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.

Enriching the international clinical nomenclature with Chinese daily used synonyms and concept recognition in physician notes.

Science.gov (United States)

Zhang, Rui; Liu, Jialin; Huang, Yong; Wang, Miye; Shi, Qingke; Chen, Jun; Zeng, Zhi

2017-05-02

It has been shown that the entities in everyday clinical text are often expressed in a way that varies from how they are expressed in the nomenclature. Owing to lots of synonyms, abbreviations, medical jargons or even misspellings in the daily used physician notes in clinical information system (CIS), the terminology without enough synonyms may not be adequately suitable for the task of Chinese clinical term recognition. This paper demonstrates a validated system to retrieve the Chinese term of clinical finding (CTCF) from CIS and map them to the corresponding concepts of international clinical nomenclature, such as SNOMED CT. The system focuses on the SNOMED CT with Chinese synonyms enrichment (SCCSE). The literal similarity and the diagnosis-related similarity metrics were used for concept mapping. Two CTCF recognition methods, the rule- and terminology-based approach (RTBA) and the conditional random field machine learner (CRF), were adopted to identify the concepts in physician notes. The system was validated against the history of present illness annotated by clinical experts. The RTBA and CRF could be combined to predict new CTCFs besides SCCSE persistently. Around 59,000 CTCF candidates were accepted as valid and 39,000 of them occurred at least once in the history of present illness. 3,729 of them were accordant with the description in referenced Chinese clinical nomenclature, which could cross map to other international nomenclature such as SNOMED CT. With the hybrid similarity metrics, another 7,454 valid CTCFs (synonyms) were succeeded in concept mapping. For CTCF recognition in physician notes, a series of experiments were performed to find out the best CRF feature set, which gained an F-score of 0.887. The RTBA achieved a better F-score of 0.919 by the CTCF dictionary created in this research. This research demonstrated that it is feasible to help the SNOMED CT with Chinese synonyms enrichment based on physician notes in CIS. With continuous

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...

FPGA-Based Implementation of Lithuanian Isolated Word Recognition Algorithm

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Tomyslav Sledevič

2013-05-01

Full Text Available The paper describes the FPGA-based implementation of Lithuanian isolated word recognition algorithm. FPGA is selected for parallel process implementation using VHDL to ensure fast signal processing at low rate clock signal. Cepstrum analysis was applied to features extraction in voice. The dynamic time warping algorithm was used to compare the vectors of cepstrum coefficients. A library of 100 words features was created and stored in the internal FPGA BRAM memory. Experimental testing with speaker dependent records demonstrated the recognition rate of 94%. The recognition rate of 58% was achieved for speaker-independent records. Calculation of cepstrum coefficients lasted for 8.52 ms at 50 MHz clock, while 100 DTWs took 66.56 ms at 25 MHz clock.Article in Lithuanian

RECOGNITION OF ADULTS’ EXPERIENTIAL COMPETENCES, IN PORTUGAL (2001-2011: ACHIEVEMENTS AND WEAKNESSES

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Pedro Abrantes

2014-06-01

Full Text Available Based on a PhD thesis and a post-doc project focused on this topic, the authors describe and analyse the programme of skills recognition, validation and certification, as it was developed in Portugal during the first decade of the 21st century, enabling the qualification of more than 5% of the active population. In the first section, this innovative theoretical and methodological framework for adult education and certification is discussed. Secondly, main agents, stages and dynamics of this process are sketched. In a third section, the main results of the programme’s national evaluations are synthetized. And in the fourth one, key social dynamics observed through qualitative research are underlined. In the conclusions, programme’s achievements and failures are systematized and some remarks for future interventions in this field are sketched.

6th International Conference on Pattern Recognition and Machine Intelligence

CERN Document Server

Gawrysiak, Piotr; Kryszkiewicz, Marzena; Rybiński, Henryk

2016-01-01

This book presents valuable contributions devoted to practical applications of Machine Intelligence and Big Data in various branches of the industry. All the contributions are extended versions of presentations delivered at the Industrial Session the 6th International Conference on Pattern Recognition and Machine Intelligence (PREMI 2015) held in Warsaw, Poland at June 30- July 3, 2015, which passed through a rigorous reviewing process. The contributions address real world problems and show innovative solutions used to solve them. This volume will serve as a bridge between researchers and practitioners, as well as between different industry branches, which can benefit from sharing ideas and results.

Intangible Wealth, between Recognition and Evaluation

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Florentina Moisescu

2016-07-01

Full Text Available The theme of this work is the recognition, assessment and importance of intangible property in the financial statements of an enterprise. The paper presents an insight into intangible assets recognition of causality, with highlight relevant aspects of how education contribute in a decisive manner, through the generation and dissemination of knowledge in order to achieve specific strategic goals of economic policy. This issue has also been a challenge and it still represents one for companies that have intangible assets overstated or that have kept in their balance sheet intangible assets without aproperly assessed market value. The paper also addresses the issue by restricting the proposed theoretical aspects in favor of those which are directly related to the accounting practice in line with International Accounting Standards and with the accounting regulations of our country.

The Recognition Of Fatigue

DEFF Research Database (Denmark)

Elsass, Peter; Jensen, Bodil; Mørup, Rikke

2007-01-01

Elsass P., Jensen B., Morup R., Thogersen M.H. (2007). The Recognition Of Fatigue: A qualitative study of life-stories from rehabilitation clients. International Journal of Psychosocial Rehabilitation. 11 (2), 75-87......Elsass P., Jensen B., Morup R., Thogersen M.H. (2007). The Recognition Of Fatigue: A qualitative study of life-stories from rehabilitation clients. International Journal of Psychosocial Rehabilitation. 11 (2), 75-87...

International Group Heterogeneity and Students' Business Project Achievement

Science.gov (United States)

Ding, Ning; Bosker, Roel J.; Xu, Xiaoyan; Rugers, Lucie; van Heugten, Petra PAM

2015-01-01

In business higher education, group project work plays an essential role. The purpose of the present study is to explore the relationship between the group heterogeneity of students' business project groups and their academic achievements at both group and individual levels. The sample consists of 536 freshmen from an International Business School…

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.)

Object feature extraction and recognition model

International Nuclear Information System (INIS)

Wan Min; Xiang Rujian; Wan Yongxing

2001-01-01

The characteristics of objects, especially flying objects, are analyzed, which include characteristics of spectrum, image and motion. Feature extraction is also achieved. To improve the speed of object recognition, a feature database is used to simplify the data in the source database. The feature vs. object relationship maps are stored in the feature database. An object recognition model based on the feature database is presented, and the way to achieve object recognition is also explained

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.

Academic Culture, Business Culture, and Measuring Achievement Differences: Internal Auditing Views

Science.gov (United States)

Roth, Benjamin S.

2012-01-01

This study explored whether university internal audit directors' views of culture and measuring achievement differences between their institutions and a business were related to how they viewed internal auditing priorities and uses. The Carnegie Classification system's 283 Doctorate-granting Universities were the target population.…

Linking TIMSS and NAEP Assessments to Evaluate International Trends in Achievement

Science.gov (United States)

Lim, Hwanggyu; Sireci, Stephen G.

2017-01-01

The Trends in International Mathematics and Science Study (TIMSS) makes it possible to compare the performance of students in the US in Mathematics and Science to the performance of students in other countries. TIMSS uses four international benchmarks for describing student achievement: Low, Intermediate, High, and Advanced. In this study, we…

The Beliefs of Students, Parents and Teachers about Internal Factors of Academic Achievement

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Helena Smrtnik Vitulić

2014-03-01

Full Text Available The main purpose of this paper was to determine the beliefs of students, teachers and parents about the internal factors of academic achievement and to verify whether their beliefs vary. In this paper the beliefs about the internal factors of academic achievement: personality traits, intellectual ability, language competence, interest in the subject and locus of control are thematised. The sample included 516 students from grades 5, 7 and 9 of 12 different basic schools in central Slovenia, 408 of their parents and 195 teachers. Amongst the broad range of personality traits in the survey questionnaire, parents selected openness and conscientiousness as the most important traits for academic success, while students selected openness and extroversion, and teachers selected agreeableness and emotional stability. In the opinion of the participants in the research, amongst other internal factors of academic success emphasised, those that have the greatest influence on academic achievement are interest in the subject and internal locus of control, while students’ intellectual ability and language competence are attributed slightly less importance. Beliefs regarding the individual factors of academic achievement vary between the groups of participants. In the future, it would be sensible to encourage students, teachers and parents to reflect on the meaning of the individual factors of academic achievement, and especially to speak with them about the factors on which each respective group can exert an influence in order to improve students’ academic achievement.

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.  .

Inclusivity in the Classroom and International Achievement in Mathematics and Science: An Exploratory Study

Science.gov (United States)

Barnard-Brak, Lucy; Wei, Tianlan; Schmidt, Marcelo; Sheffield, Rebecca

2014-01-01

Purpose: Few studies have examined the role of inclusivity in international assessments of student achievement, such as the TIMSS (Trends in International Mathematics and Science Study). The current study examined how the inclusivity of students with disabilities at the classroom level across countries may be associated with achievement scores,…

The Beliefs of Students, Parents and Teachers about Internal Factors of Academic Achievement

OpenAIRE

Helena Smrtnik Vitulić; Irena Lesar

2014-01-01

The main purpose of this paper was to determine the beliefs of students, teachers and parents about the internal factors of academic achievement and to verify whether their beliefs vary. In this paper the beliefs about the internal factors of academic achievement: personality traits, intellectual ability, language competence, interest in the subject and locus of control are thematised. The sample included 516 students from grades 5, 7 and 9 of 12 different basic schools in central Slovenia, 4...

School IPM Recognition and Certification

Science.gov (United States)

Schools and school districts can get support and recognition for implementation of school IPM. EPA is developing a program to provide recognition for school districts that are working towards or have achieved a level of success with school IPM programs.

The Rights of Pastoralist Peoples. A Framework for their Recognition in International Law

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Miguel Ángel Martín López

2016-06-01

Full Text Available Pastoralists are one of the most poverty stricken and underdeveloped existing human groups in the world. Until now, having remained practically invisible in the eyes of international law, it is desirable to open a debate concerning the recognition of their rights. The ideal situation would be to create a specific category of rights dedicated expressly to these pastoralist peoples. Therefore, one can surmise that there are two laws that constitute its essential content: the law protecting their way of life and their access rights to the land

Enhancement of Iris Recognition System Based on Phase Only Correlation

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Nuriza Pramita

2011-08-01

Full Text Available Iris recognition system is one of biometric based recognition/identification systems. Numerous techniques have been implemented to achieve a good recognition rate, including the ones based on Phase Only Correlation (POC. Significant and higher correlation peaks suggest that the system recognizes iris images of the same subject (person, while lower and unsignificant peaks correspond to recognition of those of difference subjects. Current POC methods have not investigated minimum iris point that can be used to achieve higher correlation peaks. This paper proposed a method that used only one-fourth of full normalized iris size to achieve higher (or at least the same recognition rate. Simulation on CASIA version 1.0 iris image database showed that averaged recognition rate of the proposed method achieved 67%, higher than that of using one-half (56% and full (53% iris point. Furthermore, all (100% POC peak values of the proposed method was higher than that of the method with full iris points.

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

Science.gov (United States)

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

2015-04-01

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

Societal Characteristics within the School: Inferences from the International Study of Educational Achievement.

Science.gov (United States)

Anderson, C. Arnold

1979-01-01

This paper relates the scholastic performance findings of the International Educational Achievement (IEA) Studies to social characteristics. It explores the relationship of national school achievement to economic development, national communication systems, and national social and cultural indices. This is the final article in a symposium on the…

Secure method for biometric-based recognition with integrated cryptographic functions.

Science.gov (United States)

Chiou, Shin-Yan

2013-01-01

Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied.

Secure Method for Biometric-Based Recognition with Integrated Cryptographic Functions

Directory of Open Access Journals (Sweden)

Shin-Yan Chiou

2013-01-01

Full Text Available Biometric systems refer to biometric technologies which can be used to achieve authentication. Unlike cryptography-based technologies, the ratio for certification in biometric systems needs not to achieve 100% accuracy. However, biometric data can only be directly compared through proximal access to the scanning device and cannot be combined with cryptographic techniques. Moreover, repeated use, improper storage, or transmission leaks may compromise security. Prior studies have attempted to combine cryptography and biometrics, but these methods require the synchronization of internal systems and are vulnerable to power analysis attacks, fault-based cryptanalysis, and replay attacks. This paper presents a new secure cryptographic authentication method using biometric features. The proposed system combines the advantages of biometric identification and cryptographic techniques. By adding a subsystem to existing biometric recognition systems, we can simultaneously achieve the security of cryptographic technology and the error tolerance of biometric recognition. This method can be used for biometric data encryption, signatures, and other types of cryptographic computation. The method offers a high degree of security with protection against power analysis attacks, fault-based cryptanalysis, and replay attacks. Moreover, it can be used to improve the confidentiality of biological data storage and biodata identification processes. Remote biometric authentication can also be safely applied.

Pattern recognition in bioinformatics : 5th IAPR international conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010 : proceedings

NARCIS (Netherlands)

Dijkstra, T.M.H.; Tsivtsivadze, E.; Marchiori, E.; Heskes, T.

2010-01-01

This book constitutes the refereed proceedings of the 5th International Conference on Pattern Recognition in Bioinformatics, PRIB 2010, held in Nijmegen, The Netherlands, in September 2010. The 38 revised full papers presented were carefully reviewed and selected from 46 submissions. The field of

Color descriptors for object category recognition

NARCIS (Netherlands)

van de Sande, K.E.A.; Gevers, T.; Snoek, C.G.M.

2008-01-01

Category recognition is important to access visual information on the level of objects. A common approach is to compute image descriptors first and then to apply machine learning to achieve category recognition from annotated examples. As a consequence, the choice of image descriptors is of great

Micro-Expression Recognition Using Color Spaces.

Science.gov (United States)

Wang, Su-Jing; Yan, Wen-Jing; Li, Xiaobai; Zhao, Guoying; Zhou, Chun-Guang; Fu, Xiaolan; Yang, Minghao; Tao, Jianhua

2015-12-01

Micro-expressions are brief involuntary facial expressions that reveal genuine emotions and, thus, help detect lies. Because of their many promising applications, they have attracted the attention of researchers from various fields. Recent research reveals that two perceptual color spaces (CIELab and CIELuv) provide useful information for expression recognition. This paper is an extended version of our International Conference on Pattern Recognition paper, in which we propose a novel color space model, tensor independent color space (TICS), to help recognize micro-expressions. In this paper, we further show that CIELab and CIELuv are also helpful in recognizing micro-expressions, and we indicate why these three color spaces achieve better performance. A micro-expression color video clip is treated as a fourth-order tensor, i.e., a four-dimension array. The first two dimensions are the spatial information, the third is the temporal information, and the fourth is the color information. We transform the fourth dimension from RGB into TICS, in which the color components are as independent as possible. The combination of dynamic texture and independent color components achieves a higher accuracy than does that of RGB. In addition, we define a set of regions of interests (ROIs) based on the facial action coding system and calculated the dynamic texture histograms for each ROI. Experiments are conducted on two micro-expression databases, CASME and CASME 2, and the results show that the performances for TICS, CIELab, and CIELuv are better than those for RGB or gray.

A Bayesian classifier for symbol recognition

OpenAIRE

Barrat , Sabine; Tabbone , Salvatore; Nourrissier , Patrick

2007-01-01

URL : http://www.buyans.com/POL/UploadedFile/134_9977.pdf; International audience; We present in this paper an original adaptation of Bayesian networks to symbol recognition problem. More precisely, a descriptor combination method, which enables to improve significantly the recognition rate compared to the recognition rates obtained by each descriptor, is presented. In this perspective, we use a simple Bayesian classifier, called naive Bayes. In fact, probabilistic graphical models, more spec...

Gender differences in extreme mathematical achievement: an international perspective on biological and social factors.

Science.gov (United States)

Penner, Andrew M

2008-01-01

Genetic and other biological explanations have reemerged in recent scholarship on the underrepresentation of women in mathematics and the sciences. This study engages this debate by using international data-including math achievement scores from the Third International Mathematics and Sciences Study and country-level data from the World Bank, the United Nations, the International Labour Organization, the World Values Survey, and the International Social Survey Programme-to demonstrate the importance of social factors and to estimate an upper bound for the impact of genetic factors. The author argues that international variation provides a valuable opportunity to present simple and powerful arguments for the continued importance of social factors. In addition, where previous research has, by and large, focused on differences in population means, this work examines gender differences throughout the distribution. The article shows that there is considerable variation in gender differences internationally, a finding not easily explained by strictly biological theories. Modeling the cross-national variation in gender differences with country-level predictors reveals that differences among high achievers are related to gender inequality in the labor market and differences in the overall status of men and women.

A Novel Energy-Efficient Approach for Human Activity Recognition.

Science.gov (United States)

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

2017-09-08

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

The Relation of an International Student Center's Orientation Training Sessions with International Students' Achievement and Integration to University

Science.gov (United States)

Güvendir, Meltem Acar

2018-01-01

The purpose of the research is to examine the relation of orientation training sessions with integration and achievement of the international students. The study used the Institutional Integration Scales, developed by Pascarella and Terenzini (1980), to examine the integration level of the international students. 181 freshmen undergraduate and…

Mathematical symbol hypothesis recognition with rejection option

OpenAIRE

Julca-Aguilar , Frank; Hirata , Nina ,; Viard-Gaudin , Christian; Mouchère , Harold; Medjkoune , Sofiane

2014-01-01

International audience; In the context of handwritten mathematical expressions recognition, a first step consist on grouping strokes (segmentation) to form symbol hypotheses: groups of strokes that might represent a symbol. Then, the symbol recognition step needs to cope with the identification of wrong segmented symbols (false hypotheses). However, previous works on symbol recognition consider only correctly segmented symbols. In this work, we focus on the problem of mathematical symbol reco...

Fast neuromimetic object recognition using FPGA outperforms GPU implementations.

Science.gov (United States)

Orchard, Garrick; Martin, Jacob G; Vogelstein, R Jacob; Etienne-Cummings, Ralph

2013-08-01

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically inspired models of visual object recognition, among them the hierarchical model and X (HMAX) model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive and biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial field-programmable aate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128 × 128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multiclass visual object recognition tasks.

The Computer Book of the Internal Medicine Resident: competence acquisition and achievement of learning objectives.

Science.gov (United States)

Oristrell, J; Oliva, J C; Casanovas, A; Comet, R; Jordana, R; Navarro, M

2014-01-01

The Computer Book of the Internal Medicine resident (CBIMR) is a computer program that was validated to analyze the acquisition of competences in teams of Internal Medicine residents. To analyze the characteristics of the rotations during the Internal Medicine residency and to identify the variables associated with the acquisition of clinical and communication skills, the achievement of learning objectives and resident satisfaction. All residents of our service (n=20) participated in the study during a period of 40 months. The CBIMR consisted of 22 self-assessment questionnaires specific for each rotation, with items on services (clinical workload, disease protocolization, resident responsibilities, learning environment, service organization and teamwork) and items on educational outcomes (acquisition of clinical and communication skills, achievement of learning objectives, overall satisfaction). Associations between services features and learning outcomes were analyzed using bivariate and multivariate analysis. An intense clinical workload, high resident responsibilities and disease protocolization were associated with the acquisition of clinical skills. High clinical competence and teamwork were both associated with better communication skills. Finally, an adequate learning environment was associated with increased clinical competence, the achievement of educational goals and resident satisfaction. Potentially modifiable variables related with the operation of clinical services had a significant impact on the acquisition of clinical and communication skills, the achievement of educational goals, and resident satisfaction during the specialized training in Internal Medicine. Copyright © 2013 Elsevier España, S.L. All rights reserved.

Swap transactions as a financial tool, their recognition as international accounting standard 39 and display in financial statements

Directory of Open Access Journals (Sweden)

Ali Kablan

2013-04-01

Full Text Available Developments in international financial markets concern both developed countries and developing countries closely. The transactions of institutions arising from of commercial activities display a more complex and more risky state in line with international economic developments. The globalization trend in the world economy, the extreme fluctuations in currencies, interests and product prices have rendered closely following up the developments in financial tools mandatory. Taking advantage of derivative financial tools which increase the revenue of assets by taking future risks into consideration, impact a decrease in debt costs and has the purpose of transferring risks are of vital importance with respect to the successful management of companies. At the present time in which international commerce, free market economy and globalization has gained in importance, one of the derivative products used in risk management and have a wide implementation area is swap transactions. Swap transactions can be expressed as a financial transaction including the exchange of interest, foreign currency or both between two or more parties. Swap transactions in particular are used for purposes such as protection against risks due to interest rates and exchange rates, ensuring low cost financing, changing the debt structure and entering different markets. In this study, the generally defined characteristics of swap transactions, which have an important standing within financial risk management and have been rapidly developing in the world in recent years and their recognition according to the International Accounting Standard 39 concerning the recognition of swap transactions, which has in particular termed the study have been focused on. In the framework of the standard, interest swap and foreign currency swap implementation study were included with respect to the matter.

Viewpoint Manifolds for Action Recognition

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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.

Multispectral Palmprint Recognition Using a Quaternion Matrix

Directory of Open Access Journals (Sweden)

Yafeng Li

2012-04-01

Full Text Available Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR illuminations were represented by a quaternion matrix, then principal component analysis (PCA and discrete wavelet transform (DWT were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%.

International energy technology collaboration: benefits and achievements

International Nuclear Information System (INIS)

1996-01-01

The IEA Energy Technology Collaboration Programme facilitates international collaboration on energy technology research, development and deployment. More than 30 countries are involved in Europe, America, Asia, Australasia and Africa. The aim is to accelerate the development and deployment of new energy technologies to meet energy security, environmental and economic development goals. Costs and resources are shared among participating governments, utilities, corporations and universities. By co-operating, they avoid unproductive duplication and maximize the benefits from research budgets. The IEA Programme results every year in hundreds of publications which disseminate information about the latest energy technology developments and their commercial utilisation. The IEA Energy Technology Collaboration Programme operates through a series of agreements among governments. This report details the activities and achievements of all 41 agreements, covering energy technology information centres and Research and Development projects in fossil fuels, renewable energy efficient end-use, and nuclear fusion technologies. (authors). 58 refs., 9 tabs

Recognition of names of eminent psychologists.

Science.gov (United States)

Duncan, C P

1976-10-01

Faculty members, graduate students, undergraduate majors, and introductory psychology students checked those names they recognized in the list of 228 deceased psychologists, rated for eminence, provided by Annin, Boring, and Watson. Mean percentage recognition was less than 50% for the 128 American psychologists, and less than 25% for the 100 foreign psychologists, by the faculty subjects. The other three groups of subjects gave even lower recognition scores. Recognition was probably also influenced by recency; median year of death of the American psychologists was 1955, of the foreign psychologists, 1943. High recognition (defined as recognition by 80% or more of the faculty group) was achieved by only 34 psychologists, almost all of them American. These highly recognized psychologists also had high eminence ratings, but there was an equal number of psychologists with high eminence ratings that were poorly recognized.

Vehicle logo recognition using multi-level fusion model

Science.gov (United States)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

From Off-line to On-line Handwriting Recognition

NARCIS (Netherlands)

Lallican, P.; Viard-Gaudin, C.; Knerr, S.

2004-01-01

On-line handwriting includes more information on time order of the writing signal and on the dynamics of the writing process than off-line handwriting. Therefore, on-line recognition systems achieve higher recognition rates. This can be concluded from results reported in the literature, and has been

Addressing Omitted Prior Achievement Bias in International Assessments: An Applied Example Using PIRLS-NPD Matched Data

Science.gov (United States)

Caro, Daniel H.; Kyriakides, Leonidas; Televantou, Ioulia

2018-01-01

Omitted prior achievement bias is pervasive in international assessment studies and precludes causal inference. For example, reported negative associations between student-oriented teaching strategies and student performance are against expectations and might actually reflect omitted prior achievement bias. Namely, that these teaching strategies…

International Recognition of FormAkademisk

Directory of Open Access Journals (Sweden)

Janne Beate Reitan

2017-12-01

Full Text Available FormAkademisk was invited to the Design Journal Editors' Meeting at the College of Design, Architecture, Art, and Planning (DAAP, University of Cincinnati in late October, as the only design research journal from the Nordic region. The meeting was organized in advance of the International Association of Societies of Design Research (IASDR 2017 conference.Liv Merete Nielsen, who initiated the creation of  FormAkademisk and has been a Section Editor since the start-up and I, who have been the Editor-in-Chief for the entire period, travelled to the meeting.FormAkademisk   was in good company - among the others invited, we can mention the American Design Issues and the British Design Studies, both of which are at Level 2 of the Norwegian Science Index - NVI. Other reputable journals invited were the International Journal of Design from Taiwan, She Ji - The Journal of Design, Economics, and Innovation from Tongji University in Shanghai, China, Design and Culture from the United States, Co-Design from the United Kingdom, Information Design Journal published in the Netherlands with an international editorial board, Journal of Design, Business & Society with an international editorial board, the French Sciences du Design and Visible Language published at the University of Cincinnati, USA who hosted the meeting.First, we warmed up by describing each journal's editorial profile. For FormAkademisk we emphasized that we have two equal focuses – research in design, but also research in design education for the general public. This combination seems to be unique internationally.Common issues we discussed further were challenges with the quality of submitted articles and obtaining qualified peer reviewers. We also discussed whether we would agree on a common understanding of what it means to be included as an author of an article. Based on the discussions, FormAkademisk comes well prepared compared to the other internationally leading design research

Sustainability of International Branch Campuses in the United Arab Emirates: A Vision for the Future

Science.gov (United States)

Franklin, Angela; Alzouebi, Khadeegha

2014-01-01

The United Arab Emirates is developing higher education institutions that will contribute to an educational sector providing premium degree programs. There was a belief that the recognition and achievements these institutions attained over decades in their native land would be transferable in the implementation of international branch campuses.…

Foreign language learning, hyperlexia, and early word recognition.

Science.gov (United States)

Sparks, R L; Artzer, M

2000-01-01

Children with hyperlexia read words spontaneously before the age of five, have impaired comprehension on both listening and reading tasks, and have word recognition skill above expectations based on cognitive and linguistic abilities. One student with hyperlexia and another student with higher word recognition than comprehension skills who started to read words at a very early age were followed over several years from the primary grades through high school when both were completing a second-year Spanish course. The purpose of the present study was to examine the foreign language (FL) word recognition, spelling, reading comprehension, writing, speaking, and listening skills of the two students and another high school student without hyperlexia. Results showed that the student without hyperlexia achieved higher scores than the hyperlexic student and the student with above average word recognition skills on most FL proficiency measures. The student with hyperlexia and the student with above average word recognition skills achieved higher scores on the Spanish proficiency tasks that required the exclusive use of phonological (pronunciation) and phonological/orthographic (word recognition, spelling) skills than on Spanish proficiency tasks that required the use of listening comprehension and speaking and writing skills. The findings provide support for the notion that word recognition and spelling in a FL may be modular processes and exist independently of general cognitive and linguistic skills. Results also suggest that students may have stronger FL learning skills in one language component than in other components of language, and that there may be a weak relationship between FL word recognition and oral proficiency in the FL.

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.

Nuclear Data Center International Standard Towards TSO Initiative

International Nuclear Information System (INIS)

Raja Murzaferi Raja Moktar; Mohd Fauzi Haris; Siti Nurbahyah Hamdan

2011-01-01

Nuclear Data Center is the main facility for Nuclear Malaysia Agency IT infrastructure comprising of main critical servers, research and operational data storage, HPC-clusters system and vital network core equipment. In recent years, international body such as TIA-Telecommunication Industry Association and Up time Institute have came out with proper international data center standards in order to ensure data center operation on achieving maximum operational up time and minimal downtime. The standard are currently being rated as tier level ranging from Data Center tier I up to tier IV, differentiate by facility standard and up time/ downtime percentage ratio. This paper will discuss Nuclear Data Center adopting international standards in supporting Nuclear Malaysia TSO initiative thus ensuring the critical core component of agency IT services availability and further more International standard recognitions. (author)

23rd International Conference on Systems Engineering

CERN Document Server

Zydek, Dawid; Chmaj, Grzegorz

2015-01-01

This collection of proceedings from the International Conference on Systems Engineering, Las Vegas, 2014 is orientated toward systems engineering, including topics like aerospace, power systems, industrial automation and robotics, systems theory, control theory, artificial intelligence, signal processing, decision support, pattern recognition and machine learning, information and communication technologies, image processing, and computer vision as well as its applications. The volume’s main focus is on models, algorithms, and software tools that facilitate efficient and convenient utilization of modern achievements in systems engineering.

A Hierarchical Model for Continuous Gesture Recognition Using Kinect

DEFF Research Database (Denmark)

Jensen, Søren Kejser; Moesgaard, Christoffer; Nielsen, Christoffer Samuel

2013-01-01

Human gesture recognition is an area, which has been studied thoroughly in recent years,and close to100% recognition rates in restricted environments have been achieved, often either with single separated gestures in the input stream, or with computationally intensive systems. The results are unf...

Motivation, Self-Regulated Learning Efficacy, and Academic Achievement among International and Domestic Students at an Urban Community College: A Comparison

Science.gov (United States)

Liao, Hsiang-Ann; Ferdenzi, Anita Cuttita; Edlin, Margot

2012-01-01

This study is designed to examine how intrinsic motivation, extrinsic motivation, and self-regulated learning efficacy influence academic achievement of international and domestic community college students. Results show that for both international and domestic students, motivation did not directly affect academic achievement. Self-regulated…

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.

The Economics of International Differences in Educational Achievement. NBER Working Paper No. 15949

Science.gov (United States)

Hanushek, Eric A.; Woessmann, Ludger

2010-01-01

An emerging economic literature over the past decade has made use of international tests of educational achievement to analyze the determinants and impacts of cognitive skills. The cross-country comparative approach provides a number of unique advantages over national studies: It can exploit institutional variation that does not exist within…

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.

View Invariant Gesture Recognition using 3D Motion Primitives

DEFF Research Database (Denmark)

Holte, Michael Boelstoft; Moeslund, Thomas B.

2008-01-01

This paper presents a method for automatic recognition of human gestures. The method works with 3D image data from a range camera to achieve invariance to viewpoint. The recognition is based solely on motion from characteristic instances of the gestures. These instances are denoted 3D motion...

International Assessment: A Rasch Model and Teachers' Evaluation of TIMSS Science Achievement Items

Science.gov (United States)

Glynn, Shawn M.

2012-01-01

The Trends in International Mathematics and Science Study (TIMSS) is a comparative assessment of the achievement of students in many countries. In the present study, a rigorous independent evaluation was conducted of a representative sample of TIMSS science test items because item quality influences the validity of the scores used to inform…

Legitimacy as a Precondition for the Recognition of New Governments: A Case of Libya

Directory of Open Access Journals (Sweden)

Hamed Hasyemi Saugheh

2018-01-01

Full Text Available Recognition of new Stets and governments is a political act with legal reverberations. Although the recognition of new States and governments is a traditional concept of international law but the challenging recognition of the transitional government of Libya proved that this traditional concept still can be highly exigent. Traditionally, the States in providing recognition to a new government follow their own benefits and privileges and rarely consider the structure, capacity and public support for the new government. If the rule of law and respecting democracy is going to be means of promoting peace and security is various areas of the world, is not it time to redefine the traditional concepts of international law (included of recognition of new States and government from a new perspective? Considering the fact that, the existence of a legitimate authority in a group enhances the effective functioning of that group and reduces the internal conflicts, it seems that it is time to expand the political concept of legitimacy of the authorities into the international law. Is there any State practice to support the argument? In this article, the existence of norm creating forces and role of legitimacy in the recognition of the Libyan Transitional Government is going to be analysed. The After studying the role of legitimacy of the Libyan NTC in passing the sovereignty from the past regime to the new government by the international community, the effect of lack of legitimacy on the previous regime will be examined and the question of withdrawing of recognition of governments will be addressed.

