after combined cognitive plus social cognitive training in schizophrenia.

    Science.gov (United States)

    Hooker, Christine I; Bruce, Lori; Fisher, Melissa; Verosky, Sara C; Miyakawa, Asako; Vinogradov, Sophia

    2012-08-01

    Cognitive remediation training has been shown to improve both cognitive and social cognitive deficits in people with schizophrenia, but the mechanisms that support this behavioral improvement are largely unknown. One hypothesis is that intensive behavioral training in cognition and/or social cognition restores the underlying neural mechanisms that support targeted skills. However, there is little research on the neural effects of cognitive remediation training. This study investigated whether a 50 h (10-week) remediation intervention which included both cognitive and social cognitive training would influence neural function in regions that support social cognition. Twenty-two stable, outpatient schizophrenia participants were randomized to a treatment condition consisting of auditory-based cognitive training (AT) [Brain Fitness Program/auditory module ~60 min/day] plus social cognition training (SCT) which was focused on emotion recognition [~5-15 min per day] or a placebo condition of non-specific computer games (CG) for an equal amount of time. Pre and post intervention assessments included an fMRI task of positive and negative facial emotion recognition, and standard behavioral assessments of cognition, emotion processing, and functional outcome. There were no significant intervention-related improvements in general cognition or functional outcome. fMRI results showed the predicted group-by-time interaction. Specifically, in comparison to CG, AT+SCT participants had a greater pre-to-post intervention increase in postcentral gyrus activity during emotion recognition of both positive and negative emotions. Furthermore, among all participants, the increase in postcentral gyrus activity predicted behavioral improvement on a standardized test of emotion processing (MSCEIT: Perceiving Emotions). Results indicate that combined cognition and social cognition training impacts neural mechanisms that support social cognition skills. Copyright © 2012 Elsevier B.V. All

  8. Adaptive Self-Occlusion Behavior Recognition Based on pLSA

    Directory of Open Access Journals (Sweden)

    Hong-bin Tu

    2013-01-01

    Full Text Available Human action recognition is an important area of human action recognition research. Focusing on the problem of self-occlusion in the field of human action recognition, a new adaptive occlusion state behavior recognition approach was presented based on Markov random field and probabilistic Latent Semantic Analysis (pLSA. Firstly, the Markov random field was used to represent the occlusion relationship between human body parts in terms an occlusion state variable by phase space obtained. Then, we proposed a hierarchical area variety model. Finally, we use the topic model of pLSA to recognize the human behavior. Experiments were performed on the KTH, Weizmann, and Humaneva dataset to test and evaluate the proposed method. The compared experiment results showed that what the proposed method can achieve was more effective than the compared methods.

  9. Evaluation of Activity Recognition Algorithms for Employee Performance Monitoring

    OpenAIRE

    Mehreen Mumtaz; Hafiz Adnan Habib

    2012-01-01

    Successful Human Resource Management plays a key role in success of any organization. Traditionally, human resource managers rely on various information technology solutions such as Payroll and Work Time Systems incorporating RFID and biometric technologies. This research evaluates activity recognition algorithms for employee performance monitoring. An activity recognition algorithm has been implemented that categorized the activity of employee into following in to classes: job activities and...

  10. A Review of Human Activity Recognition Methods

    Directory of Open Access Journals (Sweden)

    Michalis eVrigkas

    2015-11-01

    Full Text Available Recognizing human activities from video sequences or still images is a challenging task due to problems such as background clutter, partial occlusion, changes in scale, viewpoint, lighting, and appearance. Many applications, including video surveillance systems, human-computer interaction, and robotics for human behavior characterization, require a multiple activity recognition system. In this work, we provide a detailed review of recent and state-of-the-art research advances in the field of human activity classification. We propose a categorization of human activity methodologies and discuss their advantages and limitations. In particular, we divide human activity classification methods into two large categories according to whether they use data from different modalities or not. Then, each of these categories is further analyzed into sub-categories, which reflect how they model human activities and what type of activities they are interested in. Moreover, we provide a comprehensive analysis of the existing, publicly available human activity classification datasets and examine the requirements for an ideal human activity recognition dataset. Finally, we report the characteristics of future research directions and present some open issues on human activity recognition.

  11. Gender differences in the recognition of emotional faces: are men less efficient?

    Directory of Open Access Journals (Sweden)

    Ana Ruiz-Ibáñez

    2017-06-01

    Full Text Available As research in recollection of stimuli with emotional valence indicates, emotions influence memory. Many studies in face and emotional facial expression recognition have focused on age (young and old people and gender-associated (men and women differences. Nevertheless, this kind of studies has produced contradictory results, because of that, it would be necessary to study gender involvement in depth. The main objective of our research consists of analyzing the differences in image recognition using faces with emotional facial expressions between two groups composed by university students aged 18-30. The first group is constituted by men and the second one by women. The results showed statistically significant differences in face corrected recognition (hit rate - false alarm rate: the women demonstrated a better recognition than the men. However, other analyzed variables as time or efficiency do not provide conclusive results. Furthermore, a significant negative correlation between the time used and the efficiency when doing the task was found in the male group. This information reinforces not only the hypothesis of gender difference in face recognition, in favor of women, but also these ones that suggest a different cognitive processing of facial stimuli in both sexes. Finally, we argue the necessity of a greater research related to variables as age or sociocultural level.

  12. Multi-font printed Mongolian document recognition system

    Science.gov (United States)

    Peng, Liangrui; Liu, Changsong; Ding, Xiaoqing; Wang, Hua; Jin, Jianming

    2009-01-01

    Mongolian is one of the major ethnic languages in China. Large amount of Mongolian printed documents need to be digitized in digital library and various applications. Traditional Mongolian script has unique writing style and multi-font-type variations, which bring challenges to Mongolian OCR research. As traditional Mongolian script has some characteristics, for example, one character may be part of another character, we define the character set for recognition according to the segmented components, and the components are combined into characters by rule-based post-processing module. For character recognition, a method based on visual directional feature and multi-level classifiers is presented. For character segmentation, a scheme is used to find the segmentation point by analyzing the properties of projection and connected components. As Mongolian has different font-types which are categorized into two major groups, the parameter of segmentation is adjusted for each group. A font-type classification method for the two font-type group is introduced. For recognition of Mongolian text mixed with Chinese and English, language identification and relevant character recognition kernels are integrated. Experiments show that the presented methods are effective. The text recognition rate is 96.9% on the test samples from practical documents with multi-font-types and mixed scripts.

  13. A multi-view face recognition system based on cascade face detector and improved Dlib

    Science.gov (United States)

    Zhou, Hongjun; Chen, Pei; Shen, Wei

    2018-03-01

    In this research, we present a framework for multi-view face detect and recognition system based on cascade face detector and improved Dlib. This method is aimed to solve the problems of low efficiency and low accuracy in multi-view face recognition, to build a multi-view face recognition system, and to discover a suitable monitoring scheme. For face detection, the cascade face detector is used to extracted the Haar-like feature from the training samples, and Haar-like feature is used to train a cascade classifier by combining Adaboost algorithm. Next, for face recognition, we proposed an improved distance model based on Dlib to improve the accuracy of multiview face recognition. Furthermore, we applied this proposed method into recognizing face images taken from different viewing directions, including horizontal view, overlooks view, and looking-up view, and researched a suitable monitoring scheme. This method works well for multi-view face recognition, and it is also simulated and tested, showing satisfactory experimental results.

  14. Fundamental remote science research program. Part 2: Status report of the mathematical pattern recognition and image analysis project

    Science.gov (United States)

    Heydorn, R. P.

    1984-01-01

    The Mathematical Pattern Recognition and Image Analysis (MPRIA) Project is concerned with basic research problems related to the study of he Earth from remotely sensed measurements of its surface characteristics. The program goal is to better understand how to analyze the digital image that represents the spatial, spectral, and temporal arrangement of these measurements for purposing of making selected inferences about the Earth. This report summarizes the progress that has been made toward this program goal by each of the principal investigators in the MPRIA Program.

  15. High-performance speech recognition using consistency modeling

    Science.gov (United States)

    Digalakis, Vassilios; Murveit, Hy; Monaco, Peter; Neumeyer, Leo; Sankar, Ananth

    1994-12-01

    The goal of SRI's consistency modeling project is to improve the raw acoustic modeling component of SRI's DECIPHER speech recognition system and develop consistency modeling technology. Consistency modeling aims to reduce the number of improper independence assumptions used in traditional speech recognition algorithms so that the resulting speech recognition hypotheses are more self-consistent and, therefore, more accurate. At the initial stages of this effort, SRI focused on developing the appropriate base technologies for consistency modeling. We first developed the Progressive Search technology that allowed us to perform large-vocabulary continuous speech recognition (LVCSR) experiments. Since its conception and development at SRI, this technique has been adopted by most laboratories, including other ARPA contracting sites, doing research on LVSR. Another goal of the consistency modeling project is to attack difficult modeling problems, when there is a mismatch between the training and testing phases. Such mismatches may include outlier speakers, different microphones and additive noise. We were able to either develop new, or transfer and evaluate existing, technologies that adapted our baseline genonic HMM recognizer to such difficult conditions.

  16. Motion Primitives for Action Recognition

    DEFF Research Database (Denmark)

    Fihl, Preben; Holte, Michael Boelstoft; Moeslund, Thomas B.

    2007-01-01

    the actions as a sequence of temporal isolated instances, denoted primitives. These primitives are each defined by four features extracted from motion images. The primitives are recognized in each frame based on a trained classifier resulting in a sequence of primitives. From this sequence we recognize......The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent...... different temporal actions using a probabilistic Edit Distance method. The method is tested on different actions with and without noise and the results show recognition rates of 88.7% and 85.5%, respectively....

  17. Gait Recognition Using Image Self-Similarity

    Directory of Open Access Journals (Sweden)

    Chiraz BenAbdelkader

    2004-04-01

    Full Text Available Gait is one of the few biometrics that can be measured at a distance, and is hence useful for passive surveillance as well as biometric applications. Gait recognition research is still at its infancy, however, and we have yet to solve the fundamental issue of finding gait features which at once have sufficient discrimination power and can be extracted robustly and accurately from low-resolution video. This paper describes a novel gait recognition technique based on the image self-similarity of a walking person. We contend that the similarity plot encodes a projection of gait dynamics. It is also correspondence-free, robust to segmentation noise, and works well with low-resolution video. The method is tested on multiple data sets of varying sizes and degrees of difficulty. Performance is best for fronto-parallel viewpoints, whereby a recognition rate of 98% is achieved for a data set of 6 people, and 70% for a data set of 54 people.

  18. [Measuring impairment of facial affects recognition in schizophrenia. Preliminary study of the facial emotions recognition task (TREF)].

    Science.gov (United States)

    Gaudelus, B; Virgile, J; Peyroux, E; Leleu, A; Baudouin, J-Y; Franck, N

    2015-06-01

    The impairment of social cognition, including facial affects recognition, is a well-established trait in schizophrenia, and specific cognitive remediation programs focusing on facial affects recognition have been developed by different teams worldwide. However, even though social cognitive impairments have been confirmed, previous studies have also shown heterogeneity of the results between different subjects. Therefore, assessment of personal abilities should be measured individually before proposing such programs. Most research teams apply tasks based on facial affects recognition by Ekman et al. or Gur et al. However, these tasks are not easily applicable in a clinical exercise. Here, we present the Facial Emotions Recognition Test (TREF), which is designed to identify facial affects recognition impairments in a clinical practice. The test is composed of 54 photos and evaluates abilities in the recognition of six universal emotions (joy, anger, sadness, fear, disgust and contempt). Each of these emotions is represented with colored photos of 4 different models (two men and two women) at nine intensity levels from 20 to 100%. Each photo is presented during 10 seconds; no time limit for responding is applied. The present study compared the scores of the TREF test in a sample of healthy controls (64 subjects) and people with stabilized schizophrenia (45 subjects) according to the DSM IV-TR criteria. We analysed global scores for all emotions, as well as sub scores for each emotion between these two groups, taking into account gender differences. Our results were coherent with previous findings. Applying TREF, we confirmed an impairment in facial affects recognition in schizophrenia by showing significant differences between the two groups in their global results (76.45% for healthy controls versus 61.28% for people with schizophrenia), as well as in sub scores for each emotion except for joy. Scores for women were significantly higher than for men in the population

  19. Mother’s Happiness with Cognitive - Executive Functions and Facial Emotional Recognition in School Children with Down Syndrome

    Science.gov (United States)

    MALMIR, Maryam; SEIFENARAGHI, Maryam; FARHUD, Dariush D.; AFROOZ, G.Ali; KHANAHMADI, Mohammad

    2015-01-01

    Background: According to the mother’s key roles in bringing up emotional and cognitive abilities of mentally retarded children and respect to positive psychology in recent decades, this research is administered to assess the relation between mother’s happiness level with cognitive- executive functions (i.e. attention, working memory, inhibition and planning) and facial emotional recognition ability as two factors in learning and adjustment skills in mentally retarded children with Down syndrome. Methods: This study was an applied research and data were analyzed by Pearson correlation procedure. Population is included all school children with Down syndrome (9–12 yr) that come from Tehran, Iran. Overall, 30 children were selected as an in access sample. After selection and agreement of parents, the Wechsler Intelligence Scale for Children-Revised (WISC-R) was performed to determine the student’s IQ, and then mothers were invited to fill out the Oxford Happiness Inventory (OHI). Cognitive-executive functions were evaluated by tests as followed: Continues Performance Test (CPT), N-Back, Stroop test (day and night version) and Tower of London. Ekman emotion facial expression test was also accomplished for assessing facial emotional recognition in children with Down syndrome, individually. Results: Mother’s happiness level had a positive relation with cognitive-executive functions (attention, working memory, inhibition and planning) and facial emotional recognition in her children with Down syndrome, significantly. Conclusion: Parents’ happiness (especially mothers) is a powerful predictor for cognitive and emotional abilities of their children. PMID:26284205

  20. IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2014-09-01

    Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

  1. How many steps/day are enough? for adults

    Directory of Open Access Journals (Sweden)

    Rowe David A

    2011-07-01

    MVPA that also include estimates of habitual activity levels equate to 7,100 to 11,000 steps/day. A direct estimate of minimal amounts of MVPA accumulated in the course of objectively monitored free-living behaviour is 7,000-8,000 steps/day. A scale that spans a wide range of incremental increases in steps/day and is congruent with public health recognition that "some physical activity is better than none," yet still incorporates step-based translations of recommended amounts of time in MVPA may be useful in research and practice. The full range of users (researchers to practitioners to the general public of objective monitoring instruments that provide step-based outputs require good reference data and evidence-based recommendations to be able to design effective health messages congruent with public health physical activity guidelines, guide behaviour change, and ultimately measure, track, and interpret steps/day.

  2. 78 FR 48178 - Submission for OMB Review; 30-day Comment Request: Autism Spectrum Disorder Research Portfolio...

    Science.gov (United States)

    2013-08-07

    ...; 30-day Comment Request: Autism Spectrum Disorder Research Portfolio Analysis SUMMARY: Under the... (ASD) Research Portfolio Analysis, 0925--NEW- National Institute of Mental Health (NIMH), National Institutes of Health (NIH). Need and Use of Information Collection: The purpose of the ASD portfolio analysis...

  3. Research on Knowledge Gap Recognition Mechanism of Virtual Industry Cluster

    OpenAIRE

    Lu Cheng

    2013-01-01

    As a new organizing form, VIC gets rid of regional limit of traditional cluster, realizing virtual space agglomeration which crossing space and time. Knowledge sharing and complementary is foundation to form VIC and be one of the main goals. As preparation of the knowledge transfer, recognizing and making up for knowledge gap did not caused most scholars' attention. This study argues that, knowledge gap recognition is the premise of knowledge transfer, combined with knowledge theories, the co...

  4. Application of biomolecular recognition via magnetic nanoparticle in nanobiotechnology

    Science.gov (United States)

    Shen, Wei-Zheng; Cetinel, Sibel; Montemagno, Carlo

    2018-05-01

    The marriage of biomolecular recognition and magnetic nanoparticle creates tremendous opportunities in the development of advanced technology both in academic research and in industrial sectors. In this paper, we review current progress on the magnetic nanoparticle-biomolecule hybrid systems, particularly employing the recognition pairs of DNA-DNA, DNA-protein, protein-protein, and protein-inorganics in several nanobiotechnology application areas, including molecular biology, diagnostics, medical treatment, industrial biocatalysts, and environmental separations.

  5. Indonesian Sign Language Number Recognition using SIFT Algorithm

    Science.gov (United States)

    Mahfudi, Isa; Sarosa, Moechammad; Andrie Asmara, Rosa; Azrino Gustalika, M.

    2018-04-01

    Indonesian sign language (ISL) is generally used for deaf individuals and poor people communication in communicating. They use sign language as their primary language which consists of 2 types of action: sign and finger spelling. However, not all people understand their sign language so that this becomes a problem for them to communicate with normal people. this problem also becomes a factor they are isolated feel from the social life. It needs a solution that can help them to be able to interacting with normal people. Many research that offers a variety of methods in solving the problem of sign language recognition based on image processing. SIFT (Scale Invariant Feature Transform) algorithm is one of the methods that can be used to identify an object. SIFT is claimed very resistant to scaling, rotation, illumination and noise. Using SIFT algorithm for Indonesian sign language recognition number result rate recognition to 82% with the use of a total of 100 samples image dataset consisting 50 sample for training data and 50 sample images for testing data. Change threshold value get affect the result of the recognition. The best value threshold is 0.45 with rate recognition of 94%.

  6. Facial Expression Recognition Through Machine Learning

    Directory of Open Access Journals (Sweden)

    Nazia Perveen

    2015-08-01

    Full Text Available Facial expressions communicate non-verbal cues which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important element of normal human-machine interfaces it might likewise be utilized as a part of behavioral science and in clinical practice. In spite of the fact that people perceive facial expressions for all intents and purposes immediately solid expression recognition by machine is still a challenge. From the point of view of automatic recognition a facial expression can be considered to comprise of disfigurements of the facial parts and their spatial relations or changes in the faces pigmentation. Research into automatic recognition of the facial expressions addresses the issues encompassing the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation. We get results by utilizing the CVIPtools. We have taken train data set of six facial expressions of three persons and for train data set purpose we have total border mask sample 90 and 30 border mask sample for test data set purpose and we use RST- Invariant features and texture features for feature analysis and then classified them by using k- Nearest Neighbor classification algorithm. The maximum accuracy is 90.

  7. In-the-wild facial expression recognition in extreme poses

    Science.gov (United States)

    Yang, Fei; Zhang, Qian; Zheng, Chi; Qiu, Guoping

    2018-04-01

    In the computer research area, facial expression recognition is a hot research problem. Recent years, the research has moved from the lab environment to in-the-wild circumstances. It is challenging, especially under extreme poses. But current expression detection systems are trying to avoid the pose effects and gain the general applicable ability. In this work, we solve the problem in the opposite approach. We consider the head poses and detect the expressions within special head poses. Our work includes two parts: detect the head pose and group it into one pre-defined head pose class; do facial expression recognize within each pose class. Our experiments show that the recognition results with pose class grouping are much better than that of direct recognition without considering poses. We combine the hand-crafted features, SIFT, LBP and geometric feature, with deep learning feature as the representation of the expressions. The handcrafted features are added into the deep learning framework along with the high level deep learning features. As a comparison, we implement SVM and random forest to as the prediction models. To train and test our methodology, we labeled the face dataset with 6 basic expressions.

  8. Word Recognition Subcomponents and Passage Level Reading in a Foreign Language

    Science.gov (United States)

    Yamashita, Junko

    2013-01-01

    Despite the growing number of studies highlighting the complex process of acquiring second language (L2) word recognition skills, comparatively little research has examined the relationship between word recognition and passage-level reading ability in L2 learners; further, the existing results are inconclusive. This study aims to help fill the…

  9. Optical character recognition systems for different languages with soft computing

    CERN Document Server

    Chaudhuri, Arindam; Badelia, Pratixa; K Ghosh, Soumya

    2017-01-01

    The book offers a comprehensive survey of soft-computing models for optical character recognition systems. The various techniques, including fuzzy and rough sets, artificial neural networks and genetic algorithms, are tested using real texts written in different languages, such as English, French, German, Latin, Hindi and Gujrati, which have been extracted by publicly available datasets. The simulation studies, which are reported in details here, show that soft-computing based modeling of OCR systems performs consistently better than traditional models. Mainly intended as state-of-the-art survey for postgraduates and researchers in pattern recognition, optical character recognition and soft computing, this book will be useful for professionals in computer vision and image processing alike, dealing with different issues related to optical character recognition.

  10. Rotation-invariant neural pattern recognition system with application to coin recognition.

    Science.gov (United States)

    Fukumi, M; Omatu, S; Takeda, F; Kosaka, T

    1992-01-01

    In pattern recognition, it is often necessary to deal with problems to classify a transformed pattern. A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed. The system consists of a fixed invariance network with many slabs and a trainable multilayered network. The system was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin. The results show that the approach works well for variable rotation pattern recognition.

  11. Exploring Techniques for Vision Based Human Activity Recognition: Methods, Systems, and Evaluation

    Directory of Open Access Journals (Sweden)

    Hong Zhang

    2013-01-01

    Full Text Available With the wide applications of vision based intelligent systems, image and video analysis technologies have attracted the attention of researchers in the computer vision field. In image and video analysis, human activity recognition is an important research direction. By interpreting and understanding human activity, we can recognize and predict the occurrence of crimes and help the police or other agencies react immediately. In the past, a large number of papers have been published on human activity recognition in video and image sequences. In this paper, we provide a comprehensive survey of the recent development of the techniques, including methods, systems, and quantitative evaluation towards the performance of human activity recognition.

  12. [Advantages and Application Prospects of Deep Learning in Image Recognition and Bone Age Assessment].

    Science.gov (United States)

    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.

  13. The Influence of Emotion on Recognition Memory for Scenes

    OpenAIRE

    Pryde, Beatrice

    2012-01-01

    According to dual-process models, recognition memory is supported by two distinct processes: familiarity, a relatively automatic process that involves the retrieval of a previously encountered item, and recollection, a more effortful process that involves the retrieval of information associated with the context in which an item was encoded (Mickes, Wais & Wixted, 2009). There is a wealth of research suggesting that recognition memory performance is affected by the emotional content of stimul...

  14. Individual recognition of human infants on the basis of cries alone.

    Science.gov (United States)

    Green, J A; Gustafson, G E

    1983-11-01

    Human parents were asked to identify their infants on the basis of tape-recorded cries that they had not previously heard. The cries of twenty 30-day-old infants were recorded just prior to a feeding, then rerecorded onto a test tape containing cries from three other infants. Eighty percent of mothers were able to recognize their infants' cries, as were 45% of fathers. An additional 140 adults (non-parents) were tested in order to determine if the process of dubbing cries onto test tapes had left extraneous auditory cues to infants' identities and if the foil infants were equally discriminable. The results indicated that parents' recognition was not based on extraneous cues and that, overall, the foils were appropriate distractors in the parents' task. Thus, the majority of parents can recognize their 30-day-old infants on the sole basis of acoustic cues contained in the infants' cries. The acoustic features that underlie this recognition are now being investigated.

  15. Opportunity recognition and international new venture creation in University spin-offs

    DEFF Research Database (Denmark)

    Hannibal, Martin; Evers, Natasha; Servais, Per

    2016-01-01

    Extant research suggests that the founder’s activities and interactions are considered pivotal in driving the opportunity recognition process leading to international new venture emergence. This paper aims to explore the opportunity recognition process and international new venture emergence...... in the context of university high-technology spin-offs that are internationally market driven from inception. University spin-offs (USOs) are defined as ‘new firms created to exploit commercially some knowledge, technology or research results developed within a university’ (Pirnay et al., Small Bus Econ 21...... that the inventor-founders are typically engaged in opportunity recognition processes that are characterized as creative, driven by scientific innovations. It is indicated that the process of USO emergence and continuous development involves activities and interactions similar to typical international new ventures...

  16. Automatic sign language recognition inspired by human sign perception

    NARCIS (Netherlands)

    Ten Holt, G.A.

    2010-01-01

    Automatic sign language recognition is a relatively new field of research (since ca. 1990). Its objectives are to automatically analyze sign language utterances. There are several issues within the research area that merit investigation: how to capture the utterances (cameras, magnetic sensors,

  17. Face Recognition by Metropolitan Police Super-Recognisers.

    Science.gov (United States)

    Robertson, David J; Noyes, Eilidh; Dowsett, Andrew J; Jenkins, Rob; Burton, A Mike

    2016-01-01

    Face recognition is used to prove identity across a wide variety of settings. Despite this, research consistently shows that people are typically rather poor at matching faces to photos. Some professional groups, such as police and passport officers, have been shown to perform just as poorly as the general public on standard tests of face recognition. However, face recognition skills are subject to wide individual variation, with some people showing exceptional ability-a group that has come to be known as 'super-recognisers'. The Metropolitan Police Force (London) recruits 'super-recognisers' from within its ranks, for deployment on various identification tasks. Here we test four working super-recognisers from within this police force, and ask whether they are really able to perform at levels above control groups. We consistently find that the police 'super-recognisers' perform at well above normal levels on tests of unfamiliar and familiar face matching, with degraded as well as high quality images. Recruiting employees with high levels of skill in these areas, and allocating them to relevant tasks, is an efficient way to overcome some of the known difficulties associated with unfamiliar face recognition.

  18. Recognition of knowledge – A step towards optimization of education

    Directory of Open Access Journals (Sweden)

    Vanda Rebolj

    2011-03-01

    In her presentation of the knowledge recognition procedures, the author relies on constructivist theories on knowledge and highlights the importance of the achieved levels of knowledge, paying equal attention to the low levels (skills, higher levels and the highest levels (problem­solving, none of which should be omitted in the assessment and recognition procedures. The author then presents the experience in knowledge recognition gained in the last five years by several colleges providing part­time studies, starting with a course in accounting and proceeding with other programmes. It is essential that knowledge recognition should not be pushed into the domain of experts or become an administrative procedure; it must remain part of the regular teaching procedure and under control of the teacher. This requires implementation of appropriate teacher training. Despite the fact that the recognition procedures developed so far have proved to be valid and have gained on credibility, numerous new research issues are being raised in this field.

  19. [Effect of opioid receptors on acute stress-induced changes in recognition memory].

    Science.gov (United States)

    Liu, Ying; Wu, Yu-Wei; Qian, Zhao-Qiang; Yan, Cai-Fang; Fan, Ka-Min; Xu, Jin-Hui; Li, Xiao; Liu, Zhi-Qiang

    2016-12-25

    Although ample evidence has shown that acute stress impairs memory, the influences of acute stress on different phases of memory, such as acquisition, consolidation and retrieval, are different. Experimental data from both human and animals support that endogenous opioid system plays a role in stress, as endogenous opioid release is increased and opioid receptors are activated during stress experience. On the other hand, endogenous opioid system mediates learning and memory. The aim of the present study was to investigate the effect of acute forced swimming stress on recognition memory of C57 mice and the role of opioid receptors in this process by using a three-day pattern of new object recognition task. The results showed that 15-min acute forced swimming damaged the retrieval of recognition memory, but had no effect on acquisition and consolidation of recognition memory. No significant change of object recognition memory was found in mice that were given naloxone, an opioid receptor antagonist, by intraperitoneal injection. But intraperitoneal injection of naloxone before forced swimming stress could inhibit the impairment of recognition memory retrieval caused by forced swimming stress. The results of real-time PCR showed that acute forced swimming decreased the μ opioid receptor mRNA levels in whole brain and hippocampus, while the injection of naloxone before stress could reverse this change. These results suggest that acute stress may impair recognition memory retrieval via opioid receptors.

  20. A Diffusion Model Analysis of Decision Biases Affecting Delayed Recognition of Emotional Stimuli

    Science.gov (United States)

    Bowen, Holly J.; Spaniol, Julia; Patel, Ronak; Voss, Andreas

    2016-01-01

    Previous empirical work suggests that emotion can influence accuracy and cognitive biases underlying recognition memory, depending on the experimental conditions. The current study examines the effects of arousal and valence on delayed recognition memory using the diffusion model, which allows the separation of two decision biases thought to underlie memory: response bias and memory bias. Memory bias has not been given much attention in the literature but can provide insight into the retrieval dynamics of emotion modulated memory. Participants viewed emotional pictorial stimuli; half were given a recognition test 1-day later and the other half 7-days later. Analyses revealed that emotional valence generally evokes liberal responding, whereas high arousal evokes liberal responding only at a short retention interval. The memory bias analyses indicated that participants experienced greater familiarity with high-arousal compared to low-arousal items and this pattern became more pronounced as study-test lag increased; positive items evoke greater familiarity compared to negative and this pattern remained stable across retention interval. The findings provide insight into the separate contributions of valence and arousal to the cognitive mechanisms underlying delayed emotion modulated memory. PMID:26784108

  1. Post-Training Reversible Inactivation of the Hippocampus Enhances Novel Object Recognition Memory

    Science.gov (United States)

    Oliveira, Ana M. M.; Hawk, Joshua D.; Abel, Ted; Havekes, Robbert

    2010-01-01

    Research on the role of the hippocampus in object recognition memory has produced conflicting results. Previous studies have used permanent hippocampal lesions to assess the requirement for the hippocampus in the object recognition task. However, permanent hippocampal lesions may impact performance through effects on processes besides memory…

  2. Recognition-induced forgetting of faces in visual long-term memory.

    Science.gov (United States)

    Rugo, Kelsi F; Tamler, Kendall N; Woodman, Geoffrey F; Maxcey, Ashleigh M

    2017-10-01

    Despite more than a century of evidence that long-term memory for pictures and words are different, much of what we know about memory comes from studies using words. Recent research examining visual long-term memory has demonstrated that recognizing an object induces the forgetting of objects from the same category. This recognition-induced forgetting has been shown with a variety of everyday objects. However, unlike everyday objects, faces are objects of expertise. As a result, faces may be immune to recognition-induced forgetting. However, despite excellent memory for such stimuli, we found that faces were susceptible to recognition-induced forgetting. Our findings have implications for how models of human memory account for recognition-induced forgetting as well as represent objects of expertise and consequences for eyewitness testimony and the justice system.

  3. Technical Reviews on Pattern Recognition in Process Analytical Technology

    International Nuclear Information System (INIS)

    Kim, Jong Yun; Choi, Yong Suk; Ji, Sun Kyung; Park, Yong Joon; Song, Kyu Seok; Jung, Sung Hee

    2008-12-01

    Pattern recognition is one of the first and the most widely adopted chemometric tools among many active research area in chemometrics such as design of experiment(DoE), pattern recognition, multivariate calibration, signal processing. Pattern recognition has been used to identify the origin of a wine and the time of year that the vine was grown by using chromatography, cause of fire by using GC/MS chromatography, detection of explosives and land mines, cargo and luggage inspection in seaports and airports by using a prompt gamma-ray activation analysis, and source apportionment of environmental pollutant by using a stable isotope ratio mass spectrometry. Recently, pattern recognition has been taken into account as a major chemometric tool in the so-called 'process analytical technology (PAT)', which is a newly-developed concept in the area of process analytics proposed by US Food and Drug Administration (US FDA). For instance, identification of raw material by using a pattern recognition analysis plays an important role for the effective quality control of the production process. Recently, pattern recognition technique has been used to identify the spatial distribution and uniformity of the active ingredients present in the product such as tablet by transforming the chemical data into the visual information

  4. Medial prefrontal cortex role in recognition memory in rodents.

    Science.gov (United States)

    Morici, Juan Facundo; Bekinschtein, Pedro; Weisstaub, Noelia V

    2015-10-01

    The study of the neurobiology of recognition memory, defined by the integration of the different components of experiences that support recollection of past experiences have been a challenge for memory researches for many years. In the last twenty years, with the development of the spontaneous novel object recognition task and all its variants this has started to change. The features of recognition memory include a particular object or person ("what"), the context in which the experience took place, which can be the arena itself or the location within a particular arena ("where") and the particular time at which the event occurred ("when"). This definition instead of the historical anthropocentric one allows the study of this type of episodic memory in animal models. Some forms of recognition memory that require integration of different features recruit the medial prefrontal cortex. Focusing on findings from spontaneous recognition memory tasks performed by rodents, this review concentrates on the description of previous works that have examined the role that the medial prefrontal cortex has on the different steps of recognition memory. We conclude that this structure, independently of the task used, is required at different memory stages when the task cannot be solved by a single item strategy. Copyright © 2015 Elsevier B.V. All rights reserved.

  5. A Novel Wearable Sensor-Based Human Activity Recognition Approach Using Artificial Hydrocarbon Networks.

    Science.gov (United States)

    Ponce, Hiram; Martínez-Villaseñor, María de Lourdes; Miralles-Pechuán, Luis

    2016-07-05

    Human activity recognition has gained more interest in several research communities given that understanding user activities and behavior helps to deliver proactive and personalized services. There are many examples of health systems improved by human activity recognition. Nevertheless, the human activity recognition classification process is not an easy task. Different types of noise in wearable sensors data frequently hamper the human activity recognition classification process. In order to develop a successful activity recognition system, it is necessary to use stable and robust machine learning techniques capable of dealing with noisy data. In this paper, we presented the artificial hydrocarbon networks (AHN) technique to the human activity recognition community. Our artificial hydrocarbon networks novel approach is suitable for physical activity recognition, noise tolerance of corrupted data sensors and robust in terms of different issues on data sensors. We proved that the AHN classifier is very competitive for physical activity recognition and is very robust in comparison with other well-known machine learning methods.

  6. The effect of glucose administration on the recollection and familiarity components of recognition memory.

    Science.gov (United States)

    Sünram-Lea, Sandra I; Dewhurst, Stephen A; Foster, Jonathan K

    2008-01-01

    Previous research has demonstrated that glucose administration facilitates long-term memory performance. The aim of the present research was to evaluate the effect of glucose administration on different components of long-term recognition memory. Fifty-six healthy young individuals received (a) a drink containing 25 g of glucose or (b) an inert placebo drink. Recollection and familiarity components of recognition memory were measured using the 'remember-know' paradigm. The results revealed that glucose administration led to significantly increased proportion of recognition responses based on recollection, but had no effect on the proportion of recognition responses made through participants' detection of stimulus familiarity. Consequently, the data suggest that glucose administration appears to facilitate recognition memory that is accompanied by recollection of contextual details and episodic richness. The findings also suggest that memory tasks that result in high levels of hippocampal activity may be more likely to be enhanced by glucose administration than tasks that are less reliant on medial temporal lobe structures.

  7. Activity Recognition Using Inertial Sensing for Healthcare, Wellbeing and Sports Applications: A Survey

    NARCIS (Netherlands)

    Avci, A.; Bosch, S.; Marin Perianu, Mihai; Marin Perianu, Raluca; Havinga, Paul J.M.

    This paper surveys the current research directions of activity recognition using inertial sensors, with potential application in healthcare, wellbeing and sports. The analysis of related work is organized according to the five main steps involved in the activity recognition process: preprocessing,

  8. How does real affect affect affect recognition in speech?

    NARCIS (Netherlands)

    Truong, Khiet Phuong

    2009-01-01

    The automatic analysis of affect is a relatively new and challenging multidisciplinary research area that has gained a lot of interest over the past few years. The research and development of affect recognition systems has opened many opportunities for improving the interaction between man and

  9. Redistribution, Recognition and Representation: Working against Pedagogies of Indifference

    Science.gov (United States)

    Lingard, Bob; Keddie, Amanda

    2013-01-01

    This paper reports on an Australian government-commissioned research study that documented classroom pedagogies in 24 Queensland schools. The research created the model of "productive pedagogies", which conjoined what Nancy Fraser calls a politics of redistribution, recognition and representation. In this model pedagogies are…

  10. A multimodal approach to emotion recognition ability in autism spectrum disorders

    OpenAIRE

    Jones, C.; Pickles, A.; Falcaro, M.; Marsden, A.; Happé, F.; Scott, S.; Sauter, D.; Tregay, J.; Phillips, R.; Baird, G.; Simonoff, E.; Charman, T.

    2011-01-01

    Background:  Autism spectrum disorders (ASD) are characterised by social and communication difficulties in day-to-day life, including problems in recognising emotions. However, experimental investigations of emotion recognition ability in ASD have been equivocal, hampered by small sample sizes, narrow IQ range and over-focus on the visual modality. Methods:  We tested 99 adolescents (mean age 15;6 years, mean IQ 85) with an ASD and 57 adolescents without an ASD (mean age 15;6 years, mean IQ 8...

  11. Atypical evening cortisol profile induces visual recognition memory deficit in healthy human subjects

    Directory of Open Access Journals (Sweden)

    Gilpin Heather

    2008-08-01

    Full Text Available Abstract Background Diurnal rhythm-mediated endogenous cortisol levels in humans are characterised by a peak in secretion after awakening that declines throughout the day to an evening trough. However, a significant proportion of the population exhibits an atypical cycle of diurnal cortisol due to shift work, jet-lag, aging, and mental illness. Results The present study has demonstrated a correlation between elevation of cortisol in the evening and deterioration of visual object recognition memory. However, high evening cortisol levels have no effect on spatial memory. Conclusion This study suggests that atypical evening salivary cortisol levels have an important role in the early deterioration of recognition memory. The loss of recognition memory, which is vital for everyday life, is a major symptom of the amnesic syndrome and early stages of Alzheimer's disease. Therefore, this study will promote a potential physiologic marker of early deterioration of recognition memory and a possible diagnostic strategy for Alzheimer's disease.

  12. Environmental Sound Recognition Using Time-Frequency Intersection Patterns

    Directory of Open Access Journals (Sweden)

    Xuan Guo

    2012-01-01

    Full Text Available Environmental sound recognition is an important function of robots and intelligent computer systems. In this research, we use a multistage perceptron neural network system for environmental sound recognition. The input data is a combination of time-variance pattern of instantaneous powers and frequency-variance pattern with instantaneous spectrum at the power peak, referred to as a time-frequency intersection pattern. Spectra of many environmental sounds change more slowly than those of speech or voice, so the intersectional time-frequency pattern will preserve the major features of environmental sounds but with drastically reduced data requirements. Two experiments were conducted using an original database and an open database created by the RWCP project. The recognition rate for 20 kinds of environmental sounds was 92%. The recognition rate of the new method was about 12% higher than methods using only an instantaneous spectrum. The results are also comparable with HMM-based methods, although those methods need to treat the time variance of an input vector series with more complicated computations.

  13. Constraints in distortion-invariant target recognition system simulation

    Science.gov (United States)

    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.

  14. Pedestrian recognition using automotive radar sensors

    Science.gov (United States)

    Bartsch, A.; Fitzek, F.; Rasshofer, R. H.

    2012-09-01

    The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.

  15. DATABASES FOR RECOGNITION OF HANDWRITTEN ARABIC CHEQUES

    NARCIS (Netherlands)

    Alohali, Y.; Cheriet, M.; Suen, C.Y.

    2004-01-01

    This paper describes an effort toward building Arabic cheque databases for research in recognition of handwritten Arabic cheques. Databases of Arabic legal amounts, Arabic sub­ words, courtesy amounts, Indian digits, and Arabic cheques are provided. This paper highlights the characteristics of the

  16. Feature Fusion Algorithm for Multimodal Emotion Recognition from Speech and Facial Expression Signal

    Directory of Open Access Journals (Sweden)

    Han Zhiyan

    2016-01-01

    Full Text Available In order to overcome the limitation of single mode emotion recognition. This paper describes a novel multimodal emotion recognition algorithm, and takes speech signal and facial expression signal as the research subjects. First, fuse the speech signal feature and facial expression signal feature, get sample sets by putting back sampling, and then get classifiers by BP neural network (BPNN. Second, measure the difference between two classifiers by double error difference selection strategy. Finally, get the final recognition result by the majority voting rule. Experiments show the method improves the accuracy of emotion recognition by giving full play to the advantages of decision level fusion and feature level fusion, and makes the whole fusion process close to human emotion recognition more, with a recognition rate 90.4%.

  17. A pattern recognition account of decision making.

    Science.gov (United States)

    Massaro, D W

    1994-09-01

    In the domain of pattern recognition, experiments have shown that perceivers integrate multiple sources of information in an optimal manner. In contrast, other research has been interpreted to mean that decision making is nonoptimal. As an example, Tversky and Kahneman (1983) have shown that subjects commit a conjunction fallacy because they judge it more likely that a fictitious person named Linda is a bank teller and a feminist than just a bank teller. This judgment supposedly violates probability theory, because the probability of two events can never be greater than the probability of either event alone. The present research tests the hypothesis that subjects interpret this judgment task as a pattern recognition task. If this hypothesis is correct, subjects' judgments should be described accurately by the fuzzy logical model of perception (FLMP)--a successful model of pattern recognition. In the first experiment, the Linda task was extended to an expanded factorial design with five vocations and five avocations. The probability ratings were described well by the FLMP and described poorly by a simple probability model. The second experiment included (1) two fictitious people, Linda and Joan, as response alternatives and (2) both ratings and categorization judgments. Although the ratings were accurately described by both the FLMP and an averaging of the sources of information, the categorization judgments were described better by the FLMP. These results reveal important similarities in recognizing patterns and in decision making. Given that the FLMP is an optimal method for combining multiple sources of information, the probability judgments appear to be optimal in the same manner as pattern-recognition judgments.

  18. Face Detection and Face Recognition in Android Mobile Applications

    Directory of Open Access Journals (Sweden)

    Octavian DOSPINESCU

    2016-01-01

    Full Text Available The quality of the smartphone’s camera enables us to capture high quality pictures at a high resolution, so we can perform different types of recognition on these images. Face detection is one of these types of recognition that is very common in our society. We use it every day on Facebook to tag friends in our pictures. It is also used in video games alongside Kinect concept, or in security to allow the access to private places only to authorized persons. These are just some examples of using facial recognition, because in modern society, detection and facial recognition tend to surround us everywhere. The aim of this article is to create an appli-cation for smartphones that can recognize human faces. The main goal of this application is to grant access to certain areas or rooms only to certain authorized persons. For example, we can speak here of hospitals or educational institutions where there are rooms where only certain employees can enter. Of course, this type of application can cover a wide range of uses, such as helping people suffering from Alzheimer's to recognize the people they loved, to fill gaps persons who can’t remember the names of their relatives or for example to automatically capture the face of our own children when they smile.

  19. The automaticity of emotion recognition.

    Science.gov (United States)

    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.

  20. Multispectral iris recognition based on group selection and game theory

    Science.gov (United States)

    Ahmad, Foysal; Roy, Kaushik

    2017-05-01

    A commercially available iris recognition system uses only a narrow band of the near infrared spectrum (700-900 nm) while iris images captured in the wide range of 405 nm to 1550 nm offer potential benefits to enhance recognition performance of an iris biometric system. The novelty of this research is that a group selection algorithm based on coalition game theory is explored to select the best patch subsets. In this algorithm, patches are divided into several groups based on their maximum contribution in different groups. Shapley values are used to evaluate the contribution of patches in different groups. Results show that this group selection based iris recognition

  1. sEMG-Based Gesture Recognition with Convolution Neural Networks

    Directory of Open Access Journals (Sweden)

    Zhen Ding

    2018-06-01

    Full Text Available The traditional classification methods for limb motion recognition based on sEMG have been deeply researched and shown promising results. However, information loss during feature extraction reduces the recognition accuracy. To obtain higher accuracy, the deep learning method was introduced. In this paper, we propose a parallel multiple-scale convolution architecture. Compared with the state-of-art methods, the proposed architecture fully considers the characteristics of the sEMG signal. Larger sizes of kernel filter than commonly used in other CNN-based hand recognition methods are adopted. Meanwhile, the characteristics of the sEMG signal, that is, muscle independence, is considered when designing the architecture. All the classification methods were evaluated on the NinaPro database. The results show that the proposed architecture has the highest recognition accuracy. Furthermore, the results indicate that parallel multiple-scale convolution architecture with larger size of kernel filter and considering muscle independence can significantly increase the classification accuracy.

  2. HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition.

    Science.gov (United States)

    James, Alex Pappachen; Fedorova, Irina; Ibrayev, Timur; Kudithipudi, Dhireesha

    2017-06-01

    Hierarchical Temporal Memory (HTM) is an online machine learning algorithm that emulates the neo-cortex. The development of a scalable on-chip HTM architecture is an open research area. The two core substructures of HTM are spatial pooler and temporal memory. In this work, we propose a new Spatial Pooler circuit design with parallel memristive crossbar arrays for the 2D columns. The proposed design was validated on two different benchmark datasets, face recognition, and speech recognition. The circuits are simulated and analyzed using a practical memristor device model and 0.18 μm IBM CMOS technology model. The databases AR, YALE, ORL, and UFI, are used to test the performance of the design in face recognition. TIMIT dataset is used for the speech recognition.

  3. [Prosopagnosia and facial expression recognition].

    Science.gov (United States)

    Koyama, Shinichi

    2014-04-01

    This paper reviews clinical neuropsychological studies that have indicated that the recognition of a person's identity and the recognition of facial expressions are processed by different cortical and subcortical areas of the brain. The fusiform gyrus, especially the right fusiform gyrus, plays an important role in the recognition of identity. The superior temporal sulcus, amygdala, and medial frontal cortex play important roles in facial-expression recognition. Both facial recognition and facial-expression recognition are highly intellectual processes that involve several regions of the brain.

  4. Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2014-05-01

    Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

  5. Micro-Doppler Feature Extraction and Recognition Based on Netted Radar for Ballistic Targets

    Directory of Open Access Journals (Sweden)

    Feng Cun-qian

    2015-12-01

    Full Text Available This study examines the complexities of using netted radar to recognize and resolve ballistic midcourse targets. The application of micro-motion feature extraction to ballistic mid-course targets is analyzed, and the current status of application and research on micro-motion feature recognition is concluded for singlefunction radar networks such as low- and high-resolution imaging radar networks. Advantages and disadvantages of these networks are discussed with respect to target recognition. Hybrid-mode radar networks combine low- and high-resolution imaging radar and provide a specific reference frequency that is the basis for ballistic target recognition. Main research trends are discussed for hybrid-mode networks that apply micromotion feature extraction to ballistic mid-course targets.

  6. Culture moderates the relationship between interdependence and face recognition

    Directory of Open Access Journals (Sweden)

    Andy H Ng

    2015-10-01

    Full Text Available Recent theory suggests that face recognition accuracy is affected by people’s motivations, with people being particularly motivated to remember ingroup versus outgroup faces. In the current research we suggest that those higher in interdependence should have a greater motivation to remember ingroup faces, but this should depend on how ingroups are defined. To examine this possibility, we used a joint individual difference and cultural approach to test (a whether individual differences in interdependence would predict face recognition accuracy, and (b whether this effect would be moderated by culture. In Study 1 European Canadians higher in interdependence demonstrated greater recognition for same-race (White, but not cross-race (East Asian faces. In Study 2 we found that culture moderated this effect. Interdependence again predicted greater recognition for same-race (White, but not cross-race (East Asian faces among European Canadians; however, interdependence predicted worse recognition for both same-race (East Asian and cross-race (White faces among first-generation East Asians. The results provide insight into the role of motivation in face perception as well as cultural differences in the conception of ingroups.

  7. Achievement motivation and memory: achievement goals differentially influence immediate and delayed remember-know recognition memory.

    Science.gov (United States)

    Murayama, Kou; Elliot, Andrew J

    2011-10-01

    Little research has been conducted on achievement motivation and memory and, more specifically, on achievement goals and memory. In the present research, the authors conducted two experiments designed to examine the influence of mastery-approach and performance-approach goals on immediate and delayed remember-know recognition memory. The experiments revealed differential effects for achievement goals over time: Performance-approach goals showed higher correct remember responding on an immediate recognition test, whereas mastery-approach goals showed higher correct remember responding on a delayed recognition test. Achievement goals had no influence on overall recognition memory and no consistent influence on know responding across experiments. These findings indicate that it is important to consider quality, not just quantity, in both motivation and memory, when studying relations between these constructs.

  8. Character context: a shape descriptor for Arabic handwriting recognition

    Science.gov (United States)

    Mudhsh, Mohammed; Almodfer, Rolla; Duan, Pengfei; Xiong, Shengwu

    2017-11-01

    In the handwriting recognition field, designing good descriptors are substantial to obtain rich information of the data. However, the handwriting recognition research of a good descriptor is still an open issue due to unlimited variation in human handwriting. We introduce a "character context descriptor" that efficiently dealt with the structural characteristics of Arabic handwritten characters. First, the character image is smoothed and normalized, then the character context descriptor of 32 feature bins is built based on the proposed "distance function." Finally, a multilayer perceptron with regularization is used as a classifier. On experimentation with a handwritten Arabic characters database, the proposed method achieved a state-of-the-art performance with recognition rate equal to 98.93% and 99.06% for the 66 and 24 classes, respectively.

  9. New forms of conflict resolution: transformation, empowerment and recognition

    Directory of Open Access Journals (Sweden)

    Vicent Martínez Guzmán

    2006-04-01

    Full Text Available This paper uses the scientific field of Conflict Studies, within the framework of Peace Research. It is based on a philosophic conception of the “human condition” and is guided by the notions of empowerment and recognition as tools suitable for the pacific transformation of conflicts. It also looks at the relation of the doctrine of recognition with the problem of inequality and with a political philosophy of justice. It maintains that one cannot separate policies of recognition, more closely linked to identity and culture, from policies of justice, more attent to the pacific transformation of human inequalities, poverty, indigence, marginalization and exclusion. As an example of this proposal, it uses the most recent report of the United Nations Human Development Program.

  10. Entrepreneurship education as a factor of entrepreneurial opportunity recognition for starting a new business

    Directory of Open Access Journals (Sweden)

    Ajka Baručić

    2016-12-01

    Full Text Available One of the central issues for entrepreneurship researchers is how and why some people are able to identify and use entrepreneurial opportunity and start a business, while others are not. Research has shown that factors conditioning entrepreneurial opportunity recognition may include: creativity, work experience, social networking of entrepreneurs, prior knowledge on the market, customers’ needs and the ways to satisfy them, intuition and ability to foresee or cognitive factors. This paper presents the research into the relation between entrepreneurship education and entrepreneurial opportunity recognition, that was not a subject of interest of theoretical discussions and research of previous researchers.

  11. Use of Authentic-Speech Technique for Teaching Sound Recognition to EFL Students

    Science.gov (United States)

    Sersen, William J.

    2011-01-01

    The main objective of this research was to test an authentic-speech technique for improving the sound-recognition skills of EFL (English as a foreign language) students at Roi-Et Rajabhat University. The secondary objective was to determine the correlation, if any, between students' self-evaluation of sound-recognition progress and the actual…

  12. Pupil dilation during recognition memory: Isolating unexpected recognition from judgment uncertainty.

    Science.gov (United States)

    Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G

    2016-09-01

    Optimally discriminating familiar from novel stimuli demands a decision-making process informed by prior expectations. Here we demonstrate that pupillary dilation (PD) responses during recognition memory decisions are modulated by expectations, and more specifically, that pupil dilation increases for unexpected compared to expected recognition. Furthermore, multi-level modeling demonstrated that the time course of the dilation during each individual trial contains separable early and late dilation components, with the early amplitude capturing unexpected recognition, and the later trailing slope reflecting general judgment uncertainty or effort. This is the first demonstration that the early dilation response during recognition is dependent upon observer expectations and that separate recognition expectation and judgment uncertainty components are present in the dilation time course of every trial. The findings provide novel insights into adaptive memory-linked orienting mechanisms as well as the general cognitive underpinnings of the pupillary index of autonomic nervous system activity. Copyright © 2016 Elsevier B.V. All rights reserved.