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Directory of Open Access Journals (Sweden)

Heracleous Panikos

2007-01-01

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

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

Directory of Open Access Journals (Sweden)

Shouyi Yin

2015-01-01

Full Text Available Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

Fine-grained recognition of plants from images.

Science.gov (United States)

Šulc, Milan; Matas, Jiří

2017-01-01

Fine-grained recognition of plants from images is a challenging computer vision task, due to the diverse appearance and complex structure of plants, high intra-class variability and small inter-class differences. We review the state-of-the-art and discuss plant recognition tasks, from identification of plants from specific plant organs to general plant recognition "in the wild". We propose texture analysis and deep learning methods for different plant recognition tasks. The methods are evaluated and compared them to the state-of-the-art. Texture analysis is only applied to images with unambiguous segmentation (bark and leaf recognition), whereas CNNs are only applied when sufficiently large datasets are available. The results provide an insight in the complexity of different plant recognition tasks. The proposed methods outperform the state-of-the-art in leaf and bark classification and achieve very competitive results in plant recognition "in the wild". The results suggest that recognition of segmented leaves is practically a solved problem, when high volumes of training data are available. The generality and higher capacity of state-of-the-art CNNs makes them suitable for plant recognition "in the wild" where the views on plant organs or plants vary significantly and the difficulty is increased by occlusions and background clutter.

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

Science.gov (United States)

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

2011-01-01

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

Internal Affairs Allegations

Data.gov (United States)

Montgomery County of Maryland — This dataset contains allegations brought to the attention of the Internal Affairs Division either through external complaints or internal complaint or recognition....

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.

Balanced Scorecard Goal Four: Provide Policy Management, Advocacy and Problem Solving Measuring Achievement of Internal Customer Objectives

Science.gov (United States)

2002-06-01

Achievement of Internal Customer Objectives A Graduate Management Project Submitted to The Residency Committee In Candidacy for the Degree of Masters in...internal customer relations, the GPRMC has incorporated use of a Balanced Scorecard within its management scheme. The scorecard serves as a strategy map...headquarters. The goal, "Provide Policy Management , Advocacy and Problem Solving", addresses the relationship between the headquarters and its internal

Iris recognition based on robust principal component analysis

Science.gov (United States)

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

2014-11-01

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

Stereotyped Visual Symbols as a Mean of Public Consolidation in Context Of International Genocide Recognition

Directory of Open Access Journals (Sweden)

Elena Anatolievna Ivanova

2017-12-01

Full Text Available This article presents the results of the study devoted to stereotyped visual symbols as a part of the corporate identity complex of anti-genocide organizations aimed to reach an international genocide recognition as a part of their strategies. The relevance of the stud y is justified with the similarity of modern tools for visualizing the unique characteristics of organizations and centuries-old practice of opponents opposing each other, what was discovered in the investigation process. The effectiveness of the usage of stereotyped visual symbols as the means of public consolidation in combating the genocide, which is the purpose of this study, is proved. Using the method of structural and semiotic analysis, the authors studied visual symbols used as the means of broadcasting the public opinion coded into a key message within the framework of the anti-genocide organizations’ activities. The studied visual symbols were identified as the means of stereotyped influence aimed on the mass audience, which allowed us to conclude about the effectiveness of such symbols in solving problems in mass communications. During the generalization and systematization of the data obtained, the most frequently used symbols which enclose the codes of certain cultures were identified, which led us to the conclusion that such symbols are stereotypically used in the context of combating genocide and bringing the public forward the recognition of such conflicts.

Hybrid Speaker Recognition Using Universal Acoustic Model

Science.gov (United States)

Nishimura, Jun; Kuroda, Tadahiro

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

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

Directory of Open Access Journals (Sweden)

Min Peng

2017-10-01

Full Text Available Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition.

Science.gov (United States)

Peng, Min; Wang, Chongyang; Chen, Tong; Liu, Guangyuan; Fu, Xiaolan

2017-01-01

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve.

Dual Temporal Scale Convolutional Neural Network for Micro-Expression Recognition

Science.gov (United States)

Peng, Min; Wang, Chongyang; Chen, Tong; Liu, Guangyuan; Fu, Xiaolan

2017-01-01

Facial micro-expression is a brief involuntary facial movement and can reveal the genuine emotion that people try to conceal. Traditional methods of spontaneous micro-expression recognition rely excessively on sophisticated hand-crafted feature design and the recognition rate is not high enough for its practical application. In this paper, we proposed a Dual Temporal Scale Convolutional Neural Network (DTSCNN) for spontaneous micro-expressions recognition. The DTSCNN is a two-stream network. Different of stream of DTSCNN is used to adapt to different frame rate of micro-expression video clips. Each stream of DSTCNN consists of independent shallow network for avoiding the overfitting problem. Meanwhile, we fed the networks with optical-flow sequences to ensure that the shallow networks can further acquire higher-level features. Experimental results on spontaneous micro-expression databases (CASME I/II) showed that our method can achieve a recognition rate almost 10% higher than what some state-of-the-art method can achieve. PMID:29081753

Unvoiced Speech Recognition Using Tissue-Conductive Acoustic Sensor

Directory of Open Access Journals (Sweden)

Hiroshi Saruwatari

2007-01-01

Full Text Available We present the use of stethoscope and silicon NAM (nonaudible murmur microphones in automatic speech recognition. NAM microphones are special acoustic sensors, which are attached behind the talker's ear and can capture not only normal (audible speech, but also very quietly uttered speech (nonaudible murmur. As a result, NAM microphones can be applied in automatic speech recognition systems when privacy is desired in human-machine communication. Moreover, NAM microphones show robustness against noise and they might be used in special systems (speech recognition, speech transform, etc. for sound-impaired people. Using adaptation techniques and a small amount of training data, we achieved for a 20 k dictation task a 93.9% word accuracy for nonaudible murmur recognition in a clean environment. In this paper, we also investigate nonaudible murmur recognition in noisy environments and the effect of the Lombard reflex on nonaudible murmur recognition. We also propose three methods to integrate audible speech and nonaudible murmur recognition using a stethoscope NAM microphone with very promising results.

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…

Exhibits Recognition System for Combining Online Services and Offline Services

Science.gov (United States)

Ma, He; Liu, Jianbo; Zhang, Yuan; Wu, Xiaoyu

2017-10-01

In order to achieve a more convenient and accurate digital museum navigation, we have developed a real-time and online-to-offline museum exhibits recognition system using image recognition method based on deep learning. In this paper, the client and server of the system are separated and connected through the HTTP. Firstly, by using the client app in the Android mobile phone, the user can take pictures and upload them to the server. Secondly, the features of the picture are extracted using the deep learning network in the server. With the help of the features, the pictures user uploaded are classified with a well-trained SVM. Finally, the classification results are sent to the client and the detailed exhibition’s introduction corresponding to the classification results are shown in the client app. Experimental results demonstrate that the recognition accuracy is close to 100% and the computing time from the image uploading to the exhibit information show is less than 1S. By means of exhibition image recognition algorithm, our implemented exhibits recognition system can combine online detailed exhibition information to the user in the offline exhibition hall so as to achieve better digital navigation.

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.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

Science.gov (United States)

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

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.

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.

Fast cat-eye effect target recognition based on saliency extraction

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Li, Li; Ren, Jianlin; Wang, Xingbin

2015-09-01

Background complexity is a main reason that results in false detection in cat-eye target recognition. Human vision has selective attention property which can help search the salient target from complex unknown scenes quickly and precisely. In the paper, we propose a novel cat-eye effect target recognition method named Multi-channel Saliency Processing before Fusion (MSPF). This method combines traditional cat-eye target recognition with the selective characters of visual attention. Furthermore, parallel processing enables it to achieve fast recognition. Experimental results show that the proposed method performs better in accuracy, robustness and speed compared to other methods.

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

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

2014-02-01

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

Face recognition in the thermal infrared domain

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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.

Efficient Interaction Recognition through Positive Action Representation

Directory of Open Access Journals (Sweden)

Tao Hu

2013-01-01

Full Text Available This paper proposes a novel approach to decompose two-person interaction into a Positive Action and a Negative Action for more efficient behavior recognition. A Positive Action plays the decisive role in a two-person exchange. Thus, interaction recognition can be simplified to Positive Action-based recognition, focusing on an action representation of just one person. Recently, a new depth sensor has become widely available, the Microsoft Kinect camera, which provides RGB-D data with 3D spatial information for quantitative analysis. However, there are few publicly accessible test datasets using this camera, to assess two-person interaction recognition approaches. Therefore, we created a new dataset with six types of complex human interactions (i.e., named K3HI, including kicking, pointing, punching, pushing, exchanging an object, and shaking hands. Three types of features were extracted for each Positive Action: joint, plane, and velocity features. We used continuous Hidden Markov Models (HMMs to evaluate the Positive Action-based interaction recognition method and the traditional two-person interaction recognition approach with our test dataset. Experimental results showed that the proposed recognition technique is more accurate than the traditional method, shortens the sample training time, and therefore achieves comprehensive superiority.

Low-contrast underwater living fish recognition using PCANet

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Sun, Xin; Yang, Jianping; Wang, Changgang; Dong, Junyu; Wang, Xinhua

2018-04-01

Quantitative and statistical analysis of ocean creatures is critical to ecological and environmental studies. And living fish recognition is one of the most essential requirements for fishery industry. However, light attenuation and scattering phenomenon are present in the underwater environment, which makes underwater images low-contrast and blurry. This paper tries to design a robust framework for accurate fish recognition. The framework introduces a two stage PCA Network to extract abstract features from fish images. On a real-world fish recognition dataset, we use a linear SVM classifier and set penalty coefficients to conquer data unbalanced issue. Feature visualization results show that our method can avoid the feature distortion in boundary regions of underwater image. Experiments results show that the PCA Network can extract discriminate features and achieve promising recognition accuracy. The framework improves the recognition accuracy of underwater living fishes and can be easily applied to marine fishery industry.

How fast is famous face recognition?

Directory of Open Access Journals (Sweden)

Gladys eBarragan-Jason

2012-10-01

Full Text Available The rapid recognition of familiar faces is crucial for social interactions. However the actual speed with which recognition can be achieved remains largely unknown as most studies have been carried out without any speed constraints. Different paradigms have been used, leading to conflicting results, and although many authors suggest that face recognition is fast, the speed of face recognition has not been directly compared to fast visual tasks. In this study, we sought to overcome these limitations. Subjects performed three tasks, a familiarity categorization task (famous faces among unknown faces, a superordinate categorization task (human faces among animal ones and a gender categorization task. All tasks were performed under speed constraints. The results show that, despite the use of speed constraints, subjects were slow when they had to categorize famous faces: minimum reaction time was 467 ms, which is 180 ms more than during superordinate categorization and 160 ms more than in the gender condition. Our results are compatible with a hierarchy of face processing from the superordinate level to the familiarity level. The processes taking place between detection and recognition need to be investigated in detail.

Survey of Careers and Achievements on Delegates in JSRT International Delegation Projects.

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Kobayashi, Masato; Tanaka, Rie; Matsubara, Kosuke; Morioka, Shigeaki; Tsujioka, Katsumi; Arimura, Hidetaka; Ueda, Katsuhiko; Ogura, Akio; Miyati, Tosiaki

Japanese society of radiological technology (JSRT) categorizes three international delegation projects; short-term studying abroad program (STSAP), international academic society visit program (overseas) (IASVP), and international internship visit program (Stanford University) (IIVP) for driving globalization of JSRT. In this survey, we conducted a questionnaire evaluating effectiveness of the international delegations. The survey covered 50 delegates of STSAP, 180 delegates of IASVP, and 100 delegates of IIVP. This survey includes detailed histories of career, current position, academic articles, and presentations as a first presenter before and on, and after each program. We categorized into six groups (change career, promoted in a position in hospital, kept a current position in hospital, promoted in a position in university, kept a current position in university, and others) in three programs. The response rate is approximately 58% (191/330 delegators). In all programs, almost all the delegates were radiological technologists in the hospital. They had reported a lot of academic articles and made a lot of presentations, and promoted in the hospital and/or university. STSAP, IASVP and IIVP were descending order of the average number of the articles as a first author and presentations as a first presenter. They published more the academic articles in Japanese than in English compared to JJRT and RPT. Therefore, research achievements and human resource conducted by this project provide great technologists and technique, and education. For further JRST globalization, it is desirable that we can continue these international delegations and verify the effectiveness.

HuAc: Human Activity Recognition Using Crowdsourced WiFi Signals and Skeleton Data

Directory of Open Access Journals (Sweden)

Linlin Guo

2018-01-01

Full Text Available The joint of WiFi-based and vision-based human activity recognition has attracted increasing attention in the human-computer interaction, smart home, and security monitoring fields. We propose HuAc, the combination of WiFi-based and Kinect-based activity recognition system, to sense human activity in an indoor environment with occlusion, weak light, and different perspectives. We first construct a WiFi-based activity recognition dataset named WiAR to provide a benchmark for WiFi-based activity recognition. Then, we design a mechanism of subcarrier selection according to the sensitivity of subcarriers to human activities. Moreover, we optimize the spatial relationship of adjacent skeleton joints and draw out a corresponding relationship between CSI and skeleton-based activity recognition. Finally, we explore the fusion information of CSI and crowdsourced skeleton joints to achieve the robustness of human activity recognition. We implemented HuAc using commercial WiFi devices and evaluated it in three kinds of scenarios. Our results show that HuAc achieves an average accuracy of greater than 93% using WiAR dataset.

The Relationship between Science Achievement and Self-Concept among Gifted Students from the Third International Earth Science Olympiad

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Chang, Chun-Yen; Lin, Pei-Ling

2017-01-01

This study investigated the relationship between gifted students' academic self-concept (ASC) and academic achievement (AC) in earth science with internationally representative high-school students from the third International Earth Science Olympiad (IESO) held in Taiwan in 2009. The results of regression analysis indicated that IESO students' ASC…

Hybrid generative-discriminative approach to age-invariant face recognition

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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.

Secondary iris recognition method based on local energy-orientation feature

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Huo, Guang; Liu, Yuanning; Zhu, Xiaodong; Dong, Hongxing

2015-01-01

This paper proposes a secondary iris recognition based on local features. The application of the energy-orientation feature (EOF) by two-dimensional Gabor filter to the extraction of the iris goes before the first recognition by the threshold of similarity, which sets the whole iris database into two categories-a correctly recognized class and a class to be recognized. Therefore, the former are accepted and the latter are transformed by histogram to achieve an energy-orientation histogram feature (EOHF), which is followed by a second recognition with the chi-square distance. The experiment has proved that the proposed method, because of its higher correct recognition rate, could be designated as the most efficient and effective among its companion studies in iris recognition algorithms.

Cost-Sensitive Learning for Emotion Robust Speaker Recognition

Directory of Open Access Journals (Sweden)

Dongdong Li

2014-01-01

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

Cost-sensitive learning for emotion robust speaker recognition.

Science.gov (United States)

Li, Dongdong; Yang, Yingchun; Dai, Weihui

2014-01-01

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

Student and School Factors Affecting Mathematics Achievement: International Comparisons between Korea, Japan and the USA

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Shin, Jongho; Lee, Hyunjoo; Kim, Yongnam

2009-01-01

The purpose of the study was to comparatively investigate student- and school-level factors affecting mathematics achievement of Korean, Japanese and American students. For international comparisons, the PISA 2003 data were analysed by using the Hierarchical Linear Modeling method. The variables of competitive-learning preference, instrumental…

Assuming measurement invariance of background indicators in international comparative educational achievement studies: a challenge for the interpretation of achievement differences

Directory of Open Access Journals (Sweden)

Heike Wendt

2017-03-01

Full Text Available Abstract Background Large-scale cross-national studies designed to measure student achievement use different social, cultural, economic and other background variables to explain observed differences in that achievement. Prior to their inclusion into a prediction model, these variables are commonly scaled into latent background indices. To allow cross-national comparisons of the latent indices, measurement invariance is assumed. However, it is unclear whether the assumption of measurement invariance has some influence on the results of the prediction model, thus challenging the reliability and validity of cross-national comparisons of predicted results. Methods To establish the effect size attributed to different degrees of measurement invariance, we rescaled the ‘home resource for learning index’ (HRL for the 37 countries ( $$n=166,709$$ n = 166 , 709 students that participated in the IEA’s combined ‘Progress in International Reading Literacy Study’ (PIRLS and ‘Trends in International Mathematics and Science Study’ (TIMSS assessments of 2011. We used (a two different measurement models [one-parameter model (1PL and two-parameter model (2PL] with (b two different degrees of measurement invariance, resulting in four different models. We introduced the different HRL indices as predictors in a generalized linear mixed model (GLMM with mathematics achievement as the dependent variable. We then compared three outcomes across countries and by scaling model: (1 the differing fit-values of the measurement models, (2 the estimated discrimination parameters, and (3 the estimated regression coefficients. Results The least restrictive measurement model fitted the data best, and the degree of assumed measurement invariance of the HRL indices influenced the random effects of the GLMM in all but one country. For one-third of the countries, the fixed effects of the GLMM also related to the degree of assumed measurement invariance. Conclusion The

Semantic Activity Recognition

OpenAIRE

Thonnat , Monique

2008-01-01

International audience; Extracting automatically the semantics from visual data is a real challenge. We describe in this paper how recent work in cognitive vision leads to significative results in activity recognition for visualsurveillance and video monitoring. In particular we present work performed in the domain of video understanding in our PULSAR team at INRIA in Sophia Antipolis. Our main objective is to analyse in real-time video streams captured by static video cameras and to recogniz...

Recognition of social identity in ants

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Nick eBos

2012-03-01

Full Text Available Recognizing the identity of others, from the individual to the group level, is a hallmark of society. Ants, and other social insects, have evolved advanced societies characterized by efficient social recognition systems. Colony identity is mediated by colony specific signature mixtures, a blend of hydrocarbons present on the cuticle of every individual (the label. Recognition occurs when an ant encounters another individual, and compares the label it perceives to an internal representation of its own colony odor (the template. A mismatch between label and template leads to rejection of the encountered individual. Although advances have been made in our understanding of how the label is produced and acquired, contradictory evidence exists about information processing of recognition cues. Here, we review the literature on template acquisition in ants and address how and when the template is formed, where in the nervous system it is localized, and the possible role of learning. We combine seemingly contradictory evidence in to a novel, parsimonious theory for the information processing of nestmate recognition cues.

Print exposure, reading habits, and reading achievement among deaf and hearing college students.

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Marschark, Marc; Sarchet, Thomastine; Convertino, Carol M; Borgna, Georgianna; Morrison, Carolyn; Remelt, Sarah

2012-01-01

This study explored relations of print exposure, academic achievement, and reading habits among 100 deaf and 100 hearing college students. As in earlier studies, recognition tests for book titles and magazine titles were used as measures of print exposure, college entrance test scores were used as measures of academic achievement, and students provided self-reports of reading habits. Deaf students recognized fewer magazine titles and fewer book titles appropriate for reading levels from kindergarten through Grade 12 while reporting more weekly hours of reading. As in previous studies with hearing college students, the title recognition test proved a better predictor of deaf and hearing students' English achievement than how many hours they reported reading. The finding that the recognition tests were relatively more potent predictors of achievement for deaf students than hearing students may reflect the fact that deaf students often obtain less information through incidental learning and classroom presentations.

Promoting the Recognition and Protection of the Rights of All Migrants Using a Soft-Law International Migrants Bill of Rights

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Ian M. Kysel

2016-06-01

Full Text Available The rights and movement of people crossing international borders remain inadequately governed and incompletely protected by a fragmented patchwork of institutions and norms. In recent years, debates regarding migration law and practice globally have been focused on subcategories of migrants, such as refugees, or on particular migration contexts, such as migration as a result of crisis or climate change. In response, a transnational initiative housed at the Georgetown University Law Center has drafted a soft-law bill of rights — the International Migrants Bill of Rights (IMBR — that seeks to elaborate the law protecting all migrants, regardless of the cause of their movement across an international border. The bill draws its content from human rights, refugee, and labor law, among other areas, and is drafted to be a comprehensive and declarative tool that articulates a core set of rights to protect migrants and to apply in the migration context.This article articulates how such a tool could be used to promote the recognition and protection of the rights of all migrants, in law and in practice. It argues that a soft-law bill of rights could be leveraged to fill significant gaps and promote an improved normative and institutional infrastructure that better protects all migrants worldwide. Section I provides a brief overview of the gap that a soft-law bill of rights can address. Section II provides a brief overview of the history and content of the bill of rights and IMBR Initiative. Section III describes, specifically, how making use of a soft-law bill of rights stands to improve the recognition and protection of fundamental rights that protect all migrants — and how soft law can help fill specific protection gaps.

Graded Mirror Self-Recognition by Clark's Nutcrackers.

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Clary, Dawson; Kelly, Debbie M

2016-11-04

The traditional 'mark test' has shown some large-brained species are capable of mirror self-recognition. During this test a mark is inconspicuously placed on an animal's body where it can only be seen with the aid of a mirror. If the animal increases the number of actions directed to the mark region when presented with a mirror, the animal is presumed to have recognized the mirror image as its reflection. However, the pass/fail nature of the mark test presupposes self-recognition exists in entirety or not at all. We developed a novel mirror-recognition task, to supplement the mark test, which revealed gradation in the self-recognition of Clark's nutcrackers, a large-brained corvid. To do so, nutcrackers cached food alone, observed by another nutcracker, or with a regular or blurry mirror. The nutcrackers suppressed caching with a regular mirror, a behavioural response to prevent cache theft by conspecifics, but did not suppress caching with a blurry mirror. Likewise, during the mark test, most nutcrackers made more self-directed actions to the mark with a blurry mirror than a regular mirror. Both results suggest self-recognition was more readily achieved with the blurry mirror and that self-recognition may be more broadly present among animals than currently thought.

Hierarchical Context Modeling for Video Event Recognition.

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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.

Oxytocin improves emotion recognition for older males.

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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.

Bio-recognitive photonics of a DNA-guided organic semiconductor

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Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-01

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an `inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Bio-recognitive photonics of a DNA-guided organic semiconductor.

Science.gov (United States)

Back, Seung Hyuk; Park, Jin Hyuk; Cui, Chunzhi; Ahn, Dong June

2016-01-04

Incorporation of duplex DNA with higher molecular weights has attracted attention for a new opportunity towards a better organic light-emitting diode (OLED) capability. However, biological recognition by OLED materials is yet to be addressed. In this study, specific oligomeric DNA-DNA recognition is successfully achieved by tri (8-hydroxyquinoline) aluminium (Alq3), an organic semiconductor. Alq3 rods crystallized with guidance from single-strand DNA molecules show, strikingly, a unique distribution of the DNA molecules with a shape of an 'inverted' hourglass. The crystal's luminescent intensity is enhanced by 1.6-fold upon recognition of the perfect-matched target DNA sequence, but not in the case of a single-base mismatched one. The DNA-DNA recognition forming double-helix structure is identified to occur only in the rod's outer periphery. This study opens up new opportunities of Alq3, one of the most widely used OLED materials, enabling biological recognition.

Recognition of the dosimetric calibration capacities of Cuba by the International Bureau of Weights and Measures; Reconocimiento de las capacidades de calibracion dosimetrica de Cuba por el Buro Internacional de Pesas y Medidas

Energy Technology Data Exchange (ETDEWEB)

Walwyn S, G.; Gutierrez L, S.; Tamayo G, J.A.; Gonzalez R, N.; Alonso V, G. [CPHR, Calle 20 No. 4113 e/ 41 y 47, Playa C.P. 11300, La Habana (Cuba)]. e-mail: gonzalo@cphr.edu.cu

2006-07-01

The declared mission of the International Bureau of Weights and Measures are the world uniformity of the measurement, however until some years ago a formal mechanism didn't exist for its complete implementation. With this end arose the Mutual Recognition Agreement whose specific objective is to establish the grade of equivalence of the national standards, the one of mutually recognizing the calibration and measurement certificates and the one of providing to the governments of a sure technical tool in its commercial negotiations and regulatory matters at international level. Cuba like an associated country to the Meter Convention, signed the agreement and it intended to demonstrate the international equivalence of its standards. The best measurement and calibration capacities of the country in the dosimetric magnitudes are in the Secondary Laboratory of Dosimetric Calibration of the Protection and Hygiene of Radiations Center. This capacities were included in the Regional Metrological Organization COOMET in the year 2003. In June of the 2005 the metrological capacities have been approved and published in the databases of the International Bureau of Weights and Measures as demonstration of the high competition of the calibration works that its are carried out in the laboratory. This approval is one of the maximum international recognitions that the patterns of a country can receive and its are the result of 10 years of work of the laboratory like part of the international net OIEA/OMS, which has given it the possibility to gauge the patterns and of adopting internationally validated calibration methodologies. On the other hand, it has been decisive the participation of the laboratory in multiple international comparisons of their patterns, as well as the implementation of a system of administration of the quality credited by the competent national organ. The article reviews the technical work of the laboratory during several years that it gave as result this

Dynamic facial expression recognition based on geometric and texture features

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Li, Ming; Wang, Zengfu

2018-04-01

Recently, dynamic facial expression recognition in videos has attracted growing attention. In this paper, we propose a novel dynamic facial expression recognition method by using geometric and texture features. In our system, the facial landmark movements and texture variations upon pairwise images are used to perform the dynamic facial expression recognition tasks. For one facial expression sequence, pairwise images are created between the first frame and each of its subsequent frames. Integration of both geometric and texture features further enhances the representation of the facial expressions. Finally, Support Vector Machine is used for facial expression recognition. Experiments conducted on the extended Cohn-Kanade database show that our proposed method can achieve a competitive performance with other methods.

Optogenetic Stimulation of Prefrontal Glutamatergic Neurons Enhances Recognition Memory.

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

2016-05-04

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

Filial Piety and Academic Motivation: High-Achieving Students in an International School in South Korea

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Tam, Jonathan

2016-01-01

This study uses self-determination theory to explore the mechanisms of filial piety in the academic motivation of eight high-achieving secondary school seniors at an international school in South Korea, resulting in several findings. First, the students attributed their parents' values and expectations as a major source of the students'…

Chinese Herbal Medicine Image Recognition and Retrieval by Convolutional Neural Network.

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Sun, Xin; Qian, Huinan

2016-01-01

Chinese herbal medicine image recognition and retrieval have great potential of practical applications. Several previous studies have focused on the recognition with hand-crafted image features, but there are two limitations in them. Firstly, most of these hand-crafted features are low-level image representation, which is easily affected by noise and background. Secondly, the medicine images are very clean without any backgrounds, which makes it difficult to use in practical applications. Therefore, designing high-level image representation for recognition and retrieval in real world medicine images is facing a great challenge. Inspired by the recent progress of deep learning in computer vision, we realize that deep learning methods may provide robust medicine image representation. In this paper, we propose to use the Convolutional Neural Network (CNN) for Chinese herbal medicine image recognition and retrieval. For the recognition problem, we use the softmax loss to optimize the recognition network; then for the retrieval problem, we fine-tune the recognition network by adding a triplet loss to search for the most similar medicine images. To evaluate our method, we construct a public database of herbal medicine images with cluttered backgrounds, which has in total 5523 images with 95 popular Chinese medicine categories. Experimental results show that our method can achieve the average recognition precision of 71% and the average retrieval precision of 53% over all the 95 medicine categories, which are quite promising given the fact that the real world images have multiple pieces of occluded herbal and cluttered backgrounds. Besides, our proposed method achieves the state-of-the-art performance by improving previous studies with a large margin.

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.

Action Recognition by Joint Spatial-Temporal Motion Feature

Directory of Open Access Journals (Sweden)

Weihua Zhang

2013-01-01

Full Text Available This paper introduces a method for human action recognition based on optical flow motion features extraction. Automatic spatial and temporal alignments are combined together in order to encourage the temporal consistence on each action by an enhanced dynamic time warping (DTW algorithm. At the same time, a fast method based on coarse-to-fine DTW constraint to improve computational performance without reducing accuracy is induced. The main contributions of this study include (1 a joint spatial-temporal multiresolution optical flow computation method which can keep encoding more informative motion information than recent proposed methods, (2 an enhanced DTW method to improve temporal consistence of motion in action recognition, and (3 coarse-to-fine DTW constraint on motion features pyramids to speed up recognition performance. Using this method, high recognition accuracy is achieved on different action databases like Weizmann database and KTH database.

Finger Vein Recognition Based on Personalized Weight Maps

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Yang, Gongping; Xiao, Rongyang; Yin, Yilong; Yang, Lu

2013-01-01

Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs). The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition. PMID:24025556

Finger Vein Recognition Based on Personalized Weight Maps

Directory of Open Access Journals (Sweden)

Lu Yang

2013-09-01

Full Text Available Finger vein recognition is a promising biometric recognition technology, which verifies identities via the vein patterns in the fingers. Binary pattern based methods were thoroughly studied in order to cope with the difficulties of extracting the blood vessel network. However, current binary pattern based finger vein matching methods treat every bit of feature codes derived from different image of various individuals as equally important and assign the same weight value to them. In this paper, we propose a finger vein recognition method based on personalized weight maps (PWMs. The different bits have different weight values according to their stabilities in a certain number of training samples from an individual. Firstly we present the concept of PWM, and then propose the finger vein recognition framework, which mainly consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PWM achieves not only better performance, but also high robustness and reliability. In addition, PWM can be used as a general framework for binary pattern based recognition.

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

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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...

Molecular recognition in protein modification with rhodium metallopeptides

Science.gov (United States)

Ball, Zachary T.

2015-01-01

Chemical manipulation of natural, unengineered proteins is a daunting challenge which tests the limits of reaction design. By combining transition-metal or other catalysts with molecular recognition ideas, it is possible to achieve site-selective protein reactivity without the need for engineered recognition sequences or reactive sites. Some recent examples in this area have used ruthenium photocatalysis, pyridine organocatalysis, and rhodium(II) metallocarbene catalysis, indicating that the fundamental ideas provide opportunities for using diverse reactivity on complex protein substrates and in complex cell-like environments. PMID:25588960

Threshold models of recognition and the recognition heuristic

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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.

NutriNet: A Deep Learning Food and Drink Image Recognition System for Dietary Assessment.

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Mezgec, Simon; Koroušić Seljak, Barbara

2017-06-27

Automatic food image recognition systems are alleviating the process of food-intake estimation and dietary assessment. However, due to the nature of food images, their recognition is a particularly challenging task, which is why traditional approaches in the field have achieved a low classification accuracy. Deep neural networks have outperformed such solutions, and we present a novel approach to the problem of food and drink image detection and recognition that uses a newly-defined deep convolutional neural network architecture, called NutriNet. This architecture was tuned on a recognition dataset containing 225,953 512 × 512 pixel images of 520 different food and drink items from a broad spectrum of food groups, on which we achieved a classification accuracy of 86 . 72 % , along with an accuracy of 94 . 47 % on a detection dataset containing 130 , 517 images. We also performed a real-world test on a dataset of self-acquired images, combined with images from Parkinson's disease patients, all taken using a smartphone camera, achieving a top-five accuracy of 55 % , which is an encouraging result for real-world images. Additionally, we tested NutriNet on the University of Milano-Bicocca 2016 (UNIMIB2016) food image dataset, on which we improved upon the provided baseline recognition result. An online training component was implemented to continually fine-tune the food and drink recognition model on new images. The model is being used in practice as part of a mobile app for the dietary assessment of Parkinson's disease patients.

2.5D Multi-View Gait Recognition Based on Point Cloud Registration

Science.gov (United States)

Tang, Jin; Luo, Jian; Tjahjadi, Tardi; Gao, Yan

2014-01-01

This paper presents a method for modeling a 2.5-dimensional (2.5D) human body and extracting the gait features for identifying the human subject. To achieve view-invariant gait recognition, a multi-view synthesizing method based on point cloud registration (MVSM) to generate multi-view training galleries is proposed. The concept of a density and curvature-based Color Gait Curvature Image is introduced to map 2.5D data onto a 2D space to enable data dimension reduction by discrete cosine transform and 2D principle component analysis. Gait recognition is achieved via a 2.5D view-invariant gait recognition method based on point cloud registration. Experimental results on the in-house database captured by a Microsoft Kinect camera show a significant performance gain when using MVSM. PMID:24686727

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

Science.gov (United States)

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

2018-04-01

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

Sunspot drawings handwritten character recognition method based on deep learning

Science.gov (United States)

Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

2016-05-01

High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

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.

Electromyographic Grasp Recognition for a Five Fingered Robotic Hand

Directory of Open Access Journals (Sweden)

Nayan M. Kakoty

2012-09-01

Full Text Available This paper presents classification of grasp types based on surface electromyographic signals. Classification is through radial basis function kernel support vector machine using sum of wavelet decomposition coefficients of the EMG signals. In a study involving six subjects, we achieved an average recognition rate of 86%. The electromyographic grasp recognition together with a 8-bit microcontroller has been employed to control a fivefingered robotic hand to emulate six grasp types used during 70% daily living activities.

77 FR 6005 - Application for Recognition as a 501(c)(29) Organization

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2012-02-07

... Medicaid Services, that seek exemption from Federal income tax under the Internal Revenue Code. The text of... DEPARTMENT OF THE TREASURY Internal Revenue Service 26 CFR Part 1 [TD 9574] RIN 1545-BK64 Application for Recognition as a 501(c)(29) Organization AGENCY: Internal Revenue Service (IRS), Treasury...

Container code recognition in information auto collection system of container inspection

International Nuclear Information System (INIS)

Su Jianping; Chen Zhiqiang; Zhang Li; Gao Wenhuan; Kang Kejun

2003-01-01

Now custom needs electrical application and automatic detection. Container inspection should not only give the image of the goods, but also auto-attain container's code and weight. Its function and track control, information transfer make up the Information Auto Collection system of Container Inspection. Code Recognition is the point. The article is based on model match, the close property of character, and uses it to recognize. Base on checkout rule, design the adjustment arithmetic, form the whole recognition strategy. This strategy can achieve high recognition ratio and robust property

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

Science.gov (United States)

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

2011-01-01

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

26 CFR 1.988-2 - Recognition and computation of exchange gain or loss.

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2010-04-01

... computation of exchange gain or loss. (a) Disposition of nonfunctional currency—(1) Recognition of exchange... currency shall be governed by the recognition provisions of the Internal Revenue Code which apply to the... 1092). The disposition of nonfunctional currency in settlement of a forward contract, futures contract...

Towards online iris and periocular recognition under relaxed imaging constraints.

Science.gov (United States)

Tan, Chun-Wei; Kumar, Ajay

2013-10-01

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

Implementing an excellence in teaching recognition system: needs analysis and recommendations.

Science.gov (United States)

Schindler, Nancy; Corcoran, Julia C; Miller, Megan; Wang, Chih-Hsiung; Roggin, Kevin; Posner, Mitchell; Fryer, Jonathan; DaRosa, Debra A

2013-01-01

Teaching awards have been suggested to serve a variety of purposes. The specific characteristics of teaching awards and the associated effectiveness at achieving planned purposes are poorly understood. A needs analysis was performed to inform recommendations for an Excellence in Teaching Recognition System to meet the needs of surgical education leadership. We performed a 2-part needs analysis beginning with a review of the literature. We then, developed, piloted, and administered a survey instrument to General Surgery program leaders. The survey examined the features and perceived effectiveness of existing teaching awards systems. A multi-institution committee of program directors, clerkship directors, and Vice-Chairs of education then met to identify goals and develop recommendations for implementation of an "Excellence in Teaching Recognition System." There is limited evidence demonstrating effectiveness of existing teaching awards in medical education. Evidence supports the ability of such awards to demonstrate value placed on teaching, to inspire faculty to teach, and to contribute to promotion. Survey findings indicate that existing awards strive to achieve these purposes and that educational leaders believe awards have the potential to do this and more. Leaders are moderately satisfied with existing awards for providing recognition and demonstrating value placed on teaching, but they are less satisfied with awards for motivating faculty to participate in teaching or for contributing to promotion. Most departments and institutions honor only a few recipients annually. There is a paucity of literature addressing teaching recognition systems in medical education and little evidence to support the success of such systems in achieving their intended purposes. The ability of awards to affect outcomes such as participation in teaching and promotion may be limited by the small number of recipients for most existing awards. We propose goals for a Teaching Recognition

Behavioral Performance and Neural Areas Associated with Memory Processes Contribute to Math and Reading Achievement in 6-year-old Children.

Science.gov (United States)

Blankenship, Tashauna L; Keith, Kayla; Calkins, Susan D; Bell, Martha Ann

2018-01-01

Associations between working memory and academic achievement (math and reading) are well documented. Surprisingly, little is known of the contributions of episodic memory, segmented into temporal memory (recollection proxy) and item recognition (familiarity proxy), to academic achievement. This is the first study to observe these associations in typically developing 6-year old children. Overlap in neural correlates exists between working memory, episodic memory, and math and reading achievement. We attempted to tease apart the neural contributions of working memory, temporal memory, and item recognition to math and reading achievement. Results suggest that working memory and temporal memory, but not item recognition, are important contributors to both math and reading achievement, and that EEG power during a working memory task contributes to performance on tests of academic achievement.

Internality and Achievement in the Intermediate Grades.

Science.gov (United States)

Creek, Roy J.; And Others

Locus of control is a construct that reflects an individual's perception of control over his or her own destiny. The thesis is that people adopt either an internal or an external orientation. Internally oriented persons consider success the result of ability and effort. Externally oriented individuals attribute success to luck, fate, or powerful…

Automatic anatomy recognition in whole-body PET/CT images

Energy Technology Data Exchange (ETDEWEB)

Wang, Huiqian [College of Optoelectronic Engineering, Chongqing University, Chongqing 400044, China and Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Udupa, Jayaram K., E-mail: jay@mail.med.upenn.edu; Odhner, Dewey; Tong, Yubing; Torigian, Drew A. [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 (United States); Zhao, Liming [Medical Image Processing Group Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104 and Research Center of Intelligent System and Robotics, Chongqing University of Posts and Telecommunications, Chongqing 400065 (China)

2016-01-15

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

Automatic anatomy recognition in whole-body PET/CT images

International Nuclear Information System (INIS)

Wang, Huiqian; Udupa, Jayaram K.; Odhner, Dewey; Tong, Yubing; Torigian, Drew A.; Zhao, Liming

2016-01-01

Purpose: Whole-body positron emission tomography/computed tomography (PET/CT) has become a standard method of imaging patients with various disease conditions, especially cancer. Body-wide accurate quantification of disease burden in PET/CT images is important for characterizing lesions, staging disease, prognosticating patient outcome, planning treatment, and evaluating disease response to therapeutic interventions. However, body-wide anatomy recognition in PET/CT is a critical first step for accurately and automatically quantifying disease body-wide, body-region-wise, and organwise. This latter process, however, has remained a challenge due to the lower quality of the anatomic information portrayed in the CT component of this imaging modality and the paucity of anatomic details in the PET component. In this paper, the authors demonstrate the adaptation of a recently developed automatic anatomy recognition (AAR) methodology [Udupa et al., “Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images,” Med. Image Anal. 18, 752–771 (2014)] to PET/CT images. Their goal was to test what level of object localization accuracy can be achieved on PET/CT compared to that achieved on diagnostic CT images. Methods: The authors advance the AAR approach in this work in three fronts: (i) from body-region-wise treatment in the work of Udupa et al. to whole body; (ii) from the use of image intensity in optimal object recognition in the work of Udupa et al. to intensity plus object-specific texture properties, and (iii) from the intramodality model-building-recognition strategy to the intermodality approach. The whole-body approach allows consideration of relationships among objects in different body regions, which was previously not possible. Consideration of object texture allows generalizing the previous optimal threshold-based fuzzy model recognition method from intensity images to any derived fuzzy membership image, and in the process

A GPU-paralleled implementation of an enhanced face recognition algorithm

Science.gov (United States)

Chen, Hao; Liu, Xiyang; Shao, Shuai; Zan, Jiguo

2013-03-01

Face recognition algorithm based on compressed sensing and sparse representation is hotly argued in these years. The scheme of this algorithm increases recognition rate as well as anti-noise capability. However, the computational cost is expensive and has become a main restricting factor for real world applications. In this paper, we introduce a GPU-accelerated hybrid variant of face recognition algorithm named parallel face recognition algorithm (pFRA). We describe here how to carry out parallel optimization design to take full advantage of many-core structure of a GPU. The pFRA is tested and compared with several other implementations under different data sample size. Finally, Our pFRA, implemented with NVIDIA GPU and Computer Unified Device Architecture (CUDA) programming model, achieves a significant speedup over the traditional CPU implementations.

Soluble Molecularly Imprinted Nanorods for Homogeneous Molecular Recognition

Directory of Open Access Journals (Sweden)

Rongning Liang

2018-03-01

Full Text Available Nowadays, it is still difficult for molecularly imprinted polymers (MIPs to achieve homogeneous recognition since they cannot be easily dissolved in organic or aqueous phase. To address this issue, soluble molecularly imprinted nanorods have been synthesized by using soluble polyaniline doped with a functionalized organic protonic acid as the polymer matrix. By employing 1-naphthoic acid as a model, the proposed imprinted nanorods exhibit an excellent solubility and good homogeneous recognition ability. The imprinting factor for the soluble imprinted nanoroads is 6.8. The equilibrium dissociation constant and the apparent maximum number of the proposed imprinted nanorods are 248.5 μM and 22.1 μmol/g, respectively. We believe that such imprinted nanorods may provide an appealing substitute for natural receptors in homogeneous recognition related fields.

Soluble Molecularly Imprinted Nanorods for Homogeneous Molecular Recognition

Science.gov (United States)

Liang, Rongning; Wang, Tiantian; Zhang, Huan; Yao, Ruiqing; Qin, Wei

2018-03-01

Nowadays, it is still difficult for molecularly imprinted polymer (MIPs) to achieve homogeneous recognition since they cannot be easily dissolved in organic or aqueous phase. To address this issue, soluble molecularly imprinted nanorods have been synthesized by using soluble polyaniline doped with a functionalized organic protonic acid as the polymer matrix. By employing 1-naphthoic acid as a model, the proposed imprinted nanorods exhibit an excellent solubility and good homogeneous recognition ability. The imprinting factor for the soluble imprinted nanoroads is 6.8. The equilibrium dissociation constant and the apparent maximum number of the proposed imprinted nanorods are 248.5 μM and 22.1 μmol/g, respectively. We believe that such imprinted nanorods may provide an appealing substitute for natural receptors in homogeneous recognition related fields.

Color Face Recognition Based on Steerable Pyramid Transform and Extreme Learning Machines

Directory of Open Access Journals (Sweden)

Ayşegül Uçar

2014-01-01

Full Text Available This paper presents a novel color face recognition algorithm by means of fusing color and local information. The proposed algorithm fuses the multiple features derived from different color spaces. Multiorientation and multiscale information relating to the color face features are extracted by applying Steerable Pyramid Transform (SPT to the local face regions. In this paper, the new three hybrid color spaces, YSCr, ZnSCr, and BnSCr, are firstly constructed using the Cb and Cr component images of the YCbCr color space, the S color component of the HSV color spaces, and the Zn and Bn color components of the normalized XYZ color space. Secondly, the color component face images are partitioned into the local patches. Thirdly, SPT is applied to local face regions and some statistical features are extracted. Fourthly, all features are fused according to decision fusion frame and the combinations of Extreme Learning Machines classifiers are applied to achieve color face recognition with fast and high correctness. The experiments show that the proposed Local Color Steerable Pyramid Transform (LCSPT face recognition algorithm improves seriously face recognition performance by using the new color spaces compared to the conventional and some hybrid ones. Furthermore, it achieves faster recognition compared with state-of-the-art studies.

Internal Employability as a Strategy for Key Employee Retention

Directory of Open Access Journals (Sweden)

Ángela Sánchez-Manjavacas

2014-05-01

Full Text Available Economies the world over and particularly those in southern Europe, are suffering the crippling effects of the extremely complex economic and financial crisis. This study looks at the impact of certain human resource policies geared towards increasing internal employability as a means of retaining valued employees and promoting job flexibility within the firm, as well as increasing positive attitudes towards organizational citizenship. Satisfaction and commitment are proposed as intermediating variables of the relationship between perceived internal employability and ITQ/OCB. The proposed research model is contrasted using structural equation modeling (LISREL. The results obtained from the empirical study indicate that employability should be considered an essential factor in achieving the desired commitment, loyalty, adaptability and productivity from employees by strengthening the psychological contract between firm and worker through professional recognition.

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...

Highlights from TIMSS 2011: Mathematics and Science Achievement of U.S. Fourth- and Eighth-Grade Students in an International Context. NCES 2013-009

Science.gov (United States)

Provasnik, Stephen; Kastberg, David; Ferraro, David; Lemanski, Nita; Roey, Stephen; Jenkins, Frank

2012-01-01

The Trends in International Mathematics and Science Study (TIMSS) is an international comparative study of student achievement. TIMSS 2011 represents the fifth such study since TIMSS was first conducted in 1995. Developed and implemented at the international level by the International Association for the Evaluation of Educational Achievement…

The relation between the secrecy rate of biometric template protection and biometric recognition performance

NARCIS (Netherlands)

Veldhuis, Raymond N.J.

2015-01-01

A theoretical result relating the maximum achievable security of the family of biometric template protection systems known as key-binding systems to the recognition performance of a biometric recognition system that is optimal in Neyman-Pearson sense is derived. The relation allows for the

Interpreting the Third International Mathematics and Science Study (TIMSS) achievement scales using scale anchoring

Science.gov (United States)

Kelly, Dana L.

1999-11-01

The scale anchoring method was used to analyze and describe the TIMSS primary and middle school (Populations 1 and 2) mathematics and science achievement scales. Scale anchoring is a way of attaching meaning to a scale by describing what students know and can do at specific points on the scale. Student achievement was scrutinized at four points on the TIMSS primary and middle school achievement scales---the 25th, 50th, 75th, and 90th international percentiles for fourth and eighth grades. The scale anchoring method was adapted for the TIMSS data and items that students scoring at each of the four scale points were likely to answer correctly (with a 65 percent probability) were identified. The items were assembled in binders organized by anchor level and content area. Two ten-member panels of subject-matter specialists were convened to scrutinize the items, draft descriptions of student proficiency at the four scale points, and identify example TIMSS items to illustrate performance at each level. Following the panel meetings, the descriptions were refined through an iterative review process. The result is a content-referenced interpretation of the TIMSS scales through which TIMSS achievement results can be better communicated and understood.

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

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.

Variable Frame Rate and Length Analysis for Data Compression in Distributed Speech Recognition

DEFF Research Database (Denmark)

Kraljevski, Ivan; Tan, Zheng-Hua

2014-01-01

This paper addresses the issue of data compression in distributed speech recognition on the basis of a variable frame rate and length analysis method. The method first conducts frame selection by using a posteriori signal-to-noise ratio weighted energy distance to find the right time resolution...... length for steady regions. The method is applied to scalable source coding in distributed speech recognition where the target bitrate is met by adjusting the frame rate. Speech recognition results show that the proposed approach outperforms other compression methods in terms of recognition accuracy...... for noisy speech while achieving higher compression rates....

World Federation for Medical Education Policy on international recognition of medical schools' programme.

Science.gov (United States)

Karle, Hans

2008-12-01

The increasing globalisation of medicine, as manifested in the migration rate of medical doctors and in the growth of cross-border education providers, has inflicted a wave of quality assurance efforts in medical education, and underlined the need for definition of standards and for introduction of effective and transparent accreditation systems. In 2004, reflecting the importance of the interface between medical education and the healthcare delivery sector, a World Health Organization (WHO)/World Federation for Medical Education (WFME) Strategic Partnership to improve medical education was formed. In 2005, the partnership published Guidelines for Accreditation of Basic Medical Education. The WHO/WFME Guidelines recommend the establishment of proper accreditation systems that are effective, independent, transparent and based on medical education-specific criteria. An important prerequisite for this development was the WFME Global Standards programme, initiated in 1997 and widely endorsed. The standards are now being used in all 6 WHO/WFME regions as a basis for quality improvement of medical education throughout its continuum and as a template for national and regional accreditation standards. Promotion of national accreditation systems will have a pivotal influence on future international appraisal of medical education. Information about accreditation status - the agencies involved and the criteria and procedure used - will be an essential component of new Global Directories of Health Professions Educational Institutions. According to an agreement between the WHO and the University of Copenhagen (UC), these Directories (the Avicenna Directories) will be developed and published by the UC with the assistance of the WFME, starting with renewal of the WHO World Directory of Medical Schools, and sequentially expanding to cover educational institutions for other health professions. The Directories will be a foundation for international meta-recognition ("accrediting the

Achieving Our Potential: An Action Plan for Prior Learning Assessment and Recognition (PLAR) in Canada

Science.gov (United States)

Morrissey, Mary; Myers, Douglas; Belanger, Paul; Robitaille, Magali; Davison, Phil; Van Kleef, Joy; Williams, Rick

2008-01-01

This comprehensive publication assesses the status of prior learning assessment and recognition (PLAR) across Canada and offers insights and recommendations into the processes necessary for employers, post-secondary institutions and government to recognize and value experiential and informal learning. Acknowledging economic trends in Canada's job…

Effects of emotional and perceptual-motor stress on a voice recognition system's accuracy: An applied investigation

Science.gov (United States)

Poock, G. K.; Martin, B. J.

1984-02-01

This was an applied investigation examining the ability of a speech recognition system to recognize speakers' inputs when the speakers were under different stress levels. Subjects were asked to speak to a voice recognition system under three conditions: (1) normal office environment, (2) emotional stress, and (3) perceptual-motor stress. Results indicate a definite relationship between voice recognition system performance and the type of low stress reference patterns used to achieve recognition.

Preventing aggressive incidents and seclusions in forensic care by means of the 'Early Recognition Method'

NARCIS (Netherlands)

Fluttert, F.A.J.; Meijel, B.K.G. van; Nijman, H.L.I.; Björkly, S.; Grypdonck, M.H.F.

2010-01-01

Objective. The Early Recognition Method aims at improving collaboration between nurses and patients to prevent aggression in forensic psychiatric care. To achieve this goal, Early Recognition Method strongly focuses on early signs of aggression. In the current study, we investigated whether

Coupled bias-variance tradeoff for cross-pose face recognition.

Science.gov (United States)

Li, Annan; Shan, Shiguang; Gao, Wen

2012-01-01

Subspace-based face representation can be looked as a regression problem. From this viewpoint, we first revisited the problem of recognizing faces across pose differences, which is a bottleneck in face recognition. Then, we propose a new approach for cross-pose face recognition using a regressor with a coupled bias-variance tradeoff. We found that striking a coupled balance between bias and variance in regression for different poses could improve the regressor-based cross-pose face representation, i.e., the regressor can be more stable against a pose difference. With the basic idea, ridge regression and lasso regression are explored. Experimental results on CMU PIE, the FERET, and the Multi-PIE face databases show that the proposed bias-variance tradeoff can achieve considerable reinforcement in recognition performance.

Face recognition algorithm using extended vector quantization histogram features.

Science.gov (United States)

Yan, Yan; Lee, Feifei; Wu, Xueqian; Chen, Qiu

2018-01-01

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

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.

Automatic anatomy recognition on CT images with pathology

Science.gov (United States)

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

2016-03-01

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

Kernel Learning of Histogram of Local Gabor Phase Patterns for Face Recognition

Directory of Open Access Journals (Sweden)

Bineng Zhong

2008-06-01

Full Text Available This paper proposes a new face recognition method, named kernel learning of histogram of local Gabor phase pattern (K-HLGPP, which is based on Daugman’s method for iris recognition and the local XOR pattern (LXP operator. Unlike traditional Gabor usage exploiting the magnitude part in face recognition, we encode the Gabor phase information for face classification by the quadrant bit coding (QBC method. Two schemes are proposed for face recognition. One is based on the nearest-neighbor classifier with chi-square as the similarity measurement, and the other makes kernel discriminant analysis for HLGPP (K-HLGPP using histogram intersection and Gaussian-weighted chi-square kernels. The comparative experiments show that K-HLGPP achieves a higher recognition rate than other well-known face recognition systems on the large-scale standard FERET, FERET200, and CAS-PEAL-R1 databases.

Face recognition using slow feature analysis and contourlet transform

Science.gov (United States)

Wang, Yuehao; Peng, Lingling; Zhe, Fuchuan

2018-04-01

In this paper we propose a novel face recognition approach based on slow feature analysis (SFA) in contourlet transform domain. This method firstly use contourlet transform to decompose the face image into low frequency and high frequency part, and then takes technological advantages of slow feature analysis for facial feature extraction. We named the new method combining the slow feature analysis and contourlet transform as CT-SFA. The experimental results on international standard face database demonstrate that the new face recognition method is effective and competitive.

Reliable Gait Recognition Using 3D Reconstructions and Random Forests - An Anthropometric Approach

DEFF Research Database (Denmark)

Sandau, Martin; Heimbürger, Rikke V.; Jensen, Karl E.

2016-01-01

reliable recognition. Sixteen participants performed normal walking where 3D reconstructions were obtained continually. Segment lengths and kinematics from the extremities were manually extracted by eight expert observers. The results showed that all the participants were recognized, assuming the same...... expert annotated the data. Recognition based on data annotated by different experts was less reliable achieving 72.6% correct recognitions as some parameters were heavily affected by interobserver variability. This study verified that 3D reconstructions are feasible for forensic gait analysis...

Scene recognition based on integrating active learning with dictionary learning

Science.gov (United States)

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

2018-04-01

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

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...

Domain Regeneration for Cross-Database Micro-Expression Recognition

Science.gov (United States)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition.

Science.gov (United States)

Benatti, Simone; Casamassima, Filippo; Milosevic, Bojan; Farella, Elisabetta; Schönle, Philipp; Fateh, Schekeb; Burger, Thomas; Huang, Qiuting; Benini, Luca

2015-10-01

Wearable devices offer interesting features, such as low cost and user friendliness, but their use for medical applications is an open research topic, given the limited hardware resources they provide. In this paper, we present an embedded solution for real-time EMG-based hand gesture recognition. The work focuses on the multi-level design of the system, integrating the hardware and software components to develop a wearable device capable of acquiring and processing EMG signals for real-time gesture recognition. The system combines the accuracy of a custom analog front end with the flexibility of a low power and high performance microcontroller for on-board processing. Our system achieves the same accuracy of high-end and more expensive active EMG sensors used in applications with strict requirements on signal quality. At the same time, due to its flexible configuration, it can be compared to the few wearable platforms designed for EMG gesture recognition available on market. We demonstrate that we reach similar or better performance while embedding the gesture recognition on board, with the benefit of cost reduction. To validate this approach, we collected a dataset of 7 gestures from 4 users, which were used to evaluate the impact of the number of EMG channels, the number of recognized gestures and the data rate on the recognition accuracy and on the computational demand of the classifier. As a result, we implemented a SVM recognition algorithm capable of real-time performance on the proposed wearable platform, achieving a classification rate of 90%, which is aligned with the state-of-the-art off-line results and a 29.7 mW power consumption, guaranteeing 44 hours of continuous operation with a 400 mAh battery.

Aligning science and policy to achieve evolutionarily enlightened conservation.

Science.gov (United States)

Cook, Carly N; Sgrò, Carla M

2017-06-01

There is increasing recognition among conservation scientists that long-term conservation outcomes could be improved through better integration of evolutionary theory into management practices. Despite concerns that the importance of key concepts emerging from evolutionary theory (i.e., evolutionary principles and processes) are not being recognized by managers, there has been little effort to determine the level of integration of evolutionary theory into conservation policy and practice. We assessed conservation policy at 3 scales (international, national, and provincial) on 3 continents to quantify the degree to which key evolutionary concepts, such as genetic diversity and gene flow, are being incorporated into conservation practice. We also evaluated the availability of clear guidance within the applied evolutionary biology literature as to how managers can change their management practices to achieve better conservation outcomes. Despite widespread recognition of the importance of maintaining genetic diversity, conservation policies provide little guidance about how this can be achieved in practice and other relevant evolutionary concepts, such as inbreeding depression, are mentioned rarely. In some cases the poor integration of evolutionary concepts into management reflects a lack of decision-support tools in the literature. Where these tools are available, such as risk-assessment frameworks, they are not being adopted by conservation policy makers, suggesting that the availability of a strong evidence base is not the only barrier to evolutionarily enlightened management. We believe there is a clear need for more engagement by evolutionary biologists with policy makers to develop practical guidelines that will help managers make changes to conservation practice. There is also an urgent need for more research to better understand the barriers to and opportunities for incorporating evolutionary theory into conservation practice. © 2016 Society for Conservation

Bayesian Action–Perception Computational Model: Interaction of Production and Recognition of Cursive Letters

Science.gov (United States)

Gilet, Estelle; Diard, Julien; Bessière, Pierre

2011-01-01

In this paper, we study the collaboration of perception and action representations involved in cursive letter recognition and production. We propose a mathematical formulation for the whole perception–action loop, based on probabilistic modeling and Bayesian inference, which we call the Bayesian Action–Perception (BAP) model. Being a model of both perception and action processes, the purpose of this model is to study the interaction of these processes. More precisely, the model includes a feedback loop from motor production, which implements an internal simulation of movement. Motor knowledge can therefore be involved during perception tasks. In this paper, we formally define the BAP model and show how it solves the following six varied cognitive tasks using Bayesian inference: i) letter recognition (purely sensory), ii) writer recognition, iii) letter production (with different effectors), iv) copying of trajectories, v) copying of letters, and vi) letter recognition (with internal simulation of movements). We present computer simulations of each of these cognitive tasks, and discuss experimental predictions and theoretical developments. PMID:21674043

Texture recognition of medical images with the ICM method

International Nuclear Information System (INIS)

Kinser, Jason M.; Wang Guisong

2004-01-01

The Integrated Cortical Model (ICM) is based upon several models of the mammalian visual cortex and produces pulse images over several iterations. These pulse images tend to isolate segments, edges, and textures that are inherent in the input image. To create a texture recognition engine the pulse spectrum of individual pixels are collected and used to develop a recognition library. Recognition is performed by comparing pulse spectra of unclassified regions of images with the known regions. Because signatures are smaller than images, signature-based computation is quite efficient and parasites can be recognized quickly. The precision of this method depends on the representative of signatures and classification. Our experiment results support the theoretical findings and show perspectives of practical applications of ICM-based method. The advantage of ICM method is using signatures to represent objects. ICM can extract the internal features of objects and represent them with signatures. Signature classification is critical for the precision of recognition

Stablishment and maintenance of professional recognition in radiation protection

International Nuclear Information System (INIS)

Masse, F.X.

1994-01-01

Recognition of qualified experts in radiation protection is an issue IRPA has been concerned with from its inception. It has long been known that the recognition mechanism differs widely throughout the world community and IRPA Associated Societies have each dealt with the needs of their members in this regard in their own way. Some unification of the recognition of qualified radiation protection experts was first through to be important with the organization of the Commission of the European Communities, anticipating the need for such expertise to be able to move freely within the EC states. A number of attempts have been made to determine the feasibility of such standardization by first inter comparing the existing systems internationally. Such intercomparisons have only verified the wide diversity of existing recognition systems, confirming the difficulty that would be associated with any standardization attempt. We are therefore shifting our focus to the issue of professional education and training as a means of gradual standardization in the profession. (Author)

Robust and Effective Component-based Banknote Recognition for the Blind.

Science.gov (United States)

Hasanuzzaman, Faiz M; Yang, Xiaodong; Tian, Yingli

2012-11-01

We develop a novel camera-based computer vision technology to automatically recognize banknotes for assisting visually impaired people. Our banknote recognition system is robust and effective with the following features: 1) high accuracy: high true recognition rate and low false recognition rate, 2) robustness: handles a variety of currency designs and bills in various conditions, 3) high efficiency: recognizes banknotes quickly, and 4) ease of use: helps blind users to aim the target for image capture. To make the system robust to a variety of conditions including occlusion, rotation, scaling, cluttered background, illumination change, viewpoint variation, and worn or wrinkled bills, we propose a component-based framework by using Speeded Up Robust Features (SURF). Furthermore, we employ the spatial relationship of matched SURF features to detect if there is a bill in the camera view. This process largely alleviates false recognition and can guide the user to correctly aim at the bill to be recognized. The robustness and generalizability of the proposed system is evaluated on a dataset including both positive images (with U.S. banknotes) and negative images (no U.S. banknotes) collected under a variety of conditions. The proposed algorithm, achieves 100% true recognition rate and 0% false recognition rate. Our banknote recognition system is also tested by blind users.

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.

Toward retail product recognition on grocery shelves

Science.gov (United States)

Varol, Gül; Kuzu, Rıdvan S.

2015-03-01

This paper addresses the problem of retail product recognition on grocery shelf images. We present a technique for accomplishing this task with a low time complexity. We decompose the problem into detection and recognition. The former is achieved by a generic product detection module which is trained on a specific class of products (e.g. tobacco packages). Cascade object detection framework of Viola and Jones [1] is used for this purpose. We further make use of Support Vector Machines (SVMs) to recognize the brand inside each detected region. We extract both shape and color information; and apply feature-level fusion from two separate descriptors computed with the bag of words approach. Furthermore, we introduce a dataset (available on request) that we have collected for similar research purposes. Results are presented on this dataset of more than 5,000 images consisting of 10 tobacco brands. We show that satisfactory detection and classification can be achieved on devices with cheap computational power. Potential applications of the proposed approach include planogram compliance control, inventory management and assisting visually impaired people during shopping.

15 CFR 310.6 - Recognition by the President.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Recognition by the President. 310.6 Section 310.6 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S. GOVERNMENT...

New Achievements in Technology Education and Development

Science.gov (United States)

Soomro, Safeeullah, Ed.

2010-01-01

Since many decades Education Science and Technology has an achieved tremendous recognition and has been applied to variety of disciplines, mainly Curriculum development, methodology to develop e-learning systems and education management. Many efforts have been taken to improve knowledge of students, researchers, educationists in the field of…

Prompt recognition of brain states by their EEG signals

DEFF Research Database (Denmark)

Peters, B.O.; Pfurtscheller, G.; Flyvbjerg, H.

1997-01-01

Brain states corresponding to intention of movement of left and right index finger and right foot are classified by a ''committee'' of artificial neural networks processing individual channels of 56-electrode electroencephalograms (EEGs). Correct recognition is achieved in 83% of cases...

Interconnectivity of macroporous molecularly imprinted polymers fabricated by hydroxyapatite-stabilized Pickering high internal phase emulsions-hydrogels for the selective recognition of protein.

Science.gov (United States)

Sun, Yanhua; Li, Yuqing; Xu, Jiangfeng; Huang, Ling; Qiu, Tianyun; Zhong, Shian

2017-07-01

Hydroxyapatite hybridized molecularly imprinted polydopamine polymers with selective recognition of bovine hemoglobin (BHb) were successfully prepared via Pickering oil-in-water high internal phase emulsions-hydrogels and molecularly imprinting technique. The emulsions were stabilized by hydroxyapatite of which the wettability was modified by 3-methacryloxypropyltrimethoxysilane. The materials were characterized by SEM, IR and TGA. The results showed that the BHb imprinted polymers based on Pickering hydrogels (Hydro-MIPs) possess macropores ranging from 20μm to 50μm, and their large numbers of amino groups and hydroxyl groups result in a favorable adsorption capacity for BHb. The maximum adsorption capacity of Hydro-MIPs for BHb was 438mg/g, 3.27 times more than that of the non-imprinted polymers (Hydro-NIPs). The results indicated that Hydro-MIPs possessing well-defined hierarchical porous structures exhibited outstanding recognition behavior towards the target protein molecules. This work provided a promising alternative method for the fabrication of polymer materials with tunable and interconnected pores structures for the separation and purification of protein in vitro. Copyright © 2017. Published by Elsevier B.V.