  13. I know my neighbour: individual recognition in Octopus vulgaris.

    Science.gov (United States)

    Tricarico, Elena; Borrelli, Luciana; Gherardi, Francesca; Fiorito, Graziano

    2011-04-13

    Little is known about individual recognition (IR) in octopuses, although they have been abundantly studied for their sophisticated behaviour and learning capacities. Indeed, the ability of octopuses to recognise conspecifics is suggested by a number of clues emerging from both laboratory studies (where they appear to form and maintain dominance hierarchies) and field observations (octopuses of neighbouring dens display little agonism between each other). To fill this gap in knowledge, we investigated the behaviour of 24 size-matched pairs of Octopus vulgaris in laboratory conditions. The experimental design was composed of 3 phases: Phase 1 (acclimatization): 12 "sight-allowed" (and 12 "isolated") pairs were maintained for 3 days in contiguous tanks separated by a transparent (and opaque) partition to allow (and block) the vision of the conspecific; Phase 2 (cohabitation): members of each pair (both sight-allowed and isolated) were transferred into an experimental tank and were allowed to interact for 15 min every day for 3 consecutive days; Phase 3 (test): each pair (both sight-allowed and isolated) was subject to a switch of an octopus to form pairs composed of either familiar ("sham switches") or unfamiliar conspecifics ("real switches"). Longer latencies (i.e. the time elapsed from the first interaction) and fewer physical contacts in the familiar pairs as opposed to the unfamiliar pairs were used as proxies for recognition. Octopuses appear able to recognise conspecifics and to remember the individual previously met for at least one day. To the best of our knowledge, this is the first experimental study showing the occurrence of a form of IR in cephalopods. Future studies should clarify whether this is a "true" IR.

  14. I know my neighbour: individual recognition in Octopus vulgaris.

    Directory of Open Access Journals (Sweden)

    Elena Tricarico

    2011-04-01

    Full Text Available Little is known about individual recognition (IR in octopuses, although they have been abundantly studied for their sophisticated behaviour and learning capacities. Indeed, the ability of octopuses to recognise conspecifics is suggested by a number of clues emerging from both laboratory studies (where they appear to form and maintain dominance hierarchies and field observations (octopuses of neighbouring dens display little agonism between each other. To fill this gap in knowledge, we investigated the behaviour of 24 size-matched pairs of Octopus vulgaris in laboratory conditions.The experimental design was composed of 3 phases: Phase 1 (acclimatization: 12 "sight-allowed" (and 12 "isolated" pairs were maintained for 3 days in contiguous tanks separated by a transparent (and opaque partition to allow (and block the vision of the conspecific; Phase 2 (cohabitation: members of each pair (both sight-allowed and isolated were transferred into an experimental tank and were allowed to interact for 15 min every day for 3 consecutive days; Phase 3 (test: each pair (both sight-allowed and isolated was subject to a switch of an octopus to form pairs composed of either familiar ("sham switches" or unfamiliar conspecifics ("real switches". Longer latencies (i.e. the time elapsed from the first interaction and fewer physical contacts in the familiar pairs as opposed to the unfamiliar pairs were used as proxies for recognition.Octopuses appear able to recognise conspecifics and to remember the individual previously met for at least one day. To the best of our knowledge, this is the first experimental study showing the occurrence of a form of IR in cephalopods. Future studies should clarify whether this is a "true" IR.

  15. Study on municipal road cracking and surface deformation based on image recognition

    Science.gov (United States)

    Yuan, Haitao; Wang, Shuai; Tan, Jizong

    2017-05-01

    In recent years, the digital image recognition technology of concrete structure cracks and deformation of binocular vision technology detection of civil engineering structure have made substantial development. As a result, people's understanding of the road engineering structure cracking and surface deformation recognition gives rise to a new situation. For the research on digital image concrete structure cracking and masonry structure surface deformation recognition technology, the key is to break through in the method, and to improve the traditional recognition technology and mode. Only in this way can we continuously improve the security level of the highway, to adapt to the new requirements of the development of new urbanization and modernization. This thesis focuses on and systematically analyzes the digital image road engineering structure cracking and key technologies of surface deformation recognition and its engineering applications. In addition, we change the concrete structure cracking and masonry structure surface deformation recognition pattern, and realize the breakthrough and innovation of the road structure safety testing means and methods.

  16. Speech Emotion Recognition Based on Power Normalized Cepstral Coefficients in Noisy Conditions

    Directory of Open Access Journals (Sweden)

    M. Bashirpour

    2016-09-01

    Full Text Available Automatic recognition of speech emotional states in noisy conditions has become an important research topic in the emotional speech recognition area, in recent years. This paper considers the recognition of emotional states via speech in real environments. For this task, we employ the power normalized cepstral coefficients (PNCC in a speech emotion recognition system. We investigate its performance in emotion recognition using clean and noisy speech materials and compare it with the performances of the well-known MFCC, LPCC, RASTA-PLP, and also TEMFCC features. Speech samples are extracted from the Berlin emotional speech database (Emo DB and Persian emotional speech database (Persian ESD which are corrupted with 4 different noise types under various SNR levels. The experiments are conducted in clean train/noisy test scenarios to simulate practical conditions with noise sources. Simulation results show that higher recognition rates are achieved for PNCC as compared with the conventional features under noisy conditions.

  17. Semantic relations differentially impact associative recognition memory: electrophysiological evidence.

    Science.gov (United States)

    Kriukova, Olga; Bridger, Emma; Mecklinger, Axel

    2013-10-01

    Though associative recognition memory is thought to rely primarily on recollection, recent research indicates that familiarity might also make a substantial contribution when to-be-learned items are integrated into a coherent structure by means of an existing semantic relation. It remains unclear how different types of semantic relations, such as categorical (e.g., dancer-singer) and thematic (e.g., dancer-stage) relations might affect associative recognition, however. Using event-related potentials (ERPs), we addressed this question by manipulating the type of semantic link between paired words in an associative recognition memory experiment. An early midfrontal old/new effect, typically linked to familiarity, was observed across the relation types. In contrast, a robust left parietal old/new effect was found in the categorical condition only, suggesting a clear contribution of recollection to associative recognition for this kind of pairs. One interpretation of this pattern is that familiarity was sufficiently diagnostic for associative recognition of thematic relations, which could result from the integrative nature of the thematic relatedness compared to the similarity-based nature of categorical pairs. The present study suggests that the extent to which recollection and familiarity are involved in associative recognition is at least in part determined by the properties of semantic relations between the paired associates. Copyright © 2013 Elsevier Inc. All rights reserved.

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

    Science.gov (United States)

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

    2008-08-01

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

  19. Arguments Against a Configural Processing Account of Familiar Face Recognition.

    Science.gov (United States)

    Burton, A Mike; Schweinberger, Stefan R; Jenkins, Rob; Kaufmann, Jürgen M

    2015-07-01

    Face recognition is a remarkable human ability, which underlies a great deal of people's social behavior. Individuals can recognize family members, friends, and acquaintances over a very large range of conditions, and yet the processes by which they do this remain poorly understood, despite decades of research. Although a detailed understanding remains elusive, face recognition is widely thought to rely on configural processing, specifically an analysis of spatial relations between facial features (so-called second-order configurations). In this article, we challenge this traditional view, raising four problems: (1) configural theories are underspecified; (2) large configural changes leave recognition unharmed; (3) recognition is harmed by nonconfigural changes; and (4) in separate analyses of face shape and face texture, identification tends to be dominated by texture. We review evidence from a variety of sources and suggest that failure to acknowledge the impact of familiarity on facial representations may have led to an overgeneralization of the configural account. We argue instead that second-order configural information is remarkably unimportant for familiar face recognition. © The Author(s) 2015.

  20. Optical character recognition of handwritten Arabic using hidden Markov models

    Science.gov (United States)

    Aulama, Mohannad M.; Natsheh, Asem M.; Abandah, Gheith A.; Olama, Mohammed M.

    2011-04-01

    The problem of optical character recognition (OCR) of handwritten Arabic has not received a satisfactory solution yet. In this paper, an Arabic OCR algorithm is developed based on Hidden Markov Models (HMMs) combined with the Viterbi algorithm, which results in an improved and more robust recognition of characters at the sub-word level. Integrating the HMMs represents another step of the overall OCR trends being currently researched in the literature. The proposed approach exploits the structure of characters in the Arabic language in addition to their extracted features to achieve improved recognition rates. Useful statistical information of the Arabic language is initially extracted and then used to estimate the probabilistic parameters of the mathematical HMM. A new custom implementation of the HMM is developed in this study, where the transition matrix is built based on the collected large corpus, and the emission matrix is built based on the results obtained via the extracted character features. The recognition process is triggered using the Viterbi algorithm which employs the most probable sequence of sub-words. The model was implemented to recognize the sub-word unit of Arabic text raising the recognition rate from being linked to the worst recognition rate for any character to the overall structure of the Arabic language. Numerical results show that there is a potentially large recognition improvement by using the proposed algorithms.

  1. Infrared and visible fusion face recognition based on NSCT domain

    Science.gov (United States)

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

    2018-01-01

    Visible face recognition systems, being vulnerable to illumination, expression, and pose, can not achieve robust performance in unconstrained situations. Meanwhile, near infrared face images, being light- independent, can avoid or limit the drawbacks of face recognition in visible light, but its main challenges are low resolution and signal noise ratio (SNR). Therefore, near infrared and visible fusion face recognition has become an important direction in the field of unconstrained face recognition research. In this paper, a novel fusion algorithm in non-subsampled contourlet transform (NSCT) domain is proposed for Infrared and visible face fusion recognition. Firstly, NSCT is used respectively to process the infrared and visible face images, which exploits the image information at multiple scales, orientations, and frequency bands. Then, to exploit the effective discriminant feature and balance the power of high-low frequency band of NSCT coefficients, the local Gabor binary pattern (LGBP) and Local Binary Pattern (LBP) are applied respectively in different frequency parts to obtain the robust representation of infrared and visible face images. Finally, the score-level fusion is used to fuse the all the features for final classification. The visible and near infrared face recognition is tested on HITSZ Lab2 visible and near infrared face database. Experiments results show that the proposed method extracts the complementary features of near-infrared and visible-light images and improves the robustness of unconstrained face recognition.

  2. Goal-seeking neural net for recall and recognition

    Science.gov (United States)

    Omidvar, Omid M.

    1990-07-01

    Neural networks have been used to mimic cognitive processes which take place in animal brains. The learning capability inherent in neural networks makes them suitable candidates for adaptive tasks such as recall and recognition. The synaptic reinforcements create a proper condition for adaptation, which results in memorization, formation of perception, and higher order information processing activities. In this research a model of a goal seeking neural network is studied and the operation of the network with regard to recall and recognition is analyzed. In these analyses recall is defined as retrieval of stored information where little or no matching is involved. On the other hand recognition is recall with matching; therefore it involves memorizing a piece of information with complete presentation. This research takes the generalized view of reinforcement in which all the signals are potential reinforcers. The neuronal response is considered to be the source of the reinforcement. This local approach to adaptation leads to the goal seeking nature of the neurons as network components. In the proposed model all the synaptic strengths are reinforced in parallel while the reinforcement among the layers is done in a distributed fashion and pipeline mode from the last layer inward. A model of complex neuron with varying threshold is developed to account for inhibitory and excitatory behavior of real neuron. A goal seeking model of a neural network is presented. This network is utilized to perform recall and recognition tasks. The performance of the model with regard to the assigned tasks is presented.

  3. Making Employee Recognition a Tool for Achieving Improved Performance: Implication for Ghanaian Universities

    Science.gov (United States)

    Amoatemaa, Abena Serwaa; Kyeremeh, Dorcas Darkoah

    2016-01-01

    Many organisations are increasingly making use of employee recognition to motivate employees to achieve high performance and productivity. Research has shown that effective recognition occurs in organisations that have strong supportive culture, understand the psychology of praising employees for their good work, and apply the principles of…

  4. Towards The Deep Model : Understanding Visual Recognition Through Computational Models

    OpenAIRE

    Wang, Panqu

    2017-01-01

    Understanding how visual recognition is achieved in the human brain is one of the most fundamental questions in vision research. In this thesis I seek to tackle this problem from a neurocomputational modeling perspective. More specifically, I build machine learning-based models to simulate and explain cognitive phenomena related to human visual recognition, and I improve computational models using brain-inspired principles to excel at computer vision tasks.I first describe how a neurocomputat...

  5. An application of viola jones method for face recognition for absence process efficiency

    Science.gov (United States)

    Rizki Damanik, Rudolfo; Sitanggang, Delima; Pasaribu, Hendra; Siagian, Hendrik; Gulo, Frisman

    2018-04-01

    Absence was a list of documents that the company used to record the attendance time of each employee. The most common problem in a fingerprint machine is the identification of a slow sensor or a sensor not recognizing a finger. The employees late to work because they get difficulties at fingerprint system, they need about 3 – 5 minutes to absence when the condition of finger is wet or not fit. To overcome this problem, this research tried to utilize facial recognition for attendance process. The method used for facial recognition was Viola Jones. Through the processing phase of the RGB face image was converted into a histogram equalization face image for the next stage of recognition. The result of this research was the absence process could be done less than 1 second with a maximum slope of ± 700 and a distance of 20-200 cm. After implement facial recognition the process of absence is more efficient, just take less 1 minute to absence.

  6. Robust Behavior Recognition in Intelligent Surveillance Environments

    Directory of Open Access Journals (Sweden)

    Ganbayar Batchuluun

    2016-06-01

    Full Text Available Intelligent surveillance systems have been studied by many researchers. These systems should be operated in both daytime and nighttime, but objects are invisible in images captured by visible light camera during the night. Therefore, near infrared (NIR cameras, thermal cameras (based on medium-wavelength infrared (MWIR, and long-wavelength infrared (LWIR light have been considered for usage during the nighttime as an alternative. Due to the usage during both daytime and nighttime, and the limitation of requiring an additional NIR illuminator (which should illuminate a wide area over a great distance for NIR cameras during the nighttime, a dual system of visible light and thermal cameras is used in our research, and we propose a new behavior recognition in intelligent surveillance environments. Twelve datasets were compiled by collecting data in various environments, and they were used to obtain experimental results. The recognition accuracy of our method was found to be 97.6%, thereby confirming the ability of our method to outperform previous methods.

  7. Spontaneous object recognition: a promising approach to the comparative study of memory

    Directory of Open Access Journals (Sweden)

    Rachel eBlaser

    2015-07-01

    Full Text Available Spontaneous recognition of a novel object is a popular measure of exploratory behavior, perception and recognition memory in rodent models. Because of its relative simplicity and speed of testing, the variety of stimuli that can be used, and its ecological validity across species, it is also an attractive task for comparative research. To date, variants of this test have been used with vertebrate and invertebrate species, but the methods have seldom been sufficiently standardized to allow cross-species comparison. Here, we review the methods necessary for the study of novel object recognition in mammalian and non-mammalian models, as well as the results of these experiments. Critical to the use of this test is an understanding of the organism’s initial response to a novel object, the modulation of exploration by context, and species differences in object perception and exploratory behaviors. We argue that with appropriate consideration of species differences in perception, object affordances, and natural exploratory behaviors, the spontaneous object recognition test can be a valid and versatile tool for translational research with non-mammalian models.

  8. SAR Target Recognition Based on Multi-feature Multiple Representation Classifier Fusion

    Directory of Open Access Journals (Sweden)

    Zhang Xinzheng

    2017-10-01

    Full Text Available In this paper, we present a Synthetic Aperture Radar (SAR image target recognition algorithm based on multi-feature multiple representation learning classifier fusion. First, it extracts three features from the SAR images, namely principal component analysis, wavelet transform, and Two-Dimensional Slice Zernike Moments (2DSZM features. Second, we harness the sparse representation classifier and the cooperative representation classifier with the above-mentioned features to get six predictive labels. Finally, we adopt classifier fusion to obtain the final recognition decision. We researched three different classifier fusion algorithms in our experiments, and the results demonstrate thatusing Bayesian decision fusion gives thebest recognition performance. The method based on multi-feature multiple representation learning classifier fusion integrates the discrimination of multi-features and combines the sparse and cooperative representation classification performance to gain complementary advantages and to improve recognition accuracy. The experiments are based on the Moving and Stationary Target Acquisition and Recognition (MSTAR database,and they demonstrate the effectiveness of the proposed approach.

  9. Neural network application for thermal image recognition of low-resolution objects

    Science.gov (United States)

    Fang, Yi-Chin; Wu, Bo-Wen

    2007-02-01

    In the ever-changing situation on a battle field, accurate recognition of a distant object is critical to a commander's decision-making and the general public's safety. Efficiently distinguishing between an enemy's armoured vehicles and ordinary civilian houses under all weather conditions has become an important research topic. This study presents a system for recognizing an armoured vehicle by distinguishing marks and contours. The characteristics of 12 different shapes and 12 characters are used to explore thermal image recognition under the circumstance of long distance and low resolution. Although the recognition capability of human eyes is superior to that of artificial intelligence under normal conditions, it tends to deteriorate substantially under long-distance and low-resolution scenarios. This study presents an effective method for choosing features and processing images. The artificial neural network technique is applied to further improve the probability of accurate recognition well beyond the limit of the recognition capability of human eyes.

  10. Automatic Blastomere Recognition from a Single Embryo Image

    Directory of Open Access Journals (Sweden)

    Yun Tian

    2014-01-01

    Full Text Available The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.

  11. Recognition of "real-world" musical excerpts by cochlear implant recipients and normal-hearing adults.

    Science.gov (United States)

    Gfeller, Kate; Olszewski, Carol; Rychener, Marly; Sena, Kimberly; Knutson, John F; Witt, Shelley; Macpherson, Beth

    2005-06-01

    The purposes of this study were (a) to compare recognition of "real-world" music excerpts by postlingually deafened adults using cochlear implants and normal-hearing adults; (b) to compare the performance of cochlear implant recipients using different devices and processing strategies; and (c) to examine the variability among implant recipients in recognition of musical selections in relation to performance on speech perception tests, performance on cognitive tests, and demographic variables. Seventy-nine cochlear implant users and 30 normal-hearing adults were tested on open-set recognition of systematically selected excerpts from musical recordings heard in real life. The recognition accuracy of the two groups was compared for three musical genre: classical, country, and pop. Recognition accuracy was correlated with speech recognition scores, cognitive measures, and demographic measures, including musical background. Cochlear implant recipients were significantly less accurate in recognition of previously familiar (known before hearing loss) musical excerpts than normal-hearing adults (p genre. Implant recipients were most accurate in the recognition of country items and least accurate in the recognition of classical items. There were no significant differences among implant recipients due to implant type (Nucleus, Clarion, or Ineraid), or programming strategy (SPEAK, CIS, or ACE). For cochlear implant recipients, correlations between melody recognition and other measures were moderate to weak in strength; those with statistically significant correlations included age at time of testing (negatively correlated), performance on selected speech perception tests, and the amount of focused music listening following implantation. Current-day cochlear implants are not effective in transmitting several key structural features (i.e., pitch, harmony, timbral blends) of music essential to open-set recognition of well-known musical selections. Consequently, implant

  12. 3 CFR 8378 - Proclamation 8378 of May 11, 2009. Peace Officers Memorial Day and Police Week, 2009

    Science.gov (United States)

    2010-01-01

    ... Memorial Day and Police Week, 2009 8378 Proclamation 8378 Presidential Documents Proclamations Proclamation 8378 of May 11, 2009 Proc. 8378 Peace Officers Memorial Day and Police Week, 2009By the President of... disabled in the line of duty, and to designate that week as Police Week in recognition of their service...

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

    Science.gov (United States)

    Chetverikov, Andrey

    2014-04-01

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

  14. Pattern recognition approach to nondestructive evaluation of materials

    International Nuclear Information System (INIS)

    Chen, C.H.

    1987-01-01

    In this paper, a pattern recognition approach to the ultrasonic nondestructive evaluation of materials is examined. Emphasis is placed on identifying effective features from time and frequency domains, correlation functions and impulse responses to classify aluminum plate specimens into three major defect geometry categories: flat, angular cut and circular hole defects. A multi-stage classification procedure is developed which can further determine the angles and sizes for defect characterization and classification. The research clearly demonstrates that the pattern recognition approach can significantly improve the nondestructive material evaluation capability of the ultrasonic methods without resorting to the solution of highly complex mathematical inverse problems

  15. Human Wearable Attribute Recognition Using Probability-Map-Based Decomposition of Thermal Infrared Images

    OpenAIRE

    KRESNARAMAN, Brahmastro; KAWANISHI, Yasutomo; DEGUCHI, Daisuke; TAKAHASHI, Tomokazu; MEKADA, Yoshito; IDE, Ichiro; MURASE, Hiroshi

    2017-01-01

    This paper addresses the attribute recognition problem, a field of research that is dominated by studies in the visible spectrum. Only a few works are available in the thermal spectrum, which is fundamentally different from the visible one. This research performs recognition specifically on wearable attributes, such as glasses and masks. Usually these attributes are relatively small in size when compared with the human body, on top of a large intra-class variation of the human body itself, th...

  16. PROBABILISTIC APPROACH TO OBJECT DETECTION AND RECOGNITION FOR VIDEOSTREAM PROCESSING

    Directory of Open Access Journals (Sweden)

    Volodymyr Kharchenko

    2017-07-01

    Full Text Available Purpose: The represented research results are aimed to improve theoretical basics of computer vision and artificial intelligence of dynamical system. Proposed approach of object detection and recognition is based on probabilistic fundamentals to ensure the required level of correct object recognition. Methods: Presented approach is grounded at probabilistic methods, statistical methods of probability density estimation and computer-based simulation at verification stage of development. Results: Proposed approach for object detection and recognition for video stream data processing has shown several advantages in comparison with existing methods due to its simple realization and small time of data processing. Presented results of experimental verification look plausible for object detection and recognition in video stream. Discussion: The approach can be implemented in dynamical system within changeable environment such as remotely piloted aircraft systems and can be a part of artificial intelligence in navigation and control systems.

  17. Increased Efficiency of Face Recognition System using Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Rajani Muraleedharan

    2006-02-01

    Full Text Available This research was inspired by the need of a flexible and cost effective biometric security system. The flexibility of the wireless sensor network makes it a natural choice for data transmission. Swarm intelligence (SI is used to optimize routing in distributed time varying network. In this paper, SI maintains the required bit error rate (BER for varied channel conditions while consuming minimal energy. A specific biometric, the face recognition system, is discussed as an example. Simulation shows that the wireless sensor network is efficient in energy consumption while keeping the transmission accuracy, and the wireless face recognition system is competitive to the traditional wired face recognition system in classification accuracy.

  18. The Role of Morphology in Word Recognition of Hebrew as a Templatic Language

    Science.gov (United States)

    Oganyan, Marina

    2017-01-01

    Research on recognition of complex words has primarily focused on affixational complexity in concatenative languages. This dissertation investigates both templatic and affixational complexity in Hebrew, a templatic language, with particular focus on the role of the root and template morphemes in recognition. It also explores the role of morphology…

  19. Target recognition of log-polar ladar range images using moment invariants

    Science.gov (United States)

    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.

  20. Non-Cooperative Facial Recognition Video Dataset Collection Plan

    Energy Technology Data Exchange (ETDEWEB)

    Kimura, Marcia L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Erikson, Rebecca L. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Lombardo, Nicholas J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2013-08-31

    The Pacific Northwest National Laboratory (PNNL) will produce a non-cooperative (i.e. not posing for the camera) facial recognition video data set for research purposes to evaluate and enhance facial recognition systems technology. The aggregate data set consists of 1) videos capturing PNNL role players and public volunteers in three key operational settings, 2) photographs of the role players for enrolling in an evaluation database, and 3) ground truth data that documents when the role player is within various camera fields of view. PNNL will deliver the aggregate data set to DHS who may then choose to make it available to other government agencies interested in evaluating and enhancing facial recognition systems. The three operational settings that will be the focus of the video collection effort include: 1) unidirectional crowd flow 2) bi-directional crowd flow, and 3) linear and/or serpentine queues.

  1. Simulation Analysis on Driving Behavior during Traffic Sign Recognition

    Directory of Open Access Journals (Sweden)

    Lishan Sun

    2011-05-01

    Full Text Available The traffic signs transfer trip information to drivers through vectors like words, graphs and numbers. Traffic sign with excessive information often makes the drivers have no time to read and understand, leading to risky driving. It is still a problem of how to clarify the relationship between traffic sign recognition and risky driving behavior. This paper presents a study that is reflective of such an effort. Twenty volunteers participated in the dynamic visual recognition experiment in driving simulator, and the data of several key indicators are obtained, including visual cognition time, vehicle acceleration and the offset distance from middle lane, etc. Correlations between each indicator above are discussed in terms of risky driving. Research findings directly show that drivers' behavior changes a lot during their traffic sign recognition.

  2. Heteroditopic receptors for ion-pair recognition.

    Science.gov (United States)

    McConnell, Anna J; Beer, Paul D

    2012-05-21

    Ion-pair recognition is a new field of research emerging from cation and anion coordination chemistry. Specific types of heteroditopic receptor designs for ion pairs and the complexity of ion-pair binding are discussed to illustrate key concepts such as cooperativity. The importance of this area of research is reflected by the wide variety of potential applications of ion-pair receptors, including applications as membrane transport and salt solubilization agents and sensors. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  3. End-to-end visual speech recognition with LSTMS

    NARCIS (Netherlands)

    Petridis, Stavros; Li, Zuwei; Pantic, Maja

    2017-01-01

    Traditional visual speech recognition systems consist of two stages, feature extraction and classification. Recently, several deep learning approaches have been presented which automatically extract features from the mouth images and aim to replace the feature extraction stage. However, research on

  4. Chronic prenatal caffeine exposure impairs novel object recognition and radial arm maze behaviors in adult rats.

    Science.gov (United States)

    Soellner, Deborah E; Grandys, Theresa; Nuñez, Joseph L

    2009-12-14

    In this report, we demonstrate that chronic prenatal exposure to a moderate dose of caffeine disrupts novel object recognition and radial arm maze behaviors in adult male and female rats. Pregnant dams were administered either tap water or 75 mg/L caffeinated tap water throughout gestation. Oral self-administration in the drinking water led to an approximate maternal intake of 10mg/kg/day, equivalent to 2-3 cups of coffee/day in humans based on a metabolic body weight conversion. In adulthood, the offspring underwent testing on novel object recognition, radial arm maze, and Morris water maze tasks. Prenatal caffeine exposure was found to impair 24-h memory retention in the novel object recognition task and impair both working and reference memory in the radial arm maze. However, prenatal caffeine exposure did not alter Morris water maze performance in either a simple water maze procedure or in an advanced water maze procedure that included reversal and working memory paradigms. These findings demonstrate that chronic oral intake of caffeine throughout gestation can alter adult cognitive behaviors in rats.