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.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

Science.gov (United States)

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-01-01

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies. PMID:29695113

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

Directory of Open Access Journals (Sweden)

Dat Tien Nguyen

2018-04-01

Full Text Available Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD method for an iris recognition system (iPAD using a near infrared light (NIR camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED. Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM. Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor.

Science.gov (United States)

Nguyen, Dat Tien; Baek, Na Rae; Pham, Tuyen Danh; Park, Kang Ryoung

2018-04-24

Among biometric recognition systems such as fingerprint, finger-vein, or face, the iris recognition system has proven to be effective for achieving a high recognition accuracy and security level. However, several recent studies have indicated that an iris recognition system can be fooled by using presentation attack images that are recaptured using high-quality printed images or by contact lenses with printed iris patterns. As a result, this potential threat can reduce the security level of an iris recognition system. In this study, we propose a new presentation attack detection (PAD) method for an iris recognition system (iPAD) using a near infrared light (NIR) camera image. To detect presentation attack images, we first localized the iris region of the input iris image using circular edge detection (CED). Based on the result of iris localization, we extracted the image features using deep learning-based and handcrafted-based methods. The input iris images were then classified into real and presentation attack categories using support vector machines (SVM). Through extensive experiments with two public datasets, we show that our proposed method effectively solves the iris recognition presentation attack detection problem and produces detection accuracy superior to previous studies.

Mexican sign language recognition using normalized moments and artificial neural networks

Science.gov (United States)

Solís-V., J.-Francisco; Toxqui-Quitl, Carina; Martínez-Martínez, David; H.-G., Margarita

2014-09-01

This work presents a framework designed for the Mexican Sign Language (MSL) recognition. A data set was recorded with 24 static signs from the MSL using 5 different versions, this MSL dataset was captured using a digital camera in incoherent light conditions. Digital Image Processing was used to segment hand gestures, a uniform background was selected to avoid using gloved hands or some special markers. Feature extraction was performed by calculating normalized geometric moments of gray scaled signs, then an Artificial Neural Network performs the recognition using a 10-fold cross validation tested in weka, the best result achieved 95.83% of recognition rate.

Three-dimensional object recognition using similar triangles and decision trees

Science.gov (United States)

Spirkovska, Lilly

1993-01-01

A system, TRIDEC, that is capable of distinguishing between a set of objects despite changes in the objects' positions in the input field, their size, or their rotational orientation in 3D space is described. TRIDEC combines very simple yet effective features with the classification capabilities of inductive decision tree methods. The feature vector is a list of all similar triangles defined by connecting all combinations of three pixels in a coarse coded 127 x 127 pixel input field. The classification is accomplished by building a decision tree using the information provided from a limited number of translated, scaled, and rotated samples. Simulation results are presented which show that TRIDEC achieves 94 percent recognition accuracy in the 2D invariant object recognition domain and 98 percent recognition accuracy in the 3D invariant object recognition domain after training on only a small sample of transformed views of the objects.

Getting International Labour Rights Right at a Foreign Controlled Company in Malaysia

DEFF Research Database (Denmark)

Wad, Peter

2013-01-01

The article addresses international campaigning for labour rights and global labour networking against illegitimate labour practices of global corporations. Theoretically, the article offers an analytical framework to explain and strategise labour empowerment and disempowerment in Global Production......–Malaysian campaign in support of a worker collective in a Danish controlled joint venture in Malaysia struggling for union recognition and collective bargaining agreement. The article concludes that the GLN approach integrates the achievements of the labour agency literatures by focusing on explaining changes...... in strategic labour power from the dynamic interface of strategic opportunities and labour capacity. Moreover, it is argued that semi-comprehensive international campaigns of labour NGOs may add critical but insufficient support to labour agency in developing countries with highly legalistic and politically...

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)

Current trends in small vocabulary speech recognition for equipment control

Science.gov (United States)

Doukas, Nikolaos; Bardis, Nikolaos G.

2017-09-01

Speech recognition systems allow human - machine communication to acquire an intuitive nature that approaches the simplicity of inter - human communication. Small vocabulary speech recognition is a subset of the overall speech recognition problem, where only a small number of words need to be recognized. Speaker independent small vocabulary recognition can find significant applications in field equipment used by military personnel. Such equipment may typically be controlled by a small number of commands that need to be given quickly and accurately, under conditions where delicate manual operations are difficult to achieve. This type of application could hence significantly benefit by the use of robust voice operated control components, as they would facilitate the interaction with their users and render it much more reliable in times of crisis. This paper presents current challenges involved in attaining efficient and robust small vocabulary speech recognition. These challenges concern feature selection, classification techniques, speaker diversity and noise effects. A state machine approach is presented that facilitates the voice guidance of different equipment in a variety of situations.

Finger Vein Recognition Based on Local Directional Code

Science.gov (United States)

Meng, Xianjing; Yang, Gongping; Yin, Yilong; Xiao, Rongyang

2012-01-01

Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP), Local Derivative Pattern (LDP) and Local Line Binary Pattern (LLBP). However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD), this paper represents a new direction based local descriptor called Local Directional Code (LDC) and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP. PMID:23202194

Finger Vein Recognition Based on Local Directional Code

Directory of Open Access Journals (Sweden)

Rongyang Xiao

2012-11-01

Full Text Available Finger vein patterns are considered as one of the most promising biometric authentication methods for its security and convenience. Most of the current available finger vein recognition methods utilize features from a segmented blood vessel network. As an improperly segmented network may degrade the recognition accuracy, binary pattern based methods are proposed, such as Local Binary Pattern (LBP, Local Derivative Pattern (LDP and Local Line Binary Pattern (LLBP. However, the rich directional information hidden in the finger vein pattern has not been fully exploited by the existing local patterns. Inspired by the Webber Local Descriptor (WLD, this paper represents a new direction based local descriptor called Local Directional Code (LDC and applies it to finger vein recognition. In LDC, the local gradient orientation information is coded as an octonary decimal number. Experimental results show that the proposed method using LDC achieves better performance than methods using LLBP.

Multithread Face Recognition in Cloud

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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.

Using an expanded outcomes framework and continuing education evidence to improve facilitation of patient-centered medical home recognition and transformation.

Science.gov (United States)

Van Hoof, Thomas J; Kelvey-Albert, Michele; Katz, Matthew; Lalime, Ken; Sacks, Ken; Meehan, Thomas P

2014-01-01

The patient-centered medical home is a model for delivering primary care in the United States. Primary care clinicians and their staffs require assistance in understanding the innovation and in applying it to practice. The purpose of this article is to describe and to critique a continuing education program that is relevant to, and will become more common in, primary care. A multifaceted educational strategy prepared 20 primary care private practices to achieve National Committee for Quality Assurance Level 3 recognition as Patient-Centered Medical Homes. Eighteen (90%) practices submitted an application to the National Committee for Quality Assurance. On the first submission attempt, 13 of 18 (72%) achieved Level 3 recognition and 5 (28%) achieved Level 1 recognition. An interactive multifaceted educational strategy can be successful in preparing primary care practices for Patient-Centered Medical Homes recognition, but the strategy may not ensure transformation. Future educational activities should consider an expanded outcomes framework and the evidence of effective continuing education to be more successful with recognition and transformation.

Visual-spatial abilities relate to mathematics achievement in children with heavy prenatal alcohol exposure.

Science.gov (United States)

Crocker, Nicole; Riley, Edward P; Mattson, Sarah N

2015-01-01

The current study examined the relationship between mathematics and attention, working memory, and visual memory in children with heavy prenatal alcohol exposure and controls. Subjects were 56 children (29 AE, 27 CON) who were administered measures of global mathematics achievement (WRAT-3 Arithmetic & WISC-III Written Arithmetic), attention, (WISC-III Digit Span forward and Spatial Span forward), working memory (WISC-III Digit Span backward and Spatial Span backward), and visual memory (CANTAB Spatial Recognition Memory and Pattern Recognition Memory). The contribution of cognitive domains to mathematics achievement was analyzed using linear regression techniques. Attention, working memory, and visual memory data were entered together on Step 1 followed by group on Step 2, and the interaction terms on Step 3. Model 1 accounted for a significant amount of variance in both mathematics achievement measures; however, model fit improved with the addition of group on Step 2. Significant predictors of mathematics achievement were Spatial Span forward and backward and Spatial Recognition Memory. These findings suggest that deficits in spatial processing may be related to math impairments seen in FASD. In addition, prenatal alcohol exposure was associated with deficits in mathematics achievement, above and beyond the contribution of general cognitive abilities. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Symbol Recognition using Spatial Relations

OpenAIRE

K.C., Santosh; Lamiroy, Bart; Wendling, Laurent

2012-01-01

International audience; In this paper, we present a method for symbol recognition based on the spatio-structural description of a 'vocabulary' of extracted visual elementary parts. It is applied to symbols in electrical wiring diagrams. The method consists of first identifying vocabulary elements into different groups based on their types (e.g., circle, corner ). We then compute spatial relations between the possible pairs of labelled vocabulary types which are further used as a basis for bui...

Effect of Internal Factors and External Factors on Learning Achievement Intermediate Financial Accounting Course I

OpenAIRE

Huda, Syamsul; Diana, Nana

2017-01-01

The purpose of this study was to determine the effect of internal and external factors of students on the achievement of intermediate financial accounting courses 1 partially and simultaneously. This type of research is quantitative, while the data used in this study is primary data in the form of questionnaires and secondary data in the form of midterm semester exam on intermediate financial accounting 1 semester odd academic year 2016/2017. Hypothesis testing using multiple regression analy...

Finger Vein Recognition Based on a Personalized Best Bit Map

Science.gov (United States)

Yang, Gongping; Xi, Xiaoming; Yin, Yilong

2012-01-01

Finger vein patterns have recently been recognized as an effective biometric identifier. In this paper, we propose a finger vein recognition method based on a personalized best bit map (PBBM). Our method is rooted in a local binary pattern based method and then inclined to use the best bits only for matching. We first present the concept of PBBM and the generating algorithm. Then we propose the finger vein recognition framework, which consists of preprocessing, feature extraction, and matching. Finally, we design extensive experiments to evaluate the effectiveness of our proposal. Experimental results show that PBBM achieves not only better performance, but also high robustness and reliability. In addition, PBBM can be used as a general framework for binary pattern based recognition. PMID:22438735

Adaptive pattern recognition in real-time video-based soccer analysis

DEFF Research Database (Denmark)

Schlipsing, Marc; Salmen, Jan; Tschentscher, Marc

2017-01-01

are taken into account. Our contribution is twofold: (1) the deliberate use of machine learning and pattern recognition techniques allows us to achieve high classification accuracy in varying environments. We systematically evaluate combinations of image features and learning machines in the given online......Computer-aided sports analysis is demanded by coaches and the media. Image processing and machine learning techniques that allow for "live" recognition and tracking of players exist. But these methods are far from collecting and analyzing event data fully autonomously. To generate accurate results......, human interaction is required at different stages including system setup, calibration, supervision of classifier training, and resolution of tracking conflicts. Furthermore, the real-time constraints are challenging: in contrast to other object recognition and tracking applications, we cannot treat data...

CONSIDERATIONS CONCERNING THE RECOGNITION OF FOREIGN JUDGMENTS IN THE NEW BRAZILIAN CIVIL PROCEDURE CODE

Directory of Open Access Journals (Sweden)

Humberto Dalla Bernardina de Pinho

2016-06-01

Full Text Available The presente study aims to critically analyze the recognition of foreign judgments in the new Brazilian Civil Procedure Code, highlighting the changes brought by the new legislation. The international judicial cooperation was emphasized in the new Code, which was made clear by the prevision of the institute called “direct aid” (“auxílio direto”, the detailed regulation of the recognition of foreign judgements, including the possibility of concession of interim measures and the recognition of foreign interim measures.

Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

DEFF Research Database (Denmark)

Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

2018-01-01

, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data......PAIN)' database, for RGBDT pain level recognition in sequences. We provide a first baseline results including 5 pain levels recognition by analyzing independent visual modalities and their fusion with CNN and LSTM models. From the experimental evaluation we observe that fusion of modalities helps to enhance...... recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate....

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.

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.

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

Science.gov (United States)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

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

Continuous Chinese sign language recognition with CNN-LSTM

Science.gov (United States)

Yang, Su; Zhu, Qing

2017-07-01

The goal of sign language recognition (SLR) is to translate the sign language into text, and provide a convenient tool for the communication between the deaf-mute and the ordinary. In this paper, we formulate an appropriate model based on convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) network, in order to accomplish the continuous recognition work. With the strong ability of CNN, the information of pictures captured from Chinese sign language (CSL) videos can be learned and transformed into vector. Since the video can be regarded as an ordered sequence of frames, LSTM model is employed to connect with the fully-connected layer of CNN. As a recurrent neural network (RNN), it is suitable for sequence learning tasks with the capability of recognizing patterns defined by temporal distance. Compared with traditional RNN, LSTM has performed better on storing and accessing information. We evaluate this method on our self-built dataset including 40 daily vocabularies. The experimental results show that the recognition method with CNN-LSTM can achieve a high recognition rate with small training sets, which will meet the needs of real-time SLR system.

Multi-Lingual Deep Neural Networks for Language Recognition

Science.gov (United States)

2016-08-08

system architecture 2. I-VECTOR SYSTEM Most state-of-the- art language recognition systems are based on the i-vector framework [8] depicted in Figure 1...may be possible to achieve more gains on the Arabic and Chinese cluster by adding ad- ditional ASR corpora such as Callhome Egyptian Arabic or HKUST

Separating recognition processes of declarative memory via anodal tDCS: boosting old item recognition by temporal and new item detection by parietal stimulation.

Science.gov (United States)

Pisoni, Alberto; Turi, Zsolt; Raithel, Almuth; Ambrus, Géza Gergely; Alekseichuk, Ivan; Schacht, Annekathrin; Paulus, Walter; Antal, Andrea

2015-01-01

There is emerging evidence from imaging studies that parietal and temporal cortices act together to achieve successful recognition of declarative information; nevertheless, the precise role of these regions remains elusive. To evaluate the role of these brain areas in declarative memory retrieval, we applied bilateral tDCS, with anode over the left and cathode over the right parietal or temporal cortices separately, during the recognition phase of a verbal learning paradigm using a balanced old-new decision task. In a parallel group design, we tested three different groups of healthy adults, matched for demographic and neurocognitive status: two groups received bilateral active stimulation of either the parietal or the temporal cortex, while a third group received sham stimulation. Accuracy, discriminability index (d') and reaction times of recognition memory performance were measurements of interest. The d' sensitivity index and accuracy percentage improved in both active stimulation groups, as compared with the sham one, while reaction times remained unaffected. Moreover, the analysis of accuracy revealed a different effect of tDCS for old and new item recognition. While the temporal group showed enhanced performance for old item recognition, the parietal group was better at correctly recognising new ones. Our results support an active role of both of these areas in memory retrieval, possibly underpinning different stages of the recognition process.

Robust and Effective Component-based Banknote Recognition by SURF Features.

Science.gov (United States)

Hasanuzzaman, Faiz M; Yang, Xiaodong; Tian, YingLi

2011-01-01

Camera-based computer vision technology is able to assist visually impaired people to automatically recognize banknotes. A good banknote recognition algorithm for blind or visually impaired people should have the following features: 1) 100% accuracy, and 2) robustness to various conditions in different environments and occlusions. Most existing algorithms of banknote recognition are limited to work for restricted conditions. In this paper we propose a component-based framework for banknote recognition by using Speeded Up Robust Features (SURF). The component-based framework is effective in collecting more class-specific information and robust in dealing with partial occlusion and viewpoint changes. Furthermore, the evaluation of SURF demonstrates its effectiveness in handling background noise, image rotation, scale, and illumination changes. To authenticate the robustness and generalizability of the proposed approach, we have collected a large dataset of banknotes from a variety of conditions including occlusion, cluttered background, rotation, and changes of illumination, scaling, and viewpoints. The proposed algorithm achieves 100% recognition rate on our challenging dataset.

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

Science.gov (United States)

Fang, Fuming; Shinozaki, Takahiro

2018-01-01

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

Robust Face Recognition Via Gabor Feature and Sparse Representation

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Hao Yu-Juan

2016-01-01

Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

Recognition in Programmes for Children with Special Needs

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Marjeta Šmid

2016-09-01

Full Text Available The purpose of this article is to examine the factors that affect the inclusion of pupils in programmes for children with special needs from the perspective of the theory of recognition. The concept of recognition, which includes three aspects of social justice (economic, cultural and political, argues that the institutional arrangements that prevent ‘parity of participation’ in the school social life of the children with special needs are affected not only by economic distribution but also by the patterns of cultural values. A review of the literature shows that the arrangements of education of children with special needs are influenced primarily by the patterns of cultural values of capability and inferiority, as well as stereotypical images of children with special needs. Due to the significant emphasis on learning skills for academic knowledge and grades, less attention is dedicated to factors of recognition and representational character, making it impossible to improve some meaningful elements of inclusion. Any participation of pupils in activities, the voices of the children, visibility of the children due to achievements and the problems of arbitrariness in determining boundaries between programmes are some such elements. Moreover, aided by theories, the actions that could contribute to better inclusion are reviewed. An effective approach to changes would be the creation of transformative conditions for the recognition and balancing of redistribution, recognition, and representation.

International target values 2010 for achievable measurement uncertainties in nuclear material accountancy

Energy Technology Data Exchange (ETDEWEB)

Dias, Fabio C., E-mail: fabio@ird.gov.b [Comissao Nacional de Energia Nuclear (CNEN), Rio de Janeiro, RJ (Brazil); Almeida, Silvio G. de; Renha Junior, Geraldo, E-mail: silvio@abacc.org.b, E-mail: grenha@abacc.org.b [Agencia Brasileiro-Argentina de Contabilidade e Controle de Materiais Nucleares (ABACC), Rio de Janeiro, RJ (Brazil)

2011-07-01

The International Target Values (ITVs) are reasonable uncertainty estimates that can be used in judging the reliability of measurement techniques applied to industrial nuclear and fissile materials subject to accountancy and/or safeguards verification. In the absence of relevant experimental estimates, ITVs can also be used to select measurement techniques and calculate sample population during the planning phase of verification activities. It is important to note that ITVs represent estimates of the 'state-of-the-practice', which should be achievable under routine measurement conditions affecting both facility operators and safeguards inspectors, not only in the field, but also in laboratory. Tabulated values cover measurement methods used for the determination of volume or mass of the nuclear material, for its elemental and isotopic assays, and for its sampling. The 2010 edition represents the sixth revision of the International Target Values (ITVs), issued by the International Atomic Energy Agency (IAEA) as a Safeguards Technical Report (STR-368). The first version was released as 'Target Values' in 1979 by the Working Group on Techniques and Standards for Destructive Analysis (WGDA) of the European Safeguards Research and Development Association (ESARDA) and focused on destructive analytical methods. In the latest 2010 revision, international standards in estimating and expressing uncertainties have been considered while maintaining a format that allows comparison with the previous editions of the ITVs. Those standards have been usually applied in QC/QA programmes, as well as qualification of methods, techniques and instruments. Representatives of the Brazilian Nuclear Energy Commission (CNEN) and the Brazilian-Argentine Agency for Accounting and Control of Nuclear Materials (ABACC) participated in previous Consultants Group Meetings since the one convened to establish the first list of ITVs released in 1993 and in subsequent revisions

International target values 2010 for achievable measurement uncertainties in nuclear material accountancy

International Nuclear Information System (INIS)

Dias, Fabio C.; Almeida, Silvio G. de; Renha Junior, Geraldo

2011-01-01

The International Target Values (ITVs) are reasonable uncertainty estimates that can be used in judging the reliability of measurement techniques applied to industrial nuclear and fissile materials subject to accountancy and/or safeguards verification. In the absence of relevant experimental estimates, ITVs can also be used to select measurement techniques and calculate sample population during the planning phase of verification activities. It is important to note that ITVs represent estimates of the 'state-of-the-practice', which should be achievable under routine measurement conditions affecting both facility operators and safeguards inspectors, not only in the field, but also in laboratory. Tabulated values cover measurement methods used for the determination of volume or mass of the nuclear material, for its elemental and isotopic assays, and for its sampling. The 2010 edition represents the sixth revision of the International Target Values (ITVs), issued by the International Atomic Energy Agency (IAEA) as a Safeguards Technical Report (STR-368). The first version was released as 'Target Values' in 1979 by the Working Group on Techniques and Standards for Destructive Analysis (WGDA) of the European Safeguards Research and Development Association (ESARDA) and focused on destructive analytical methods. In the latest 2010 revision, international standards in estimating and expressing uncertainties have been considered while maintaining a format that allows comparison with the previous editions of the ITVs. Those standards have been usually applied in QC/QA programmes, as well as qualification of methods, techniques and instruments. Representatives of the Brazilian Nuclear Energy Commission (CNEN) and the Brazilian-Argentine Agency for Accounting and Control of Nuclear Materials (ABACC) participated in previous Consultants Group Meetings since the one convened to establish the first list of ITVs released in 1993 and in subsequent revisions, including the latest one

Israel’s Achievements in Mathematics in the Last International Examinations: Part I: The TIMSS 2011

Directory of Open Access Journals (Sweden)

Hanna DAVID

2014-06-01

Full Text Available After more than two decades of deterioration in the Israeli TIMSS results in mathematics, Israel scored number 7 among all countries participating in the world. As the probability of such a sudden, huge improvement seems negligible, this article sheds a new light on the Israel “achievements”. It shows that the students participating were not a sample according to statistical definitions and in additions – two sub-populations with the lowest Israeli achievements did not participate in this international examination.

Eigen-Gradients for Traffic Sign Recognition

Directory of Open Access Journals (Sweden)

Sheila Esmeralda Gonzalez-Reyna

2013-01-01

Full Text Available Traffic sign detection and recognition systems include a variety of applications like autonomous driving, road sign inventory, and driver support systems. Machine learning algorithms provide useful tools for traffic sign identification tasks. However, classification algorithms depend on the preprocessing stage to obtain high accuracy rates. This paper proposes a road sign characterization method based on oriented gradient maps and the Karhunen-Loeve transform in order to improve classification performance. Dimensionality reduction may be important for portable applications on resource constrained devices like FPGAs; therefore, our approach focuses on achieving a good classification accuracy by using a reduced amount of attributes compared to some state-of-the-art methods. The proposed method was tested using German Traffic Sign Recognition Benchmark, reaching a dimensionality reduction of 99.3% and a classification accuracy of 95.9% with a Multi-Layer Perceptron.

Facial expression recognition in the wild based on multimodal texture features

Science.gov (United States)

Sun, Bo; Li, Liandong; Zhou, Guoyan; He, Jun

2016-11-01

Facial expression recognition in the wild is a very challenging task. We describe our work in static and continuous facial expression recognition in the wild. We evaluate the recognition results of gray deep features and color deep features, and explore the fusion of multimodal texture features. For the continuous facial expression recognition, we design two temporal-spatial dense scale-invariant feature transform (SIFT) features and combine multimodal features to recognize expression from image sequences. For the static facial expression recognition based on video frames, we extract dense SIFT and some deep convolutional neural network (CNN) features, including our proposed CNN architecture. We train linear support vector machine and partial least squares classifiers for those kinds of features on the static facial expression in the wild (SFEW) and acted facial expression in the wild (AFEW) dataset, and we propose a fusion network to combine all the extracted features at decision level. The final achievement we gained is 56.32% on the SFEW testing set and 50.67% on the AFEW validation set, which are much better than the baseline recognition rates of 35.96% and 36.08%.

Sistem Kontrol Akses Berbasis Real Time Face Recognition dan Gender Information

Directory of Open Access Journals (Sweden)

Putri Nurmala

2015-06-01

Full Text Available Face recognition and gender information is a computer application for automatically identifying or verifying a person's face from a camera to capture a person's face. It is usually used in access control systemsand it can be compared to other biometrics such as finger print identification system or iris. Many of face recognition algorithms have been developed in recent years. Face recognition system and gender information inthis system based on the Principal Component Analysis method (PCA. Computational method has a simple and fast compared with the use of the method requires a lot of learning, such as artificial neural network. In thisaccess control system, relay used and Arduino controller. In this essay focuses on face recognition and gender - based information in real time using the method of Principal Component Analysis ( PCA . The result achievedfrom the application design is the identification of a person’s face with gender using PCA. The results achieved by the application is face recognition system using PCA can obtain good results the 85 % success rate in face recognition with face images that have been tested by a few people and a fairly high degree of accuracy.

Delayed Self-Recognition in Autism: A Unique Difficulty?

Science.gov (United States)

Dunphy-Lelii, Sarah; Wellman, Henry M.

2012-01-01

Achieving a sense of self is a crucial task of ordinary development. With which aspects of self do children with autism have particular difficulty? Two prior studies concluded that children with autism are unimpaired in delayed self-recognition; we confirm and clarify this conclusion by examining it in conjunction with another key aspect of self…

Unalienated Recognition as a Feature of Democratic Schooling

Science.gov (United States)

Rheingold, Alison

2012-01-01

The current era of standards and accountability in U.S. public schooling narrows recognition and assessment to an almost exclusive focus on the production of test scores as legitimate markers of student achievement. This climate prevents rather than encourages democratic forms of exchange within and across social worlds. Via a case study of one…

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.

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

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

Science.gov (United States)

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

2018-04-01

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

Gait recognition using kinect and locally linear embedding

African Journals Online (AJOL)

2017-09-10

Sep 10, 2017 ... [10] Zhang M, Sawchuk A A. Manifold learning and recognition of human activity using body-area sensors. In 10th IEEE International Conference on Machine Learning and. Applications and Workshops, 2011, pp. 7-13. [11] Azlee Z, Nooritawati M T, Ihsan M Y, Zairi I R. The performance of binary artificial ...

An Edge-Based Macao License Plate Recognition System

Directory of Open Access Journals (Sweden)

Chi-Man Pun

2011-04-01

Full Text Available This paper presents a system to recognize Macao license plates. Sobel edge detector is employed to extract the vertical edges, and an edge composition algorithm is proposed to combine the edges into candidate plate regions. They are further examined on the existence of the character qMq by a verification algorithm. A row separation algorithm is also proposed to cater both one-row and two-row types of plates. Projection analysis and template matching methods are exploited to segment and recognize the characters. Various pre and post processing steps are proposed other than traditional implementation so as to improve the recognition accuracy. This work achieves a high recognition rate of 95%.

Low-resolution expression recognition based on central oblique average CS-LBP with adaptive threshold

Science.gov (United States)

Han, Sheng; Xi, Shi-qiong; Geng, Wei-dong

2017-11-01

In order to solve the problem of low recognition rate of traditional feature extraction operators under low-resolution images, a novel algorithm of expression recognition is proposed, named central oblique average center-symmetric local binary pattern (CS-LBP) with adaptive threshold (ATCS-LBP). Firstly, the features of face images can be extracted by the proposed operator after pretreatment. Secondly, the obtained feature image is divided into blocks. Thirdly, the histogram of each block is computed independently and all histograms can be connected serially to create a final feature vector. Finally, expression classification is achieved by using support vector machine (SVM) classifier. Experimental results on Japanese female facial expression (JAFFE) database show that the proposed algorithm can achieve a recognition rate of 81.9% when the resolution is as low as 16×16, which is much better than that of the traditional feature extraction operators.

Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification

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Srdjan Sladojevic

2016-01-01

Full Text Available The latest generation of convolutional neural networks (CNNs has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

Experiences of families with a high-achiever child in sport: Case ...

African Journals Online (AJOL)

The family, not only the coach, plays a major role in the pursuit of children to reach the highest level in sport. Yet, it is mainly the high achiever, and sometimes the coach, who get recognition for success in this regard. This study explored the experiences of families with high-achieving adolescent athletes aspiring to compete ...

ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

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VIMALA C.

2015-05-01

Full Text Available In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW, Statistical Pattern Matching techniques such as Hidden Markov Model (HMM and Gaussian Mixture Models (GMM, Machine Learning techniques such as Neural Networks (NN, Support Vector Machine (SVM, and Decision Trees (DT are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

Cognitive ability, academic achievement and academic self-concept: extending the internal/external frame of reference model.

Science.gov (United States)

Chen, Ssu-Kuang; Hwang, Fang-Ming; Yeh, Yu-Chen; Lin, Sunny S J

2012-06-01

Marsh's internal/external (I/E) frame of reference model depicts the relationship between achievement and self-concept in specific academic domains. Few efforts have been made to examine concurrent relationships among cognitive ability, achievement, and academic self-concept (ASC) within an I/E model framework. To simultaneously examine the influences of domain-specific cognitive ability and grades on domain self-concept in an extended I/E model, including the indirect effect of domain-specific cognitive ability on domain self-concept via grades. Tenth grade respondents (628 male, 452 female) to a national adolescent survey conducted in Taiwan. Respondents completed surveys designed to measure maths and verbal aptitudes. Data on Maths and Chinese class grades and self-concepts were also collected. Statistically significant and positive path coefficients were found between cognitive ability and self-concept in the same domain (direct effect) and between these two constructs via grades (indirect effect). The cross-domain effects of either ability or grades on ASC were negatively significant. Taiwanese 10th graders tend to evaluate their ASCs based on a mix of ability and achievement, with achievement as a mediator exceeding ability as a predictor. In addition, the cross-domain effects suggest that Taiwanese students are likely to view Maths and verbal abilities and achievements as distinctly different. ©2011 The British Psychological Society.

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

An Investigation of International Science Achievement Using the OECD's PISA 2006 Data Set

Science.gov (United States)

Milford, Todd

School Effectiveness Research (SER) is concerned with efforts to better understand the effectiveness enhancing relationship between student and school variables and how these variables primarily influence academic achievement (Scheerens, 2004). However, one identified methodological shortcoming in SER is the absence of cross-cultural perspectives (Kyriakides, 2006). This is a concern as what may prove effective in one nation does not necessarily mean that it can be easily and seamlessly imported into another with the same results. This study looked at the relationships between science self-beliefs and academic achievement in science across all nations who participated in the Programme for International Student Assessment (PISA) in 2006. It further explored the variance accounted for by cultural, social and economic capital (the elements of the PISA socioeconomic status variable) for each country in PISA 2006 when predicting scientific literacy. Lastly, it used hierarchical linear modeling (HLM) to analyze data from PISA 2006 for nations experiencing high rates of immigration (i.e., Germany, Spain, Canada, the United States, Australia and New Zealand). The outcome measures used for these countries were achievement scores in science, mathematics and reading. The variables examined at the student level were science self-efficacy, science self-concept, immigrant status and socioeconomic status. The variables examined at the school level were student level aggregates of school proportion of immigrants and school socioeconomic status. In the correlation analysis between science literacy and either science self-concept of science self-efficacy, findings suggest that at the student level, students with both higher science self-concept and higher science self-efficacy tend to achieve higher academically. However, at the country level the relationship was negative between self-concept and academic achievement in science (i.e., countries with higher science self-concept tend

Object recognition with hierarchical discriminant saliency networks.