  5. Effects of repeated collaborative retrieval on individual memory vary as a function of recall versus recognition tasks.

    Science.gov (United States)

    Blumen, Helena M; Rajaram, Suparna

    2009-11-01

    Our research examines how prior group collaboration modulates later individual memory. We recently showed that repeated collaborative recall sessions benefit later individual recall more than a single collaborative recall session (Blumen & Rajaram, 2008). Current research compared the effects of repeated collaborative recall and repeated collaborative recognition on later individual recall and later individual recognition. A total of 192 participants studied a list of nouns and then completed three successive retrieval sessions in one of four conditions. While two collaborative recall sessions and two collaborative recognition sessions generated comparable levels of individual recall (CRecall-CRecall-I Recall approximately CRecognition-CRecognition-I Recall , Experiment 1a), two collaborative recognition sessions generated greater levels of individual recognition than two collaborative recall sessions (CRecognition-CRecognition- IRecognition > CRecall-CRecall- I Recognition , Experiment 1b). These findings are discussed in terms of two opposing mechanisms that operate during collaborative retrieval-re-exposure and retrieval disruption-and in terms of transfer-appropriate processing across collaborative and individual retrieval sessions.

  6. The Improvement of Behavior Recognition Accuracy of Micro Inertial Accelerometer by Secondary Recognition Algorithm

    Directory of Open Access Journals (Sweden)

    Yu Liu

    2014-05-01

    Full Text Available Behaviors of “still”, “walking”, “running”, “jumping”, “upstairs” and “downstairs” can be recognized by micro inertial accelerometer of low cost. By using the features as inputs to the well-trained BP artificial neural network which is selected as classifier, those behaviors can be recognized. But the experimental results show that the recognition accuracy is not satisfactory. This paper presents secondary recognition algorithm and combine it with BP artificial neural network to improving the recognition accuracy. The Algorithm is verified by the Android mobile platform, and the recognition accuracy can be improved more than 8 %. Through extensive testing statistic analysis, the recognition accuracy can reach 95 % through BP artificial neural network and the secondary recognition, which is a reasonable good result from practical point of view.

  7. Human embryo research and the 14-day rule.

    Science.gov (United States)

    Pera, Martin F

    2017-06-01

    In many jurisdictions, restrictions prohibit the culture of human embryos beyond 14 days of development. However, recent reports describing the successful maintenance of embryos in vitro to this stage have prompted many in the field to question whether the rule is still appropriate. This Spotlight article looks at the original rationale behind the 14-day rule and its relevance today in light of advances in human embryo culture and in the derivation of embryonic-like structures from human pluripotent stem cells. © 2017. Published by The Company of Biologists Ltd.

  8. Hippocampal Infusion of Zeta Inhibitory Peptide Impairs Recent, but Not Remote, Recognition Memory in Rats

    Directory of Open Access Journals (Sweden)

    Jena B. Hales

    2015-01-01

    Full Text Available Spatial memory in rodents can be erased following the infusion of zeta inhibitory peptide (ZIP into the dorsal hippocampus via indwelling guide cannulas. It is believed that ZIP impairs spatial memory by reversing established late-phase long-term potentiation (LTP. However, it is unclear whether other forms of hippocampus-dependent memory, such as recognition memory, are also supported by hippocampal LTP. In the current study, we tested recognition memory in rats following hippocampal ZIP infusion. In order to combat the limited targeting of infusions via cannula, we implemented a stereotaxic approach for infusing ZIP throughout the dorsal, intermediate, and ventral hippocampus. Rats infused with ZIP 3–7 days after training on the novel object recognition task exhibited impaired object recognition memory compared to control rats (those infused with aCSF. In contrast, rats infused with ZIP 1 month after training performed similar to control rats. The ability to form new memories after ZIP infusions remained intact. We suggest that enhanced recognition memory for recent events is supported by hippocampal LTP, which can be reversed by hippocampal ZIP infusion.

  9. Heterogeneity of long-history migration predicts emotion recognition accuracy.

    Science.gov (United States)

    Wood, Adrienne; Rychlowska, Magdalena; Niedenthal, Paula M

    2016-06-01

    Recent work (Rychlowska et al., 2015) demonstrated the power of a relatively new cultural dimension, historical heterogeneity, in predicting cultural differences in the endorsement of emotion expression norms. Historical heterogeneity describes the number of source countries that have contributed to a country's present-day population over the last 500 years. People in cultures originating from a large number of source countries may have historically benefited from greater and clearer emotional expressivity, because they lacked a common language and well-established social norms. We therefore hypothesized that in addition to endorsing more expressive display rules, individuals from heterogeneous cultures will also produce facial expressions that are easier to recognize by people from other cultures. By reanalyzing cross-cultural emotion recognition data from 92 papers and 82 cultures, we show that emotion expressions of people from heterogeneous cultures are more easily recognized by observers from other cultures than are the expressions produced in homogeneous cultures. Heterogeneity influences expression recognition rates alongside the individualism-collectivism of the perceivers' culture, as more individualistic cultures were more accurate in emotion judgments than collectivistic cultures. This work reveals the present-day behavioral consequences of long-term historical migration patterns and demonstrates the predictive power of historical heterogeneity. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  10. Adults' strategies for simple addition and multiplication: verbal self-reports and the operand recognition paradigm.

    Science.gov (United States)

    Metcalfe, Arron W S; Campbell, Jamie I D

    2011-05-01

    Accurate measurement of cognitive strategies is important in diverse areas of psychological research. Strategy self-reports are a common measure, but C. Thevenot, M. Fanget, and M. Fayol (2007) proposed a more objective method to distinguish different strategies in the context of mental arithmetic. In their operand recognition paradigm, speed of recognition memory for problem operands after solving a problem indexes strategy (e.g., direct memory retrieval vs. a procedural strategy). Here, in 2 experiments, operand recognition time was the same following simple addition or multiplication, but, consistent with a wide variety of previous research, strategy reports indicated much greater use of procedures (e.g., counting) for addition than multiplication. Operation, problem size (e.g., 2 + 3 vs. 8 + 9), and operand format (digits vs. words) had interactive effects on reported procedure use that were not reflected in recognition performance. Regression analyses suggested that recognition time was influenced at least as much by the relative difficulty of the preceding problem as by the strategy used. The findings indicate that the operand recognition paradigm is not a reliable substitute for strategy reports and highlight the potential impact of difficulty-related carryover effects in sequential cognitive tasks.

  11. Experience moderates overlap between object and face recognition, suggesting a common ability.

    Science.gov (United States)

    Gauthier, Isabel; McGugin, Rankin W; Richler, Jennifer J; Herzmann, Grit; Speegle, Magen; Van Gulick, Ana E

    2014-07-03

    Some research finds that face recognition is largely independent from the recognition of other objects; a specialized and innate ability to recognize faces could therefore have little or nothing to do with our ability to recognize objects. We propose a new framework in which recognition performance for any category is the product of domain-general ability and category-specific experience. In Experiment 1, we show that the overlap between face and object recognition depends on experience with objects. In 256 subjects we measured face recognition, object recognition for eight categories, and self-reported experience with these categories. Experience predicted neither face recognition nor object recognition but moderated their relationship: Face recognition performance is increasingly similar to object recognition performance with increasing object experience. If a subject has a lot of experience with objects and is found to perform poorly, they also prove to have a low ability with faces. In a follow-up survey, we explored the dimensions of experience with objects that may have contributed to self-reported experience in Experiment 1. Different dimensions of experience appear to be more salient for different categories, with general self-reports of expertise reflecting judgments of verbal knowledge about a category more than judgments of visual performance. The complexity of experience and current limitations in its measurement support the importance of aggregating across multiple categories. Our findings imply that both face and object recognition are supported by a common, domain-general ability expressed through experience with a category and best measured when accounting for experience. © 2014 ARVO.

  12. Pengaruh Iklan terhadap Minat Beli Pengguna Youtube dengan Brand Recognition sebagai Variabel Intervening

    Directory of Open Access Journals (Sweden)

    Herdian Rizky Yuniyanto

    2018-03-01

    Full Text Available The Influence of Advertising on The Buying Interest of Youtube Users with Brand Recognition as Intervening VariableInternet media can be used to do marketing, one of which is Youtube. This study aims to explain the effect of advertising on buying interest on Youtube users using brand recognition as an intervening variable. The sample in this study amounted 180 students of  Universitas Kristen Satya Wacana who saw Nike product advertising on Youtube. The analysis technique used is path analysis. The result of the research shows that advertising have a significant positive effect to brand recognition, brand recognition have significant positive effect to buying interest, and brand recognition can not function as intervening variable in relationship between advertising and buying interest.DOI: 10.15408/ess.v8i1.5885

  13. Research Into the Use of Speech Recognition Enhanced Microworlds in an Authorable Language Tutor

    National Research Council Canada - National Science Library

    Plott, Beth

    1999-01-01

    .... Once the first microworld exercise was completed and integrated into MILT, ARI funded the investigation of the use of discreet speech recognition technology in language learning using the microworld exercise as a basis...

  14. 2nd International Symposium on Signal Processing and Intelligent Recognition Systems

    CERN Document Server

    Bandyopadhyay, Sanghamitra; Krishnan, Sri; Li, Kuan-Ching; Mosin, Sergey; Ma, Maode

    2016-01-01

    This Edited Volume contains a selection of refereed and revised papers originally presented at the second International Symposium on Signal Processing and Intelligent Recognition Systems (SIRS-2015), December 16-19, 2015, Trivandrum, India. The program committee received 175 submissions. Each paper was peer reviewed by at least three or more independent referees of the program committee and the 59 papers were finally selected. The papers offer stimulating insights into biometrics, digital watermarking, recognition systems, image and video processing, signal and speech processing, pattern recognition, machine learning and knowledge-based systems. The book is directed to the researchers and scientists engaged in various field of signal processing and related areas. .

  15. Recognition during recall failure: Semantic feature matching as a mechanism for recognition of semantic cues when recall fails.

    Science.gov (United States)

    Cleary, Anne M; Ryals, Anthony J; Wagner, Samantha R

    2016-01-01

    Research suggests that a feature-matching process underlies cue familiarity-detection when cued recall with graphemic cues fails. When a test cue (e.g., potchbork) overlaps in graphemic features with multiple unrecalled studied items (e.g., patchwork, pitchfork, pocketbook, pullcork), higher cue familiarity ratings are given during recall failure of all of the targets than when the cue overlaps in graphemic features with only one studied target and that target fails to be recalled (e.g., patchwork). The present study used semantic feature production norms (McRae et al., Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005) to examine whether the same holds true when the cues are semantic in nature (e.g., jaguar is used to cue cheetah). Indeed, test cues (e.g., cedar) that overlapped in semantic features (e.g., a_tree, has_bark, etc.) with four unretrieved studied items (e.g., birch, oak, pine, willow) received higher cue familiarity ratings during recall failure than test cues that overlapped in semantic features with only two (also unretrieved) studied items (e.g., birch, oak), which in turn received higher familiarity ratings during recall failure than cues that did not overlap in semantic features with any studied items. These findings suggest that the feature-matching theory of recognition during recall failure can accommodate recognition of semantic cues during recall failure, providing a potential mechanism for conceptually-based forms of cue recognition during target retrieval failure. They also provide converging evidence for the existence of the semantic features envisaged in feature-based models of semantic knowledge representation and for those more concretely specified by the production norms of McRae et al. (Behavior Research Methods, Instruments, & Computers, 37, 547-559, 2005).

  16. 2D Methods for pose invariant face recognition

    CSIR Research Space (South Africa)

    Mokoena, Ntabiseng

    2016-12-01

    Full Text Available The ability to recognise face images under random pose is a task that is done effortlessly by human beings. However, for a computer system, recognising face images under varying poses still remains an open research area. Face recognition across pose...

  17. Applications of evolutionary computation in image processing and pattern recognition

    CERN Document Server

    Cuevas, Erik; Perez-Cisneros, Marco

    2016-01-01

    This book presents the use of efficient Evolutionary Computation (EC) algorithms for solving diverse real-world image processing and pattern recognition problems. It provides an overview of the different aspects of evolutionary methods in order to enable the reader in reaching a global understanding of the field and, in conducting studies on specific evolutionary techniques that are related to applications in image processing and pattern recognition. It explains the basic ideas of the proposed applications in a way that can also be understood by readers outside of the field. Image processing and pattern recognition practitioners who are not evolutionary computation researchers will appreciate the discussed techniques beyond simple theoretical tools since they have been adapted to solve significant problems that commonly arise on such areas. On the other hand, members of the evolutionary computation community can learn the way in which image processing and pattern recognition problems can be translated into an...

  18. Smartphone-based human activity recognition

    OpenAIRE

    Reyes Ortiz, Jorge Luis

    2014-01-01

    Cotutela Universitat Politècnica de Catalunya i Università degli Studi di Genova Human Activity Recognition (HAR) is a multidisciplinary research field that aims to gather data regarding people's behavior and their interaction with the environment in order to deliver valuable context-aware information. It has nowadays contributed to develop human-centered areas of study such as Ambient Intelligence and Ambient Assisted Living, which concentrate on the improvement of people's Quality of Lif...

  19. REAL-TIME FACE RECOGNITION BASED ON OPTICAL FLOW AND HISTOGRAM EQUALIZATION

    Directory of Open Access Journals (Sweden)

    D. Sathish Kumar

    2013-05-01

    Full Text Available Face recognition is one of the intensive areas of research in computer vision and pattern recognition but many of which are focused on recognition of faces under varying facial expressions and pose variation. A constrained optical flow algorithm discussed in this paper, recognizes facial images involving various expressions based on motion vector computation. In this paper, an optical flow computation algorithm which computes the frames of varying facial gestures, and integrating with synthesized image in a probabilistic environment has been proposed. Also Histogram Equalization technique has been used to overcome the effect of illuminations while capturing the input data using camera devices. It also enhances the contrast of the image for better processing. The experimental results confirm that the proposed face recognition system is more robust and recognizes the facial images under varying expressions and pose variations more accurately.

  20. Common constraints limit Korean and English character recognition in peripheral vision.

    Science.gov (United States)

    He, Yingchen; Kwon, MiYoung; Legge, Gordon E

    2018-01-01

    The visual span refers to the number of adjacent characters that can be recognized in a single glance. It is viewed as a sensory bottleneck in reading for both normal and clinical populations. In peripheral vision, the visual span for English characters can be enlarged after training with a letter-recognition task. Here, we examined the transfer of training from Korean to English characters for a group of bilingual Korean native speakers. In the pre- and posttests, we measured visual spans for Korean characters and English letters. Training (1.5 hours × 4 days) consisted of repetitive visual-span measurements for Korean trigrams (strings of three characters). Our training enlarged the visual spans for Korean single characters and trigrams, and the benefit transferred to untrained English symbols. The improvement was largely due to a reduction of within-character and between-character crowding in Korean recognition, as well as between-letter crowding in English recognition. We also found a negative correlation between the size of the visual span and the average pattern complexity of the symbol set. Together, our results showed that the visual span is limited by common sensory (crowding) and physical (pattern complexity) factors regardless of the language script, providing evidence that the visual span reflects a universal bottleneck for text recognition.

  1. I Know My Neighbour: Individual Recognition in Octopus vulgaris

    Science.gov (United States)

    Tricarico, Elena; Borrelli, Luciana; Gherardi, Francesca; Fiorito, Graziano

    2011-01-01

    Background Little is known about individual recognition (IR) in octopuses, although they have been abundantly studied for their sophisticated behaviour and learning capacities. Indeed, the ability of octopuses to recognise conspecifics is suggested by a number of clues emerging from both laboratory studies (where they appear to form and maintain dominance hierarchies) and field observations (octopuses of neighbouring dens display little agonism between each other). To fill this gap in knowledge, we investigated the behaviour of 24 size-matched pairs of Octopus vulgaris in laboratory conditions. Methodology/Principal Findings The experimental design was composed of 3 phases: Phase 1 (acclimatization): 12 “sight-allowed” (and 12 “isolated”) pairs were maintained for 3 days in contiguous tanks separated by a transparent (and opaque) partition to allow (and block) the vision of the conspecific; Phase 2 (cohabitation): members of each pair (both sight-allowed and isolated) were transferred into an experimental tank and were allowed to interact for 15 min every day for 3 consecutive days; Phase 3 (test): each pair (both sight-allowed and isolated) was subject to a switch of an octopus to form pairs composed of either familiar (“sham switches”) or unfamiliar conspecifics (“real switches”). Longer latencies (i.e. the time elapsed from the first interaction) and fewer physical contacts in the familiar pairs as opposed to the unfamiliar pairs were used as proxies for recognition. Conclusions Octopuses appear able to recognise conspecifics and to remember the individual previously met for at least one day. To the best of our knowledge, this is the first experimental study showing the occurrence of a form of IR in cephalopods. Future studies should clarify whether this is a “true” IR. PMID:21533257

  2. Understanding gender bias in face recognition: effects of divided attention at encoding.

    Science.gov (United States)

    Palmer, Matthew A; Brewer, Neil; Horry, Ruth

    2013-03-01

    Prior research has demonstrated a female own-gender bias in face recognition, with females better at recognizing female faces than male faces. We explored the basis for this effect by examining the effect of divided attention during encoding on females' and males' recognition of female and male faces. For female participants, divided attention impaired recognition performance for female faces to a greater extent than male faces in a face recognition paradigm (Study 1; N=113) and an eyewitness identification paradigm (Study 2; N=502). Analysis of remember-know judgments (Study 2) indicated that divided attention at encoding selectively reduced female participants' recollection of female faces at test. For male participants, divided attention selectively reduced recognition performance (and recollection) for male stimuli in Study 2, but had similar effects on recognition of male and female faces in Study 1. Overall, the results suggest that attention at encoding contributes to the female own-gender bias by facilitating the later recollection of female faces. Copyright © 2013 Elsevier B.V. All rights reserved.

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

    Science.gov (United States)

    Christ, K. A.

    1984-08-01

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

  4. Performance Assessment of Dynaspeak Speech Recognition System on Inflight Databases

    National Research Council Canada - National Science Library

    Barry, Timothy

    2004-01-01

    .... To aid in the assessment of various commercially available speech recognition systems, several aircraft speech databases have been developed at the Air Force Research Laboratory's Human Effectiveness Directorate...

  5. The Legal Recognition of Sign Languages

    Science.gov (United States)

    De Meulder, Maartje

    2015-01-01

    This article provides an analytical overview of the different types of explicit legal recognition of sign languages. Five categories are distinguished: constitutional recognition, recognition by means of general language legislation, recognition by means of a sign language law or act, recognition by means of a sign language law or act including…

  6. Ear recognition from one sample per person.

    Directory of Open Access Journals (Sweden)

    Long Chen

    Full Text Available Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

  7. Ear recognition from one sample per person.

    Science.gov (United States)

    Chen, Long; Mu, Zhichun; Zhang, Baoqing; Zhang, Yi

    2015-01-01

    Biometrics has the advantages of efficiency and convenience in identity authentication. As one of the most promising biometric-based methods, ear recognition has received broad attention and research. Previous studies have achieved remarkable performance with multiple samples per person (MSPP) in the gallery. However, most conventional methods are insufficient when there is only one sample per person (OSPP) available in the gallery. To solve the OSPP problem by maximizing the use of a single sample, this paper proposes a hybrid multi-keypoint descriptor sparse representation-based classification (MKD-SRC) ear recognition approach based on 2D and 3D information. Because most 3D sensors capture 3D data accessorizing the corresponding 2D data, it is sensible to use both types of information. First, the ear region is extracted from the profile. Second, keypoints are detected and described for both the 2D texture image and 3D range image. Then, the hybrid MKD-SRC algorithm is used to complete the recognition with only OSPP in the gallery. Experimental results on a benchmark dataset have demonstrated the feasibility and effectiveness of the proposed method in resolving the OSPP problem. A Rank-one recognition rate of 96.4% is achieved for a gallery of 415 subjects, and the time involved in the computation is satisfactory compared to conventional methods.

  8. Finger vein recognition based on convolutional neural network

    Directory of Open Access Journals (Sweden)

    Meng Gesi

    2017-01-01

    Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

  9. The Modality-Match Effect in Recognition Memory

    Science.gov (United States)

    Mulligan, Neil W.; Osborn, Katherine

    2009-01-01

    The modality-match effect in recognition refers to superior memory for words presented in the same modality at study and test. Prior research on this effect is ambiguous and inconsistent. The present study demonstrates that the modality-match effect is found when modality is rendered salient at either encoding or retrieval. Specifically, in…

  10. Experiments on Automatic Recognition of Nonnative Arabic Speech

    Directory of Open Access Journals (Sweden)

    Douglas O'Shaughnessy

    2008-05-01

    Full Text Available The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well, the nonnative speech data available for training are generally insufficient. Moreover, as compared to other languages, the Arabic language has sparked a relatively small number of research efforts. In this paper, we are concerned with the problem of nonnative speech in a speaker independent, large-vocabulary speech recognition system for modern standard Arabic (MSA. We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The WestPoint modern standard Arabic database from the language data consortium (LDC and the hidden Markov Model Toolkit (HTK are used throughout all experiments. Our study shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both training and testing phases. This is not the case when a language model and phonetic lattice networks are incorporated in the system. At the phonetic level, the results show that female nonnative speakers perform better than nonnative male speakers, and that emphatic phonemes yield a significant decrease in performance when they are uttered by both male and female nonnative speakers.

  11. Experiments on Automatic Recognition of Nonnative Arabic Speech

    Directory of Open Access Journals (Sweden)

    Selouani Sid-Ahmed

    2008-01-01

    Full Text Available The automatic recognition of foreign-accented Arabic speech is a challenging task since it involves a large number of nonnative accents. As well, the nonnative speech data available for training are generally insufficient. Moreover, as compared to other languages, the Arabic language has sparked a relatively small number of research efforts. In this paper, we are concerned with the problem of nonnative speech in a speaker independent, large-vocabulary speech recognition system for modern standard Arabic (MSA. We analyze some major differences at the phonetic level in order to determine which phonemes have a significant part in the recognition performance for both native and nonnative speakers. Special attention is given to specific Arabic phonemes. The performance of an HMM-based Arabic speech recognition system is analyzed with respect to speaker gender and its native origin. The WestPoint modern standard Arabic database from the language data consortium (LDC and the hidden Markov Model Toolkit (HTK are used throughout all experiments. Our study shows that the best performance in the overall phoneme recognition is obtained when nonnative speakers are involved in both training and testing phases. This is not the case when a language model and phonetic lattice networks are incorporated in the system. At the phonetic level, the results show that female nonnative speakers perform better than nonnative male speakers, and that emphatic phonemes yield a significant decrease in performance when they are uttered by both male and female nonnative speakers.

  12. Automatic Facial Expression Recognition and Operator Functional State

    Science.gov (United States)

    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

  13. Automatic Facial Expression Recognition and Operator Functional State

    Science.gov (United States)

    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.

  14. Recognition and Toleration

    DEFF Research Database (Denmark)

    Lægaard, Sune

    2010-01-01

    Recognition and toleration are ways of relating to the diversity characteristic of multicultural societies. The article concerns the possible meanings of toleration and recognition, and the conflict that is often claimed to exist between these two approaches to diversity. Different forms or inter...

  15. User adaptation in long-term, open-loop myoelectric training: implications for EMG pattern recognition in prosthesis control

    Science.gov (United States)

    He, Jiayuan; Zhang, Dingguo; Jiang, Ning; Sheng, Xinjun; Farina, Dario; Zhu, Xiangyang

    2015-08-01

    Objective. Recent studies have reported that the classification performance of electromyographic (EMG) signals degrades over time without proper classification retraining. This problem is relevant for the applications of EMG pattern recognition in the control of active prostheses. Approach. In this study we investigated the changes in EMG classification performance over 11 consecutive days in eight able-bodied subjects and two amputees. Main results. It was observed that, when the classifier was trained on data from one day and tested on data from the following day, the classification error decreased exponentially but plateaued after four days for able-bodied subjects and six to nine days for amputees. The between-day performance became gradually closer to the corresponding within-day performance. Significance. These results indicate that the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions are increased. The performance of the motor tasks is thus more consistent over time, resulting in more repeatable EMG patterns, even if the subjects do not have any external feedback on their performance. The learning curves for both able-bodied subjects and subjects with limb deficiencies could be modeled as an exponential function. These results provide important insights into the user adaptation characteristics during practical long-term myoelectric control applications, with implications for the design of an adaptive pattern recognition system.

  16. Enhancing emotion recognition in VIPs with haptic feedback

    NARCIS (Netherlands)

    Buimer, Hendrik; Bittner, Marian; Kostelijk, Tjerk; van der Geest, Thea; van Wezel, Richard Jack Anton; Zhao, Yan; Stephanidis, Constantine

    2016-01-01

    The rise of smart technologies has created new opportunities to support blind and visually impaired persons (VIPs). One of the biggest problems we identified in our previous research on problems VIPs face during activities of daily life concerned the recognition of persons and their facial

  17. Fine-grained vehicle type recognition based on deep convolution neural networks

    Directory of Open Access Journals (Sweden)

    Hongcai CHEN

    2017-12-01

    Full Text Available Public security and traffic department put forward higher requirements for real-time performance and accuracy of vehicle type recognition in complex traffic scenes. Aiming at the problems of great plice forces occupation, low retrieval efficiency, and lacking of intelligence for dealing with false license, fake plate vehicles and vehicles without plates, this paper proposes a vehicle type fine-grained recognition method based GoogleNet deep convolution neural networks. The filter size and numbers of convolution neural network are designed, the activation function and vehicle type classifier are optimally selected, and a new network framework is constructed for vehicle type fine-grained recognition. The experimental results show that the proposed method has 97% accuracy for vehicle type fine-grained recognition and has greater improvement than the original GoogleNet model. Moreover, the new model effectively reduces the number of training parameters, and saves computer memory. Fine-grained vehicle type recognition can be used in intelligent traffic management area, and has important theoretical research value and practical significance.

  18. Research on the selection of innovation compound using Possibility Construction Space Theory and fuzzy pattern recognition

    Science.gov (United States)

    Xie, Songhua; Li, Dehua; Nie, Hui

    2009-10-01

    There are a large number of fuzzy concepts and fuzzy phenomena in traditional Chinese medicine, which have led to great difficulties for study of traditional Chinese medicine. In this paper, the mathematical methods are used to quantify fuzzy concepts of drugs and prescription. We put forward the process of innovation formulations and selection method in Chinese medicine based on the Possibility Construction Space Theory (PCST) and fuzzy pattern recognition. Experimental results show that the method of selecting medicines from a number of characteristics of traditional Chinese medicine is consistent with the basic theory of traditional Chinese medicine. The results also reflect the integrated effects of the innovation compound. Through the use of the innovation formulations system, we expect to provide software tools for developing new traditional Chinese medicine and to inspire traditional Chinese medicine researchers to develop novel drugs.

  19. Sex differences in facial emotion recognition across varying expression intensity levels from videos.

    Science.gov (United States)

    Wingenbach, Tanja S H; Ashwin, Chris; Brosnan, Mark

    2018-01-01

    There has been much research on sex differences in the ability to recognise facial expressions of emotions, with results generally showing a female advantage in reading emotional expressions from the face. However, most of the research to date has used static images and/or 'extreme' examples of facial expressions. Therefore, little is known about how expression intensity and dynamic stimuli might affect the commonly reported female advantage in facial emotion recognition. The current study investigated sex differences in accuracy of response (Hu; unbiased hit rates) and response latencies for emotion recognition using short video stimuli (1sec) of 10 different facial emotion expressions (anger, disgust, fear, sadness, surprise, happiness, contempt, pride, embarrassment, neutral) across three variations in the intensity of the emotional expression (low, intermediate, high) in an adolescent and adult sample (N = 111; 51 male, 60 female) aged between 16 and 45 (M = 22.2, SD = 5.7). Overall, females showed more accurate facial emotion recognition compared to males and were faster in correctly recognising facial emotions. The female advantage in reading expressions from the faces of others was unaffected by expression intensity levels and emotion categories used in the study. The effects were specific to recognition of emotions, as males and females did not differ in the recognition of neutral faces. Together, the results showed a robust sex difference favouring females in facial emotion recognition using video stimuli of a wide range of emotions and expression intensity variations.

  20. Sex differences in facial emotion recognition across varying expression intensity levels from videos

    Science.gov (United States)

    2018-01-01

    There has been much research on sex differences in the ability to recognise facial expressions of emotions, with results generally showing a female advantage in reading emotional expressions from the face. However, most of the research to date has used static images and/or ‘extreme’ examples of facial expressions. Therefore, little is known about how expression intensity and dynamic stimuli might affect the commonly reported female advantage in facial emotion recognition. The current study investigated sex differences in accuracy of response (Hu; unbiased hit rates) and response latencies for emotion recognition using short video stimuli (1sec) of 10 different facial emotion expressions (anger, disgust, fear, sadness, surprise, happiness, contempt, pride, embarrassment, neutral) across three variations in the intensity of the emotional expression (low, intermediate, high) in an adolescent and adult sample (N = 111; 51 male, 60 female) aged between 16 and 45 (M = 22.2, SD = 5.7). Overall, females showed more accurate facial emotion recognition compared to males and were faster in correctly recognising facial emotions. The female advantage in reading expressions from the faces of others was unaffected by expression intensity levels and emotion categories used in the study. The effects were specific to recognition of emotions, as males and females did not differ in the recognition of neutral faces. Together, the results showed a robust sex difference favouring females in facial emotion recognition using video stimuli of a wide range of emotions and expression intensity variations. PMID:29293674

  1. Sex differences in facial emotion recognition across varying expression intensity levels from videos.

    Directory of Open Access Journals (Sweden)

    Tanja S H Wingenbach

    Full Text Available There has been much research on sex differences in the ability to recognise facial expressions of emotions, with results generally showing a female advantage in reading emotional expressions from the face. However, most of the research to date has used static images and/or 'extreme' examples of facial expressions. Therefore, little is known about how expression intensity and dynamic stimuli might affect the commonly reported female advantage in facial emotion recognition. The current study investigated sex differences in accuracy of response (Hu; unbiased hit rates and response latencies for emotion recognition using short video stimuli (1sec of 10 different facial emotion expressions (anger, disgust, fear, sadness, surprise, happiness, contempt, pride, embarrassment, neutral across three variations in the intensity of the emotional expression (low, intermediate, high in an adolescent and adult sample (N = 111; 51 male, 60 female aged between 16 and 45 (M = 22.2, SD = 5.7. Overall, females showed more accurate facial emotion recognition compared to males and were faster in correctly recognising facial emotions. The female advantage in reading expressions from the faces of others was unaffected by expression intensity levels and emotion categories used in the study. The effects were specific to recognition of emotions, as males and females did not differ in the recognition of neutral faces. Together, the results showed a robust sex difference favouring females in facial emotion recognition using video stimuli of a wide range of emotions and expression intensity variations.

  2. Super-recognition in development: A case study of an adolescent with extraordinary face recognition skills.

    Science.gov (United States)

    Bennetts, Rachel J; Mole, Joseph; Bate, Sarah

    2017-09-01

    Face recognition abilities vary widely. While face recognition deficits have been reported in children, it is unclear whether superior face recognition skills can be encountered during development. This paper presents O.B., a 14-year-old female with extraordinary face recognition skills: a "super-recognizer" (SR). O.B. demonstrated exceptional face-processing skills across multiple tasks, with a level of performance that is comparable to adult SRs. Her superior abilities appear to be specific to face identity: She showed an exaggerated face inversion effect and her superior abilities did not extend to object processing or non-identity aspects of face recognition. Finally, an eye-movement task demonstrated that O.B. spent more time than controls examining the nose - a pattern previously reported in adult SRs. O.B. is therefore particularly skilled at extracting and using identity-specific facial cues, indicating that face and object recognition are dissociable during development, and that super recognition can be detected in adolescence.

  3. Early age-dependent impairments of context-dependent extinction learning, object recognition, and object-place learning occur in rats.

    Science.gov (United States)

    Wiescholleck, Valentina; Emma André, Marion Agnès; Manahan-Vaughan, Denise

    2014-03-01

    The hippocampus is vulnerable to age-dependent memory decline. Multiple forms of memory depend on adequate hippocampal function. Extinction learning comprises active inhibition of no longer relevant learned information concurrent with suppression of a previously learned reaction. It is highly dependent on context, and evidence exists that it requires hippocampal activation. In this study, we addressed whether context-based extinction as well as hippocampus-dependent tasks, such as object recognition and object-place recognition, are equally affected by moderate aging. Young (7-8 week old) and older (7-8 month old) Wistar rats were used. For the extinction study, animals learned that a particular floor context indicated that they should turn into one specific arm (e.g., left) to receive a food reward. On the day after reaching the learning criterion of 80% correct choices, the floor context was changed, no reward was given and animals were expected to extinguish the learned response. Both, young and older rats managed this first extinction trial in the new context with older rats showing a faster extinction performance. One day later, animals were returned to the T-maze with the original floor context and renewal effects were assessed. In this case, only young but not older rats showed the expected renewal effect (lower extinction ratio as compared to the day before). To assess general memory abilities, animals were tested in the standard object recognition and object-place memory tasks. Evaluations were made at 5 min, 1 h and 7 day intervals. Object recognition memory was poor at short-term and intermediate time-points in older but not young rats. Object-place memory performance was unaffected at 5 min, but impaired at 1 h in older but not young rats. Both groups were impaired at 7 days. These findings support that not only aspects of general memory, but also context-dependent extinction learning, are affected by moderate aging. This may reflect less flexibility in

  4. Vehicle license plate recognition based on geometry restraints and multi-feature decision

    Science.gov (United States)

    Wu, Jianwei; Wang, Zongyue

    2005-10-01

    Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

  5. A motivational determinant of facial emotion recognition: regulatory focus affects recognition of emotions in faces.

    Science.gov (United States)

    Sassenrath, Claudia; Sassenberg, Kai; Ray, Devin G; Scheiter, Katharina; Jarodzka, Halszka

    2014-01-01

    Two studies examined an unexplored motivational determinant of facial emotion recognition: observer regulatory focus. It was predicted that a promotion focus would enhance facial emotion recognition relative to a prevention focus because the attentional strategies associated with promotion focus enhance performance on well-learned or innate tasks - such as facial emotion recognition. In Study 1, a promotion or a prevention focus was experimentally induced and better facial emotion recognition was observed in a promotion focus compared to a prevention focus. In Study 2, individual differences in chronic regulatory focus were assessed and attention allocation was measured using eye tracking during the facial emotion recognition task. Results indicated that the positive relation between a promotion focus and facial emotion recognition is mediated by shorter fixation duration on the face which reflects a pattern of attention allocation matched to the eager strategy in a promotion focus (i.e., striving to make hits). A prevention focus did not have an impact neither on perceptual processing nor on facial emotion recognition. Taken together, these findings demonstrate important mechanisms and consequences of observer motivational orientation for facial emotion recognition.

  6. Pattern recognition & machine learning

    CERN Document Server

    Anzai, Y

    1992-01-01

    This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

  7. A REVIEW: OPTICAL CHARACTER RECOGNITION

    OpenAIRE

    Swati Tomar*1 & Amit Kishore2

    2018-01-01

    This paper presents detailed review in the field of Optical Character Recognition. Various techniques are determine that have been proposed to realize the center of character recognition in an optical character recognition system. Even though, sufficient studies and papers are describes the techniques for converting textual content from a paper document into machine readable form. Optical character recognition is a process where the computer understands automatically the image of handwritten ...

  8. Orexin signaling during social defeat stress influences subsequent social interaction behaviour and recognition memory.

    Science.gov (United States)

    Eacret, Darrell; Grafe, Laura A; Dobkin, Jane; Gotter, Anthony L; Rengerb, John J; Winrow, Christopher J; Bhatnagar, Seema

    2018-06-11

    Orexins are neuropeptides synthesized in the lateral hypothalamus that influence arousal, feeding, reward pathways, and the response to stress. However, the role of orexins in repeated stress is not fully characterized. Here, we examined how orexins and their receptors contribute to the coping response during repeated social defeat and subsequent anxiety-like and memory-related behaviors. Specifically, we used Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) to stimulate orexins prior to each of five consecutive days of social defeat stress in adult male rats. Additionally, we determined the role of the orexin 2 receptor in these behaviors by using a selective orexin 2 receptor antagonist (MK-1064) administered prior to each social defeat. Following the 5 day social defeat conditioning period, rats were evaluated in social interaction and novel object recognition paradigms to assess anxiety-like behavior and recognition memory, respectively. Activation of orexin neurons by DREADDs prior to each social defeat decreased the average latency to become defeated across 5 days, indicative of a passive coping strategy that we have previously linked to a stress vulnerable phenotype. Moreover, stimulation of orexin signaling during defeat conditioning decreased subsequent social interaction and performance in the novel object recognition test indicating increased subsequent anxiety-like behavior and reduced working memory. Blocking the orexin 2 receptor during repeated defeat did not alter these effects. Together, our results suggest that orexin neuron activation produces a passive coping phenotype during social defeat leading to subsequent anxiety-like behaviors and memory deficits. Copyright © 2018. Published by Elsevier B.V.

  9. Face memory and face recognition in children and adolescents with attention deficit hyperactivity disorder: A systematic review.

    Science.gov (United States)

    Romani, Maria; Vigliante, Miriam; Faedda, Noemi; Rossetti, Serena; Pezzuti, Lina; Guidetti, Vincenzo; Cardona, Francesco

    2018-06-01

    This review focuses on facial recognition abilities in children and adolescents with attention deficit hyperactivity disorder (ADHD). A systematic review, using PRISMA guidelines, was conducted to identify original articles published prior to May 2017 pertaining to memory, face recognition, affect recognition, facial expression recognition and recall of faces in children and adolescents with ADHD. The qualitative synthesis based on different studies shows a particular focus of the research on facial affect recognition without paying similar attention to the structural encoding of facial recognition. In this review, we further investigate facial recognition abilities in children and adolescents with ADHD, providing synthesis of the results observed in the literature, while detecting face recognition tasks used on face processing abilities in ADHD and identifying aspects not yet explored. Copyright © 2018 Elsevier Ltd. All rights reserved.

  10. Social power and recognition of emotional prosody: High power is associated with lower recognition accuracy than low power.

    Science.gov (United States)

    Uskul, Ayse K; Paulmann, Silke; Weick, Mario

    2016-02-01

    Listeners have to pay close attention to a speaker's tone of voice (prosody) during daily conversations. This is particularly important when trying to infer the emotional state of the speaker. Although a growing body of research has explored how emotions are processed from speech in general, little is known about how psychosocial factors such as social power can shape the perception of vocal emotional attributes. Thus, the present studies explored how social power affects emotional prosody recognition. In a correlational study (Study 1) and an experimental study (Study 2), we show that high power is associated with lower accuracy in emotional prosody recognition than low power. These results, for the first time, suggest that individuals experiencing high or low power perceive emotional tone of voice differently. (c) 2016 APA, all rights reserved).

  11. Functional architecture of visual emotion recognition ability: A latent variable approach.

    Science.gov (United States)

    Lewis, Gary J; Lefevre, Carmen E; Young, Andrew W

    2016-05-01

    Emotion recognition has been a focus of considerable attention for several decades. However, despite this interest, the underlying structure of individual differences in emotion recognition ability has been largely overlooked and thus is poorly understood. For example, limited knowledge exists concerning whether recognition ability for one emotion (e.g., disgust) generalizes to other emotions (e.g., anger, fear). Furthermore, it is unclear whether emotion recognition ability generalizes across modalities, such that those who are good at recognizing emotions from the face, for example, are also good at identifying emotions from nonfacial cues (such as cues conveyed via the body). The primary goal of the current set of studies was to address these questions through establishing the structure of individual differences in visual emotion recognition ability. In three independent samples (Study 1: n = 640; Study 2: n = 389; Study 3: n = 303), we observed that the ability to recognize visually presented emotions is based on different sources of variation: a supramodal emotion-general factor, supramodal emotion-specific factors, and face- and within-modality emotion-specific factors. In addition, we found evidence that general intelligence and alexithymia were associated with supramodal emotion recognition ability. Autism-like traits, empathic concern, and alexithymia were independently associated with face-specific emotion recognition ability. These results (a) provide a platform for further individual differences research on emotion recognition ability, (b) indicate that differentiating levels within the architecture of emotion recognition ability is of high importance, and (c) show that the capacity to understand expressions of emotion in others is linked to broader affective and cognitive processes. (c) 2016 APA, all rights reserved).

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

    Science.gov (United States)

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

    2016-09-01

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

  13. Novel insights into the ontogeny of nestmate recognition in Polistes social wasps.

    Science.gov (United States)

    Signorotti, Lisa; Cappa, Federico; d'Ettorre, Patrizia; Cervo, Rita

    2014-01-01

    The importance of early experience in animals' life is unquestionable, and imprinting-like phenomena may shape important aspects of behaviour. Early learning typically occurs during a sensitive period, which restricts crucial processes of information storage to a specific developmental phase. The characteristics of the sensitive period have been largely investigated in vertebrates, because of their complexity and plasticity, both in behaviour and neurophysiology, but early learning occurs also in invertebrates. In social insects, early learning appears to influence important social behaviours such as nestmate recognition. Yet, the mechanisms underlying recognition systems are not fully understood. It is currently believed that Polistes social wasps are able to discriminate nestmates from non-nestmates following the perception of olfactory cues present on the paper of their nest, which are learned during a strict sensitive period, immediately after emergence. Here, through differential odour experience experiments, we show that workers of Polistes dominula develop correct nestmate recognition abilities soon after emergence even in absence of what have been so far considered the necessary cues (the chemicals spread on nest paper). P. dominula workers were exposed for the first four days of adult life to paper fragments from their nest, or from a foreign conspecific nest or to a neutral condition. Wasps were then transferred to their original nests where recognition abilities were tested. Our results show that wasps do not alter their recognition ability if exposed only to nest material, or in absence of nest material, during the early phase of adult life. It thus appears that the nest paper is not used as a source of recognition cues to be learned in a specific time window, although we discuss possible alternative explanations. Our study provides a novel perspective for the study of the ontogeny of nestmate recognition in Polistes wasps and in other social insects.

  14. Relating the Content and Confidence of Recognition Judgments

    Science.gov (United States)

    Selmeczy, Diana; Dobbins, Ian G.

    2014-01-01

    The Remember/Know procedure, developed by Tulving (1985) to capture the distinction between the conscious correlates of episodic and semantic retrieval, has spawned considerable research and debate. However, only a handful of reports have examined the recognition content beyond this dichotomous simplification. To address this, we collected…

  15. Challenging ocular image recognition

    Science.gov (United States)

    Pauca, V. Paúl; Forkin, Michael; Xu, Xiao; Plemmons, Robert; Ross, Arun A.

    2011-06-01

    Ocular recognition is a new area of biometric investigation targeted at overcoming the limitations of iris recognition performance in the presence of non-ideal data. There are several advantages for increasing the area beyond the iris, yet there are also key issues that must be addressed such as size of the ocular region, factors affecting performance, and appropriate corpora to study these factors in isolation. In this paper, we explore and identify some of these issues with the goal of better defining parameters for ocular recognition. An empirical study is performed where iris recognition methods are contrasted with texture and point operators on existing iris and face datasets. The experimental results show a dramatic recognition performance gain when additional features are considered in the presence of poor quality iris data, offering strong evidence for extending interest beyond the iris. The experiments also highlight the need for the direct collection of additional ocular imagery.

  16. Noisy Ocular Recognition Based on Three Convolutional Neural Networks

    Directory of Open Access Journals (Sweden)

    Min Beom Lee

    2017-12-01

    Full Text Available In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user’s eyes looking somewhere else, not into the front of the camera, specular reflection (SR and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs. Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II training dataset (selected from the university of Beira iris (UBIRIS.v2 database, mobile iris challenge evaluation (MICHE database, and institute of automation of Chinese academy of sciences (CASIA-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  17. Noisy Ocular Recognition Based on Three Convolutional Neural Networks.

    Science.gov (United States)

    Lee, Min Beom; Hong, Hyung Gil; Park, Kang Ryoung

    2017-12-17

    In recent years, the iris recognition system has been gaining increasing acceptance for applications such as access control and smartphone security. When the images of the iris are obtained under unconstrained conditions, an issue of undermined quality is caused by optical and motion blur, off-angle view (the user's eyes looking somewhere else, not into the front of the camera), specular reflection (SR) and other factors. Such noisy iris images increase intra-individual variations and, as a result, reduce the accuracy of iris recognition. A typical iris recognition system requires a near-infrared (NIR) illuminator along with an NIR camera, which are larger and more expensive than fingerprint recognition equipment. Hence, many studies have proposed methods of using iris images captured by a visible light camera without the need for an additional illuminator. In this research, we propose a new recognition method for noisy iris and ocular images by using one iris and two periocular regions, based on three convolutional neural networks (CNNs). Experiments were conducted by using the noisy iris challenge evaluation-part II (NICE.II) training dataset (selected from the university of Beira iris (UBIRIS).v2 database), mobile iris challenge evaluation (MICHE) database, and institute of automation of Chinese academy of sciences (CASIA)-Iris-Distance database. As a result, the method proposed by this study outperformed previous methods.

  18. Hybrid generative-discriminative approach to age-invariant face recognition

    Science.gov (United States)

    Sajid, Muhammad; Shafique, Tamoor

    2018-03-01

    Age-invariant face recognition is still a challenging research problem due to the complex aging process involving types of facial tissues, skin, fat, muscles, and bones. Most of the related studies that have addressed the aging problem are focused on generative representation (aging simulation) or discriminative representation (feature-based approaches). Designing an appropriate hybrid approach taking into account both the generative and discriminative representations for age-invariant face recognition remains an open problem. We perform a hybrid matching to achieve robustness to aging variations. This approach automatically segments the eyes, nose-bridge, and mouth regions, which are relatively less sensitive to aging variations compared with the rest of the facial regions that are age-sensitive. The aging variations of age-sensitive facial parts are compensated using a demographic-aware generative model based on a bridged denoising autoencoder. The age-insensitive facial parts are represented by pixel average vector-based local binary patterns. Deep convolutional neural networks are used to extract relative features of age-sensitive and age-insensitive facial parts. Finally, the feature vectors of age-sensitive and age-insensitive facial parts are fused to achieve the recognition results. Extensive experimental results on morphological face database II (MORPH II), face and gesture recognition network (FG-NET), and Verification Subset of cross-age celebrity dataset (CACD-VS) demonstrate the effectiveness of the proposed method for age-invariant face recognition well.

  19. Important themes in research on and education of young children in day care centres: Finnish viewpoints

    Directory of Open Access Journals (Sweden)

    Maritta Hännikäinen

    2013-10-01

    Full Text Available The aim of this article is to outline important themes, according to Finnish early childhood education researchers, that need to be addressed in researching and educating children under three years of age in Finland. To achieve this aim, the article divides into two parts. First, we present and discuss the results of a small-scale survey, conducted in Finland, on the views of key informants in the early childhood education units of Finnish universities. Second, the views presented in the survey are used as a starting point to introduce two ongoing qualitative case studies on the everyday life of toddlers in Finnish day care centres. In line with the survey findings, these case studies emphasize in particular the importance of the relational, social nature of children, the educational community, and the sensitivity of the adult for children’s wellbeing in day care groups.

  20. Facial Expression Recognition Deficits and Faulty Learning: Implications for Theoretical Models and Clinical Applications

    Science.gov (United States)

    Sheaffer, Beverly L.; Golden, Jeannie A.; Averett, Paige

    2009-01-01

    The ability to recognize facial expressions of emotion is integral in social interaction. Although the importance of facial expression recognition is reflected in increased research interest as well as in popular culture, clinicians may know little about this topic. The purpose of this article is to discuss facial expression recognition literature…

  1. USE OF IMAGE ENHANCEMENT TECHNIQUES FOR IMPROVING REAL TIME FACE RECOGNITION EFFICIENCY ON WEARABLE GADGETS

    Directory of Open Access Journals (Sweden)

    MUHAMMAD EHSAN RANA

    2017-01-01

    Full Text Available The objective of this research is to study the effects of image enhancement techniques on face recognition performance of wearable gadgets with an emphasis on recognition rate.In this research, a number of image enhancement techniques are selected that include brightness normalization, contrast normalization, sharpening, smoothing, and various combinations of these. Subsequently test images are obtained from AT&T database and Yale Face Database B to investigate the effect of these image enhancement techniques under various conditions such as change of illumination and face orientation and expression.The evaluation of data, collected during this research, revealed that the effect of image pre-processing techniques on face recognition highly depends on the illumination condition under which these images are taken. It is revealed that the benefit of applying image enhancement techniques on face images is best seen when there is high variation of illumination among images. Results also indicate that highest recognition rate is achieved when images are taken under low light condition and image contrast is enhanced using histogram equalization technique and then image noise is reduced using median smoothing filter. Additionally combination of contrast normalization and mean smoothing filter shows good result in all scenarios. Results obtained from test cases illustrate up to 75% improvement in face recognition rate when image enhancement is applied to images in given scenarios.

  2. 59th Clinical Research Division Research Day Briefing

    Science.gov (United States)

    2016-10-27

    College of Lab Animal Medicine; Certified by American College of Veterinary Pathology 1 - PhD, Physiology/Biochem - Clinical Research Admin...Molecular Biology/Genomics - Next Generation Sequencing - Real Time PCR - Multi-Plex Assays Cell Biology - Flow Cytometry Microbiology Coagulation

  3. Opportunity recognition in entrepreneurship education, design principles on fostering competent entrepreneurs in the science domain

    NARCIS (Netherlands)

    Nab, J.; Beugels, J.; van Keulen, H.; Oost, H.; Pilot, A.

    2008-01-01

    This paper is part of a research project focusing on educational design principles that should help students with a background in Science to become competent with respect to opportunity recognition in business. The recognition of business opportunities is one of the basic competencies of

  4. Opportunity recognition: delineating the process and motivators for serial entrepreneurs

    Directory of Open Access Journals (Sweden)

    Boris Urban

    2011-04-01

    Full Text Available Opportunity recognition is a fundamental research issue in entrepreneurship which this paper empirically investigates for serial entrepreneurs. Initially key definitions and boundary conditions of opportunity recognition are explored to elucidate the relevant motivators driving serial entrepreneurs. After operationalising the various concepts, data is collected by surveying serial entrepreneurs (n= 77 based on pre-determined selection criteria. Since the study’s objective is to build solid theory on these new phenomena, descriptive analysis on the empirical results is provided. To test the hypotheses inferential statistics employing parametric and non-parametric tests are used. The findings reveal that the opportunity recognition behaviours are manifest among serial entrepreneurs, with few significant differences on how many new, major businesses have been pursued, or whether they can be said to be successes.

  5. Overview of radar intra-pulse modulation recognition

    Science.gov (United States)

    Zang, Hanlin; Li, Yanling

    2018-05-01

    This paper introduces the current radar intra-pulse modulation method, describes the status quo and development direction of the intentional modulation and unintentional modulation in the pulse, and summarizes the existing problems and prospects for the future. Looking forward to the future, and providing a reference direction for the research on radar signal recognition in the next step.

  6. An 8-year longitudinal study of mirror self-recognition in chimpanzees (Pan troglodytes).

    Science.gov (United States)

    de Veer, Monique W; Gallup, Gordon G; Theall, Laura A; van den Bos, Ruud; Povinelli, Daniel J

    2003-01-01

    In a previous cross-sectional study of mirror self-recognition involving 92 chimpanzees, Povinelli et al. [Journal of Comparative Psychology 107 (1993) 347] reported a peak in the proportion of animals exhibiting self-recognition in the adolescent/young adult sample (8-15 years), with 75% being classified as positive. In contrast, only 26% of the older animals (16-39 years) were classified as positive, suggesting a marked decline in self-recognition in middle to late adulthood. In the present study, all of the chimpanzees from the 8-15-year-old group in the Povinelli et al. study (n=12) were again tested for self-recognition, 8 years later. Using the same criteria, 67% of the animals were classified the same. Although a higher proportion of the adult animals in this study (50%) exhibited self-recognition than would be inferred on the basis of the previous study (25%), all changes in self-recognition status were in the negative direction. These results show that mirror self-recognition is a highly stable trait in many chimpanzees, but may be subject to decline with age. Connections with human research are briefly discussed.