Science.gov (United States)

Han, Sunhyoung; Vasconcelos, Nuno

2014-01-01

The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as a pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognition model, the hierarchical discriminant saliency network (HDSN), whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. As a model of neural computation, the HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a convolutional neural network implementation, all layers are convolutional and implement a combination of filtering, rectification, and pooling. The rectification is performed with a parametric extension of the now popular rectified linear units (ReLUs), whose parameters can be tuned for the detection of target object classes. This enables a number of functional enhancements over neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation of saliency responses by the discriminant power of the underlying features, and the ability to detect both feature presence and absence. In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity to target object classes and invariance. The performance of the network in saliency and object recognition tasks is compared to those of models from the biological and

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...

A hierarchical classification method for finger knuckle print recognition

Science.gov (United States)

Kong, Tao; Yang, Gongping; Yang, Lu

2014-12-01

Finger knuckle print has recently been seen as an effective biometric technique. In this paper, we propose a hierarchical classification method for finger knuckle print recognition, which is rooted in traditional score-level fusion methods. In the proposed method, we firstly take Gabor feature as the basic feature for finger knuckle print recognition and then a new decision rule is defined based on the predefined threshold. Finally, the minor feature speeded-up robust feature is conducted for these users, who cannot be recognized by the basic feature. Extensive experiments are performed to evaluate the proposed method, and experimental results show that it can achieve a promising performance.

Incremental Tensor Principal Component Analysis for Handwritten Digit Recognition

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Chang Liu

2014-01-01

Full Text Available To overcome the shortcomings of traditional dimensionality reduction algorithms, incremental tensor principal component analysis (ITPCA based on updated-SVD technique algorithm is proposed in this paper. This paper proves the relationship between PCA, 2DPCA, MPCA, and the graph embedding framework theoretically and derives the incremental learning procedure to add single sample and multiple samples in detail. The experiments on handwritten digit recognition have demonstrated that ITPCA has achieved better recognition performance than that of vector-based principal component analysis (PCA, incremental principal component analysis (IPCA, and multilinear principal component analysis (MPCA algorithms. At the same time, ITPCA also has lower time and space complexity.

The Convention on the Recognition and Enforcement of Foreign ...

African Journals Online (AJOL)

The Convention on the Recognition and Enforcement of Foreign Arbitral Awards, often referred to as the New York Convention, has established itself as a regulatory and enforcement instrument which is crucial to international trade. This is evident from the fact that more than 150 countries have so far ratified the convention.

Glyph Identification and Character Recognition for Sindhi OCR

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NISAR AHMEDMEMON

2017-10-01

Full Text Available A computer can read and write multiple languages and today?s computers are capable of understanding various human languages. A computer can be given instructions through various input methods but OCR (Optical Character Recognition and handwritten character recognition are the input methods in which a scanned page containing text is converted into written or editable text. The change in language text available on scanned page demands different algorithm to recognize text because every language and script pose varying number of challenges to recognize text. The Latin language recognition pose less difficulties compared to Arabic script and languages that use Arabic script for writing and OCR systems for these Latin languages are near to perfection. Very little work has been done on regional languages of Pakistan. In this paper the Sindhi glyphs are identified and the number of characters and connected components are identified for this regional language of Pakistan. A graphical user interface has been created to perform identification task for glyphs and characters of Sindhi language. The glyphs of characters are successfully identified from scanned page and this information can be used to recognize characters. The language glyph identification can be used to apply suitable algorithm to identify language as well as to achieve a higher recognition rate.

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.

Modular Neural Networks and Type-2 Fuzzy Systems for Pattern Recognition

CERN Document Server

Melin, Patricia

2012-01-01

This book describes hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. Type-2 fuzzy logic is an extension of traditional type-1 fuzzy logic that enables managing higher levels of uncertainty in complex real world problems, which are of particular importance in the area of pattern recognition. The book is organized in three main parts, each containing a group of chapters built around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which are the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural ne...

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

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Xin Li

2014-06-01

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

Gait Recognition Based on Outermost Contour

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Lili Liu

2011-10-01

Full Text Available Gait recognition aims to identify people by the way they walk. In this paper, a simple but e ective gait recognition method based on Outermost Contour is proposed. For each gait image sequence, an adaptive silhouette extraction algorithm is firstly used to segment the frames of the sequence and a series of postprocessing is applied to obtain the normalized silhouette images with less noise. Then a novel feature extraction method based on Outermost Contour is performed. Principal Component Analysis (PCA is adopted to reduce the dimensionality of the distance signals derived from the Outermost Contours of silhouette images. Then Multiple Discriminant Analysis (MDA is used to optimize the separability of gait features belonging to di erent classes. Nearest Neighbor (NN classifier and Nearest Neighbor classifier with respect to class Exemplars (ENN are used to classify the final feature vectors produced by MDA. In order to verify the e ectiveness and robustness of our feature extraction algorithm, we also use two other classifiers: Backpropagation Neural Network (BPNN and Support Vector Machine (SVM for recognition. Experimental results on a gait database of 100 people show that the accuracy of using MDA, BPNN and SVM can achieve 97.67%, 94.33% and 94.67%, respectively.

Negotiating Managerialism: Professional Recognition and Teachers of Sustainable Development Education

Science.gov (United States)

Ross, Hamish

2015-01-01

Policy strategies to reward teachers for field-specific expertise have become internationally widespread and have been criticized for being manifestations of neoliberal globalization. In Scotland, there is political commitment to such strategies, including one to award recognition to teachers for expertise in sustainable development education…

Handwritten Character Recognition Based on the Specificity and the Singularity of the Arabic Language

Directory of Open Access Journals (Sweden)

Youssef Boulid

2017-08-01

Full Text Available A good Arabic handwritten recognition system must consider the characteristics of Arabic letters which can be explicit such as the presence of diacritics or implicit such as the baseline information (a virtual line on which cursive text are aligned and/join. In order to find an adequate method of features extraction, we have taken into consideration the nature of the Arabic characters. The paper investigate two methods based on two different visions: one describes the image in terms of the distribution of pixels, and the other describes it in terms of local patterns. Spatial Distribution of Pixels (SDP is used according to the first vision; whereas Local Binary Patterns (LBP are used for the second one. Tested on the Arabic portion of the Isolated Farsi Handwritten Character Database (IFHCDB and using neural networks as a classifier, SDP achieve a recognition rate around 94% while LBP achieve a recognition rate of about 96%.

FPGA IMPLEMENTATION OF ADAPTIVE INTEGRATED SPIKING NEURAL NETWORK FOR EFFICIENT IMAGE RECOGNITION SYSTEM

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T. Pasupathi

2014-05-01

Full Text Available Image recognition is a technology which can be used in various applications such as medical image recognition systems, security, defense video tracking, and factory automation. In this paper we present a novel pipelined architecture of an adaptive integrated Artificial Neural Network for image recognition. In our proposed work we have combined the feature of spiking neuron concept with ANN to achieve the efficient architecture for image recognition. The set of training images are trained by ANN and target output has been identified. Real time videos are captured and then converted into frames for testing purpose and the image were recognized. The machine can operate at up to 40 frames/sec using images acquired from the camera. The system has been implemented on XC3S400 SPARTAN-3 Field Programmable Gate Arrays.

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...

A Modified Sparse Representation Method for Facial Expression Recognition

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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.

Deep Multimodal Pain Recognition: A Database and Comparison of Spatio-Temporal Visual Modalities

DEFF Research Database (Denmark)

Haque, Mohammad Ahsanul; Nasrollahi, Kamal; Moeslund, Thomas B.

2018-01-01

, exploiting both spatial and temporal information of the face to assess pain level, and second, incorporating multiple visual modalities to capture complementary face information related to pain. Most works in the literature focus on merely exploiting spatial information on chromatic (RGB) video data...... recognition performance of pain levels in comparison to isolated ones. In particular, the combination of RGB, D, and T in an early fusion fashion achieved the best recognition rate....

Gesture recognition for smart home applications using portable radar sensors.

Science.gov (United States)

Wan, Qian; Li, Yiran; Li, Changzhi; Pal, Ranadip

2014-01-01

In this article, we consider the design of a human gesture recognition system based on pattern recognition of signatures from a portable smart radar sensor. Powered by AAA batteries, the smart radar sensor operates in the 2.4 GHz industrial, scientific and medical (ISM) band. We analyzed the feature space using principle components and application-specific time and frequency domain features extracted from radar signals for two different sets of gestures. We illustrate that a nearest neighbor based classifier can achieve greater than 95% accuracy for multi class classification using 10 fold cross validation when features are extracted based on magnitude differences and Doppler shifts as compared to features extracted through orthogonal transformations. The reported results illustrate the potential of intelligent radars integrated with a pattern recognition system for high accuracy smart home and health monitoring purposes.

Autonomous target recognition using remotely sensed surface vibration measurements

Science.gov (United States)

Geurts, James; Ruck, Dennis W.; Rogers, Steven K.; Oxley, Mark E.; Barr, Dallas N.

1993-09-01

The remotely measured surface vibration signatures of tactical military ground vehicles are investigated for use in target classification and identification friend or foe (IFF) systems. The use of remote surface vibration sensing by a laser radar reduces the effects of partial occlusion, concealment, and camouflage experienced by automatic target recognition systems using traditional imagery in a tactical battlefield environment. Linear Predictive Coding (LPC) efficiently represents the vibration signatures and nearest neighbor classifiers exploit the LPC feature set using a variety of distortion metrics. Nearest neighbor classifiers achieve an 88 percent classification rate in an eight class problem, representing a classification performance increase of thirty percent from previous efforts. A novel confidence figure of merit is implemented to attain a 100 percent classification rate with less than 60 percent rejection. The high classification rates are achieved on a target set which would pose significant problems to traditional image-based recognition systems. The targets are presented to the sensor in a variety of aspects and engine speeds at a range of 1 kilometer. The classification rates achieved demonstrate the benefits of using remote vibration measurement in a ground IFF system. The signature modeling and classification system can also be used to identify rotary and fixed-wing targets.

Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors.

Science.gov (United States)

Yurtman, Aras; Barshan, Billur

2017-08-09

Most activity recognition studies that employ wearable sensors assume that the sensors are attached at pre-determined positions and orientations that do not change over time. Since this is not the case in practice, it is of interest to develop wearable systems that operate invariantly to sensor position and orientation. We focus on invariance to sensor orientation and develop two alternative transformations to remove the effect of absolute sensor orientation from the raw sensor data. We test the proposed methodology in activity recognition with four state-of-the-art classifiers using five publicly available datasets containing various types of human activities acquired by different sensor configurations. While the ordinary activity recognition system cannot handle incorrectly oriented sensors, the proposed transformations allow the sensors to be worn at any orientation at a given position on the body, and achieve nearly the same activity recognition performance as the ordinary system for which the sensor units are not rotatable. The proposed techniques can be applied to existing wearable systems without much effort, by simply transforming the time-domain sensor data at the pre-processing stage.

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

Science.gov (United States)

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

2015-11-01

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

Evaluation of missing data techniques for in-car automatic speech recognition

OpenAIRE

Wang, Y.; Vuerinckx, R.; Gemmeke, J.F.; Cranen, B.; Hamme, H. Van

2009-01-01

Wang Y., Vuerinckx R., Gemmeke J., Cranen B., Van hamme H., ''Evaluation of missing data techniques for in-car automatic speech recognition'', Proceedings NAG/DAGA 2009 - international conference on acoustics, 4 pp., March 23-26, 2009, Rotterdam, The Netherlands.

26 CFR 1.737-1 - Recognition of precontribution gain.

Science.gov (United States)

2010-04-01

... 26 Internal Revenue 8 2010-04-01 2010-04-01 false Recognition of precontribution gain. 1.737-1... gain. (a) Determination of gain—(1) In general. A partner that receives a distribution of property (other than money) must recognize gain under section 737 and this section in an amount equal to the...

Study of the Factors Affecting the Mathematics Achievement of Turkish Students According to Data from the Programme for International Student Assessment (PISA) 2012

Science.gov (United States)

Güzeller, Cem Oktay; Eser, Mehmet Taha; Aksu, Gökhan

2016-01-01

This study attempts to determine the factors affecting the mathematics achievement of students in Turkey based on data from the Programme for International Student Assessment 2012 and the correct classification ratio of the established model. The study used mathematics achievement as a dependent variable while sex, having a study room, preparation…

A Malaysian Vehicle License Plate Localization and Recognition System

Directory of Open Access Journals (Sweden)

Ganapathy Velappa

2008-02-01

Full Text Available Technological intelligence is a highly sought after commodity even in traffic-based systems. These intelligent systems do not only help in traffic monitoring but also in commuter safety, law enforcement and commercial applications. In this paper, a license plate localization and recognition system for vehicles in Malaysia is proposed. This system is developed based on digital images and can be easily applied to commercial car park systems for the use of documenting access of parking services, secure usage of parking houses and also to prevent car theft issues. The proposed license plate localization algorithm is based on a combination of morphological processes with a modified Hough Transform approach and the recognition of the license plates is achieved by the implementation of the feed-forward backpropagation artificial neural network. Experimental results show an average of 95% successful license plate localization and recognition in a total of 589 images captured from a complex outdoor environment.

The adaptive use of recognition in group decision making.

Science.gov (United States)

Kämmer, Juliane E; Gaissmaier, Wolfgang; Reimer, Torsten; Schermuly, Carsten C

2014-06-01

Applying the framework of ecological rationality, the authors studied the adaptivity of group decision making. In detail, they investigated whether groups apply decision strategies conditional on their composition in terms of task-relevant features. The authors focused on the recognition heuristic, so the task-relevant features were the validity of the group members' recognition and knowledge, which influenced the potential performance of group strategies. Forty-three three-member groups performed an inference task in which they had to infer which of two German companies had the higher market capitalization. Results based on the choice data support the hypothesis that groups adaptively apply the strategy that leads to the highest theoretically achievable performance. Time constraints had no effect on strategy use but did have an effect on the proportions of different types of arguments. Possible mechanisms underlying the adaptive use of recognition in group decision making are discussed. © 2014 Cognitive Science Society, Inc.

Two-dimensional shape recognition using oriented-polar representation

Science.gov (United States)

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

1997-10-01

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

A Markov Random Field Groupwise Registration Framework for Face Recognition.

Science.gov (United States)

Liao, Shu; Shen, Dinggang; Chung, Albert C S

2014-04-01

In this paper, we propose a new framework for tackling face recognition problem. The face recognition problem is formulated as groupwise deformable image registration and feature matching problem. The main contributions of the proposed method lie in the following aspects: (1) Each pixel in a facial image is represented by an anatomical signature obtained from its corresponding most salient scale local region determined by the survival exponential entropy (SEE) information theoretic measure. (2) Based on the anatomical signature calculated from each pixel, a novel Markov random field based groupwise registration framework is proposed to formulate the face recognition problem as a feature guided deformable image registration problem. The similarity between different facial images are measured on the nonlinear Riemannian manifold based on the deformable transformations. (3) The proposed method does not suffer from the generalizability problem which exists commonly in learning based algorithms. The proposed method has been extensively evaluated on four publicly available databases: FERET, CAS-PEAL-R1, FRGC ver 2.0, and the LFW. It is also compared with several state-of-the-art face recognition approaches, and experimental results demonstrate that the proposed method consistently achieves the highest recognition rates among all the methods under comparison.

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.

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

Science.gov (United States)

Yin, Xi; Liu, Xiaoming

2018-02-01

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

Ordinal measures for iris recognition.

Science.gov (United States)

Sun, Zhenan; Tan, Tieniu

2009-12-01

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

Event Recognition Based on Deep Learning in Chinese Texts.

Directory of Open Access Journals (Sweden)

Yajun Zhang

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

Event Recognition Based on Deep Learning in Chinese Texts.

Science.gov (United States)

Zhang, Yajun; Liu, Zongtian; Zhou, Wen

2016-01-01

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

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...

A gesture-controlled Serious Game for teaching emotion recognition skills to preschoolers with autism

OpenAIRE

Christinaki, Eirini; Triantafyllidis, Georgios; Vidakis, Nikolaos

2013-01-01

The recognition of facial expressions is important for the perception of emotions. Understanding emotions is essential in human communication and social interaction. Children with autism have been reported to exhibit deficits in the recognition of affective expressions. With the appropriate intervention, elimination of those deficits can be achieved. Interventions are proposed to start as early as possible. Computer-based programs have been widely used with success to teach people with autism...

Activity and function recognition for moving and static objects in urban environments from wide-area persistent surveillance inputs

Science.gov (United States)

Levchuk, Georgiy; Bobick, Aaron; Jones, Eric

2010-04-01

In this paper, we describe results from experimental analysis of a model designed to recognize activities and functions of moving and static objects from low-resolution wide-area video inputs. Our model is based on representing the activities and functions using three variables: (i) time; (ii) space; and (iii) structures. The activity and function recognition is achieved by imposing lexical, syntactic, and semantic constraints on the lower-level event sequences. In the reported research, we have evaluated the utility and sensitivity of several algorithms derived from natural language processing and pattern recognition domains. We achieved high recognition accuracy for a wide range of activity and function types in the experiments using Electro-Optical (EO) imagery collected by Wide Area Airborne Surveillance (WAAS) platform.

Evaluation of iris recognition system for wavefront-guided laser in situ keratomileusis for myopic astigmatism.

Science.gov (United States)

Ghosh, Sudipta; Couper, Terry A; Lamoureux, Ecosse; Jhanji, Vishal; Taylor, Hugh R; Vajpayee, Rasik B

2008-02-01

To evaluate the visual and refractive outcomes of wavefront-guided laser in situ keratomileusis (LASIK) using an iris recognition system for the correction of myopic astigmatism. Centre for Eye Research Australia, Melbourne Excimer Laser Research Group, and Royal Victorian Eye and Ear Hospital, East Melbourne, Victoria, Australia. A comparative analysis of wavefront-guided LASIK was performed with an iris recognition system (iris recognition group) and without iris recognition (control group). The main parameters were uncorrected visual acuity (UCVA), best spectacle-corrected visual acuity, amount of residual cylinder, manifest spherical equivalent (SE), and the index of success using the Alpins method of astigmatism analysis 1 and 3 months postoperatively. A P value less than 0.05 was considered statistically significant. Preoperatively, the mean SE was -4.32 diopters (D) +/- 1.59 (SD) in the iris recognition group (100 eyes) and -4.55 +/- 1.87 D in the control group (98 eyes) (P = .84). At 3 months, the mean SE was -0.05 +/- 0.21 D and -0.20 +/- 0.40 D, respectively (P = .001), and an SE within +/-0.50 D of emmetropia was achieved in 92.0% and 85.7% of eyes, respectively (P = .07). At 3 months, the UCVA was 20/20 or better in 90.0% and 76.5% of eyes, respectively. A statistically significant difference in the amount of astigmatic correction was seen between the 2 groups (P = .00 and P = .01 at 1 and 3 months, respectively). The index of success was 98.0% in the iris recognition group and 81.6% in the control group (P = .03). Iris recognition software may achieve better visual and refractive outcomes in wavefront-guided LASIK for myopic astigmatism.

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.

Optimization Methods in Emotion Recognition System

Directory of Open Access Journals (Sweden)

L. Povoda

2016-09-01

Full Text Available Emotions play big role in our everyday communication and contain important information. This work describes a novel method of automatic emotion recognition from textual data. The method is based on well-known data mining techniques, novel approach based on parallel run of SVM (Support Vector Machine classifiers, text preprocessing and 3 optimization methods: sequential elimination of attributes, parameter optimization based on token groups, and method of extending train data sets during practical testing and production release final tuning. We outperformed current state of the art methods and the results were validated on bigger data sets (3346 manually labelled samples which is less prone to overfitting when compared to related works. The accuracy achieved in this work is 86.89% for recognition of 5 emotional classes. The experiments were performed in the real world helpdesk environment, was processing Czech language but the proposed methodology is general and can be applied to many different languages.

Dynamic Recognition of Driver’s Propensity Based on GPS Mobile Sensing Data and Privacy Protection

Directory of Open Access Journals (Sweden)

Xiaoyuan Wang

2016-01-01

Full Text Available Driver’s propensity is a dynamic measurement of driver’s emotional preference characteristics in driving process. It is a core parameter to compute driver’s intention and consciousness in safety driving assist system, especially in vehicle collision warning system. It is also an important influence factor to achieve the Driver-Vehicle-Environment Collaborative Wisdom and Control macroscopically. In this paper, dynamic recognition model of driver’s propensity based on support vector machine is established taking the vehicle safety controlled technology and respecting and protecting the driver’s privacy as precondition. The experiment roads travel time obtained through GPS is taken as the characteristic parameter. The sensing information of Driver-Vehicle-Environment was obtained through psychological questionnaire tests, real vehicle experiments, and virtual driving experiments, and the information is used for parameter calibration and validation of the model. Results show that the established recognition model of driver’s propensity is reasonable and feasible, which can achieve the dynamic recognition of driver’s propensity to some extent. The recognition model provides reference and theoretical basis for personalized vehicle active safety systems taking people as center especially for the vehicle safety technology based on the networking.

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...

Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis

Directory of Open Access Journals (Sweden)

Taeho Hur

2017-04-01

Full Text Available Activity recognition through smartphones has been proposed for a variety of applications. The orientation of the smartphone has a significant effect on the recognition accuracy; thus, researchers generally propose using features invariant to orientation or displacement to achieve this goal. However, those features reduce the capability of the recognition system to differentiate among some specific commuting activities (e.g., bus and subway that normally involve similar postures. In this work, we recognize those activities by analyzing the vibrations of the vehicle in which the user is traveling. We extract natural vibration features of buses and subways to distinguish between them and address the confusion that can arise because the activities are both static in terms of user movement. We use the gyroscope to fix the accelerometer to the direction of gravity to achieve an orientation-free use of the sensor. We also propose a correction algorithm to increase the accuracy when used in free living conditions and a battery saving algorithm to consume less power without reducing performance. Our experimental results show that the proposed system can adequately recognize each activity, yielding better accuracy in the detection of bus and subway activities than existing methods.

Smartphone Location-Independent Physical Activity Recognition Based on Transportation Natural Vibration Analysis.

Science.gov (United States)

Hur, Taeho; Bang, Jaehun; Kim, Dohyeong; Banos, Oresti; Lee, Sungyoung

2017-04-23

Activity recognition through smartphones has been proposed for a variety of applications. The orientation of the smartphone has a significant effect on the recognition accuracy; thus, researchers generally propose using features invariant to orientation or displacement to achieve this goal. However, those features reduce the capability of the recognition system to differentiate among some specific commuting activities (e.g., bus and subway) that normally involve similar postures. In this work, we recognize those activities by analyzing the vibrations of the vehicle in which the user is traveling. We extract natural vibration features of buses and subways to distinguish between them and address the confusion that can arise because the activities are both static in terms of user movement. We use the gyroscope to fix the accelerometer to the direction of gravity to achieve an orientation-free use of the sensor. We also propose a correction algorithm to increase the accuracy when used in free living conditions and a battery saving algorithm to consume less power without reducing performance. Our experimental results show that the proposed system can adequately recognize each activity, yielding better accuracy in the detection of bus and subway activities than existing methods.

International conference, ICPRAM 2012

CERN Document Server

Sánchez, J; Fred, Ana; Pattern recognition : applications and methods : revised selected papers

2013-01-01

This edited book includes extended and revised versions of a set of selected papers from the First International Conference on Pattern Recognition (ICPRAM 2012), held in Vilamoura, Algarve, Portugal, from 6 to 8 February, 2012, sponsored by the Institute for Systems and Technologies of Information Control and Communication (INSTICC) and held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI) and Pattern Analysis, Statistical Modelling and Computational Learning (PASCAL2). The conference brought together researchers, engineers and practitioners interested on the areas of Pattern Recognition, both from theoretical and application perspectives.

Activity Recognition Using Hybrid Generative/Discriminative Models on Home Environments Using Binary Sensors

Directory of Open Access Journals (Sweden)

Araceli Sanchis

2013-04-01

Full Text Available Activities of daily living are good indicators of elderly health status, and activity recognition in smart environments is a well-known problem that has been previously addressed by several studies. In this paper, we describe the use of two powerful machine learning schemes, ANN (Artificial Neural Network and SVM (Support Vector Machines, within the framework of HMM (Hidden Markov Model in order to tackle the task of activity recognition in a home setting. The output scores of the discriminative models, after processing, are used as observation probabilities of the hybrid approach. We evaluate our approach by comparing these hybrid models with other classical activity recognition methods using five real datasets. We show how the hybrid models achieve significantly better recognition performance, with significance level p < 0:05, proving that the hybrid approach is better suited for the addressed domain.

The nuclear fuel rod character recognition system based on neural network technique

International Nuclear Information System (INIS)

Kim, Woong-Ki; Park, Soon-Yong; Lee, Yong-Bum; Kim, Seung-Ho; Lee, Jong-Min; Chien, Sung-Il.

1994-01-01

The nuclear fuel rods should be discriminated and managed systematically by numeric characters which are printed at the end part of each rod in the process of producing fuel assembly. The characters are used to examine manufacturing process of the fuel rods in the inspection process of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies to establish automatic manufacturing process of fuel assembly. In the developed character recognition system, mesh feature set extracted from each character written in the fuel rod is employed to train a neural network based on back-propagation algorithm as a classifier for character recognition system. Performance evaluation has been achieved on a test set which is not included in a training character set. (author)

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.

Exploring global recognition of quality midwifery education: Vision or fiction?

Science.gov (United States)

Luyben, Ans; Barger, Mary; Avery, Melissa; Bharj, Kuldip Kaur; O'Connell, Rhona; Fleming, Valerie; Thompson, Joyce; Sherratt, Della

2017-06-01

Midwifery education is the foundation for preparing competent midwives to provide a high standard of safe, evidence-based care for women and their newborns. Global competencies and standards for midwifery education have been defined as benchmarks for establishing quality midwifery education and practice worldwide. However, wide variations in type and nature of midwifery education programs exist. To explore and discuss the opportunities and challenges of a global quality assurance process as a strategy to promote quality midwifery education. Accreditation and recognition as two examples of quality assurance processes in education are discussed. A global recognition process, with its opportunities and challenges, is explored from the perspective of four illustrative case studies from Ireland, Kosovo, Latin America and Bangladesh. The discussion highlights that the establishment of a global recognition process may assist in promoting quality of midwifery education programs world-wide, but cannot take the place of formal national accreditation. In addition, a recognition process will not be feasible for many institutions without additional resources, such as financial support or competent evaluators. In order to achieve quality midwifery education through a global recognition process the authors present 5 Essential Challenges for Quality Midwifery Education. Quality midwifery education is vital for establishing a competent workforce, and improving maternal and newborn health. Defining a global recognition process could be instrumental in moving toward this goal, but dealing with the identified challenges will be essential. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Generating solutions : summary of the Electricity Sector Council's review of foreign credential recognition

International Nuclear Information System (INIS)

2008-03-01

The Electricity Sector Council has recognized the increasing requirement to recruit and retain internationally trained workers to offset the anticipated retirement of up to 40 per cent of skilled workers in this sector by 2014. This document provided a brief summary of the review of foreign credential recognition in Canada's Electricity Council which was prepared in February 2008. The purpose of the study was to capture a perceptive picture of Canada's electricity labour force and to assist the Electricity Sector Council in the potential development and implementation of strategies to facilitate the integration of internationally trained workers into Canada's electricity sector. This synopsis report presented the analysis of the study including a discussion of immigration trends; foreign credential recognition in Canada's electricity sector; immigration profiles by region; case study profiles; and recommendations. It was recommended that resources be researched, developed and provided in order to help stakeholders attract, recruit, retain and integrate internationally trained workers. 2 refs

An Efficient Solution for Hand Gesture Recognition from Video Sequence

Directory of Open Access Journals (Sweden)

PRODAN, R.-C.

2012-08-01

Full Text Available The paper describes a system of hand gesture recognition by image processing for human robot interaction. The recognition and interpretation of the hand postures acquired through a video camera allow the control of the robotic arm activity: motion - translation and rotation in 3D - and tightening/releasing the clamp. A gesture dictionary was defined and heuristic algorithms for recognition were developed and tested. The system can be used for academic and industrial purposes, especially for those activities where the movements of the robotic arm were not previously scheduled, for training the robot easier than using a remote control. Besides the gesture dictionary, the novelty of the paper consists in a new technique for detecting the relative positions of the fingers in order to recognize the various hand postures, and in the achievement of a robust system for controlling robots by postures of the hands.

Early mathematics development and later achievement: Further evidence

Science.gov (United States)

Aubrey, Carol; Godfrey, Ray; Dahl, Sarah

2006-05-01

There is a growing international recognition of the importance of the early years of schooling as well as an interest being shown in the relationship of early education to later achievement. This article focuses on a cohort of English pupils who have been tracked through primary school during the first five years of the new National Numeracy Strategy. It reports a limited longitudinal study of young children's early mathematical development, initially within three testing cycles: at the mid-point and towards the end of their reception year (at five years-of-age) and again at the mid-point of Year 1 (at six years-ofage). These cycles were located within the broader context of progress through to the end of Key Stage 1 (at seven years) and Key Stage 2 (at eleven years) on the basis of national standardised assessment tests (SATs). Results showed that children who bring into school early mathematical knowledge do appear to be advantaged in terms of their mathematical progress through primary school. Numerical attainment increases in importance across the primary years and practical problem solving remains an important element of this. This finding is significant given the current emphasis on numerical calculation in the English curriculum. It is concluded that without active intervention, it is likely that children with little mathematical knowledge at the beginning of formal schooling will remain low achievers throughout their primary years and, probably, beyond.

Neurocomputing methods for pattern recognition in nuclear physics

Energy Technology Data Exchange (ETDEWEB)

Gyulassy, M.; Dong, D.; Harlander, M. [Lawrence Berkeley Lab., CA (United States)

1991-12-31

We review recent progress on the development and applications of novel neurocomputing techniques for pattern recognition problems of relevance to RHIC experiments. The Elastic Tracking algorithm is shown to achieve sub-pad two track resolution without preprocessing. A high pass neural filter is developed for jet analysis and singular deconvolution methods are shown to recover the primordial jet distribution to a surprising high degree of accuracy.

Listening for recollection: a multi-voxel pattern analysis of recognition memory retrieval strategies

Directory of Open Access Journals (Sweden)

Joel R Quamme

2010-08-01

Full Text Available Recent studies of recognition memory indicate that subjects can strategically vary how much they rely on recollection of specific details vs. feelings of familiarity when making recognition judgments. One possible explanation of these results is that subjects can establish an internally-directed attentional state (listening for recollection that enhances retrieval of studied details; fluctuations in this attentional state over time should be associated with fluctuations in subjects' recognition behavior. In this study, we used multi-voxel pattern analysis of fMRI data to identify brain regions that are involved in listening for recollection. Specifically, we looked for brain regions that met the following criteria: 1 Distinct neural patterns should be present when subjects are instructed to rely on recollection vs. familiarity, and 2 fluctuations in these neural patterns should be related to recognition behavior in the manner predicted by dual-process theories of recognition: Specifically, the presence of the recollection pattern during the pre-stimulus interval (indicating that subjects are listening for recollection at that moment should be associated with a selective decrease in false alarms to related lures. We found that pre-stimulus activity in the right supramarginal gyrus met all of these criteria, suggesting that this region proactively establishes an internally-directed attentional state that fosters recollection. We also found other regions (e.g., left middle temporal gyrus where the pattern of neural activity was related to subjects’ responding to related lures after stimulus onset (but not before, suggesting that these regions implement processes that are engaged in a reactive fashion to boost recollection.