  7. Genetic specificity of face recognition.

    Science.gov (United States)

    Shakeshaft, Nicholas G; Plomin, Robert

    2015-10-13

    Specific cognitive abilities in diverse domains are typically found to be highly heritable and substantially correlated with general cognitive ability (g), both phenotypically and genetically. Recent twin studies have found the ability to memorize and recognize faces to be an exception, being similarly heritable but phenotypically substantially uncorrelated both with g and with general object recognition. However, the genetic relationships between face recognition and other abilities (the extent to which they share a common genetic etiology) cannot be determined from phenotypic associations. In this, to our knowledge, first study of the genetic associations between face recognition and other domains, 2,000 18- and 19-year-old United Kingdom twins completed tests assessing their face recognition, object recognition, and general cognitive abilities. Results confirmed the substantial heritability of face recognition (61%), and multivariate genetic analyses found that most of this genetic influence is unique and not shared with other cognitive abilities.

  8. A Modified Sparse Representation Method for Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Wei Wang

    2016-01-01

    Full Text Available In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit method is used to speed up the convergence of OMP (orthogonal matching pursuit. Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

  9. The effects of end-of-day picture review and a sensor-based picture capture procedure on autobiographical memory using SenseCam.

    Science.gov (United States)

    Finley, Jason R; Brewer, William F; Benjamin, Aaron S

    2011-10-01

    Emerging "life-logging" technologies have tremendous potential to augment human autobiographical memory by recording and processing vast amounts of information from an individual's experiences. In this experiment undergraduate participants wore a SenseCam, a small, sensor-equipped digital camera, as they went about their normal daily activities for five consecutive days. Pictures were captured either at fixed intervals or as triggered by SenseCam's sensors. On two of five nights, participants watched an end-of-day review of a random subset of pictures captured that day. Participants were tested with a variety of memory measures at intervals of 1, 3, and 8 weeks. The most fruitful of six measures were recognition rating (on a 1-7 scale) and picture-cued recall length. On these tests, end-of-day review enhanced performance relative to no review, while pictures triggered by SenseCam's sensors showed little difference in performance compared to those taken at fixed time intervals. We discuss the promise of SenseCam as a tool for research and for improving autobiographical memory.

  10. Comparison and evaluation of datasets for off-angle iris recognition

    Science.gov (United States)

    Kurtuncu, Osman M.; Cerme, Gamze N.; Karakaya, Mahmut

    2016-05-01

    In this paper, we investigated the publicly available iris recognition datasets and their data capture procedures in order to determine if they are suitable for the stand-off iris recognition research. Majority of the iris recognition datasets include only frontal iris images. Even if a few datasets include off-angle iris images, the frontal and off-angle iris images are not captured at the same time. The comparison of the frontal and off-angle iris images shows not only differences in the gaze angle but also change in pupil dilation and accommodation as well. In order to isolate the effect of the gaze angle from other challenging issues including dilation and accommodation, the frontal and off-angle iris images are supposed to be captured at the same time by using two different cameras. Therefore, we developed an iris image acquisition platform by using two cameras in this work where one camera captures frontal iris image and the other one captures iris images from off-angle. Based on the comparison of Hamming distance between frontal and off-angle iris images captured with the two-camera- setup and one-camera-setup, we observed that Hamming distance in two-camera-setup is less than one-camera-setup ranging from 0.05 to 0.001. These results show that in order to have accurate results in the off-angle iris recognition research, two-camera-setup is necessary in order to distinguish the challenging issues from each other.

  11. Research and implementation of finger-vein recognition algorithm

    Science.gov (United States)

    Pang, Zengyao; Yang, Jie; Chen, Yilei; Liu, Yin

    2017-06-01

    In finger vein image preprocessing, finger angle correction and ROI extraction are important parts of the system. In this paper, we propose an angle correction algorithm based on the centroid of the vein image, and extract the ROI region according to the bidirectional gray projection method. Inspired by the fact that features in those vein areas have similar appearance as valleys, a novel method was proposed to extract center and width of palm vein based on multi-directional gradients, which is easy-computing, quick and stable. On this basis, an encoding method was designed to determine the gray value distribution of texture image. This algorithm could effectively overcome the edge of the texture extraction error. Finally, the system was equipped with higher robustness and recognition accuracy by utilizing fuzzy threshold determination and global gray value matching algorithm. Experimental results on pairs of matched palm images show that, the proposed method has a EER with 3.21% extracts features at the speed of 27ms per image. It can be concluded that the proposed algorithm has obvious advantages in grain extraction efficiency, matching accuracy and algorithm efficiency.

  12. Sex influence on face recognition memory moderated by presentation duration and reencoding.

    Science.gov (United States)

    Weirich, Sebastian; Hoffmann, Ferdinand; Meissner, Lucia; Heinz, Andreas; Bengner, Thomas

    2011-11-01

    It has been suggested that women have a better face recognition memory than men. Here we analyzed whether this advantage depends on a better encoding or consolidation of information and if the advantage is visible during short-term memory (STM), only, or whether it also remains evident in long-term memory (LTM). We tested short- and long-term face recognition memory in 36 nonclinical participants (19 women). We varied the duration of item presentation (1, 5, and 10 s), the time of testing (immediately after the study phase, 1 hr, and 24 hr later), and the possibility to reencode items (none, immediately after the study phase, after 1 hr). Women showed better overall face recognition memory than men (ηp² = .15, p face recognition was visible mainly if participants had the possibility to reencode faces during former test trials. Our results suggest women do not have a better face recognition memory than men per se, but may profit more than men from longer durations of presentation during encoding or the possibility for reencoding. Future research on sex differences in face recognition memory should explicate possible causes for the better encoding of face information in women.

  13. The "parts and wholes" of face recognition: A review of the literature.

    Science.gov (United States)

    Tanaka, James W; Simonyi, Diana

    2016-10-01

    It has been claimed that faces are recognized as a "whole" rather than by the recognition of individual parts. In a paper published in the Quarterly Journal of Experimental Psychology in 1993, Martha Farah and I attempted to operationalize the holistic claim using the part/whole task. In this task, participants studied a face and then their memory presented in isolation and in the whole face. Consistent with the holistic view, recognition of the part was superior when tested in the whole-face condition compared to when it was tested in isolation. The "whole face" or holistic advantage was not found for faces that were inverted, or scrambled, nor for non-face objects, suggesting that holistic encoding was specific to normal, intact faces. In this paper, we reflect on the part/whole paradigm and how it has contributed to our understanding of what it means to recognize a face as a "whole" stimulus. We describe the value of part/whole task for developing theories of holistic and non-holistic recognition of faces and objects. We discuss the research that has probed the neural substrates of holistic processing in healthy adults and people with prosopagnosia and autism. Finally, we examine how experience shapes holistic face recognition in children and recognition of own- and other-race faces in adults. The goal of this article is to summarize the research on the part/whole task and speculate on how it has informed our understanding of holistic face processing.

  14. A Survey of Online Activity Recognition Using Mobile Phones

    NARCIS (Netherlands)

    Shoaib, M.; Bosch, S.; Durmaz, O.; Scholten, Johan; Havinga, Paul J.M.

    2015-01-01

    Physical activity recognition using embedded sensors has enabled many context-aware applications in different areas, such as healthcare. Initially, one or more dedicated wearable sensors were used for such applications. However, recently, many researchers started using mobile phones for this

  15. Wax combs mediate nestmate recognition by guard honeybees

    DEFF Research Database (Denmark)

    D'Ettorre, Patrizia; Wenseleers, Tom; Dawson, Jenny

    2006-01-01

    Research has shown that the wax combs are important in the acquisition of colony odour in the honeybee, Apis mellifera. However, many of these studies were conducted in the laboratory or under artificial conditions. We investigated the role of the wax combs in nestmate recognition in the natural...

  16. Forensic Face Recognition: A Survey

    NARCIS (Netherlands)

    Ali, Tauseef; Spreeuwers, Lieuwe Jan; Veldhuis, Raymond N.J.; Quaglia, Adamo; Epifano, Calogera M.

    2012-01-01

    The improvements of automatic face recognition during the last 2 decades have disclosed new applications like border control and camera surveillance. A new application field is forensic face recognition. Traditionally, face recognition by human experts has been used in forensics, but now there is a

  17. Impairment of object recognition memory by maternal bisphenol A exposure is associated with inhibition of Akt and ERK/CREB/BDNF pathway in the male offspring hippocampus.

    Science.gov (United States)

    Wang, Chong; Li, Zhihui; Han, Haijun; Luo, Guangying; Zhou, Bingrui; Wang, Shaolin; Wang, Jundong

    2016-02-03

    Bisphenol A (BPA) is a commonly used endocrine-disrupting chemical used as a component of polycarbonates plastics that has potential adverse effects on human health. Exposure to BPA during development has been implicated in memory deficits, but the mechanism of action underlying the effect is not fully understood. In this study, we investigated the effect of maternal exposure to BPA on object recognition memory and the expressions of proteins important for memory, especially focusing on the ERK/CREB/BDNF pathway. Pregnant Sprague-Dawley female rats were orally treated with either vehicle or BPA (0.05, 0.5, 5 or 50 mg/kg BW/day) during days 9-20 of gestation. Male offspring were tested on postnatal day 21 with the object recognition task. Recognition memory was assessed using the object recognition index (index=the time spent exploring the novel object/(the time spent exploring the novel object+the time spent exploring the familiar object)). In the test session performed 90 min after the training session, BPA-exposed male offspring not only spent more time in exploring the familiar object at the highest dose than the control, but also displayed a significantly decreased the object recognition index at the doses of 0.5, 5 and 50 mg/kg BW/day. During the test session performed 24h after the training session, BPA-treated males did not change the time spent exploring the familiar object, but had a decreased object recognition index at 5 and 50 mg/kg BW/day, when compared to control group. These findings indicate that object recognition memory was susceptible to maternal BPA exposure. Western blot analysis of hippocampi from BPA-treated male offspring revealed a decrease in Akt, phospho-Akt, p44/42 MAPK and phospho-p44/42 MAPK protein levels, compared to controls. In addition, BPA significantly inhibited the levels of phosphorylation of CREB and BDNF in the hippocampus. Our results show that maternal BPA exposure may full impair object recognition memory, and that

  18. Voice Recognition in Face-Blind Patients

    Science.gov (United States)

    Liu, Ran R.; Pancaroglu, Raika; Hills, Charlotte S.; Duchaine, Brad; Barton, Jason J. S.

    2016-01-01

    Right or bilateral anterior temporal damage can impair face recognition, but whether this is an associative variant of prosopagnosia or part of a multimodal disorder of person recognition is an unsettled question, with implications for cognitive and neuroanatomic models of person recognition. We assessed voice perception and short-term recognition of recently heard voices in 10 subjects with impaired face recognition acquired after cerebral lesions. All 4 subjects with apperceptive prosopagnosia due to lesions limited to fusiform cortex had intact voice discrimination and recognition. One subject with bilateral fusiform and anterior temporal lesions had a combined apperceptive prosopagnosia and apperceptive phonagnosia, the first such described case. Deficits indicating a multimodal syndrome of person recognition were found only in 2 subjects with bilateral anterior temporal lesions. All 3 subjects with right anterior temporal lesions had normal voice perception and recognition, 2 of whom performed normally on perceptual discrimination of faces. This confirms that such lesions can cause a modality-specific associative prosopagnosia. PMID:25349193

  19. End-Stop Exemplar Based Recognition

    DEFF Research Database (Denmark)

    Olsen, Søren I.

    2003-01-01

    An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring int...... interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion....

  20. Speech Recognition on Mobile Devices

    DEFF Research Database (Denmark)

    Tan, Zheng-Hua; Lindberg, Børge

    2010-01-01

    in the mobile context covering motivations, challenges, fundamental techniques and applications. Three ASR architectures are introduced: embedded speech recognition, distributed speech recognition and network speech recognition. Their pros and cons and implementation issues are discussed. Applications within......The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...

  1. Effect of the time-of-day of training on explicit memory.

    Science.gov (United States)

    Barbosa, F F; Albuquerque, F S

    2008-06-01

    Studies have shown a time-of-day of training effect on long-term explicit memory with a greater effect being shown in the afternoon than in the morning. However, these studies did not control the chronotype variable. Therefore, the purpose of this study was to assess if the time-of-day effect on explicit memory would continue if this variable were controlled, in addition to identifying the occurrence of a possible synchronic effect. A total of 68 undergraduates were classified as morning, intermediate, or afternoon types. The subjects listened to a list of 10 words during the training phase and immediately performed a recognition task, a procedure which they repeated twice. One week later, they underwent an unannounced recognition test. The target list and the distractor words were the same in all series. The subjects were allocated to two groups according to acquisition time: a morning group (N = 32), and an afternoon group (N = 36). One week later, some of the subjects in each of these groups were subjected to a test in the morning (N = 35) or in the afternoon (N = 33). The groups had similar chronotypes. Long-term explicit memory performance was not affected by test time-of-day or by chronotype. However, there was a training time-of-day effect [F (1,56) = 53.667; P = 0.009] with better performance for those who trained in the afternoon. Our data indicated that the advantage of training in the afternoon for long-term memory performance does not depend on chronotype and also that this performance is not affected by the synchronic effect.

  2. Effect of the time-of-day of training on explicit memory

    Directory of Open Access Journals (Sweden)

    F.F. Barbosa

    2008-06-01

    Full Text Available Studies have shown a time-of-day of training effect on long-term explicit memory with a greater effect being shown in the afternoon than in the morning. However, these studies did not control the chronotype variable. Therefore, the purpose of this study was to assess if the time-of-day effect on explicit memory would continue if this variable were controlled, in addition to identifying the occurrence of a possible synchronic effect. A total of 68 undergraduates were classified as morning, intermediate, or afternoon types. The subjects listened to a list of 10 words during the training phase and immediately performed a recognition task, a procedure which they repeated twice. One week later, they underwent an unannounced recognition test. The target list and the distractor words were the same in all series. The subjects were allocated to two groups according to acquisition time: a morning group (N = 32, and an afternoon group (N = 36. One week later, some of the subjects in each of these groups were subjected to a test in the morning (N = 35 or in the afternoon (N = 33. The groups had similar chronotypes. Long-term explicit memory performance was not affected by test time-of-day or by chronotype. However, there was a training time-of-day effect [F (1,56 = 53.667; P = 0.009] with better performance for those who trained in the afternoon. Our data indicated that the advantage of training in the afternoon for long-term memory performance does not depend on chronotype and also that this performance is not affected by the synchronic effect.

  3. Multimodal approaches for emotion recognition: a survey

    Science.gov (United States)

    Sebe, Nicu; Cohen, Ira; Gevers, Theo; Huang, Thomas S.

    2005-01-01

    Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.

  4. Bidirectional Long Short-Term Memory Network for Vehicle Behavior Recognition

    Directory of Open Access Journals (Sweden)

    Jiasong Zhu

    2018-06-01

    Full Text Available Vehicle behavior recognition is an attractive research field which is useful for many computer vision and intelligent traffic analysis tasks. This paper presents an all-in-one behavior recognition framework for moving vehicles based on the latest deep learning techniques. Unlike traditional traffic analysis methods which rely on low-resolution videos captured by road cameras, we capture 4K ( 3840 × 2178 traffic videos at a busy road intersection of a modern megacity by flying a unmanned aerial vehicle (UAV during the rush hours. We then manually annotate locations and types of road vehicles. The proposed method consists of the following three steps: (1 vehicle detection and type recognition based on deep neural networks; (2 vehicle tracking by data association and vehicle trajectory modeling; (3 vehicle behavior recognition by nearest neighbor search and by bidirectional long short-term memory network, respectively. This paper also presents experimental results of the proposed framework in comparison with state-of-the-art approaches on the 4K testing traffic video, which demonstrated the effectiveness and superiority of the proposed method.

  5. Examining ERP correlates of recognition memory: Evidence of accurate source recognition without recollection

    Science.gov (United States)

    Addante, Richard, J.; Ranganath, Charan; Yonelinas, Andrew, P.

    2012-01-01

    Recollection is typically associated with high recognition confidence and accurate source memory. However, subjects sometimes make accurate source memory judgments even for items that are not confidently recognized, and it is not known whether these responses are based on recollection or some other memory process. In the current study, we measured event related potentials (ERPs) while subjects made item and source memory confidence judgments in order to determine whether recollection supported accurate source recognition responses for items that were not confidently recognized. In line with previous studies, we found that recognition memory was associated with two ERP effects: an early on-setting FN400 effect, and a later parietal old-new effect [Late Positive Component (LPC)], which have been associated with familiarity and recollection, respectively. The FN400 increased gradually with item recognition confidence, whereas the LPC was only observed for highly confident recognition responses. The LPC was also related to source accuracy, but only for items that had received a high confidence item recognition response; accurate source judgments to items that were less confidently recognized did not exhibit the typical ERP correlate of recollection or familiarity, but rather showed a late, broadly distributed negative ERP difference. The results indicate that accurate source judgments of episodic context can occur even when recollection fails. PMID:22548808

  6. Evaluating music emotion recognition:Lessons from music genre recognition?

    OpenAIRE

    Sturm, Bob L.

    2013-01-01

    A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER syste...

  7. The effects of acute social isolation on long-term social recognition memory.

    Science.gov (United States)

    Leser, Noam; Wagner, Shlomo

    2015-10-01

    The abilities to recognize individual animals of the same species and to distinguish them from other individuals are the basis for all mammalian social organizations and relationships. These abilities, termed social recognition memory, can be explored in mice and rats using their innate tendency to investigate novel social stimuli more persistently than familiar ones. Using this methodology it was found that social recognition memory is mediated by a specific neural network in the brain, the activity of which is modulated by several molecules, such the neuropeptides oxytocin and vasopressin. During the last 15 years several independent studies have revealed that social recognition memory of mice and rats depends upon their housing conditions. Specifically, long-term social recognition memory cannot be formed as shortly as few days following social isolation of the animal. This rapid and reversible impairment caused by acute social isolation seems to be specific to social memory and has not been observed in other types of memory. Here we review these studies and suggest that this unique system may serve for exploring of the mechanisms underlying the well-known negative effects of partial or perceived social isolation on human mental health. Copyright © 2015 Elsevier Inc. All rights reserved.

  8. Erythropoietin improves mood and modulates the cognitive and neural processing of emotion 3 days post administration

    DEFF Research Database (Denmark)

    Miskowiak, Kamilla; Inkster, Becky; Selvaraj, Sudhakar

    2008-01-01

    the reliability of the rapid mood improvement and its neuropsychological basis. Neuronal responses during the processing of happy and fearful faces were investigated using functional magnetic resonance imaging (fMRI); facial expression recognition performance was assessed after the fMRI scan. Daily ratings...... of mood were obtained for 3 days after Epo/saline administration. During faces processing Epo enhanced activation in the left amygdala and right precuneus to happy and fearful expressions. This was paired with improved recognition of all facial expressions, in particular of low intensity happiness...

  9. Word Recognition in Auditory Cortex

    Science.gov (United States)

    DeWitt, Iain D. J.

    2013-01-01

    Although spoken word recognition is more fundamental to human communication than text recognition, knowledge of word-processing in auditory cortex is comparatively impoverished. This dissertation synthesizes current models of auditory cortex, models of cortical pattern recognition, models of single-word reading, results in phonetics and results in…

  10. [Comparative studies of face recognition].

    Science.gov (United States)

    Kawai, Nobuyuki

    2012-07-01

    Every human being is proficient in face recognition. However, the reason for and the manner in which humans have attained such an ability remain unknown. These questions can be best answered-through comparative studies of face recognition in non-human animals. Studies in both primates and non-primates show that not only primates, but also non-primates possess the ability to extract information from their conspecifics and from human experimenters. Neural specialization for face recognition is shared with mammals in distant taxa, suggesting that face recognition evolved earlier than the emergence of mammals. A recent study indicated that a social insect, the golden paper wasp, can distinguish their conspecific faces, whereas a closely related species, which has a less complex social lifestyle with just one queen ruling a nest of underlings, did not show strong face recognition for their conspecifics. Social complexity and the need to differentiate between one another likely led humans to evolve their face recognition abilities.

  11. Selective attention and recognition: effects of congruency on episodic learning.

    Science.gov (United States)

    Rosner, Tamara M; D'Angelo, Maria C; MacLellan, Ellen; Milliken, Bruce

    2015-05-01

    Recent research on cognitive control has focused on the learning consequences of high selective attention demands in selective attention tasks (e.g., Botvinick, Cognit Affect Behav Neurosci 7(4):356-366, 2007; Verguts and Notebaert, Psychol Rev 115(2):518-525, 2008). The current study extends these ideas by examining the influence of selective attention demands on remembering. In Experiment 1, participants read aloud the red word in a pair of red and green spatially interleaved words. Half of the items were congruent (the interleaved words had the same identity), and the other half were incongruent (the interleaved words had different identities). Following the naming phase, participants completed a surprise recognition memory test. In this test phase, recognition memory was better for incongruent than for congruent items. In Experiment 2, context was only partially reinstated at test, and again recognition memory was better for incongruent than for congruent items. In Experiment 3, all of the items contained two different words, but in one condition the words were presented close together and interleaved, while in the other condition the two words were spatially separated. Recognition memory was better for the interleaved than for the separated items. This result rules out an interpretation of the congruency effects on recognition in Experiments 1 and 2 that hinges on stronger relational encoding for items that have two different words. Together, the results support the view that selective attention demands for incongruent items lead to encoding that improves recognition.

  12. Multimedia Content Development as a Facial Expression Datasets for Recognition of Human Emotions

    Science.gov (United States)

    Mamonto, N. E.; Maulana, H.; Liliana, D. Y.; Basaruddin, T.

    2018-02-01

    Datasets that have been developed before contain facial expression from foreign people. The development of multimedia content aims to answer the problems experienced by the research team and other researchers who will conduct similar research. The method used in the development of multimedia content as facial expression datasets for human emotion recognition is the Villamil-Molina version of the multimedia development method. Multimedia content developed with 10 subjects or talents with each talent performing 3 shots with each capturing talent having to demonstrate 19 facial expressions. After the process of editing and rendering, tests are carried out with the conclusion that the multimedia content can be used as a facial expression dataset for recognition of human emotions.

  13. MYRRHA successfully launched. After Belgium, a European recognition

    International Nuclear Information System (INIS)

    Abderrahim, H.A.

    2011-01-01

    The green light of the Belgian governments to MYRRHA in 2010 was one of the major milestones for SCK-CEN. In the years to come, a research team will look for answers to unresolved scientific and technological issues, and will finalise the design of this cutting-edge research reactor. In 2010, MYRRHA received wide recognition at the international level as well. MYRRHA was selected as one of the most promising nuclear technologies in the European Sustainable Nuclear Industrial Initiative (ESNII) and received a prominent place in the list of priority research facilities of the European Strategy Forum on Research Infrastructures (ESFRI).

  14. Real-Time Hand Posture Recognition Using a Range Camera

    Science.gov (United States)

    Lahamy, Herve

    The basic goal of human computer interaction is to improve the interaction between users and computers by making computers more usable and receptive to the user's needs. Within this context, the use of hand postures in replacement of traditional devices such as keyboards, mice and joysticks is being explored by many researchers. The goal is to interpret human postures via mathematical algorithms. Hand posture recognition has gained popularity in recent years, and could become the future tool for humans to interact with computers or virtual environments. An exhaustive description of the frequently used methods available in literature for hand posture recognition is provided. It focuses on the different types of sensors and data used, the segmentation and tracking methods, the features used to represent the hand postures as well as the classifiers considered in the recognition process. Those methods are usually presented as highly robust with a recognition rate close to 100%. However, a couple of critical points necessary for a successful real-time hand posture recognition system require major improvement. Those points include the features used to represent the hand segment, the number of postures simultaneously recognizable, the invariance of the features with respect to rotation, translation and scale and also the behavior of the classifiers against non-perfect hand segments for example segments including part of the arm or missing part of the palm. A 3D time-of-flight camera named SR4000 has been chosen to develop a new methodology because of its capability to provide in real-time and at high frame rate 3D information on the scene imaged. This sensor has been described and evaluated for its capability for capturing in real-time a moving hand. A new recognition method that uses the 3D information provided by the range camera to recognize hand postures has been proposed. The different steps of this methodology including the segmentation, the tracking, the hand

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

    Science.gov (United States)

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

    2017-12-01

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

  16. A Review on Human Activity Recognition Using Vision-Based Method.

    Science.gov (United States)

    Zhang, Shugang; Wei, Zhiqiang; Nie, Jie; Huang, Lei; Wang, Shuang; Li, Zhen

    2017-01-01

    Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.

  17. Neuro System Structure for Vehicle Recognition and Count in Floating Bridge Specific Conditions

    Directory of Open Access Journals (Sweden)

    Slobodan Beroš

    2012-10-01

    Full Text Available The paper presents the research of the sophisticated vehiclerecognition and count system based on the application of theneural network. The basic elements of neural network andadaptive logic network for object recognition are discussed. Theadaptive logic network solution ability based on simple digitalcircuits as crucial in real-time applications is pointed out. Thesimulation based on the use of reduced high level noise pictureand a tree 2. 7. software have shown excellent results. The consideredand simulated adaptive neural network based systemwith its good recognition and convergence is a useful real-timesolution for vehicle recognition and count in the floating bridgesevere conditions.