The environmental impact of coal technology: Politics and methods of achieving an international convention to protect the earth's atmosphere

International Nuclear Information System (INIS)

1992-01-01

Particular attention will be paid to the following points during the international Round Table discussions: Technology transfer and political opportunities for the environmentally-compatible use of coal in developing and transitional countries who rely on coal to meet their energy needs. Supporting this challenge for the international community of coal-based energy conservation in these countries, particularly through development aid. To increasingly accept our collective responsibility for energy considerations and environmental effects, based on a better understanding of the countries' differing limits and obligations in achieving the goal of a convention to protect the earth's atmosphere. (orig.) [de

Face recognition based on two-dimensional discriminant sparse preserving projection

Science.gov (United States)

Zhang, Dawei; Zhu, Shanan

2018-04-01

In this paper, a supervised dimensionality reduction algorithm named two-dimensional discriminant sparse preserving projection (2DDSPP) is proposed for face recognition. In order to accurately model manifold structure of data, 2DDSPP constructs within-class affinity graph and between-class affinity graph by the constrained least squares (LS) and l1 norm minimization problem, respectively. Based on directly operating on image matrix, 2DDSPP integrates graph embedding (GE) with Fisher criterion. The obtained projection subspace preserves within-class neighborhood geometry structure of samples, while keeping away samples from different classes. The experimental results on the PIE and AR face databases show that 2DDSPP can achieve better recognition performance.

Action recognition using mined hierarchical compound features.

Science.gov (United States)

Gilbert, Andrew; Illingworth, John; Bowden, Richard

2011-05-01

The field of Action Recognition has seen a large increase in activity in recent years. Much of the progress has been through incorporating ideas from single-frame object recognition and adapting them for temporal-based action recognition. Inspired by the success of interest points in the 2D spatial domain, their 3D (space-time) counterparts typically form the basic components used to describe actions, and in action recognition the features used are often engineered to fire sparsely. This is to ensure that the problem is tractable; however, this can sacrifice recognition accuracy as it cannot be assumed that the optimum features in terms of class discrimination are obtained from this approach. In contrast, we propose to initially use an overcomplete set of simple 2D corners in both space and time. These are grouped spatially and temporally using a hierarchical process, with an increasing search area. At each stage of the hierarchy, the most distinctive and descriptive features are learned efficiently through data mining. This allows large amounts of data to be searched for frequently reoccurring patterns of features. At each level of the hierarchy, the mined compound features become more complex, discriminative, and sparse. This results in fast, accurate recognition with real-time performance on high-resolution video. As the compound features are constructed and selected based upon their ability to discriminate, their speed and accuracy increase at each level of the hierarchy. The approach is tested on four state-of-the-art data sets, the popular KTH data set to provide a comparison with other state-of-the-art approaches, the Multi-KTH data set to illustrate performance at simultaneous multiaction classification, despite no explicit localization information provided during training. Finally, the recent Hollywood and Hollywood2 data sets provide challenging complex actions taken from commercial movie sequences. For all four data sets, the proposed hierarchical

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

The Era of Global Disputes and Mass Media Distortions. Dialogue on Recognition, Justice and Democracy

Czech Academy of Sciences Publication Activity Database

Bittar, E.; Hrubec, Marek

2017-01-01

Roč. 8, č. 2 (2017), s. 146-154 ISSN 1338-130X Grant - others:AV ČR(CZ) StrategieAV21/15 Program:StrategieAV Institutional support: RVO:67985955 Keywords : global conflicts * international law * justice * mass media * recognition * democracy Subject RIV: AA - Philosophy ; Religion OBOR OECD: Political science https://www.communicationtoday.sk/era-global-disputes-mass-media-distortions-dialogue-recognition-justice-democracy-interview-marek-hrubec/

Mutual recognition and the right to damages for criminal investigations

DEFF Research Database (Denmark)

Bang Fuglsang Madsen Sørensen, Henning

2015-01-01

This article considers how the citizen who has been surrendered on an EAW in a case ending with acquittal or non-prosecution may achieve compensation for the loss of freedom and the surrender to another Member State. The analysis shows how the EAW and the principle of mutual recognition have been...

Gold Medal Award for Life Achievement in the Practice of Psychology: Arthur L. Kovacs.

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American Psychologist, 2004

2004-01-01

The 2004 Gold Medal Award for Life Achievement in the Practice of Psychology is awarded to Arthur L. Kovacs. He is recognized for making outstanding contributions to achieving statutory recognition and securing insurance reimbursement, and as a pioneer in the professional school movement, having trained several generations of practitioners.

On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition

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Marco Zennaro

2010-12-01

Full Text Available Achieving situation recognition in ubiquitous sensor networks (USNs is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.

Effects of compression and individual variability on face recognition performance

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McGarry, Delia P.; Arndt, Craig M.; McCabe, Steven A.; D'Amato, Donald P.

2004-08-01

The Enhanced Border Security and Visa Entry Reform Act of 2002 requires that the Visa Waiver Program be available only to countries that have a program to issue to their nationals machine-readable passports incorporating biometric identifiers complying with applicable standards established by the International Civil Aviation Organization (ICAO). In June 2002, the New Technologies Working Group of ICAO unanimously endorsed the use of face recognition (FR) as the globally interoperable biometric for machine-assisted identity confirmation with machine-readable travel documents (MRTDs), although Member States may elect to use fingerprint and/or iris recognition as additional biometric technologies. The means and formats are still being developed through which biometric information might be stored in the constrained space of integrated circuit chips embedded within travel documents. Such information will be stored in an open, yet unalterable and very compact format, probably as digitally signed and efficiently compressed images. The objective of this research is to characterize the many factors that affect FR system performance with respect to the legislated mandates concerning FR. A photograph acquisition environment and a commercial face recognition system have been installed at Mitretek, and over 1,400 images have been collected of volunteers. The image database and FR system are being used to analyze the effects of lossy image compression, individual differences, such as eyeglasses and facial hair, and the acquisition environment on FR system performance. Images are compressed by varying ratios using JPEG2000 to determine the trade-off points between recognition accuracy and compression ratio. The various acquisition factors that contribute to differences in FR system performance among individuals are also being measured. The results of this study will be used to refine and test efficient face image interchange standards that ensure highly accurate recognition, both

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

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

2006-05-01

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

Compressed sensing method for human activity recognition using tri-axis accelerometer on mobile phone

Institute of Scientific and Technical Information of China (English)

Song Hui; Wang Zhongmin

2017-01-01

The diversity in the phone placements of different mobile users' dailylife increases the difficulty of recognizing human activities by using mobile phone accelerometer data.To solve this problem,a compressed sensing method to recognize human activities that is based on compressed sensing theory and utilizes both raw mobile phone accelerometer data and phone placement information is proposed.First,an over-complete dictionary matrix is constructed using sufficient raw tri-axis acceleration data labeled with phone placement information.Then,the sparse coefficient is evaluated for the samples that need to be tested by resolving L1 minimization.Finally,residual values are calculated and the minimum value is selected as the indicator to obtain the recognition results.Experimental results show that this method can achieve a recognition accuracy reaching 89.86％,which is higher than that of a recognition method that does not adopt the phone placement information for the recognition process.The recognition accuracy of the proposed method is effective and satisfactory.

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

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

2013-07-01

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

The IOC as an international organization

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EFTHALIA CHATZIGIANNI

2006-01-01

Full Text Available The purpose of this article is to demonstrate the role of the International Olympic Committee as an International Organization in the field of interdependent world politics. Contemporary international community is organized on the basis of international organizations that contribute to the cooperation and understanding of the people especially in areas that enjoy human recognition worldwide. These organizations may function as agents of world solidarity and aim directly or indirectly at the promotion of understanding between people and consequently at the establishment of peace. They also carry out activities that aim at influencing national and international politics relevant to their respective goals. In this field, the IOC, as the most important International Non-Governmental Organization in the field of sport, plays a significant role. With an activity spanning more than a century, the IOC has been able to unify nations under the notion of Olympic ideals. This article aims at contributing partly to the theoretical discussion concerning the ability of the IOC to act as an International Non Governmental Organization and fit in the pieces of world governance in terms of structure and activities. More specifically, it examines this ability in relation to the following facts: a the IOC enjoys international recognition and has a well-established international network, b it has a dynamic character and c it has the authority and financial capacity to function on international and national level as the representative of its 202 members, the National Olympic Committees (NOCs.

Jumping for recognition: Women's ski jumping viewed as a struggle for rights.

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Andersen, W; Loland, S

2017-03-01

With the campaign for women's participation in international and Olympic ski jumping as a practical case, sport's potential for recognition of individual rights is explored. In line with Honneth's influential ethical theory, recognition of rights refers to a mutual recognition between persons of each other as rational and responsible agents with an equal right to take part in the public formation and development of their community or practice. The argument is that women ski jumpers were entitled to compete as they had actual and/or potential capabilities and skills to contribute in the public formation and development of their sport. Their exclusion was a violation of individual rights. At a more general level, sport is discussed as a sphere for recognition of rights. It is argued that the basic principles of equal opportunity to take part and to perform make sport a particularly clear and potent sphere for such recognition, and also for the identification of rights violations. In sport, rights, or the violation of rights, are demonstrated in concrete and embodied ways. It is concluded that struggles for recognition and individual rights are a continuous process in sport as in most other human institutions and practices. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

A Cooking Recipe Recommendation System with Visual Recognition of Food Ingredients

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Keiji Yanai

2014-04-01

Full Text Available In this paper, we propose a cooking recipe recommendation system which runs on a consumer smartphone as an interactive mobile application. The proposed system employs real-time visual object recognition of food ingredients, and recommends cooking recipes related to the recognized food ingredients. Because of visual recognition, by only pointing a built-in camera on a smartphone to food ingredients, a user can get to know a related cooking recipes instantly. The objective of the proposed system is to assist people who cook to decide a cooking recipe at grocery stores or at a kitchen. In the current implementation, the system can recognize 30 kinds of food ingredient in 0.15 seconds, and it has achieved the 83.93% recognition rate within the top six candidates. By the user study, we confirmed the effectiveness of the proposed system.

A Support System for the Electric Appliance Control Using Pose Recognition

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Kawano, Takuya; Yamamoto, Kazuhiko; Kato, Kunihito; Hongo, Hitoshi

In this paper, we propose an electric appliance control support system for aged and bedridden people using pose recognition. We proposed a pose recognition system that distinguishes between seven poses of the user on the bed. First, the face and arm regions of the user are detected by using the skin color. Our system focuses a recognition region surrounding the face region. Next, the higher order local autocorrelation features within the region are extracted. The linear discriminant analysis creates the coefficient matrix that can optimally distinguish among training data from the seven poses. Our algorithm can recognize the seven poses even if the subject wears different clothes and slightly shifts or slants on the bed. From the experimental results, our system achieved an accuracy rate of over 99 %. Then, we show that it possibles to construct one of a user-friendly system.

An effective approach for iris recognition using phase-based image matching.

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Miyazawa, Kazuyuki; Ito, Koichi; Aoki, Takafumi; Kobayashi, Koji; Nakajima, Hiroshi

2008-10-01

This paper presents an efficient algorithm for iris recognition using phase-based image matching--an image matching technique using phase components in 2D Discrete Fourier Transforms (DFTs) of given images. Experimental evaluation using CASIA iris image databases (versions 1.0 and 2.0) and Iris Challenge Evaluation (ICE) 2005 database clearly demonstrates that the use of phase components of iris images makes possible to achieve highly accurate iris recognition with a simple matching algorithm. This paper also discusses major implementation issues of our algorithm. In order to reduce the size of iris data and to prevent the visibility of iris images, we introduce the idea of 2D Fourier Phase Code (FPC) for representing iris information. The 2D FPC is particularly useful for implementing compact iris recognition devices using state-of-the-art Digital Signal Processing (DSP) technology.

Automatic Recognition Method for Optical Measuring Instruments Based on Machine Vision

Institute of Scientific and Technical Information of China (English)

SONG Le; LIN Yuchi; HAO Liguo

2008-01-01

Based on a comprehensive study of various algorithms, the automatic recognition of traditional ocular optical measuring instruments is realized. Taking a universal tools microscope (UTM) lens view image as an example, a 2-layer automatic recognition model for data reading is established after adopting a series of pre-processing algorithms. This model is an optimal combination of the correlation-based template matching method and a concurrent back propagation (BP) neural network. Multiple complementary feature extraction is used in generating the eigenvectors of the concurrent network. In order to improve fault-tolerance capacity, rotation invariant features based on Zernike moments are extracted from digit characters and a 4-dimensional group of the outline features is also obtained. Moreover, the operating time and reading accuracy can be adjusted dynamically by setting the threshold value. The experimental result indicates that the newly developed algorithm has optimal recognition precision and working speed. The average reading ratio can achieve 97.23%. The recognition method can automatically obtain the results of optical measuring instruments rapidly and stably without modifying their original structure, which meets the application requirements.

Vision models for target detection and recognition in memory of Arthur Menendez

CERN Document Server

Peli, Eli

1995-01-01

This book is an international collection of contributions from academia, industry and the armed forces. It addresses current and emerging Spatial Vision Models and their application to the understanding, prediction and evaluation of the tasks of target detection and recognition. The discussion in many of the chapters is framed in terms of military targets and military vision aids. However, the techniques analyses and problems are by no means limited to this area of application. The detection and recognition of an armored vehicle from a reconnaissance image are performed by the same visual syst

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

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

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

The Internal/External Frame of Reference Model of Self-Concept and Achievement Relations: Age-Cohort and Cross-Cultural Differences

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Marsh, Herbert W.; Abduljabbar, Adel Salah; Parker, Philip D.; Morin, Alexandre J. S.; Abdelfattah, Faisal; Nagengast, Benjamin; Möller, Jens; Abu-Hilal, Maher M.

2015-01-01

The internal/external frame of reference (I/E) model and dimensional comparison theory posit paradoxical relations between achievement (ACH) and self-concept (SC) in mathematics (M) and verbal (V) domains; ACH in each domain positively affects SC in the matching domain (e.g., MACH to MSC) but negatively in the nonmatching domain (e.g., MACH to…

[Graduate Students in Medicine Course: Motivation, Socialization and Academic Recognition].

Science.gov (United States)

Magalhães-Alves, Cristina; Barbosa, Joselina; Ribeiro, Laura; Ferreira, Maria Amélia

2017-04-28

Students with a previous degree have personal and professional experiences that can contribute to a different academic path during the medical course. This study aims to: 1) analyze both satisfaction and impact of academic recognition; 2) investigate whether motivations and expectations at entrance are maintained along the course; 3) to evaluate socialization after regress to higher education. To accomplish the first objective a questionnaire was administered to 82 students who entered the medical school from 2011/2012 to 2013/2014. For the second and third goals a focus group was run (three groups with five students each, representing the three academic years). Students felt satisfied with the recognition, and 50% of them believe that accreditations replace knowledge acquired with the curricular units, and 47% preferred to obtain accreditation. Academic achievement was negatively associated with the satisfaction of recognition and positively with age, background and registration cycle. Socialization of these students is distinct from the younger ones, their motivations at entrance are intrinsic and, contrary to expectations, are maintained along the course. Students prefer recognition instead of attending the curricular units. The most satisfied with the recognition accomplish less credits and the younger ones, from health area and enrolled in the clinical cycle, accomplish more. Along the course, motivations become more solid, expectations change and socialization is carried out with greater responsibility.

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,

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.

Cluster-Based Adaptation Using Density Forest for HMM Phone Recognition

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Tan, Zheng-Hua; Christensen, Mads Græsbøll

2014-01-01

The dissimilarity between the training and test data in speech recognition systems is known to have a considerable effect on the recognition accuracy. To solve this problem, we use density forest to cluster the data and use maximum a posteriori (MAP) method to build a cluster-based adapted Gaussian...... mixture models (GMMs) in HMM speech recognition. Specifically, a set of bagged versions of the training data for each state in the HMM is generated, and each of these versions is used to generate one GMM and one tree in the density forest. Thereafter, an acoustic model forest is built by replacing...... the data of each leaf (cluster) in each tree with the corresponding GMM adapted by the leaf data using the MAP method. The results show that the proposed approach achieves 3:8% (absolute) lower phone error rate compared with the standard HMM/GMM and 0:8% (absolute) lower PER compared with bagged HMM/GMM....

Investigating the Impact of Possession-Way of a Smartphone on Action Recognition

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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.

Enhancing the Employability of Chinese International Students: Identifying Achievements and Gaps in the Research Field

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Xuemeng Cao

2017-10-01

Full Text Available This article shows what achievements have been made by existing studies on graduate employability, and what gaps need to be filled in this field. It starts with a retrospective account of the changing concept of employability, followed by a presentation of the practices that have been used to support graduate employability enhancement in different countries. Moreover, this article gives a critical review of Chinese contexts of graduate labour market. Last but not least, limitations of existing studies are identified, which reflect an expectation for future research on graduate employability to meet the demand of an increasingly international dimension of higher education.

Object recognition with hierarchical discriminant saliency networks

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Sunhyoung eHan

2014-09-01

Full Text Available The benefits of integrating attention and object recognition are investigated. While attention is frequently modeled as pre-processor for recognition, we investigate the hypothesis that attention is an intrinsic component of recognition and vice-versa. This hypothesis is tested with a recognitionmodel, the hierarchical discriminant saliency network (HDSN, whose layers are top-down saliency detectors, tuned for a visual class according to the principles of discriminant saliency. The HDSN has two possible implementations. In a biologically plausible implementation, all layers comply with the standard neurophysiological model of visual cortex, with sub-layers of simple and complex units that implement a combination of filtering, divisive normalization, pooling, and non-linearities. In a neuralnetwork implementation, all layers are convolutional and implement acombination of filtering, rectification, and pooling. The rectificationis performed with a parametric extension of the now popular rectified linearunits (ReLUs, whose parameters can be tuned for the detection of targetobject classes. This enables a number of functional enhancementsover neural network models that lack a connection to saliency, including optimal feature denoising mechanisms for recognition, modulation ofsaliency responses by the discriminant power of the underlying features,and the ability to detect both feature presence and absence.In either implementation, each layer has a precise statistical interpretation, and all parameters are tuned by statistical learning. Each saliency detection layer learns more discriminant saliency templates than its predecessors and higher layers have larger pooling fields. This enables the HDSN to simultaneously achieve high selectivity totarget object classes and invariance. The resulting performance demonstrates benefits for all the functional enhancements of the HDSN.

ACTION RECOGNITION USING SALIENT NEIGHBORING HISTOGRAMS

DEFF Research Database (Denmark)

Ren, Huamin; Moeslund, Thomas B.

2013-01-01

Combining spatio-temporal interest points with Bag-of-Words models achieves state-of-the-art performance in action recognition. However, existing methods based on “bag-ofwords” models either are too local to capture the variance in space/time or fail to solve the ambiguity problem in spatial...... and temporal dimensions. Instead, we propose a salient vocabulary construction algorithm to select visual words from a global point of view, and form compact descriptors to represent discriminative histograms in the neighborhoods. Those salient neighboring histograms are then trained to model different actions...

International Conference "Social Sciences: Achievements and Prospects"

OpenAIRE

Open European Academy of Public Sciences

2018-01-01

The Organizing Committee of the International Scientific and Practical Conference of the Open European Academy of Social Sciences(Spain, Barcelona), in partnership with the Barcelona University (Spain, Barcelona), the Berlin University (Germany, Berlin) Conference sections: Anthropology, Demography and Ethnography, Journalism, Art History and Culturology History and archeology, Political science, Psychology, Pedagogy Regional studies and socio-economic geography, Relig...

World Association for the Advancement of Veterinary Parasitology (WAAVP): the 50th anniversary in 2013--history, achievements, and future perspectives.

Science.gov (United States)

Eckert, J

2013-08-01

In 2013 the World Association for the Advancement of Veterinary Parasitology (WAAVP) can celebrate its 50th anniversary. At this occasion in this article selected historical data are updated, and the achievements and future perspectives of the WAAVP are discussed. Although the WAAVP is a small association with only a few hundred members, it has been able to develop remarkable activities. Between 1963 and 2011 the WAAVP has organized 23 international scientific congresses, and the 24th conference will take place in Perth, Western Australia, in 2013. These conferences have achieved a high degree of international recognition as indicated by relatively large numbers of participants (up to ~800). Furthermore, the WAAVP has promoted veterinary parasitology in various ways, such as publishing international guidelines (efficacy evaluation of antiparasitic drugs, parasitological methods, standardized nomenclature of animal parasitic diseases "SNOAPAD"), stimulating international discussions on teaching and continued education ("colleges of veterinary parasitology") and by supporting the high quality journal "Veterinary Parasitology" which is the official organ of the WAAVP. In retrospect, the development of the WAAVP can be classified as very successful. New challenges associated with global changes (growth of the world population, urbanization, climate change, new developments in animal and plant production, etc.) will require new efforts in research in various fields, including veterinary parasitology. Future activities of WAAVP may include inter alia: (a) support of international parasitological networks; (b) stimulation of coordinated research aimed at the solution of defined problems; (c) increasing the exposure of WAAVP to parasitology from hitherto neglected regions of the world; (d) strengthening of official links to international organizations (FAO, WHO, etc.); (e) continuation of guideline preparation; and (d) preparation and international distribution of high

Automatic Target Recognition in Synthetic Aperture Sonar Images Based on Geometrical Feature Extraction

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J. Del Rio Vera

2009-01-01

Full Text Available This paper presents a new supervised classification approach for automated target recognition (ATR in SAS images. The recognition procedure starts with a novel segmentation stage based on the Hilbert transform. A number of geometrical features are then extracted and used to classify observed objects against a previously compiled database of target and non-target features. The proposed approach has been tested on a set of 1528 simulated images created by the NURC SIGMAS sonar model, achieving up to 95% classification accuracy.

Segment-based acoustic models for continuous speech recognition

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Ostendorf, Mari; Rohlicek, J. R.

1993-07-01

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

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

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

2017-01-01

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

Speech recognition by means of a three-integrated-circuit set

Energy Technology Data Exchange (ETDEWEB)

Zoicas, A.

1983-11-03

The author uses pattern recognition methods for detecting word boundaries, and monitors incoming speech at 12 millisecond intervals. Frequency is divided into eight bands and analysis is achieved in an analogue interface integrated circuit, a pipeline digital processor and a control integrated circuit. Applications are suggested, including speech input to personal computers. 3 references.

Invariant recognition drives neural representations of action sequences.

Directory of Open Access Journals (Sweden)

Andrea Tacchetti

2017-12-01

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

Recognition of Arabic Sign Language Alphabet Using Polynomial Classifiers

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M. Al-Rousan

2005-08-01

Full Text Available Building an accurate automatic sign language recognition system is of great importance in facilitating efficient communication with deaf people. In this paper, we propose the use of polynomial classifiers as a classification engine for the recognition of Arabic sign language (ArSL alphabet. Polynomial classifiers have several advantages over other classifiers in that they do not require iterative training, and that they are highly computationally scalable with the number of classes. Based on polynomial classifiers, we have built an ArSL system and measured its performance using real ArSL data collected from deaf people. We show that the proposed system provides superior recognition results when compared with previously published results using ANFIS-based classification on the same dataset and feature extraction methodology. The comparison is shown in terms of the number of misclassified test patterns. The reduction in the rate of misclassified patterns was very significant. In particular, we have achieved a 36% reduction of misclassifications on the training data and 57% on the test data.

Palm vein recognition based on directional empirical mode decomposition

Science.gov (United States)

Lee, Jen-Chun; Chang, Chien-Ping; Chen, Wei-Kuei

2014-04-01

Directional empirical mode decomposition (DEMD) has recently been proposed to make empirical mode decomposition suitable for the processing of texture analysis. Using DEMD, samples are decomposed into a series of images, referred to as two-dimensional intrinsic mode functions (2-D IMFs), from finer to large scale. A DEMD-based 2 linear discriminant analysis (LDA) for palm vein recognition is proposed. The proposed method progresses through three steps: (i) a set of 2-D IMF features of various scale and orientation are extracted using DEMD, (ii) the 2LDA method is then applied to reduce the dimensionality of the feature space in both the row and column directions, and (iii) the nearest neighbor classifier is used for classification. We also propose two strategies for using the set of 2-D IMF features: ensemble DEMD vein representation (EDVR) and multichannel DEMD vein representation (MDVR). In experiments using palm vein databases, the proposed MDVR-based 2LDA method achieved recognition accuracy of 99.73%, thereby demonstrating its feasibility for palm vein recognition.

Chinese License Plates Recognition Method Based on A Robust and Efficient Feature Extraction and BPNN Algorithm

Science.gov (United States)

Zhang, Ming; Xie, Fei; Zhao, Jing; Sun, Rui; Zhang, Lei; Zhang, Yue

2018-04-01

The prosperity of license plate recognition technology has made great contribution to the development of Intelligent Transport System (ITS). In this paper, a robust and efficient license plate recognition method is proposed which is based on a combined feature extraction model and BPNN (Back Propagation Neural Network) algorithm. Firstly, the candidate region of the license plate detection and segmentation method is developed. Secondly, a new feature extraction model is designed considering three sets of features combination. Thirdly, the license plates classification and recognition method using the combined feature model and BPNN algorithm is presented. Finally, the experimental results indicate that the license plate segmentation and recognition both can be achieved effectively by the proposed algorithm. Compared with three traditional methods, the recognition accuracy of the proposed method has increased to 95.7% and the consuming time has decreased to 51.4ms.

PRIVATE LAW EFFECTS OF THE NON-RECOGNITION OF STATES' EXISTENCE AND TERRITORIAL CHANGES

Directory of Open Access Journals (Sweden)

Ioan-Luca VLAD

2015-07-01

Full Text Available The study presents an outline of the effects in private law (including private international law of the non-recognition of a state or a change of territory. Specifically, it addresses the question of what measures can another state take, in the field of private law, in order to give effect to its policy of not recognizing a state or a territorial annexation, and, in parallel, what are the means available to private parties with links to the unrecognized state or territory. The study is structured in two parts, namely 1 the effects in private law of the non-recognition of a state; and 2 the effect in private law of the non-recognition of an annexation of territory. I will make specific references in particular to the situation in Transnistria and Crimea, as examples of the two issues being addressed. The study intends to be a guide of past and present state practice at the legislative and judicial level, as well as presenting the connections between instruments of public international law, such as Sanctions Resolutions of the UN Security Council, and normative instruments of private law, such as rules of civil procedure, which must adapt to the policy of non-recognition adopted by (or imposed on states. The study also presents specific examples of situations or administrative practices which create practical problems, and result from the existence of a non-recognized entity or change of territory: issues like air traffic coordination, postal traffic, the change in the official currency of a territory, questions of citizenship etc., the aim being to present the reader with a full picture of the issues and intricacies resulting from irregularities existing at the level of the international community of states.

The Development of an Online Instrument for Prior Learning Assessment and Recognition of Internationally Educated Nurses: A Pilot Study

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Elaine Elizabeth Santa Mina

2011-01-01

Full Text Available A fully online prior learning assessment and recognition (PLAR tool for internationally educated nurses (IENs was developed and tested by an inter-professional team at Ryerson University. The tool consisted of two stages: a self-assessment component followed by a multiple-choice examination and narrative (vignette evaluation. The purposes of the study were to describe the demographic profile of the IEN registered nurse (RN, to develop the benchmark responses that demonstrate competency at the entry-to-practice level of the typical IEN RN, and to describe the experience of completing an online PLAR tool. A mixed-method approach was used. Findings demonstrated that IEN RNs who immigrate to Ontario, Canada, are of various ages and come from a wide spectrum of countries. The PLAR process holds promise for an objective assessment of IEN’s eligibility to write the Canadian Registered Nurses Examination (CRNE and to meet a global need. Further testing of the tool across a broader sample is required.

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.

Anti Theft Mechanism Through Face recognition Using FPGA

Science.gov (United States)

Sundari, Y. B. T.; Laxminarayana, G.; Laxmi, G. Vijaya

2012-11-01

The use of vehicle is must for everyone. At the same time, protection from theft is also very important. Prevention of vehicle theft can be done remotely by an authorized person. The location of the car can be found by using GPS and GSM controlled by FPGA. In this paper, face recognition is used to identify the persons and comparison is done with the preloaded faces for authorization. The vehicle will start only when the authorized personís face is identified. In the event of theft attempt or unauthorized personís trial to drive the vehicle, an MMS/SMS will be sent to the owner along with the location. Then the authorized person can alert the security personnel for tracking and catching the vehicle. For face recognition, a Principal Component Analysis (PCA) algorithm is developed using MATLAB. The control technique for GPS and GSM is developed using VHDL over SPTRAN 3E FPGA. The MMS sending method is written in VB6.0. The proposed application can be implemented with some modifications in the systems wherever the face recognition or detection is needed like, airports, international borders, banking applications etc.

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

Science.gov (United States)

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-01-01

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

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

Science.gov (United States)

Chen, Jing; Cao, Ruochen; Wang, Yongtian

2015-12-10

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

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

Directory of Open Access Journals (Sweden)

Jing Chen

2015-12-01

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

Motion-sensor fusion-based gesture recognition and its VLSI architecture design for mobile devices

Science.gov (United States)

Zhu, Wenping; Liu, Leibo; Yin, Shouyi; Hu, Siqi; Tang, Eugene Y.; Wei, Shaojun

2014-05-01

With the rapid proliferation of smartphones and tablets, various embedded sensors are incorporated into these platforms to enable multimodal human-computer interfaces. Gesture recognition, as an intuitive interaction approach, has been extensively explored in the mobile computing community. However, most gesture recognition implementations by now are all user-dependent and only rely on accelerometer. In order to achieve competitive accuracy, users are required to hold the devices in predefined manner during the operation. In this paper, a high-accuracy human gesture recognition system is proposed based on multiple motion sensor fusion. Furthermore, to reduce the energy overhead resulted from frequent sensor sampling and data processing, a high energy-efficient VLSI architecture implemented on a Xilinx Virtex-5 FPGA board is also proposed. Compared with the pure software implementation, approximately 45 times speed-up is achieved while operating at 20 MHz. The experiments show that the average accuracy for 10 gestures achieves 93.98% for user-independent case and 96.14% for user-dependent case when subjects hold the device randomly during completing the specified gestures. Although a few percent lower than the conventional best result, it still provides competitive accuracy acceptable for practical usage. Most importantly, the proposed system allows users to hold the device randomly during operating the predefined gestures, which substantially enhances the user experience.

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 ...

15 CFR 310.5 - Report of the Secretary on Federal recognition.

Science.gov (United States)

2010-01-01

... 15 Commerce and Foreign Trade 2 2010-01-01 2010-01-01 false Report of the Secretary on Federal recognition. 310.5 Section 310.5 Commerce and Foreign Trade Regulations Relating to Commerce and Foreign Trade (Continued) INTERNATIONAL TRADE ADMINISTRATION, DEPARTMENT OF COMMERCE MISCELLANEOUS REGULATIONS OFFICIAL U.S...

Job stress, recognition, job performance and intention to stay at work among Jordanian hospital nurses.

Science.gov (United States)

AbuAlRub, Raeda Fawzi; Al-Zaru, Ibtisam Moawiah

2008-04-01

To investigate: (1) relationships between job stress, recognition of nurses' performance, job performance and intention to stay among hospital nurses; and (2) the buffering effect of recognition of staff performance on the 'stress-intention to stay at work' relationship. Workplace stress tremendously affects today's workforce. Recognition of nurses' performance needs further investigation to determine if it enhances the level of intention to stay at work and if it can buffer the negative effects of stress on nurses' intention to stay at work. The sample of the present study was a convenience one. It consisted of 206 Jordanian staff nurses who completed a structured questionnaire. The findings of the study indicated a direct and a buffering effect of recognition of nurses' performance on job stress and the level of intention to stay at work. The results of the study indicated the importance of recognition for outstanding performance as well as achievements. Implications for nursing management The results of this study support the need to focus on the implementation of recognition strategies in the workplace to reduce job stress and enhance retention.

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

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Zhou, Zhi; Cao, Zongjie; Pi, Yiming

2017-12-21

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

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.

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

Science.gov (United States)

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

2017-02-09

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

The VIII International Congress on Stress Proteins in Biology and Medicine: täynnä henkeä.

Science.gov (United States)

Bonorino, Cristina; Sistonen, Lea; Eriksson, John; Mezger, Valérie; Santoro, Gabriella; Hightower, Lawrence E

2018-03-01

About 150 international scientists gathered in Turku, Finland, in August of 2017 for the eighth in a series of international congresses about the roles of stress proteins in biology and medicine. The scientific theme and title of the 2017 Congress was "Stress Management Mechanisms and Pathways." The meeting covered a broad range of topics, reflecting the wide scope of the Cell Stress Society International (CSSI) and highlighting the numerous recent breakthroughs in stress response biology and medicine. The keynote lecturers included Marja Jäättelä, Richard Morimoto, Anne Bertolotti, and Peter Walter. The Executive Council of the CSSI elected new Fellows and Senior Fellows. The Spirit of Budapest Award was presented to Peter Csermely, Wolfgang Schumann, and Subhash Lakhotia in recognition of pioneering service contributions to the CSSI. The CSSI Medallion for Career Achievement was awarded to Larry Hightower and CSSI president Gabriella Santoro proclaimed Tuesday, August 15, 2017, Robert M. Tanguay Day at the congress in recognition of Robert's many years of scientific accomplishment and work on behalf of the CSSI. Additional special events were the awarding of the Ferruccio Ritossa Early Career Award to Serena Carra and the Alfred Tissières Young Investigator Award to Ayesha Murshid. As is the tradition at CSSI congresses, there were social events that included an exciting piano performance by a trio of young Finnish pianists, at the Sibelius Museum.

Multi-task pose-invariant face recognition.

Science.gov (United States)

Ding, Changxing; Xu, Chang; Tao, Dacheng

2015-03-01

Face images captured in unconstrained environments usually contain significant pose variation, which dramatically degrades the performance of algorithms designed to recognize frontal faces. This paper proposes a novel face identification framework capable of handling the full range of pose variations within ±90° of yaw. The proposed framework first transforms the original pose-invariant face recognition problem into a partial frontal face recognition problem. A robust patch-based face representation scheme is then developed to represent the synthesized partial frontal faces. For each patch, a transformation dictionary is learnt under the proposed multi-task learning scheme. The transformation dictionary transforms the features of different poses into a discriminative subspace. Finally, face matching is performed at patch level rather than at the holistic level. Extensive and systematic experimentation on FERET, CMU-PIE, and Multi-PIE databases shows that the proposed method consistently outperforms single-task-based baselines as well as state-of-the-art methods for the pose problem. We further extend the proposed algorithm for the unconstrained face verification problem and achieve top-level performance on the challenging LFW data set.

Extending the Capture Volume of an Iris Recognition System Using Wavefront Coding and Super-Resolution.

Science.gov (United States)

Hsieh, Sheng-Hsun; Li, Yung-Hui; Tien, Chung-Hao; Chang, Chin-Chen

2016-12-01

Iris recognition has gained increasing popularity over the last few decades; however, the stand-off distance in a conventional iris recognition system is too short, which limits its application. In this paper, we propose a novel hardware-software hybrid method to increase the stand-off distance in an iris recognition system. When designing the system hardware, we use an optimized wavefront coding technique to extend the depth of field. To compensate for the blurring of the image caused by wavefront coding, on the software side, the proposed system uses a local patch-based super-resolution method to restore the blurred image to its clear version. The collaborative effect of the new hardware design and software post-processing showed great potential in our experiment. The experimental results showed that such improvement cannot be achieved by using a hardware-or software-only design. The proposed system can increase the capture volume of a conventional iris recognition system by three times and maintain the system's high recognition rate.

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Results 3861 - 3870 of 6341 ... Participatory Geographic Information Systems (P-GIS) for natural ... Achieving food security remains a complex issue, crossing sectors such ... brings people power and recognition, our weak connection to nature, ...

Spaced Learning Enhances Subsequent Recognition Memory by Reducing Neural Repetition Suppression

Science.gov (United States)

Xue, Gui; Mei, Leilei; Chen, Chuansheng; Lu, Zhong-Lin; Poldrack, Russell; Dong, Qi

2011-01-01

Spaced learning usually leads to better recognition memory as compared with massed learning, yet the underlying neural mechanisms remain elusive. One open question is whether the spacing effect is achieved by reducing neural repetition suppression. In this fMRI study, participants were scanned while intentionally memorizing 120 novel faces, half…

TU-FG-209-12: Treatment Site and View Recognition in X-Ray Images with Hierarchical Multiclass Recognition Models

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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

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

Embedded palmprint recognition system using OMAP 3530.

Science.gov (United States)

Shen, Linlin; Wu, Shipei; Zheng, Songhao; Ji, Zhen

2012-01-01

We have proposed in this paper an embedded palmprint recognition system using the dual-core OMAP 3530 platform. An improved algorithm based on palm code was proposed first. In this method, a Gabor wavelet is first convolved with the palmprint image to produce a response image, where local binary patterns are then applied to code the relation among the magnitude of wavelet response at the central pixel with that of its neighbors. The method is fully tested using the public PolyU palmprint database. While palm code achieves only about 89% accuracy, over 96% accuracy is achieved by the proposed G-LBP approach. The proposed algorithm was then deployed to the DSP processor of OMAP 3530 and work together with the ARM processor for feature extraction. When complicated algorithms run on the DSP processor, the ARM processor can focus on image capture, user interface and peripheral control. Integrated with an image sensing module and central processing board, the designed device can achieve accurate and real time performance.

Recent advances in Automatic Speech Recognition for Vietnamese

OpenAIRE

Le , Viet-Bac; Besacier , Laurent; Seng , Sopheap; Bigi , Brigitte; Do , Thi-Ngoc-Diep

2008-01-01

International audience; This paper presents our recent activities for automatic speech recognition for Vietnamese. First, our text data collection and processing methods and tools are described. For language modeling, we investigate word, sub-word and also hybrid word/sub-word models. For acoustic modeling, when only limited speech data are available for Vietnamese, we propose some crosslingual acoustic modeling techniques. Furthermore, since the use of sub-word units can reduce the high out-...

An inverse problem approach to pattern recognition in industry

Directory of Open Access Journals (Sweden)

Ali Sever

2015-01-01

Full Text Available Many works have shown strong connections between learning and regularization techniques for ill-posed inverse problems. A careful analysis shows that a rigorous connection between learning and regularization for inverse problem is not straightforward. In this study, pattern recognition will be viewed as an ill-posed inverse problem and applications of methods from the theory of inverse problems to pattern recognition are studied. A new learning algorithm derived from a well-known regularization model is generated and applied to the task of reconstruction of an inhomogeneous object as pattern recognition. Particularly, it is demonstrated that pattern recognition can be reformulated in terms of inverse problems defined by a Riesz-type kernel. This reformulation can be employed to design a learning algorithm based on a numerical solution of a system of linear equations. Finally, numerical experiments have been carried out with synthetic experimental data considering a reasonable level of noise. Good recoveries have been achieved with this methodology, and the results of these simulations are compatible with the existing methods. The comparison results show that the Regularization-based learning algorithm (RBA obtains a promising performance on the majority of the test problems. In prospects, this method can be used for the creation of automated systems for diagnostics, testing, and control in various fields of scientific and applied research, as well as in industry.

General tensor discriminant analysis and gabor features for gait recognition.

Science.gov (United States)

Tao, Dacheng; Li, Xuelong; Wu, Xindong; Maybank, Stephen J

2007-10-01

The traditional image representations are not suited to conventional classification methods, such as the linear discriminant analysis (LDA), because of the under sample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA compared with existing preprocessing methods, e.g., principal component analysis (PCA) and 2DLDA, include 1) the USP is reduced in subsequent classification by, for example, LDA; 2) the discriminative information in the training tensors is preserved; and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, while that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor function based image decompositions for image understanding and object recognition, we develop three different Gabor function based image representations: 1) the GaborD representation is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS and GaborSD representations are applied to the problem of recognizing people from their averaged gait images.A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS or GaborSD image representation, then using GDTA to extract features and finally using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the USF HumanID Database. Experimental comparisons are made with nine

Active AU Based Patch Weighting for Facial Expression Recognition

Directory of Open Access Journals (Sweden)

Weicheng Xie

2017-01-01

Full Text Available Facial expression has many applications in human-computer interaction. Although feature extraction and selection have been well studied, the specificity of each expression variation is not fully explored in state-of-the-art works. In this work, the problem of multiclass expression recognition is converted into triplet-wise expression recognition. For each expression triplet, a new feature optimization model based on action unit (AU weighting and patch weight optimization is proposed to represent the specificity of the expression triplet. The sparse representation-based approach is then proposed to detect the active AUs of the testing sample for better generalization. The algorithm achieved competitive accuracies of 89.67% and 94.09% for the Jaffe and Cohn–Kanade (CK+ databases, respectively. Better cross-database performance has also been observed.

Character recognition from trajectory by recurrent spiking neural networks.

Science.gov (United States)

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

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

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.

Empowering women | IDRC - International Development Research ...

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2018-03-08

Mar 8, 2018 ... In recognition of International Women's Day 2018, IDRC proudly reaffirms its proven ... and girls to equal opportunity, and to lead lives free of violence and discrimination. ... Perspectives on gender and women's empowerment.

Real-time image restoration for iris recognition systems.

Science.gov (United States)

Kang, Byung Jun; Park, Kang Ryoung

2007-12-01

In the field of biometrics, it has been reported that iris recognition techniques have shown high levels of accuracy because unique patterns of the human iris, which has very many degrees of freedom, are used. However, because conventional iris cameras have small depth-of-field (DOF) areas, input iris images can easily be blurred, which can lead to lower recognition performance, since iris patterns are transformed by the blurring caused by optical defocusing. To overcome these problems, an autofocusing camera can be used. However, this inevitably increases the cost, size, and complexity of the system. Therefore, we propose a new real-time iris image-restoration method, which can increase the camera's DOF without requiring any additional hardware. This paper presents five novelties as compared to previous works: 1) by excluding eyelash and eyelid regions, it is possible to obtain more accurate focus scores from input iris images; 2) the parameter of the point spread function (PSF) can be estimated in terms of camera optics and measured focus scores; therefore, parameter estimation is more accurate than it has been in previous research; 3) because the PSF parameter can be obtained by using a predetermined equation, iris image restoration can be done in real-time; 4) by using a constrained least square (CLS) restoration filter that considers noise, performance can be greatly enhanced; and 5) restoration accuracy can also be enhanced by estimating the weight value of the noise-regularization term of the CLS filter according to the amount of image blurring. Experimental results showed that iris recognition errors when using the proposed restoration method were greatly reduced as compared to those results achieved without restoration or those achieved using previous iris-restoration methods.

Gender Differences in the Recognition of Vocal Emotions

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Adi Lausen

2018-06-01

Full Text Available The conflicting findings from the few studies conducted with regard to gender differences in the recognition of vocal expressions of emotion have left the exact nature of these differences unclear. Several investigators have argued that a comprehensive understanding of gender differences in vocal emotion recognition can only be achieved by replicating these studies while accounting for influential factors such as stimulus type, gender-balanced samples, number of encoders, decoders, and emotional categories. This study aimed to account for these factors by investigating whether emotion recognition from vocal expressions differs as a function of both listeners' and speakers' gender. A total of N = 290 participants were randomly and equally allocated to two groups. One group listened to words and pseudo-words, while the other group listened to sentences and affect bursts. Participants were asked to categorize the stimuli with respect to the expressed emotions in a fixed-choice response format. Overall, females were more accurate than males when decoding vocal emotions, however, when testing for specific emotions these differences were small in magnitude. Speakers' gender had a significant impact on how listeners' judged emotions from the voice. The group listening to words and pseudo-words had higher identification rates for emotions spoken by male than by female actors, whereas in the group listening to sentences and affect bursts the identification rates were higher when emotions were uttered by female than male actors. The mixed pattern for emotion-specific effects, however, indicates that, in the vocal channel, the reliability of emotion judgments is not systematically influenced by speakers' gender and the related stereotypes of emotional expressivity. Together, these results extend previous findings by showing effects of listeners' and speakers' gender on the recognition of vocal emotions. They stress the importance of distinguishing these

Gender Differences in the Recognition of Vocal Emotions

Science.gov (United States)

Lausen, Adi; Schacht, Annekathrin

2018-01-01

The conflicting findings from the few studies conducted with regard to gender differences in the recognition of vocal expressions of emotion have left the exact nature of these differences unclear. Several investigators have argued that a comprehensive understanding of gender differences in vocal emotion recognition can only be achieved by replicating these studies while accounting for influential factors such as stimulus type, gender-balanced samples, number of encoders, decoders, and emotional categories. This study aimed to account for these factors by investigating whether emotion recognition from vocal expressions differs as a function of both listeners' and speakers' gender. A total of N = 290 participants were randomly and equally allocated to two groups. One group listened to words and pseudo-words, while the other group listened to sentences and affect bursts. Participants were asked to categorize the stimuli with respect to the expressed emotions in a fixed-choice response format. Overall, females were more accurate than males when decoding vocal emotions, however, when testing for specific emotions these differences were small in magnitude. Speakers' gender had a significant impact on how listeners' judged emotions from the voice. The group listening to words and pseudo-words had higher identification rates for emotions spoken by male than by female actors, whereas in the group listening to sentences and affect bursts the identification rates were higher when emotions were uttered by female than male actors. The mixed pattern for emotion-specific effects, however, indicates that, in the vocal channel, the reliability of emotion judgments is not systematically influenced by speakers' gender and the related stereotypes of emotional expressivity. Together, these results extend previous findings by showing effects of listeners' and speakers' gender on the recognition of vocal emotions. They stress the importance of distinguishing these factors to explain

Molecularly imprinted polymers for the recognition of proteins: the state of the art.

Science.gov (United States)

Bossi, A; Bonini, F; Turner, A P F; Piletsky, S A

2007-01-15

Molecular imprinting has proved to be an effective technique for the creation of recognition sites on a polymer scaffold. Protein imprinting has been a focus for many chemists working in the area of molecular recognition, since the creation of synthetic polymers that can specifically recognise proteins is a very challenging but potentially extremely rewarding objective. It is expected that molecularly imprinted polymers (MIPs) with specificity for proteins will find application in medicine, diagnostics, proteomics, environmental analysis, sensors and drug delivery. In this review, the authors provide an overview of the progress achieved in the decade between 1994 and 2005, with respect to the challenging area of MIPs for protein recognition. The discussion furnishes a comparative analysis of different approaches developed, underlining their relative advantages and disadvantages and highlighting trends and possible future directions.

Achieving competitiveness through supply chain integration

DEFF Research Database (Denmark)

Arlbjørn, Jan Stentoft; Wong, Chee Yew; Seerup, Søren

2007-01-01

Supply chain development can take place in several steps, from functional optimisation, then internal integration, dyadic integration and last, integration in chains and networks. Before external integration gives true value, order in own house must be achieved. This paper presents a case study...... of a Danish manufacturer that has gone through a major transformation process, and the paper intends to discuss how such a Business Process Reengineering (BPR) project aimed to achieve internal integration. The paper demonstrates how improved competitiveness can be obtained through a synchronous...

Generating solutions : summary of the Electricity Sector Council's review of foreign credential recognition

Energy Technology Data Exchange (ETDEWEB)

NONE

2008-03-15

The Electricity Sector Council has recognized the increasing requirement to recruit and retain internationally trained workers to offset the anticipated retirement of up to 40 per cent of skilled workers in this sector by 2014. This document provided a brief summary of the review of foreign credential recognition in Canada's Electricity Council which was prepared in February 2008. The purpose of the study was to capture a perceptive picture of Canada's electricity labour force and to assist the Electricity Sector Council in the potential development and implementation of strategies to facilitate the integration of internationally trained workers into Canada's electricity sector. This synopsis report presented the analysis of the study including a discussion of immigration trends; foreign credential recognition in Canada's electricity sector; immigration profiles by region; case study profiles; and recommendations. It was recommended that resources be researched, developed and provided in order to help stakeholders attract, recruit, retain and integrate internationally trained workers. 2 refs.

Feature and score fusion based multiple classifier selection for iris recognition.

Science.gov (United States)

Islam, Md Rabiul

2014-01-01

The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Feature and Score Fusion Based Multiple Classifier Selection for Iris Recognition

Directory of Open Access Journals (Sweden)

Md. Rabiul Islam

2014-01-01

Full Text Available The aim of this work is to propose a new feature and score fusion based iris recognition approach where voting method on Multiple Classifier Selection technique has been applied. Four Discrete Hidden Markov Model classifiers output, that is, left iris based unimodal system, right iris based unimodal system, left-right iris feature fusion based multimodal system, and left-right iris likelihood ratio score fusion based multimodal system, is combined using voting method to achieve the final recognition result. CASIA-IrisV4 database has been used to measure the performance of the proposed system with various dimensions. Experimental results show the versatility of the proposed system of four different classifiers with various dimensions. Finally, recognition accuracy of the proposed system has been compared with existing N hamming distance score fusion approach proposed by Ma et al., log-likelihood ratio score fusion approach proposed by Schmid et al., and single level feature fusion approach proposed by Hollingsworth et al.

Simultaneous nitrification-denitrification achieved by an innovative internal-loop airlift MBR: comparative study.

Science.gov (United States)

Li, Y Z; He, Y L; Ohandja, D G; Ji, J; Li, J F; Zhou, T

2008-09-01

This study assessed the performance of different single-stage continuous aerated submerged membrane bioreactors (MBR) for nitrogen removal. Almost complete nitrification was achieved in each MBR irrespective of operating mode and biomass system. Denitrification was found to be the rate-limiting step for total nitrogen (T-N) removal. The MBR with internal-loop airlift reactor (ALR) configuration performed better as regards T-N removal compared with continuous stirred-tank reactor (CSTR). It was demonstrated that simultaneous nitrification and denitrification (SND) is the mechanism leading to nitrogen removal and the contribution of microenvironment on SND is more remarkable for the MBRs with hybrid biomass. Macroenvironment analyses showed that gradient distribution of dissolved oxygen (DO) level in airlift MBRs imposed a significant effect on SND. Higher mixed liquor suspended solid (MLSS) concentration led to the improvement in T-N removal by enhancing anoxic microenvironment. Apparent nitrite accumulation coupled with higher nitrogen reduction was accomplished at MLSS concentration exceeded 12.6 g/L.

Method for secure electronic voting system: face recognition based approach

Science.gov (United States)

Alim, M. Affan; Baig, Misbah M.; Mehboob, Shahzain; Naseem, Imran

2017-06-01

In this paper, we propose a framework for low cost secure electronic voting system based on face recognition. Essentially Local Binary Pattern (LBP) is used for face feature characterization in texture format followed by chi-square distribution is used for image classification. Two parallel systems are developed based on smart phone and web applications for face learning and verification modules. The proposed system has two tire security levels by using person ID followed by face verification. Essentially class specific threshold is associated for controlling the security level of face verification. Our system is evaluated three standard databases and one real home based database and achieve the satisfactory recognition accuracies. Consequently our propose system provides secure, hassle free voting system and less intrusive compare with other biometrics.

[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.

Disclosure of Diagnosis in Early Recognition of Psychosis.

Science.gov (United States)

Blessing, Andreas; Studer, Anna; Gross, Amelie; Gruss, L Forest; Schneider, Roland; Dammann, Gerhard

2017-10-01

There is a debate concerning risks and benefits of early intervention in psychosis, especially concerning diagnosis disclosure. The present study reports preliminary findings on self-reported locus of control and psychological distress after the disclosure of diagnosis in an early recognition center. We compared the ratings of the locus of control and psychological distress before and after communication of diagnosis. The study included individuals with an at-risk mental state (ARMS) (n = 10), schizophrenia (n = 9), and other psychiatric disorders (n = 11). Results indicate greater endorsement of the internal locus of control in individuals with ARMS after communication of diagnosis in contrast to the other groups. Our results suggest that disclosure of diagnosis in an early recognition center leads to a reduction of psychological distress and increased feelings of control over one's health. Persons with ARMS seem to particularly benefit from disclosure of diagnosis as part of early intervention.

Reading component skills in dyslexia: word recognition, comprehension and processing speed.

Science.gov (United States)

de Oliveira, Darlene G; da Silva, Patrícia B; Dias, Natália M; Seabra, Alessandra G; Macedo, Elizeu C

2014-01-01

The cognitive model of reading comprehension (RC) posits that RC is a result of the interaction between decoding and linguistic comprehension. Recently, the notion of decoding skill was expanded to include word recognition. In addition, some studies suggest that other skills could be integrated into this model, like processing speed, and have consistently indicated that this skill influences and is an important predictor of the main components of the model, such as vocabulary for comprehension and phonological awareness of word recognition. The following study evaluated the components of the RC model and predictive skills in children and adolescents with dyslexia. 40 children and adolescents (8-13 years) were divided in a Dyslexic Group (DG; 18 children, MA = 10.78, SD = 1.66) and control group (CG 22 children, MA = 10.59, SD = 1.86). All were students from the 2nd to 8th grade of elementary school and groups were equivalent in school grade, age, gender, and IQ. Oral and RC, word recognition, processing speed, picture naming, receptive vocabulary, and phonological awareness were assessed. There were no group differences regarding the accuracy in oral and RC, phonological awareness, naming, and vocabulary scores. DG performed worse than the CG in word recognition (general score and orthographic confusion items) and were slower in naming. Results corroborated the literature regarding word recognition and processing speed deficits in dyslexia. However, dyslexics can achieve normal scores on RC test. Data supports the importance of delimitation of different reading strategies embedded in the word recognition component. The role of processing speed in reading problems remain unclear.

Long Short-Term Memory Projection Recurrent Neural Network Architectures for Piano’s Continuous Note Recognition

Directory of Open Access Journals (Sweden)

YuKang Jia

2017-01-01

Full Text Available Long Short-Term Memory (LSTM is a kind of Recurrent Neural Networks (RNN relating to time series, which has achieved good performance in speech recogniton and image recognition. Long Short-Term Memory Projection (LSTMP is a variant of LSTM to further optimize speed and performance of LSTM by adding a projection layer. As LSTM and LSTMP have performed well in pattern recognition, in this paper, we combine them with Connectionist Temporal Classification (CTC to study piano’s continuous note recognition for robotics. Based on the Beijing Forestry University music library, we conduct experiments to show recognition rates and numbers of iterations of LSTM with a single layer, LSTMP with a single layer, and Deep LSTM (DLSTM, LSTM with multilayers. As a result, the single layer LSTMP proves performing much better than the single layer LSTM in both time and the recognition rate; that is, LSTMP has fewer parameters and therefore reduces the training time, and, moreover, benefiting from the projection layer, LSTMP has better performance, too. The best recognition rate of LSTMP is 99.8%. As for DLSTM, the recognition rate can reach 100% because of the effectiveness of the deep structure, but compared with the single layer LSTMP, DLSTM needs more training time.

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.

Frame-Based Facial Expression Recognition Using Geometrical Features

Directory of Open Access Journals (Sweden)

Anwar Saeed

2014-01-01

Full Text Available To improve the human-computer interaction (HCI to be as good as human-human interaction, building an efficient approach for human emotion recognition is required. These emotions could be fused from several modalities such as facial expression, hand gesture, acoustic data, and biophysiological data. In this paper, we address the frame-based perception of the universal human facial expressions (happiness, surprise, anger, disgust, fear, and sadness, with the help of several geometrical features. Unlike many other geometry-based approaches, the frame-based method does not rely on prior knowledge of a person-specific neutral expression; this knowledge is gained through human intervention and not available in real scenarios. Additionally, we provide a method to investigate the performance of the geometry-based approaches under various facial point localization errors. From an evaluation on two public benchmark datasets, we have found that using eight facial points, we can achieve the state-of-the-art recognition rate. However, this state-of-the-art geometry-based approach exploits features derived from 68 facial points and requires prior knowledge of the person-specific neutral expression. The expression recognition rate using geometrical features is adversely affected by the errors in the facial point localization, especially for the expressions with subtle facial deformations.

User-Independent Motion State Recognition Using Smartphone Sensors.

Science.gov (United States)

Gu, Fuqiang; Kealy, Allison; Khoshelham, Kourosh; Shang, Jianga

2015-12-04

The recognition of locomotion activities (e.g., walking, running, still) is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users' data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people's motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human's motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

User-Independent Motion State Recognition Using Smartphone Sensors

Directory of Open Access Journals (Sweden)

Fuqiang Gu

2015-12-01

Full Text Available The recognition of locomotion activities (e.g., walking, running, still is important for a wide range of applications like indoor positioning, navigation, location-based services, and health monitoring. Recently, there has been a growing interest in activity recognition using accelerometer data. However, when utilizing only acceleration-based features, it is difficult to differentiate varying vertical motion states from horizontal motion states especially when conducting user-independent classification. In this paper, we also make use of the newly emerging barometer built in modern smartphones, and propose a novel feature called pressure derivative from the barometer readings for user motion state recognition, which is proven to be effective for distinguishing vertical motion states and does not depend on specific users’ data. Seven types of motion states are defined and six commonly-used classifiers are compared. In addition, we utilize the motion state history and the characteristics of people’s motion to improve the classification accuracies of those classifiers. Experimental results show that by using the historical information and human’s motion characteristics, we can achieve user-independent motion state classification with an accuracy of up to 90.7%. In addition, we analyze the influence of the window size and smartphone pose on the accuracy.

Robust Face Recognition via Multi-Scale Patch-Based Matrix Regression.

Directory of Open Access Journals (Sweden)

Guangwei Gao

Full Text Available In many real-world applications such as smart card solutions, law enforcement, surveillance and access control, the limited training sample size is the most fundamental problem. By making use of the low-rank structural information of the reconstructed error image, the so-called nuclear norm-based matrix regression has been demonstrated to be effective for robust face recognition with continuous occlusions. However, the recognition performance of nuclear norm-based matrix regression degrades greatly in the face of the small sample size problem. An alternative solution to tackle this problem is performing matrix regression on each patch and then integrating the outputs from all patches. However, it is difficult to set an optimal patch size across different databases. To fully utilize the complementary information from different patch scales for the final decision, we propose a multi-scale patch-based matrix regression scheme based on which the ensemble of multi-scale outputs can be achieved optimally. Extensive experiments on benchmark face databases validate the effectiveness and robustness of our method, which outperforms several state-of-the-art patch-based face recognition algorithms.

Higher-order neural network software for distortion invariant object recognition

Science.gov (United States)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

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

Heterogeneous sharpness for cross-spectral face recognition

Science.gov (United States)

Cao, Zhicheng; Schmid, Natalia A.

2017-05-01

Matching images acquired in different electromagnetic bands remains a challenging problem. An example of this type of comparison is matching active or passive infrared (IR) against a gallery of visible face images, known as cross-spectral face recognition. Among many unsolved issues is the one of quality disparity of the heterogeneous images. Images acquired in different spectral bands are of unequal image quality due to distinct imaging mechanism, standoff distances, or imaging environment, etc. To reduce the effect of quality disparity on the recognition performance, one can manipulate images to either improve the quality of poor-quality images or to degrade the high-quality images to the level of the quality of their heterogeneous counterparts. To estimate the level of discrepancy in quality of two heterogeneous images a quality metric such as image sharpness is needed. It provides a guidance in how much quality improvement or degradation is appropriate. In this work we consider sharpness as a relative measure of heterogeneous image quality. We propose a generalized definition of sharpness by first achieving image quality parity and then finding and building a relationship between the image quality of two heterogeneous images. Therefore, the new sharpness metric is named heterogeneous sharpness. Image quality parity is achieved by experimentally finding the optimal cross-spectral face recognition performance where quality of the heterogeneous images is varied using a Gaussian smoothing function with different standard deviation. This relationship is established using two models; one of them involves a regression model and the other involves a neural network. To train, test and validate the model, we use composite operators developed in our lab to extract features from heterogeneous face images and use the sharpness metric to evaluate the face image quality within each band. Images from three different spectral bands visible light, near infrared, and short

THE DEAL WITH IRAN IS THE BEST-CASE SCENARIO THE INTERNATIONAL COMMUNITY COULD ACHIEVE IN THE CURRENT GEOPOLITICAL ENVIRONMENT

Science.gov (United States)

2016-02-16

nonproliferation regime. However, the international community retains all options to achieve the objective of preventing Iran from producing a nuclear weapon...of U.S. Iran policy since 1979 but the imposition of U.N. Security Council and worldwide sanctions escalated after 2006 and increased dramatically...Diplomacy 2011), 333. 15 Celia L/ Reynolds and Wilfred T. Wan, "Empirical trends in sanctions and possitive inducements in nonproliferation ", in

Face recognition performance of individuals with Asperger syndrome on the Cambridge Face Memory Test.

Science.gov (United States)

Hedley, Darren; Brewer, Neil; Young, Robyn

2011-12-01

Although face recognition deficits in individuals with Autism Spectrum Disorder (ASD), including Asperger syndrome (AS), are widely acknowledged, the empirical evidence is mixed. This in part reflects the failure to use standardized and psychometrically sound tests. We contrasted standardized face recognition scores on the Cambridge Face Memory Test (CFMT) for 34 individuals with AS with those for 42, IQ-matched non-ASD individuals, and age-standardized scores from a large Australian cohort. We also examined the influence of IQ, autistic traits, and negative affect on face recognition performance. Overall, participants with AS performed significantly worse on the CFMT than the non-ASD participants and when evaluated against standardized test norms. However, while 24% of participants with AS presented with severe face recognition impairment (>2 SDs below the mean), many individuals performed at or above the typical level for their age: 53% scored within +/- 1 SD of the mean and 9% demonstrated superior performance (>1 SD above the mean). Regression analysis provided no evidence that IQ, autistic traits, or negative affect significantly influenced face recognition: diagnostic group membership was the only significant predictor of face recognition performance. In sum, face recognition performance in ASD is on a continuum, but with average levels significantly below non-ASD levels of performance. Copyright © 2011, International Society for Autism Research, Wiley-Liss, Inc.

Application of Video Recognition Technology in Landslide Monitoring System

Directory of Open Access Journals (Sweden)

Qingjia Meng

2018-01-01

Full Text Available The video recognition technology is applied to the landslide emergency remote monitoring system. The trajectories of the landslide are identified by this system in this paper. The system of geological disaster monitoring is applied synthetically to realize the analysis of landslide monitoring data and the combination of video recognition technology. Landslide video monitoring system will video image information, time point, network signal strength, power supply through the 4G network transmission to the server. The data is comprehensively analysed though the remote man-machine interface to conduct to achieve the threshold or manual control to determine the front-end video surveillance system. The system is used to identify the target landslide video for intelligent identification. The algorithm is embedded in the intelligent analysis module, and the video frame is identified, detected, analysed, filtered, and morphological treatment. The algorithm based on artificial intelligence and pattern recognition is used to mark the target landslide in the video screen and confirm whether the landslide is normal. The landslide video monitoring system realizes the remote monitoring and control of the mobile side, and provides a quick and easy monitoring technology.

Contribution to automatic speech recognition. Analysis of the direct acoustical signal. Recognition of isolated words and phoneme identification

International Nuclear Information System (INIS)

Dupeyrat, Benoit

1981-01-01

This report deals with the acoustical-phonetic step of the automatic recognition of the speech. The parameters used are the extrema of the acoustical signal (coded in amplitude and duration). This coding method, the properties of which are described, is simple and well adapted to a digital processing. The quality and the intelligibility of the coded signal after reconstruction are particularly satisfactory. An experiment for the automatic recognition of isolated words has been carried using this coding system. We have designed a filtering algorithm operating on the parameters of the coding. Thus the characteristics of the formants can be derived under certain conditions which are discussed. Using these characteristics the identification of a large part of the phonemes for a given speaker was achieved. Carrying on the studies has required the development of a particular methodology of real time processing which allowed immediate evaluation of the improvement of the programs. Such processing on temporal coding of the acoustical signal is extremely powerful and could represent, used in connection with other methods an efficient tool for the automatic processing of the speech.(author) [fr

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

International group heterogeneity and students’ business project achievement

NARCIS (Netherlands)

Ding, Ning; Bosker, Roel J.; Xu, Xiaoyan; Rugers, Lucie; van Heugten, Petra

2015-01-01

In business higher education, group project work plays an essential role. The purpose of the present study is to explore the relationship between the group heterogeneity of students’ business project groups and their academic achievements at both group and individual levels. The sample consists of

International Group Heterogeneity and Students’ Business Project Achievement.