  18. Perceptual and affective mechanisms in facial expression recognition: An integrative review.

    Science.gov (United States)

    Calvo, Manuel G; Nummenmaa, Lauri

    2016-09-01

    Facial expressions of emotion involve a physical component of morphological changes in a face and an affective component conveying information about the expresser's internal feelings. It remains unresolved how much recognition and discrimination of expressions rely on the perception of morphological patterns or the processing of affective content. This review of research on the role of visual and emotional factors in expression recognition reached three major conclusions. First, behavioral, neurophysiological, and computational measures indicate that basic expressions are reliably recognized and discriminated from one another, albeit the effect may be inflated by the use of prototypical expression stimuli and forced-choice responses. Second, affective content along the dimensions of valence and arousal is extracted early from facial expressions, although this coarse affective representation contributes minimally to categorical recognition of specific expressions. Third, the physical configuration and visual saliency of facial features contribute significantly to expression recognition, with "emotionless" computational models being able to reproduce some of the basic phenomena demonstrated in human observers. We conclude that facial expression recognition, as it has been investigated in conventional laboratory tasks, depends to a greater extent on perceptual than affective information and mechanisms.

  19. Semantic and visual determinants of face recognition in a prosopagnosic patient.

    Science.gov (United States)

    Dixon, M J; Bub, D N; Arguin, M

    1998-05-01

    Prosopagnosia is the neuropathological inability to recognize familiar people by their faces. It can occur in isolation or can coincide with recognition deficits for other nonface objects. Often, patients whose prosopagnosia is accompanied by object recognition difficulties have more trouble identifying certain categories of objects relative to others. In previous research, we demonstrated that objects that shared multiple visual features and were semantically close posed severe recognition difficulties for a patient with temporal lobe damage. We now demonstrate that this patient's face recognition is constrained by these same parameters. The prosopagnosic patient ELM had difficulties pairing faces to names when the faces shared visual features and the names were semantically related (e.g., Tonya Harding, Nancy Kerrigan, and Josee Chouinard -three ice skaters). He made tenfold fewer errors when the exact same faces were associated with semantically unrelated people (e.g., singer Celine Dion, actress Betty Grable, and First Lady Hillary Clinton). We conclude that prosopagnosia and co-occurring category-specific recognition problems both stem from difficulties disambiguating the stored representations of objects that share multiple visual features and refer to semantically close identities or concepts.

  20. Channel normalization technique for speech recognition in mismatched conditions

    CSIR Research Space (South Africa)

    Kleynhans, N

    2008-11-01

    Full Text Available , where one wishes to use any available training data for a variety of purposes. Research into a new channel normalization (CN) technique for channel mismatched speech recognition is presented. A process of inverse linear filtering is used in order...

  1. A model based method for automatic facial expression recognition

    NARCIS (Netherlands)

    Kuilenburg, H. van; Wiering, M.A.; Uyl, M. den

    2006-01-01

    Automatic facial expression recognition is a research topic with interesting applications in the field of human-computer interaction, psychology and product marketing. The classification accuracy for an automatic system which uses static images as input is however largely limited by the image

  2. A Multimodal Database for Affect Recognition and Implicit Tagging

    NARCIS (Netherlands)

    Soleymani, Mohammad; Lichtenauer, Jeroen; Pun, Thierry; Pantic, Maja

    MAHNOB-HCI is a multimodal database recorded in response to affective stimuli with the goal of emotion recognition and implicit tagging research. A multimodal setup was arranged for synchronized recording of face videos, audio signals, eye gaze data, and peripheral/central nervous system

  3. Visual Recognition Memory across Contexts

    Science.gov (United States)

    Jones, Emily J. H.; Pascalis, Olivier; Eacott, Madeline J.; Herbert, Jane S.

    2011-01-01

    In two experiments, we investigated the development of representational flexibility in visual recognition memory during infancy using the Visual Paired Comparison (VPC) task. In Experiment 1, 6- and 9-month-old infants exhibited recognition when familiarization and test occurred in the same room, but showed no evidence of recognition when…

  4. The value of comparative research in major day surgery.

    Science.gov (United States)

    Llop-Gironés, Alba; Vergara-Duarte, Montse; Sánchez, Josep Anton; Tarafa, Gemma; Benach, Joan

    2017-05-19

    To measure time trends in major day surgery rates according to hospital ownership and other hospital characteristics among the providers of the public healthcare network of Catalonia, Spain. Data from the Statistics of Health Establishments providing Inpatient Care. A generalized linear mixed model with Gaussian response and random intercept and random slopes. The greatest growth in the rate of major day surgery was observed among private for-profit hospitals: 42.9 (SD: 22.5) in 2009 versus 2.7 (SD: 6.7) in 1996. These hospitals exhibited a significant increase in major day surgery compared to public hospitals (coefficient 2; p-value <0.01) CONCLUSIONS: The comparative evaluation of hospital performance is a decisive tool to ensure that public resources are used as rationally and efficiently as possible. Copyright © 2017 SESPAS. Publicado por Elsevier España, S.L.U. All rights reserved.

  5. Emotional Faces in Context: Age Differences in Recognition Accuracy and Scanning Patterns

    Science.gov (United States)

    Noh, Soo Rim; Isaacowitz, Derek M.

    2014-01-01

    While age-related declines in facial expression recognition are well documented, previous research relied mostly on isolated faces devoid of context. We investigated the effects of context on age differences in recognition of facial emotions and in visual scanning patterns of emotional faces. While their eye movements were monitored, younger and older participants viewed facial expressions (i.e., anger, disgust) in contexts that were emotionally congruent, incongruent, or neutral to the facial expression to be identified. Both age groups had highest recognition rates of facial expressions in the congruent context, followed by the neutral context, and recognition rates in the incongruent context were worst. These context effects were more pronounced for older adults. Compared to younger adults, older adults exhibited a greater benefit from congruent contextual information, regardless of facial expression. Context also influenced the pattern of visual scanning characteristics of emotional faces in a similar manner across age groups. In addition, older adults initially attended more to context overall. Our data highlight the importance of considering the role of context in understanding emotion recognition in adulthood. PMID:23163713

  6. An observational study of implicit motor imagery using laterality recognition of the hand after stroke.

    Science.gov (United States)

    Amesz, Sarah; Tessari, Alessia; Ottoboni, Giovanni; Marsden, Jon

    2016-01-01

    To explore the relationship between laterality recognition after stroke and impairments in attention, 3D object rotation and functional ability. Observational cross-sectional study. Acute care teaching hospital. Thirty-two acute and sub-acute people with stroke and 36 healthy, age-matched controls. Laterality recognition, attention and mental rotation of objects. Within the stroke group, the relationship between laterality recognition and functional ability, neglect, hemianopia and dyspraxia were further explored. People with stroke were significantly less accurate (69% vs 80%) and showed delayed reaction times (3.0 vs 1.9 seconds) when determining the laterality of a pictured hand. Deficits either in accuracy or reaction times were seen in 53% of people with stroke. The accuracy of laterality recognition was associated with reduced functional ability (R(2) = 0.21), less accurate mental rotation of objects (R(2) = 0.20) and dyspraxia (p = 0.03). Implicit motor imagery is affected in a significant number of patients after stroke with these deficits related to lesions to the motor networks as well as other deficits seen after stroke. This research provides new insights into how laterality recognition is related to a number of other deficits after stroke, including the mental rotation of 3D objects, attention and dyspraxia. Further research is required to determine if treatment programmes can improve deficits in laterality recognition and impact functional outcomes after stroke.

  7. Recognition of face and non-face stimuli in autistic spectrum disorder.

    Science.gov (United States)

    Arkush, Leo; Smith-Collins, Adam P R; Fiorentini, Chiara; Skuse, David H

    2013-12-01

    The ability to remember faces is critical for the development of social competence. From childhood to adulthood, we acquire a high level of expertise in the recognition of facial images, and neural processes become dedicated to sustaining competence. Many people with autism spectrum disorder (ASD) have poor face recognition memory; changes in hairstyle or other non-facial features in an otherwise familiar person affect their recollection skills. The observation implies that they may not use the configuration of the inner face to achieve memory competence, but bolster performance in other ways. We aimed to test this hypothesis by comparing the performance of a group of high-functioning unmedicated adolescents with ASD and a matched control group on a "surprise" face recognition memory task. We compared their memory for unfamiliar faces with their memory for images of houses. To evaluate the role that is played by peripheral cues in assisting recognition memory, we cropped both sets of pictures, retaining only the most salient central features. ASD adolescents had poorer recognition memory for faces than typical controls, but their recognition memory for houses was unimpaired. Cropping images of faces did not disproportionately influence their recall accuracy, relative to controls. House recognition skills (cropped and uncropped) were similar in both groups. In the ASD group only, performance on both sets of task was closely correlated, implying that memory for faces and other complex pictorial stimuli is achieved by domain-general (non-dedicated) cognitive mechanisms. Adolescents with ASD apparently do not use domain-specialized processing of inner facial cues to support face recognition memory. © 2013 International Society for Autism Research, Wiley Periodicals, Inc.

  8. The what, when, where, and how of visual word recognition.

    Science.gov (United States)

    Carreiras, Manuel; Armstrong, Blair C; Perea, Manuel; Frost, Ram

    2014-02-01

    A long-standing debate in reading research is whether printed words are perceived in a feedforward manner on the basis of orthographic information, with other representations such as semantics and phonology activated subsequently, or whether the system is fully interactive and feedback from these representations shapes early visual word recognition. We review recent evidence from behavioral, functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and biologically plausible connectionist modeling approaches, focusing on how each approach provides insight into the temporal flow of information in the lexical system. We conclude that, consistent with interactive accounts, higher-order linguistic representations modulate early orthographic processing. We also discuss how biologically plausible interactive frameworks and coordinated empirical and computational work can advance theories of visual word recognition and other domains (e.g., object recognition). Copyright © 2013 Elsevier Ltd. All rights reserved.

  9. Extensions of the picture superiority effect in associative recognition.

    Science.gov (United States)

    Hockley, William E; Bancroft, Tyler

    2011-12-01

    Previous research has shown that the picture superiority effect (PSE) is seen in tests of associative recognition for random pairs of line drawings compared to pairs of concrete words (Hockley, 2008). In the present study we demonstrated that the PSE for associative recognition is still observed when subjects have correctly identified the individual items of each pair as old (Experiment 1), and that this effect is not due to rehearsal borrowing (Experiment 2). The PSE for associative recognition also is shown to be present but attenuated for mixed picture-word pairs (Experiment 3), and similar in magnitude for pairs of simple black and white line drawings and coloured photographs of detailed objects (Experiment 4). The results are consistent with the view that the semantic meaning of nameable pictures is activated faster than that of words thereby affording subjects more time to generate and elaborate meaningful associations between items depicted in picture form. PsycINFO Database Record (c) 2011 APA, all rights reserved.

  10. Emotion and language: Valence and arousal affect word recognition

    Science.gov (United States)

    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

  11. The Effects of Day-to-Day Interaction via Social Network Sites on Interpersonal Relationships

    OpenAIRE

    Houghton, David J

    2012-01-01

    The current research identifies the impact of sharing day-to-day information insocial network sites (SNS) on the relationships we hold within and outside of them. Stemming from the literature on self-disclosure, uncertainty reduction, personal relationships, privacy and computer-mediated communication (CMC), a concurrent triangulation research strategy is adopted to identify the patterns of relationship development and interaction in SNS. Using a mixed methods approach, five studies were cond...

  12. Morphing Images: A Potential Tool for Teaching Word Recognition to Children with Severe Learning Difficulties

    Science.gov (United States)

    Sheehy, Kieron

    2005-01-01

    Children with severe learning difficulties who fail to begin word recognition can learn to recognise pictures and symbols relatively easily. However, finding an effective means of using pictures to teach word recognition has proved problematic. This research explores the use of morphing software to support the transition from picture to word…

  13. Automatic recognition of the unconscious reactions from physiological signals

    NARCIS (Netherlands)

    Ivonin, L.; Chang, H.M.; Chen, W.; Rauterberg, G.W.M.; Holzinger, A.; Ziefle, M.; Hitz, M.; Debevc, M.

    2013-01-01

    While the research in affective computing has been exclusively dealing with the recognition of explicit affective and cognitive states, carefully designed psychological and neuroimaging studies indicated that a considerable part of human experiences is tied to a deeper level of a psyche and not

  14. Narrowing the gap between automatic and human word recognition

    NARCIS (Netherlands)

    Scharenborg, O.E.

    2005-01-01

    In everyday life, speech is all around us, on the radio, television, and in human-human interaction. We are continually confronted with novel utterances, and usually we have no problem recognising and understanding them. Several research fields investigate the speech recognition process. This thesis

  15. GENDER RECOGNITION BASED ON SIFT FEATURES

    OpenAIRE

    Sahar Yousefi; Morteza Zahedi

    2011-01-01

    This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates align...

  16. Hand Gesture Recognition with Leap Motion

    OpenAIRE

    Du, Youchen; Liu, Shenglan; Feng, Lin; Chen, Menghui; Wu, Jie

    2017-01-01

    The recent introduction of depth cameras like Leap Motion Controller allows researchers to exploit the depth information to recognize hand gesture more robustly. This paper proposes a novel hand gesture recognition system with Leap Motion Controller. A series of features are extracted from Leap Motion tracking data, we feed these features along with HOG feature extracted from sensor images into a multi-class SVM classifier to recognize performed gesture, dimension reduction and feature weight...

  17. Comparing Face Detection and Recognition Techniques

    OpenAIRE

    Korra, Jyothi

    2016-01-01

    This paper implements and compares different techniques for face detection and recognition. One is find where the face is located in the images that is face detection and second is face recognition that is identifying the person. We study three techniques in this paper: Face detection using self organizing map (SOM), Face recognition by projection and nearest neighbor and Face recognition using SVM.

  18. Exemplar Based Recognition of Visual Shapes

    DEFF Research Database (Denmark)

    Olsen, Søren I.

    2005-01-01

    This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoint....... The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images.......This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoints...

  19. Superficial Priming in Episodic Recognition

    Science.gov (United States)

    Dopkins, Stephen; Sargent, Jesse; Ngo, Catherine T.

    2010-01-01

    We explored the effect of superficial priming in episodic recognition and found it to be different from the effect of semantic priming in episodic recognition. Participants made recognition judgments to pairs of items, with each pair consisting of a prime item and a test item. Correct positive responses to the test item were impeded if the prime…

  20. Specification for projects of radiogeologic recognition

    International Nuclear Information System (INIS)

    1979-01-01

    This instruction is a guidance to achievement of radiogeologic recognition projects. The radiogeologic recognition is a prospecting method that join the classic geologic recognition with measures of rock radioactivity. (C.M.)

  1. Memory Distortion and Its Avoidance: An Event-Related Potentials Study on False Recognition and Correct Rejection.

    Science.gov (United States)

    Cadavid, Sara; Beato, Maria Soledad

    2016-01-01

    Memory researchers have long been captivated by the nature of memory distortions and have made efforts to identify the neural correlates of true and false memories. However, the underlying mechanisms of avoiding false memories by correctly rejecting related lures remains underexplored. In this study, we employed a variant of the Deese/Roediger-McDermott paradigm to explore neural signatures of committing and avoiding false memories. ERP were obtained for True recognition, False recognition, Correct rejection of new items, and, more importantly, Correct rejection of related lures. With these ERP data, early-frontal, left-parietal, and late right-frontal old/new effects (associated with familiarity, recollection, and monitoring processes, respectively) were analysed. Results indicated that there were similar patterns for True and False recognition in all three old/new effects analysed in our study. Also, False recognition and Correct rejection of related lures activities seemed to share common underlying familiarity-based processes. The ERP similarities between False recognition and Correct rejection of related lures disappeared when recollection processes were examined because only False recognition presented a parietal old/new effect. This finding supported the view that actual false recollections underlie false memories, providing evidence consistent with previous behavioural research and with most ERP and neuroimaging studies. Later, with the onset of monitoring processes, False recognition and Correct rejection of related lures waveforms presented, again, clearly dissociated patterns. Specifically, False recognition and True recognition showed more positive going patterns than Correct rejection of related lures signal and Correct rejection of new items signature. Since False recognition and Correct rejection of related lures triggered familiarity-recognition processes, our results suggest that deciding which items are studied is based more on recollection

  2. Memory Distortion and Its Avoidance: An Event-Related Potentials Study on False Recognition and Correct Rejection.

    Directory of Open Access Journals (Sweden)

    Sara Cadavid

    Full Text Available Memory researchers have long been captivated by the nature of memory distortions and have made efforts to identify the neural correlates of true and false memories. However, the underlying mechanisms of avoiding false memories by correctly rejecting related lures remains underexplored. In this study, we employed a variant of the Deese/Roediger-McDermott paradigm to explore neural signatures of committing and avoiding false memories. ERP were obtained for True recognition, False recognition, Correct rejection of new items, and, more importantly, Correct rejection of related lures. With these ERP data, early-frontal, left-parietal, and late right-frontal old/new effects (associated with familiarity, recollection, and monitoring processes, respectively were analysed. Results indicated that there were similar patterns for True and False recognition in all three old/new effects analysed in our study. Also, False recognition and Correct rejection of related lures activities seemed to share common underlying familiarity-based processes. The ERP similarities between False recognition and Correct rejection of related lures disappeared when recollection processes were examined because only False recognition presented a parietal old/new effect. This finding supported the view that actual false recollections underlie false memories, providing evidence consistent with previous behavioural research and with most ERP and neuroimaging studies. Later, with the onset of monitoring processes, False recognition and Correct rejection of related lures waveforms presented, again, clearly dissociated patterns. Specifically, False recognition and True recognition showed more positive going patterns than Correct rejection of related lures signal and Correct rejection of new items signature. Since False recognition and Correct rejection of related lures triggered familiarity-recognition processes, our results suggest that deciding which items are studied is based more on

  3. Why recognition is rational

    Directory of Open Access Journals (Sweden)

    Clintin P. Davis-Stober

    2010-07-01

    Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.

  4. A formative evaluation of a staff reward and recognition programme

    Directory of Open Access Journals (Sweden)

    Saleemah Salie

    2012-07-01

    Research purpose: The main aim of this evaluation was to test the plausibility of the programme theory underlying a staff reward and recognition programme within a retail setting. Secondary aims were to assess whether or not the programme was implemented as intended and whether or not its outcomes were well defined. Motivation for the study: Different groups of people may have different assumptions about whether a reward and recognition programme works or not. This evaluation was motivated by the different assumptions held by programme stakeholders, programme recipients and social science researchers regarding the programme. Research design, approach and method: This formative evaluation used a descriptive design. Primary qualitative data were collected by means of structured interviews with the Human Resource Development (HRD Facilitator and ten programme participants. Main findings: The results showed that the programme theory was not plausible and that the programme was not implemented as intended. Although the HRD Facilitator and the participants agreed that the programme led to improved customer service, they disagreed about the other programme outcomes. Practical/managerial implications: This evaluation contains practical suggestions for improving the programme theory, the programme implementation process and the redefinition of the outcomes of the programme as standard performance indicators. Contribution/value-add: This evaluation contributed to the limited literature on the effect of reward and recognition programmes. Whilst there is a vast amount of literature pertaining to such programmes, very few formal evaluations exist about them.

  5. Comparison of Object Recognition Behavior in Human and Monkey

    Science.gov (United States)

    Rajalingham, Rishi; Schmidt, Kailyn

    2015-01-01

    Although the rhesus monkey is used widely as an animal model of human visual processing, it is not known whether invariant visual object recognition behavior is quantitatively comparable across monkeys and humans. To address this question, we systematically compared the core object recognition behavior of two monkeys with that of human subjects. To test true object recognition behavior (rather than image matching), we generated several thousand naturalistic synthetic images of 24 basic-level objects with high variation in viewing parameters and image background. Monkeys were trained to perform binary object recognition tasks on a match-to-sample paradigm. Data from 605 human subjects performing the same tasks on Mechanical Turk were aggregated to characterize “pooled human” object recognition behavior, as well as 33 separate Mechanical Turk subjects to characterize individual human subject behavior. Our results show that monkeys learn each new object in a few days, after which they not only match mean human performance but show a pattern of object confusion that is highly correlated with pooled human confusion patterns and is statistically indistinguishable from individual human subjects. Importantly, this shared human and monkey pattern of 3D object confusion is not shared with low-level visual representations (pixels, V1+; models of the retina and primary visual cortex) but is shared with a state-of-the-art computer vision feature representation. Together, these results are consistent with the hypothesis that rhesus monkeys and humans share a common neural shape representation that directly supports object perception. SIGNIFICANCE STATEMENT To date, several mammalian species have shown promise as animal models for studying the neural mechanisms underlying high-level visual processing in humans. In light of this diversity, making tight comparisons between nonhuman and human primates is particularly critical in determining the best use of nonhuman primates to

  6. Structure of the mouse galectin-4 N-terminal carbohydrate-recognition domain reveals the mechanism of oligosaccharide recognition

    Czech Academy of Sciences Publication Activity Database

    Krejčiříková, Veronika; Pachl, Petr; Fábry, Milan; Malý, Petr; Řezáčová, Pavlína; Brynda, Jiří

    2011-01-01

    Roč. 67, Pt3 (2011), 204-211 ISSN 0907-4449 R&D Projects: GA ČR GA203/09/0820; GA ČR GA304/03/0090; GA ČR GA301/07/0600 Institutional research plan: CEZ:AV0Z50520514; CEZ:AV0Z50520701; CEZ:AV0Z40550506 Keywords : S-type lectins * carbohydrate binding * molecular recognition Subject RIV: EB - Genetics ; Molecular Biology Impact factor: 12.619, year: 2011

  7. A simple centrality index for scientific social recognition

    Science.gov (United States)

    Kinouchi, Osame; Soares, Leonardo D. H.; Cardoso, George C.

    2018-02-01

    We introduce a new centrality index for bipartite networks of papers and authors that we call K-index. The K-index grows with the citation performance of the papers that cite a given researcher and can be seen as a measure of scientific social recognition. Indeed, the K-index measures the number of hubs, defined in a self-consistent way in the bipartite network, that cites a given author. We show that the K-index can be computed by simple inspection of the Web of Science platform and presents several advantages over other centrality indexes, in particular Hirsch h-index. The K-index is robust to self-citations, is not limited by the total number of papers published by a researcher as occurs for the h-index and can distinguish in a consistent way researchers that have the same h-index but very different scientific social recognition. The K-index easily detects a known case of a researcher with inflated number of papers, citations and h-index due to scientific misconduct. Finally, we show that, in a sample of twenty-eight physics Nobel laureates and twenty-eight highly cited non-Nobel-laureate physicists, the K-index correlates better to the achievement of the prize than the number of papers, citations, citations per paper, citing articles or the h-index. Clustering researchers in a K versus h plot reveals interesting outliers that suggest that these two indexes can present complementary independent information.

  8. Human Activity Recognition in a Car with Embedded Devices

    Directory of Open Access Journals (Sweden)

    Danilo Burbano

    2015-11-01

    Full Text Available Detection and prediction of drowsiness is key for the implementation of intelligent vehicles aimed to prevent highway crashes. There are several approaches for such solution. In thispaper the computer vision approach will be analysed, where embedded devices (e.g.videocameras are used along with machine learning and pattern recognition techniques for implementing suitable solutions for detecting driver fatigue. Most of the research in computer vision systems focused on the analysis of blinks, this is a notable solution when it is combined with additional patterns like yawing or head motion for the recognition of drowsiness. The first step in this approach is the face recognition, where AdaBoost algorithm shows accurate results for the feature extraction, whereas regarding the detection of drowsiness the data-driven classifiers such as Support Vector Machine (SVM yields remarkable results. One underlying component for implementing a computer vision technology for detection of drowsiness is a database of spontaneous images from the Facial Action Coding System (FACS, where the classifier can be trained accordingly. This paper introduces a straightforward prototype for detection of drowsiness, where the Viola-Jones method is used for face recognition and cascade classifier is used for the detection of a contiguous sequence of eyes closed, which a reconsidered as drowsiness.

  9. What are the visual features underlying rapid object recognition?

    Directory of Open Access Journals (Sweden)

    Sébastien M Crouzet

    2011-11-01

    Full Text Available Research progress in machine vision has been very significant in recent years. Robust face detection and identification algorithms are already readily available to consumers, and modern computer vision algorithms for generic object recognition are now coping with the richness and complexity of natural visual scenes. Unlike early vision models of object recognition that emphasized the role of figure-ground segmentation and spatial information between parts, recent successful approaches are based on the computation of loose collections of image features without prior segmentation or any explicit encoding of spatial relations. While these models remain simplistic models of visual processing, they suggest that, in principle, bottom-up activation of a loose collection of image features could support the rapid recognition of natural object categories and provide an initial coarse visual representation before more complex visual routines and attentional mechanisms take place. Focusing on biologically-plausible computational models of (bottom-up pre-attentive visual recognition, we review some of the key visual features that have been described in the literature. We discuss the consistency of these feature-based representations with classical theories from visual psychology and test their ability to account for human performance on a rapid object categorization task.

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

    Science.gov (United States)

    Malmberg, Kenneth J

    2008-12-01

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

  11. Gesture recognition based on computer vision and glove sensor for remote working environments

    Energy Technology Data Exchange (ETDEWEB)

    Chien, Sung Il; Kim, In Chul; Baek, Yung Mok; Kim, Dong Su; Jeong, Jee Won; Shin, Kug [Kyungpook National University, Taegu (Korea)

    1998-04-01

    In this research, we defined a gesture set needed for remote monitoring and control of a manless system in atomic power station environments. Here, we define a command as the loci of a gesture. We aim at the development of an algorithm using a vision sensor and glove sensors in order to implement the gesture recognition system. The gesture recognition system based on computer vision tracks a hand by using cross correlation of PDOE image. To recognize the gesture word, the 8 direction code is employed as the input symbol for discrete HMM. Another gesture recognition based on sensor has introduced Pinch glove and Polhemus sensor as an input device. The extracted feature through preprocessing now acts as an input signal of the recognizer. For recognition 3D loci of Polhemus sensor, discrete HMM is also adopted. The alternative approach of two foregoing recognition systems uses the vision and and glove sensors together. The extracted mesh feature and 8 direction code from the locus tracking are introduced for further enhancing recognition performance. MLP trained by backpropagation is introduced here and its performance is compared to that of discrete HMM. (author). 32 refs., 44 figs., 21 tabs.

  12. A Study on Efficient Robust Speech Recognition with Stochastic Dynamic Time Warping

    OpenAIRE

    孫, 喜浩

    2014-01-01

    In recent years, great progress has been made in automatic speech recognition (ASR) system. The hidden Markov model (HMM) and dynamic time warping (DTW) are the two main algorithms which have been widely applied to ASR system. Although, HMM technique achieves higher recognition accuracy in clear speech environment and noisy environment. It needs large-set of words and realizes the algorithm more complexly.Thus, more and more researchers have focused on DTW-based ASR system.Dynamic time warpin...

  13. The interplay between perceptual organization and object recognition: Temporal dynamics and neuropsychology

    OpenAIRE

    Torfs, Katrien

    2012-01-01

    The ease and efficiency with which we perceive objects in daily life masks the complexity of the processes involved. The main goal of my doctoral research was to enhance our understanding of the complex interplay between perceptual organization and object recognition. To this end, we investigated the dynamic interplay between different component processes of object recognition, and their temporal dynamics. In the first part of this thesis, I present three behavioral studies focusing on the ro...

  14. Probabilistic Open Set Recognition

    Science.gov (United States)

    Jain, Lalit Prithviraj

    Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary

  15. TU-FG-209-12: Treatment Site and View Recognition in X-Ray Images with Hierarchical Multiclass Recognition Models

    Energy Technology Data Exchange (ETDEWEB)

    Chang, X; Mazur, T; Yang, D [Washington University in St Louis, St Louis, MO (United States)

    2016-06-15

    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 transformation 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 View

  16. TU-FG-209-12: Treatment Site and View Recognition in X-Ray Images with Hierarchical Multiclass Recognition Models

    International Nuclear Information System (INIS)

    Chang, X; Mazur, T; Yang, D

    2016-01-01

    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 transformation 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 View

  17. How Learning Goal Orientation Fosters Leadership Recognition in Self-managed Teams

    DEFF Research Database (Denmark)

    Lee, Yih-Teen; Paunova, Minna

    2017-01-01

    understudied. Drawing on social exchange theory, we propose and test an individual-level two-stage process model of generalised exchange linking LGO and leadership recognition in self-managed teams. Specifically, we posit that learning-oriented individuals will tend to feel safer in self-managed teams, which......Defined as a mental framework for how individuals interpret and respond to achievement situations, learning goal orientation (LGO) has received increasing attention in organisational research. However, its effect on leadership, especially in contexts absent of formal leadership, remains......, but that contextual role behavior alone does not mediate the effect of LGO on leadership recognition. LGO has an indirect effect on leadership recognition through the joint mediation of felt safety and contextual role behavior. Our results offer insight on the link between LGO and leadership, with practical...

  18. The Effects of Inversion and Familiarity on Face versus Body Cues to Person Recognition

    Science.gov (United States)

    Robbins, Rachel A.; Coltheart, Max

    2012-01-01

    Extensive research has focused on face recognition, and much is known about this topic. However, much of this work seems to be based on an assumption that faces are the most important aspect of person recognition. Here we test this assumption in two experiments. We show that when viewers are forced to choose, they "do" use the face more than the…

  19. Hemispheric lateralization of linguistic prosody recognition in comparison to speech and speaker recognition.

    Science.gov (United States)

    Kreitewolf, Jens; Friederici, Angela D; von Kriegstein, Katharina

    2014-11-15

    Hemispheric specialization for linguistic prosody is a controversial issue. While it is commonly assumed that linguistic prosody and emotional prosody are preferentially processed in the right hemisphere, neuropsychological work directly comparing processes of linguistic prosody and emotional prosody suggests a predominant role of the left hemisphere for linguistic prosody processing. Here, we used two functional magnetic resonance imaging (fMRI) experiments to clarify the role of left and right hemispheres in the neural processing of linguistic prosody. In the first experiment, we sought to confirm previous findings showing that linguistic prosody processing compared to other speech-related processes predominantly involves the right hemisphere. Unlike previous studies, we controlled for stimulus influences by employing a prosody and speech task using the same speech material. The second experiment was designed to investigate whether a left-hemispheric involvement in linguistic prosody processing is specific to contrasts between linguistic prosody and emotional prosody or whether it also occurs when linguistic prosody is contrasted against other non-linguistic processes (i.e., speaker recognition). Prosody and speaker tasks were performed on the same stimulus material. In both experiments, linguistic prosody processing was associated with activity in temporal, frontal, parietal and cerebellar regions. Activation in temporo-frontal regions showed differential lateralization depending on whether the control task required recognition of speech or speaker: recognition of linguistic prosody predominantly involved right temporo-frontal areas when it was contrasted against speech recognition; when contrasted against speaker recognition, recognition of linguistic prosody predominantly involved left temporo-frontal areas. The results show that linguistic prosody processing involves functions of both hemispheres and suggest that recognition of linguistic prosody is based on

  20. Data Mining and Pattern Recognition Models for Identifying Inherited Diseases: Challenges and Implications

    OpenAIRE

    Iddamalgoda, Lahiru; Das, Partha S.; Aponso, Achala; Sundararajan, Vijayaraghava S.; Suravajhala, Prashanth; Valadi, Jayaraman K.

    2016-01-01

    Data mining and pattern recognition methods reveal interesting findings in genetic studies, especially on how the genetic makeup is associated with inherited diseases. Although researchers have proposed various data mining models for biomedical approaches, there remains a challenge in accurately prioritizing the single nucleotide polymorphisms (SNP) associated with the disease. In this commentary, we review the state-of-art data mining and pattern recognition models for identifying inherited ...

  1. Human face recognition using eigenface in cloud computing environment

    Science.gov (United States)

    Siregar, S. T. M.; Syahputra, M. F.; Rahmat, R. F.

    2018-02-01

    Doing a face recognition for one single face does not take a long time to process, but if we implement attendance system or security system on companies that have many faces to be recognized, it will take a long time. Cloud computing is a computing service that is done not on a local device, but on an internet connected to a data center infrastructure. The system of cloud computing also provides a scalability solution where cloud computing can increase the resources needed when doing larger data processing. This research is done by applying eigenface while collecting data as training data is also done by using REST concept to provide resource, then server can process the data according to existing stages. After doing research and development of this application, it can be concluded by implementing Eigenface, recognizing face by applying REST concept as endpoint in giving or receiving related information to be used as a resource in doing model formation to do face recognition.

  2. Cognition and speech-in-noise recognition: the role of proactive interference.

    Science.gov (United States)

    Ellis, Rachel J; Rönnberg, Jerker

    2014-01-01

    Complex working memory (WM) span tasks have been shown to predict speech-in-noise (SIN) recognition. Studies of complex WM span tasks suggest that, rather than indexing a single cognitive process, performance on such tasks may be governed by separate cognitive subprocesses embedded within WM. Previous research has suggested that one such subprocess indexed by WM tasks is proactive interference (PI), which refers to difficulties memorizing current information because of interference from previously stored long-term memory representations for similar information. The aim of the present study was to investigate phonological PI and to examine the relationship between PI (semantic and phonological) and SIN perception. A within-subjects experimental design was used. An opportunity sample of 24 young listeners with normal hearing was recruited. Measures of resistance to, and release from, semantic and phonological PI were calculated alongside the signal-to-noise ratio required to identify 50% of keywords correctly in a SIN recognition task. The data were analyzed using t-tests and correlations. Evidence of release from and resistance to semantic interference was observed. These measures correlated significantly with SIN recognition. Limited evidence of phonological PI was observed. The results show that capacity to resist semantic PI can be used to predict SIN recognition scores in young listeners with normal hearing. On the basis of these findings, future research will focus on investigating whether tests of PI can be used in the treatment and/or rehabilitation of hearing loss. American Academy of Audiology.

  3. ANALYSIS OF MULTIMODAL FUSION TECHNIQUES FOR AUDIO-VISUAL SPEECH RECOGNITION

    Directory of Open Access Journals (Sweden)

    D.V. Ivanko

    2016-05-01

    Full Text Available The paper deals with analytical review, covering the latest achievements in the field of audio-visual (AV fusion (integration of multimodal information. We discuss the main challenges and report on approaches to address them. One of the most important tasks of the AV integration is to understand how the modalities interact and influence each other. The paper addresses this problem in the context of AV speech processing and speech recognition. In the first part of the review we set out the basic principles of AV speech recognition and give the classification of audio and visual features of speech. Special attention is paid to the systematization of the existing techniques and the AV data fusion methods. In the second part we provide a consolidated list of tasks and applications that use the AV fusion based on carried out analysis of research area. We also indicate used methods, techniques, audio and video features. We propose classification of the AV integration, and discuss the advantages and disadvantages of different approaches. We draw conclusions and offer our assessment of the future in the field of AV fusion. In the further research we plan to implement a system of audio-visual Russian continuous speech recognition using advanced methods of multimodal fusion.

  4. Prenatal Alcohol Consumption Between Conception and Recognition of Pregnancy.

    Science.gov (United States)

    McCormack, Clare; Hutchinson, Delyse; Burns, Lucy; Wilson, Judy; Elliott, Elizabeth; Allsop, Steve; Najman, Jake; Jacobs, Sue; Rossen, Larissa; Olsson, Craig; Mattick, Richard

    2017-02-01

    Current estimates of the rates of alcohol-exposed pregnancies may underestimate prenatal alcohol exposure if alcohol consumption in early trimester 1, prior to awareness of pregnancy, is not considered. Extant literature describes predictors of alcohol consumption during pregnancy; however, alcohol consumption prior to awareness of pregnancy is a distinct behavior from consumption after becoming aware of pregnancy and thus may be associated with different predictors. The purpose of this study was therefore to examine prevalence and predictors of alcohol consumption by women prior to awareness of their pregnancy, and trajectories of change to alcohol use following pregnancy recognition. Pregnant women (n = 1,403) were prospectively recruited from general antenatal clinics of 4 public hospitals in Australian metropolitan areas between 2008 and 2013. Women completed detailed interviews about alcohol use before and after recognition of pregnancy. Most women (n = 850, 60.6%) drank alcohol between conception and pregnancy recognition. Binge and heavy drinking were more prevalent than low-level drinking. The proportion of women who drank alcohol reduced to 18.3% (n = 257) after recognition of pregnancy. Of women who drank alcohol, 70.5% ceased drinking, 18.3% reduced consumption, and 11.1% made no reduction following awareness of pregnancy. Socioeconomic status (SES) was the strongest predictor of alcohol use, with drinkers more likely to be of high rather than low SES compared with abstainers (OR = 3.30, p alcohol use prior to pregnancy recognition, age, pregnancy planning, and illicit substance use. In this sample of relatively high SES women, most women ceased or reduced drinking once aware of their pregnancy. However, the rate of alcohol-exposed pregnancies was higher than previous estimates when the period prior to pregnancy recognition was taken into account. Copyright © 2017 by the Research Society on Alcoholism.

  5. Iris Recognition Using Feature Extraction of Box Counting Fractal Dimension

    Science.gov (United States)

    Khotimah, C.; Juniati, D.

    2018-01-01

    Biometrics is a science that is now growing rapidly. Iris recognition is a biometric modality which captures a photo of the eye pattern. The markings of the iris are distinctive that it has been proposed to use as a means of identification, instead of fingerprints. Iris recognition was chosen for identification in this research because every human has a special feature that each individual is different and the iris is protected by the cornea so that it will have a fixed shape. This iris recognition consists of three step: pre-processing of data, feature extraction, and feature matching. Hough transformation is used in the process of pre-processing to locate the iris area and Daugman’s rubber sheet model to normalize the iris data set into rectangular blocks. To find the characteristics of the iris, it was used box counting method to get the fractal dimension value of the iris. Tests carried out by used k-fold cross method with k = 5. In each test used 10 different grade K of K-Nearest Neighbor (KNN). The result of iris recognition was obtained with the best accuracy was 92,63 % for K = 3 value on K-Nearest Neighbor (KNN) method.

  6. Researching family through the everyday lives of children across home and day care in Denmark

    DEFF Research Database (Denmark)

    Kousholt, Dorte

    2011-01-01

    The article investigates family as a conflictual community with a specific starting point in exploring children's lives across day-care institution and home. Children's development is theorised in relation to taking part in different communities across different contexts. The article draws...... on an ethnographically inspired research project with 6 families living in a small town in Denmark. The analysis points to how the children's possibilities of participation are created across their different life contexts and that the social interplay and conflicts between the children in the day-care institution have...... impacts on the relation and interaction between parents and children. Parenting in that way reaches far beyond the family and includes taking into account various issues in the other places where the children spend their time. The children's developmental possibilities are shaped by the relations...

  7. Researching family through the everyday lives of children across home and day care in Denmark

    DEFF Research Database (Denmark)

    Kousholt, Dorte

    2011-01-01

    on an ethnographically inspired research project with 6 families living in a small town in Denmark. The analysis points to how the children's possibilities of participation are created across their different life contexts and that the social interplay and conflicts between the children in the day-care institution have......The article investigates family as a conflictual community with a specific starting point in exploring children's lives across day-care institution and home. Children's development is theorised in relation to taking part in different communities across different contexts. The article draws...... impacts on the relation and interaction between parents and children. Parenting in that way reaches far beyond the family and includes taking into account various issues in the other places where the children spend their time. The children's developmental possibilities are shaped by the relations...

  8. Viewpoint Manifolds for Action Recognition

    Directory of Open Access Journals (Sweden)

    Souvenir Richard

    2009-01-01

    Full Text Available Abstract Action recognition from video is a problem that has many important applications to human motion analysis. In real-world settings, the viewpoint of the camera cannot always be fixed relative to the subject, so view-invariant action recognition methods are needed. Previous view-invariant methods use multiple cameras in both the training and testing phases of action recognition or require storing many examples of a single action from multiple viewpoints. In this paper, we present a framework for learning a compact representation of primitive actions (e.g., walk, punch, kick, sit that can be used for video obtained from a single camera for simultaneous action recognition and viewpoint estimation. Using our method, which models the low-dimensional structure of these actions relative to viewpoint, we show recognition rates on a publicly available dataset previously only achieved using multiple simultaneous views.

  9. On techniques for angle compensation in nonideal iris recognition.

    Science.gov (United States)

    Schuckers, Stephanie A C; Schmid, Natalia A; Abhyankar, Aditya; Dorairaj, Vivekanand; Boyce, Christopher K; Hornak, Lawrence A

    2007-10-01

    The popularity of the iris biometric has grown considerably over the past two to three years. Most research has been focused on the development of new iris processing and recognition algorithms for frontal view iris images. However, a few challenging directions in iris research have been identified, including processing of a nonideal iris and iris at a distance. In this paper, we describe two nonideal iris recognition systems and analyze their performance. The word "nonideal" is used in the sense of compensating for off-angle occluded iris images. The system is designed to process nonideal iris images in two steps: 1) compensation for off-angle gaze direction and 2) processing and encoding of the rotated iris image. Two approaches are presented to account for angular variations in the iris images. In the first approach, we use Daugman's integrodifferential operator as an objective function to estimate the gaze direction. After the angle is estimated, the off-angle iris image undergoes geometric transformations involving the estimated angle and is further processed as if it were a frontal view image. The encoding technique developed for a frontal image is based on the application of the global independent component analysis. The second approach uses an angular deformation calibration model. The angular deformations are modeled, and calibration parameters are calculated. The proposed method consists of a closed-form solution, followed by an iterative optimization procedure. The images are projected on the plane closest to the base calibrated plane. Biorthogonal wavelets are used for encoding to perform iris recognition. We use a special dataset of the off-angle iris images to quantify the performance of the designed systems. A series of receiver operating characteristics demonstrate various effects on the performance of the nonideal-iris-based recognition system.

  10. Physiological arousal in processing recognition information

    Directory of Open Access Journals (Sweden)

    Guy Hochman

    2010-07-01

    Full Text Available The recognition heuristic (RH; Goldstein and Gigerenzer, 2002 suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.

  11. Page Recognition: Quantum Leap In Recognition Technology

    Science.gov (United States)

    Miller, Larry

    1989-07-01

    No milestone has proven as elusive as the always-approaching "year of the LAN," but the "year of the scanner" might claim the silver medal. Desktop scanners have been around almost as long as personal computers. And everyone thinks they are used for obvious desktop-publishing and business tasks like scanning business documents, magazine articles and other pages, and translating those words into files your computer understands. But, until now, the reality fell far short of the promise. Because it's true that scanners deliver an accurate image of the page to your computer, but the software to recognize this text has been woefully disappointing. Old optical-character recognition (OCR) software recognized such a limited range of pages as to be virtually useless to real users. (For example, one OCR vendor specified 12-point Courier font from an IBM Selectric typewriter: the same font in 10-point, or from a Diablo printer, was unrecognizable!) Computer dealers have told me the chasm between OCR expectations and reality is so broad and deep that nine out of ten prospects leave their stores in disgust when they learn the limitations. And this is a very important, very unfortunate gap. Because the promise of recognition -- what people want it to do -- carries with it tremendous improvements in our productivity and ability to get tons of written documents into our computers where we can do real work with it. The good news is that a revolutionary new development effort has led to the new technology of "page recognition," which actually does deliver the promise we've always wanted from OCR. I'm sure every reader appreciates the breakthrough represented by the laser printer and page-makeup software, a combination so powerful it created new reasons for buying a computer. A similar breakthrough is happening right now in page recognition: the Macintosh (and, I must admit, other personal computers) equipped with a moderately priced scanner and OmniPage software (from Caere

  12. Day-to-day repeatability of the Pulse Time Index of Norm

    Directory of Open Access Journals (Sweden)

    Posokhov IN

    2014-02-01

    Full Text Available Igor N Posokhov,1 Aleksandra O Konradi,2 Eugeny V Shlyakhto,2 Oleg V Mamontov,2 Artemy V Orlov,3 Anatoly N Rogoza4 1Hemodynamic Laboratory Ltd, Nizhniy Novgorod, 2Almazov Federal Heart, Blood and Endocrinology Centre, Saint Petersburg, 3Department 65 Competitive System Analysis, National Research Nuclear University, Moscow, 4Cardiology Research Center, Moscow, Russia Abstract: The pulse wave velocity (PWV threshold for hypertensive target organ damage is presently set at 10 meters per second. New 24-hour monitors (eg, BPLab® and Vasotens® provide several PWV measurements over a period of 24–72 hours. A new parameter, ie, the Pulse Time Index of Norm (PTIN, can be calculated from these data. The PTIN is defined as the percentage of a 24-hour period during which the PWV does not exceed 10 meters per second. The aim of the present study was to test the new PTIN for clinical feasibility using day-to-day repeatability analysis. Oscillometrically generated waveform files (n=85, which were previously used for research studies, were reanalyzed using the new 2013 version software of the Vasotens technology program, which enables calculation of PTIN. The intraclass correlation coefficient was 0.98 and Cronbach's alpha was 0.97, indicating that the PTIN has excellent day-to-day repeatability and internal consistency. The present results show adequate repeatability, and PTIN assessment using the Vasotens technology appears to be feasible. Keywords: pulse wave velocity, ambulatory, 24-hour, monitoring, Pulse Time Index of Norm, arterial stiffness

  13. Kinect Who’s Coming - Applying Kinect to Human Body Height Measurement to Improve Character Recognition Performance

    Directory of Open Access Journals (Sweden)

    Hau-Wei Lee

    2015-05-01

    Full Text Available A great deal of relevant research on character recognition has been carried out, but a certain amount of time is needed to compare faces from a large database. The Kinect is able to obtain three-dimensional coordinates for an object (x & y axes and depth, and in recent years research on its applications has expanded from use in gaming to that of image measurement. This study uses Kinect skeleton information to conduct body height measurements with the aim of improving character recognition performance. Time spent searching and comparing characters is reduced by creating height categories. The margin of error for height used in this investigation was ± 5 cm; therefore, face comparisons were only executed for people in the database within ±5 cm of the body height measured, reducing the search time needed. In addition, using height and facial features simultaneously to conduct character recognition can also reduce the frequency of mistaken recognition. The Kinect was placed on a rotary stage and the position of the head on the body frame was used to conduct body tracking. Body tracking can be used to reduce image distortion caused by the lens of the Kinect. EmguCV was used for image processing and character recognition. The methods proposed in this study can be used in public safety, student attendance registration, commercial VIP recognition and many others.

  14. Entity Recognition Via Multimodal Sensor Fusion With Smart Phones

    Science.gov (United States)

    2015-03-26

    sensor’s data. In the research Preprocessing Techniques for Context Recognition from Accelerom- eter Data, Figo, Diniz , Ferreira, and Cardoso provide...International Conference on, pages 13–24. IEEE, 2011. 13. Davide Figo, Pedro C. Diniz , Diogo R. Ferreira, and João M. P. Cardoso. Pre- processing

  15. Effect of nitrogen narcosis on free recall and recognition memory in open water.

    Science.gov (United States)

    Hobbs, M; Kneller, W

    2009-01-01

    Previous research has demonstrated that nitrogen narcosis causes decrements in memory performance but the precise aspect of memory impaired is not clear in the literature. The present research investigated the effect of narcosis on free recall and recognition memory by appling signal detection theory (SDT) to the analysis of the recognition data. Using a repeated measures design, the free recall and recognition memory of 20 divers was tested in four learning-recall conditions: shallow-shallow (SS), deep-deep (DD), shallow-deep (SD) and deep-shallow (DS). The data was collected in the ocean offDahab, Egypt with shallow water representing a depth of 0-10m (33ft) and deep water 37-40m (121-131ft). The presence of narcosis was independently indexed with subjective ratings. In comparison to the SS condition there was a clear impairment of free recall in the DD and DS conditions, but not the SD condition. Recognition memory remained unaffected by narcosis. It was concluded narcosis-induced memory decrements cannot be explained as simply an impairment of input into long term memory or of self-guided search and it is suggested instead that narcosis acts to reduce the level of processing/encoding of information.

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

    Science.gov (United States)

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

    2016-08-01

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

  17. Investigating the Impact of Possession-Way of a Smartphone on Action Recognition

    Directory of Open Access Journals (Sweden)

    Zae Myung Kim

    2016-06-01

    Full Text Available For the past few decades, action recognition has been attracting many researchers due to its wide use in a variety of applications. Especially with the increasing number of smartphone users, many studies have been conducted using sensors within a smartphone. However, a lot of these studies assume that the users carry the device in specific ways such as by hand, in a pocket, in a bag, etc. This paper investigates the impact of providing an action recognition system with the information of the possession-way of a smartphone, and vice versa. The experimental dataset consists of five possession-ways (hand, backpack, upper-pocket, lower-pocket, and shoulder-bag and two actions (walking and running gathered by seven users separately. Various machine learning models including recurrent neural network architectures are employed to explore the relationship between the action recognition and the possession-way recognition. The experimental results show that the assumption of possession-ways of smartphones do affect the performance of action recognition, and vice versa. The results also reveal that a good performance is achieved when both actions and possession-ways are recognized simultaneously.

  18. On the particular vulnerability of face recognition to aging: A review of three hypotheses

    Directory of Open Access Journals (Sweden)

    Isabelle eBoutet

    2015-08-01

    Full Text Available Age-related face recognition deficits are characterized by high false alarms to unfamiliar faces, are not as pronounced for other complex stimuli, and are only partially related to general age-related impairments in cognition. This paper reviews some of the underlying processes likely to be implicated in theses deficits by focusing on areas where contradictions abound as a means to highlight avenues for future research. Research pertaining to three following hypotheses is presented: (i perceptual deterioration, (ii encoding of configural information, and (iii difficulties in recollecting contextual information. The evidence surveyed provides support for the idea that all three factors are likely to contribute, under certain conditions, to the deficits in face recognition seen in older adults. We discuss how these different factors might interact in the context of a generic framework of the different stages implicated in face recognition. Several suggestions for future investigations are outlined.

  19. Improving on hidden Markov models: An articulatorily constrained, maximum likelihood approach to speech recognition and speech coding

    Energy Technology Data Exchange (ETDEWEB)

    Hogden, J.

    1996-11-05

    The goal of the proposed research is to test a statistical model of speech recognition that incorporates the knowledge that speech is produced by relatively slow motions of the tongue, lips, and other speech articulators. This model is called Maximum Likelihood Continuity Mapping (Malcom). Many speech researchers believe that by using constraints imposed by articulator motions, we can improve or replace the current hidden Markov model based speech recognition algorithms. Unfortunately, previous efforts to incorporate information about articulation into speech recognition algorithms have suffered because (1) slight inaccuracies in our knowledge or the formulation of our knowledge about articulation may decrease recognition performance, (2) small changes in the assumptions underlying models of speech production can lead to large changes in the speech derived from the models, and (3) collecting measurements of human articulator positions in sufficient quantity for training a speech recognition algorithm is still impractical. The most interesting (and in fact, unique) quality of Malcom is that, even though Malcom makes use of a mapping between acoustics and articulation, Malcom can be trained to recognize speech using only acoustic data. By learning the mapping between acoustics and articulation using only acoustic data, Malcom avoids the difficulties involved in collecting articulator position measurements and does not require an articulatory synthesizer model to estimate the mapping between vocal tract shapes and speech acoustics. Preliminary experiments that demonstrate that Malcom can learn the mapping between acoustics and articulation are discussed. Potential applications of Malcom aside from speech recognition are also discussed. Finally, specific deliverables resulting from the proposed research are described.

  20. Feature selection in classification of eye movements using electrooculography for activity recognition.

    Science.gov (United States)

    Mala, S; Latha, K

    2014-01-01

    Activity recognition is needed in different requisition, for example, reconnaissance system, patient monitoring, and human-computer interfaces. Feature selection plays an important role in activity recognition, data mining, and machine learning. In selecting subset of features, an efficient evolutionary algorithm Differential Evolution (DE), a very efficient optimizer, is used for finding informative features from eye movements using electrooculography (EOG). Many researchers use EOG signals in human-computer interactions with various computational intelligence methods to analyze eye movements. The proposed system involves analysis of EOG signals using clearness based features, minimum redundancy maximum relevance features, and Differential Evolution based features. This work concentrates more on the feature selection algorithm based on DE in order to improve the classification for faultless activity recognition.