NARCIS (Netherlands)

Dr. Ning Ding; Drs. Petra van Heugten; Drs. Lucie Rugers; Dr. Roel Bosker; Dr. Xiaoyan Xu

2015-01-01

In business higher education, group project work plays an essential role. The purpose of the present study is to explore the relationship between the group heterogeneity of students’ business project groups and their academic achievements at both group and individual levels. The sample consists of

Marginalised Stacked Denoising Autoencoders for Robust Representation of Real-Time Multi-View Action Recognition

Directory of Open Access Journals (Sweden)

Feng Gu

2015-07-01

Full Text Available Multi-view action recognition has gained a great interest in video surveillance, human computer interaction, and multimedia retrieval, where multiple cameras of different types are deployed to provide a complementary field of views. Fusion of multiple camera views evidently leads to more robust decisions on both tracking multiple targets and analysing complex human activities, especially where there are occlusions. In this paper, we incorporate the marginalised stacked denoising autoencoders (mSDA algorithm to further improve the bag of words (BoWs representation in terms of robustness and usefulness for multi-view action recognition. The resulting representations are fed into three simple fusion strategies as well as a multiple kernel learning algorithm at the classification stage. Based on the internal evaluation, the codebook size of BoWs and the number of layers of mSDA may not significantly affect recognition performance. According to results on three multi-view benchmark datasets, the proposed framework improves recognition performance across all three datasets and outputs record recognition performance, beating the state-of-art algorithms in the literature. It is also capable of performing real-time action recognition at a frame rate ranging from 33 to 45, which could be further improved by using more powerful machines in future applications.

An integrated modeling approach to age invariant face recognition

Science.gov (United States)

Alvi, Fahad Bashir; Pears, Russel

2015-03-01

This Research study proposes a novel method for face recognition based on Anthropometric features that make use of an integrated approach comprising of a global and personalized models. The system is aimed to at situations where lighting, illumination, and pose variations cause problems in face recognition. A Personalized model covers the individual aging patterns while a Global model captures general aging patterns in the database. We introduced a de-aging factor that de-ages each individual in the database test and training sets. We used the k nearest neighbor approach for building a personalized model and global model. Regression analysis was applied to build the models. During the test phase, we resort to voting on different features. We used FG-Net database for checking the results of our technique and achieved 65 percent Rank 1 identification rate.

Reflowing-driven paragraph recognition for electronic books in PDF

Science.gov (United States)

Fang, Jing; Tang, Zhi; Gao, Liangcai

2011-01-01

When reading electronic books on handheld devices, content sometimes should be reflowed and recomposed to adapt for small-screen mobile devices. According to people's reading practice, it is reasonable to reflow the text content based on paragraphs. Hence, this paper addresses the requirement and proposes a set of novel methods on paragraph recognition for electronic books in PDF. The proposed methods consist of three steps, namely, physical structure analysis, paragraph segmentation, and reading order detection. We make use of locally ordered property of PDF documents and layout style of books to improve traditional page recognition results. In addition, we employ the optimal matching of Bipartite Graph technology to detect paragraphs' reading order. Experiments show that our methods achieve high accuracy. It is noteworthy that, the research has been applied in a commercial software package for Chinese E-book production.

Pain management : Internationally a nursing responsibility

OpenAIRE

Petrini, Marcia, A

1999-01-01

Pain management by nurses internationally has increased with the awareness of the importance of relief from pain in the healing process. Studies of the physiological mechanisms of pain and the impact on healing havepromoted the recognition for pain relief

International development workshops. Final technical report

International Nuclear Information System (INIS)

1997-01-01

The US Department of Energy (DOE) and the Nuclear Energy Agency of the Organization for Economic Cooperation and Development/Nuclear Energy Agency (OECD/NEA) began to act on their recognition of the importance of education in nuclear literacy, specifically in radioactive waste management (RWM), several years ago. To address this Goal for nuclear literacy, the US DOE; through the Information and Education Division of the Office of Civilian Radioactive Waste Management (OCRWM) and in cooperation with the OECD/NEA, organized an ''International Workshop on Education in the Field of Radioactive Waste Management'' in Engelberg, Switzerland in June of 1991. To this end, a grant to support nuclear literacy and RWM was written and funded by the OCRWM and the education division of the DOE Yucca Mountain Office in 1990. The over-riding Goal of that workshop and the DOE grant was to find ways of raising the level of nuclear literacy in the general public through educational programs in radioactive waste management (RWM). The two Main Objectives of the workshop were: first, to contribute to an information base for education systems, on global aspects of radioactive waste management; and second, to achieve international consensus on the basic tools and methods required to develop the information base. These two objectives also became the principal objectives of the DOE International Workshops grant. In other words, the global and local (Nevada) objectives were one and the same. Workshop overviews and accomplishments are summarized in this report

International development workshops. Final technical report

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-08-06

The US Department of Energy (DOE) and the Nuclear Energy Agency of the Organization for Economic Cooperation and Development/Nuclear Energy Agency (OECD/NEA) began to act on their recognition of the importance of education in nuclear literacy, specifically in radioactive waste management (RWM), several years ago. To address this Goal for nuclear literacy, the US DOE; through the Information and Education Division of the Office of Civilian Radioactive Waste Management (OCRWM) and in cooperation with the OECD/NEA, organized an ``International Workshop on Education in the Field of Radioactive Waste Management`` in Engelberg, Switzerland in June of 1991. To this end, a grant to support nuclear literacy and RWM was written and funded by the OCRWM and the education division of the DOE Yucca Mountain Office in 1990. The over-riding Goal of that workshop and the DOE grant was to find ways of raising the level of nuclear literacy in the general public through educational programs in radioactive waste management (RWM). The two Main Objectives of the workshop were: first, to contribute to an information base for education systems, on global aspects of radioactive waste management; and second, to achieve international consensus on the basic tools and methods required to develop the information base. These two objectives also became the principal objectives of the DOE International Workshops grant. In other words, the global and local (Nevada) objectives were one and the same. Workshop overviews and accomplishments are summarized in this report.

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.

Mapping face recognition information use across cultures

Directory of Open Access Journals (Sweden)

Sébastien eMiellet

2013-02-01

Full Text Available Face recognition is not rooted in a universal eye movement information-gathering strategy. Western observers favor a local facial feature sampling strategy, whereas Eastern observers prefer sampling face information from a global, central fixation strategy. Yet, the precise qualitative (the diagnostic and quantitative (the amount information underlying these cultural perceptual biases in face recognition remains undetermined.To this end, we monitored the eye movements of Western and Eastern observers during a face recognition task, with a novel gaze-contingent technique: the Expanding Spotlight. We used 2° Gaussian apertures centered on the observers' fixations expanding dynamically at a rate of 1° every 25ms at each fixation - the longer the fixation duration, the larger the aperture size. Identity-specific face information was only displayed within the Gaussian aperture; outside the aperture, an average face template was displayed to facilitate saccade planning. Thus, the Expanding Spotlight simultaneously maps out the facial information span at each fixation location.Data obtained with the Expanding Spotlight technique confirmed that Westerners extract more information from the eye region, whereas Easterners extract more information from the nose region. Interestingly, this quantitative difference was paired with a qualitative disparity. Retinal filters based on spatial frequency decomposition built from the fixations maps revealed that Westerners used local high-spatial frequency information sampling, covering all the features critical for effective face recognition (the eyes and the mouth. In contrast, Easterners achieved a similar result by using global low-spatial frequency information from those facial features.Our data show that the face system flexibly engages into local or global eye movement strategies across cultures, by relying on distinct facial information span and culturally tuned spatially filtered information. Overall, our

Recognition of speaker-dependent continuous speech with KEAL

Science.gov (United States)

Mercier, G.; Bigorgne, D.; Miclet, L.; Le Guennec, L.; Querre, M.

1989-04-01

A description of the speaker-dependent continuous speech recognition system KEAL is given. An unknown utterance, is recognized by means of the followng procedures: acoustic analysis, phonetic segmentation and identification, word and sentence analysis. The combination of feature-based, speaker-independent coarse phonetic segmentation with speaker-dependent statistical classification techniques is one of the main design features of the acoustic-phonetic decoder. The lexical access component is essentially based on a statistical dynamic programming technique which aims at matching a phonemic lexical entry containing various phonological forms, against a phonetic lattice. Sentence recognition is achieved by use of a context-free grammar and a parsing algorithm derived from Earley's parser. A speaker adaptation module allows some of the system parameters to be adjusted by matching known utterances with their acoustical representation. The task to be performed, described by its vocabulary and its grammar, is given as a parameter of the system. Continuously spoken sentences extracted from a 'pseudo-Logo' language are analyzed and results are presented.

Speech emotion recognition based on statistical pitch model

Institute of Scientific and Technical Information of China (English)

WANG Zhiping; ZHAO Li; ZOU Cairong

2006-01-01

A modified Parzen-window method, which keep high resolution in low frequencies and keep smoothness in high frequencies, is proposed to obtain statistical model. Then, a gender classification method utilizing the statistical model is proposed, which have a 98% accuracy of gender classification while long sentence is dealt with. By separation the male voice and female voice, the mean and standard deviation of speech training samples with different emotion are used to create the corresponding emotion models. Then the Bhattacharyya distance between the test sample and statistical models of pitch, are utilized for emotion recognition in speech.The normalization of pitch for the male voice and female voice are also considered, in order to illustrate them into a uniform space. Finally, the speech emotion recognition experiment based on K Nearest Neighbor shows that, the correct rate of 81% is achieved, where it is only 73.85%if the traditional parameters are utilized.

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

Science.gov (United States)

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

2018-04-01

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

Can the Outputs of LGN Y-Cells Support Emotion Recognition? A Computational Study

Directory of Open Access Journals (Sweden)

Andrea De Cesarei

2015-01-01

Full Text Available It has been suggested that emotional visual input is processed along both a slower cortical pathway and a faster subcortical pathway which comprises the lateral geniculate nucleus (LGN, the superior colliculus, the pulvinar, and finally the amygdala. However, anatomical as well as functional evidence concerning the subcortical route is lacking. Here, we adopt a computational approach in order to investigate whether the visual representation that is achieved in the LGN may support emotion recognition and emotional response along the subcortical route. In four experiments, we show that the outputs of LGN Y-cells support neither facial expression categorization nor the same/different expression matching by an artificial classificator. However, the same classificator is able to perform at an above chance level in a statistics-based categorization of scenes containing animals and scenes containing people and of light and dark patterns. It is concluded that the visual representation achieved in the LGN is insufficient to allow for the recognition of emotional facial expression.

Active Discriminative Dictionary Learning for Weather Recognition

Directory of Open Access Journals (Sweden)

Caixia Zheng

2016-01-01

Full Text Available Weather recognition based on outdoor images is a brand-new and challenging subject, which is widely required in many fields. This paper presents a novel framework for recognizing different weather conditions. Compared with other algorithms, the proposed method possesses the following advantages. Firstly, our method extracts both visual appearance features of the sky region and physical characteristics features of the nonsky region in images. Thus, the extracted features are more comprehensive than some of the existing methods in which only the features of sky region are considered. Secondly, unlike other methods which used the traditional classifiers (e.g., SVM and K-NN, we use discriminative dictionary learning as the classification model for weather, which could address the limitations of previous works. Moreover, the active learning procedure is introduced into dictionary learning to avoid requiring a large number of labeled samples to train the classification model for achieving good performance of weather recognition. Experiments and comparisons are performed on two datasets to verify the effectiveness of the proposed method.

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.

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.

Biochemical study of multiple drug recognition sites on central benzodiazepine receptors

Energy Technology Data Exchange (ETDEWEB)

Trifiletti, R.R.

1986-01-01

The benzodiazepine receptor complex of mammalian brain possesses recognition sites which mediate (at least in part) the pharmacologic actions of the 1,4-benzodiazepines and barbiturates. Evidence is provided suggesting the existence of least seven distinct drug recognition sites on this complex. Interactions between the various recognition sites have been explored using radioligand binding techniques. This information is utilized to provide a comprehensive scheme for characterizing receptor-active drugs on an anxiolytic-anticonvulsant/proconvulsant continuum using radioligand binding techniques, as well as a comprehensive program for identifying potential endogenous receptor-active substances. Further evidence is provided here supporting the notion of benzodiazepine recognition site heterogeneity. Classical 1,4-benzodiazepines do not appear to differentiate two populations of benzodiazepine receptors in an equilibrium sense, but appear to do so in a kinetic sense. An apparent physical separation of the two receptor subtypes can be achieved by differential solubilization. The benzodiazepine binding subunit can be identified by photoaffinity labeling with the benzodiazepine agonist (/sup 3/H)flunitrazepan. Conditions for reproducible partial proteolytic mapping of (/sup 3/H)flunitrazepam photoaffinity labeled receptors are established. From these maps, it is concluded that there are probably no major differences in the primary sequence of the benzodiazepine binding subunit in various regions of the rat central nervous system.

Circle Hough transform implementation for dots recognition in braille cells

Science.gov (United States)

Jacinto Gómez, Edwar; Montiel Ariza, Holman; Martínez Sarmiento, Fredy Hernán.

2017-02-01

This paper shows a technique based on CHT (Circle Hough Transform) to achieve the optical Braille recognition (OBR). Unlike other papers developed around the same topic, this one is made by using Hough Transform to process the recognition and transcription of Braille cells, proving CHT to be an appropriate technique to go over different non-systematics factors who can affect the process, as the paper type where the text to traduce is placed, some lightning factors, input image resolution and some flaws derived from the capture process, which is realized using a scanner. Tests are performed with a local database using text generated by visual nondisabled people and some transcripts by sightless people; all of this with the support of National Institute for Blind People (INCI for their Spanish acronym) placed in Colombia.

Products recognition on shop-racks from local scale-invariant features

Science.gov (United States)

Zawistowski, Jacek; Kurzejamski, Grzegorz; Garbat, Piotr; Naruniec, Jacek

2016-04-01

This paper presents a system designed for the multi-object detection purposes and adjusted for the application of product search on the market shelves. System uses well known binary keypoint detection algorithms for finding characteristic points in the image. One of the main idea is object recognition based on Implicit Shape Model method. Authors of the article proposed many improvements of the algorithm. Originally fiducial points are matched with a very simple function. This leads to the limitations in the number of objects parts being success- fully separated, while various methods of classification may be validated in order to achieve higher performance. Such an extension implies research on training procedure able to deal with many objects categories. Proposed solution opens a new possibilities for many algorithms demanding fast and robust multi-object recognition.

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.

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network

Science.gov (United States)

Islam, Kh Tohidul; Raj, Ram Gopal

2017-01-01

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are ‘traffic light ahead’ or ‘pedestrian crossing’ indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications. PMID:28406471

Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network.

Science.gov (United States)

Islam, Kh Tohidul; Raj, Ram Gopal

2017-04-13

Road sign recognition is a driver support function that can be used to notify and warn the driver by showing the restrictions that may be effective on the current stretch of road. Examples for such regulations are 'traffic light ahead' or 'pedestrian crossing' indications. The present investigation targets the recognition of Malaysian road and traffic signs in real-time. Real-time video is taken by a digital camera from a moving vehicle and real world road signs are then extracted using vision-only information. The system is based on two stages, one performs the detection and another one is for recognition. In the first stage, a hybrid color segmentation algorithm has been developed and tested. In the second stage, an introduced robust custom feature extraction method is used for the first time in a road sign recognition approach. Finally, a multilayer artificial neural network (ANN) has been created to recognize and interpret various road signs. It is robust because it has been tested on both standard and non-standard road signs with significant recognition accuracy. This proposed system achieved an average of 99.90% accuracy with 99.90% of sensitivity, 99.90% of specificity, 99.90% of f-measure, and 0.001 of false positive rate (FPR) with 0.3 s computational time. This low FPR can increase the system stability and dependability in real-time applications.

Time-frequency feature analysis and recognition of fission neutrons signal based on support vector machine

International Nuclear Information System (INIS)

Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi

2010-01-01

Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)

A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades

Directory of Open Access Journals (Sweden)

Jialin Tang

2017-11-01

Full Text Available The identification of particular types of damage in wind turbine blades using acoustic emission (AE techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency−frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency−MARSE, and average frequency−peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope. The results show that these parameters are representative for the classification of the failure modes.

A Pattern Recognition Approach to Acoustic Emission Data Originating from Fatigue of Wind Turbine Blades.

Science.gov (United States)

Tang, Jialin; Soua, Slim; Mares, Cristinel; Gan, Tat-Hean

2017-11-01

The identification of particular types of damage in wind turbine blades using acoustic emission (AE) techniques is a significant emerging field. In this work, a 45.7-m turbine blade was subjected to flap-wise fatigue loading for 21 days, during which AE was measured by internally mounted piezoelectric sensors. This paper focuses on using unsupervised pattern recognition methods to characterize different AE activities corresponding to different fracture mechanisms. A sequential feature selection method based on a k-means clustering algorithm is used to achieve a fine classification accuracy. The visualization of clusters in peak frequency-frequency centroid features is used to correlate the clustering results with failure modes. The positions of these clusters in time domain features, average frequency-MARSE, and average frequency-peak amplitude are also presented in this paper (where MARSE represents the Measured Area under Rectified Signal Envelope). The results show that these parameters are representative for the classification of the failure modes.

An Evaluation of Imitation Recognition Abilities in Typically Developing Children and Young Children with Autism Spectrum Disorder.

Science.gov (United States)

Berger, Natalie I; Ingersoll, Brooke

2015-08-01

Previous work has indicated that both typically developing children and children with Autism Spectrum Disorder (ASD) display a range of imitation recognition behaviors in response to a contingent adult imitator. However, it is unknown how the two groups perform comparatively on this construct. In this study, imitation recognition behaviors for children with ASD and typically developing children were observed during periods of contingent imitation imbedded in a naturalistic imitation task. Results from this study indicate that children with ASD are impaired in their ability to recognize being imitated relative to typically developing peers as demonstrated both by behaviors representing basic social attention and more mature imitation recognition. Display of imitation recognition behaviors was independent of length of contingent imitation period in typically developing children, but rate of engagement in imitation recognition behaviors was positively correlated with length of contingent imitation period in children with ASD. Exploratory findings also suggest a link between the ability to demonstrate recognition of being imitated and ASD symptom severity, language, and object imitation for young children with ASD. © 2015 International Society for Autism Research, Wiley Periodicals, Inc.

Reading component skills in dyslexia: word recognition, comprehension and processing speed

Directory of Open Access Journals (Sweden)

Darlene Godoy Oliveira

2014-11-01

Full Text Available The cognitive model of reading comprehension posits that reading comprehension is a result of the interaction between decoding and linguistic comprehension. Recently, the notion of decoding skill was expanded to include word recognition. In addition, some studies suggest that other skills could be integrated into this model, like processing speed, and have consistently indicated that this skill influences and is an important predictor of the main components of the model, such as vocabulary for comprehension and phonological awareness of word recognition. The following study evaluated the components of the reading comprehension model and predictive skills in children and adolescents with dyslexia. 40 children and adolescents (8-13 years were divided in a Dyslexic Group (DG, 18 children, MA = 10.78, SD = 1.66 and Control Group (CG 22 children, MA = 10.59, SD = 1.86. All were students from the 2nd to 8th grade of elementary school and groups were equivalent in school grade, age, gender, and IQ. Oral and reading comprehension, word recognition, processing speed, picture naming, receptive vocabulary and phonological awareness were assessed. There were no group differences regarding the accuracy in oral and reading comprehension, phonological awareness, naming, and vocabulary scores. DG performed worse than the CG in word recognition (general score and orthographic confusion items and were slower in naming. Results corroborated the literature regarding word recognition and processing speed deficits in dyslexia. However, dyslexics can achieve normal scores on reading comprehension test. Data supports the importance of delimitation of different reading strategies embedded in the word recognition component. The role of processing speed in reading problems remain unclear.

Financial and workflow analysis of radiology reporting processes in the planning phase of implementation of a speech recognition system

Science.gov (United States)

Whang, Tom; Ratib, Osman M.; Umamoto, Kathleen; Grant, Edward G.; McCoy, Michael J.

2002-05-01

The goal of this study is to determine the financial value and workflow improvements achievable by replacing traditional transcription services with a speech recognition system in a large, university hospital setting. Workflow metrics were measured at two hospitals, one of which exclusively uses a transcription service (UCLA Medical Center), and the other which exclusively uses speech recognition (West Los Angeles VA Hospital). Workflow metrics include time spent per report (the sum of time spent interpreting, dictating, reviewing, and editing), transcription turnaround, and total report turnaround. Compared to traditional transcription, speech recognition resulted in radiologists spending 13-32% more time per report, but it also resulted in reduction of report turnaround time by 22-62% and reduction of marginal cost per report by 94%. The model developed here helps justify the introduction of a speech recognition system by showing that the benefits of reduced operating costs and decreased turnaround time outweigh the cost of increased time spent per report. Whether the ultimate goal is to achieve a financial objective or to improve operational efficiency, it is important to conduct a thorough analysis of workflow before implementation.

Emotion recognition from speech: tools and challenges

Science.gov (United States)

Al-Talabani, Abdulbasit; Sellahewa, Harin; Jassim, Sabah A.

2015-05-01

Human emotion recognition from speech is studied frequently for its importance in many applications, e.g. human-computer interaction. There is a wide diversity and non-agreement about the basic emotion or emotion-related states on one hand and about where the emotion related information lies in the speech signal on the other side. These diversities motivate our investigations into extracting Meta-features using the PCA approach, or using a non-adaptive random projection RP, which significantly reduce the large dimensional speech feature vectors that may contain a wide range of emotion related information. Subsets of Meta-features are fused to increase the performance of the recognition model that adopts the score-based LDC classifier. We shall demonstrate that our scheme outperform the state of the art results when tested on non-prompted databases or acted databases (i.e. when subjects act specific emotions while uttering a sentence). However, the huge gap between accuracy rates achieved on the different types of datasets of speech raises questions about the way emotions modulate the speech. In particular we shall argue that emotion recognition from speech should not be dealt with as a classification problem. We shall demonstrate the presence of a spectrum of different emotions in the same speech portion especially in the non-prompted data sets, which tends to be more "natural" than the acted datasets where the subjects attempt to suppress all but one emotion.

Automatic target recognition using a feature-based optical neural network

Science.gov (United States)

Chao, Tien-Hsin

1992-01-01

An optical neural network based upon the Neocognitron paradigm (K. Fukushima et al. 1983) is introduced. A novel aspect of the architectural design is shift-invariant multichannel Fourier optical correlation within each processing layer. Multilayer processing is achieved by iteratively feeding back the output of the feature correlator to the input spatial light modulator and updating the Fourier filters. By training the neural net with characteristic features extracted from the target images, successful pattern recognition with intra-class fault tolerance and inter-class discrimination is achieved. A detailed system description is provided. Experimental demonstration of a two-layer neural network for space objects discrimination is also presented.

Convincing State-Builders? Disaggregating Internal Legitimacy in Abkhazia

OpenAIRE

Bakke, K. M.; O Loughlin, J.; Toal, G.; Ward, M. D.

2013-01-01

De facto states, functional on the ground but unrecognized by most states, have long been black boxes for systematic empirical research. This study investigates de facto states’ internal legitimacy—people's confidence in the entity itself, the regime, and institutions. While internal legitimacy is important for any state, it is particularly important for de facto states, whose lack of external legitimacy has made internal legitimacy integral to their quest for recognition. We propose that the...

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…

Prestimulus default mode activity influences depth of processing and recognition in an emotional memory task.

Science.gov (United States)

Soravia, Leila M; Witmer, Joëlle S; Schwab, Simon; Nakataki, Masahito; Dierks, Thomas; Wiest, Roland; Henke, Katharina; Federspiel, Andrea; Jann, Kay

2016-03-01

Low self-referential thoughts are associated with better concentration, which leads to deeper encoding and increases learning and subsequent retrieval. There is evidence that being engaged in externally rather than internally focused tasks is related to low neural activity in the default mode network (DMN) promoting open mind and the deep elaboration of new information. Thus, reduced DMN activity should lead to enhanced concentration, comprehensive stimulus evaluation including emotional categorization, deeper stimulus processing, and better long-term retention over one whole week. In this fMRI study, we investigated brain activation preceding and during incidental encoding of emotional pictures and on subsequent recognition performance. During fMRI, 24 subjects were exposed to 80 pictures of different emotional valence and subsequently asked to complete an online recognition task one week later. Results indicate that neural activity within the medial temporal lobes during encoding predicts subsequent memory performance. Moreover, a low activity of the default mode network preceding incidental encoding leads to slightly better recognition performance independent of the emotional perception of a picture. The findings indicate that the suppression of internally-oriented thoughts leads to a more comprehensive and thorough evaluation of a stimulus and its emotional valence. Reduced activation of the DMN prior to stimulus onset is associated with deeper encoding and enhanced consolidation and retrieval performance even one week later. Even small prestimulus lapses of attention influence consolidation and subsequent recognition performance. © 2015 Wiley Periodicals, Inc.

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. .

Depth-based human activity recognition: A comparative perspective study on feature extraction

Directory of Open Access Journals (Sweden)

Heba Hamdy Ali

2018-06-01

Full Text Available Depth Maps-based Human Activity Recognition is the process of categorizing depth sequences with a particular activity. In this problem, some applications represent robust solutions in domains such as surveillance system, computer vision applications, and video retrieval systems. The task is challenging due to variations inside one class and distinguishes between activities of various classes and video recording settings. In this study, we introduce a detailed study of current advances in the depth maps-based image representations and feature extraction process. Moreover, we discuss the state of art datasets and subsequent classification procedure. Also, a comparative study of some of the more popular depth-map approaches has provided in greater detail. The proposed methods are evaluated on three depth-based datasets “MSR Action 3D”, “MSR Hand Gesture”, and “MSR Daily Activity 3D”. Experimental results achieved 100%, 95.83%, and 96.55% respectively. While combining depth and color features on “RGBD-HuDaAct” Dataset, achieved 89.1%. Keywords: Activity recognition, Depth, Feature extraction, Video, Human body detection, Hand gesture

GASB Achieves Standardization, Recognition.

Science.gov (United States)

Bissell, George E.

1986-01-01

In 1984 the Governmental Accounting Standards Board, created to solidify accounting principles for government entities, enumerated Generally Accepted Accounting Principles endorsed by the American Institute of Certified Public Accountants and the National Council on Governmental Accounting. These principles have recently been approved for school…

The Psychiatric Association of Bosnia-Herzegovina--distinctive role in national and international framework.

Science.gov (United States)

Račetović, Goran

2012-10-01

Following the initiative and after preparation that lasted about a year, a national association of experts dealing with psychiatry has been formed in our country named Psychiatric Association of Bosnia-Herzegovina (PABH). On March 17th 2008 PABH was formally started with its work that would be since 2009 been actively promoted and profiling as one of the best organized professional associations in B-H. Recognition on the international level and the active role of the PABH were substantially achieved in 2010 in the World (WPA), and from 2011 in European (EPA) Psychiatric Association. The Third Congress of Psychiatrists of B-H with International participation is the first in a series of future Congresses organized by PABH. This retrospective review describes the development and significance of the PABH both nationally and internationally through the documentation and archives of the PABH. The PABH is included in major psychiatric events in the country (active participation in the organization, logistics and scientific programme) and experts from our country are involved in an increasing number of international professional bodies. The PABH is the leading psychiatric association B-H, an active member of the largest and most important international organizations, with a continuous increase of the number of members who recognize the importance, relevance and quality of the PABH and further progress in its development and tendencies to harmonize psychiatric practice in the country and internationally.

Bayesian feature weighting for unsupervised learning, with application to object recognition

OpenAIRE

Carbonetto , Peter; De Freitas , Nando; Gustafson , Paul; Thompson , Natalie

2003-01-01

International audience; We present a method for variable selection/weighting in an unsupervised learning context using Bayesian shrinkage. The basis for the model parameters and cluster assignments can be computed simultaneous using an efficient EM algorithm. Applying our Bayesian shrinkage model to a complex problem in object recognition (Duygulu, Barnard, de Freitas and Forsyth 2002), our experiments yied good results.

Recognition of sign language with an inertial sensor-based data glove.

Science.gov (United States)

Kim, Kyung-Won; Lee, Mi-So; Soon, Bo-Ram; Ryu, Mun-Ho; Kim, Je-Nam

2015-01-01

Communication between people with normal hearing and hearing impairment is difficult. Recently, a variety of studies on sign language recognition have presented benefits from the development of information technology. This study presents a sign language recognition system using a data glove composed of 3-axis accelerometers, magnetometers, and gyroscopes. Each data obtained by the data glove is transmitted to a host application (implemented in a Window program on a PC). Next, the data is converted into angle data, and the angle information is displayed on the host application and verified by outputting three-dimensional models to the display. An experiment was performed with five subjects, three females and two males, and a performance set comprising numbers from one to nine was repeated five times. The system achieves a 99.26% movement detection rate, and approximately 98% recognition rate for each finger's state. The proposed system is expected to be a more portable and useful system when this algorithm is applied to smartphone applications for use in some situations such as in emergencies.

Robust representation and recognition of facial emotions using extreme sparse learning.

Science.gov (United States)

Shojaeilangari, Seyedehsamaneh; Yau, Wei-Yun; Nandakumar, Karthik; Li, Jun; Teoh, Eam Khwang

2015-07-01

Recognition of natural emotions from human faces is an interesting topic with a wide range of potential applications, such as human-computer interaction, automated tutoring systems, image and video retrieval, smart environments, and driver warning systems. Traditionally, facial emotion recognition systems have been evaluated on laboratory controlled data, which is not representative of the environment faced in real-world applications. To robustly recognize the facial emotions in real-world natural situations, this paper proposes an approach called extreme sparse learning, which has the ability to jointly learn a dictionary (set of basis) and a nonlinear classification model. The proposed approach combines the discriminative power of extreme learning machine with the reconstruction property of sparse representation to enable accurate classification when presented with noisy signals and imperfect data recorded in natural settings. In addition, this paper presents a new local spatio-temporal descriptor that is distinctive and pose-invariant. The proposed framework is able to achieve the state-of-the-art recognition accuracy on both acted and spontaneous facial emotion databases.

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

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...

Wavelet-based ground vehicle recognition using acoustic signals

Science.gov (United States)

Choe, Howard C.; Karlsen, Robert E.; Gerhart, Grant R.; Meitzler, Thomas J.

1996-03-01

not present the mathematics involved in this research. Instead, the focus of this paper is on the application of various techniques used to achieve our goal of successful recognition.

Face to face: blocking facial mimicry can selectively impair recognition of emotional expressions.

Science.gov (United States)

Oberman, Lindsay M; Winkielman, Piotr; Ramachandran, Vilayanur S

2007-01-01

People spontaneously mimic a variety of behaviors, including emotional facial expressions. Embodied cognition theories suggest that mimicry reflects internal simulation of perceived emotion in order to facilitate its understanding. If so, blocking facial mimicry should impair recognition of expressions, especially of emotions that are simulated using facial musculature. The current research tested this hypothesis using four expressions (happy, disgust, fear, and sad) and two mimicry-interfering manipulations (1) biting on a pen and (2) chewing gum, as well as two control conditions. Experiment 1 used electromyography over cheek, mouth, and nose regions. The bite manipulation consistently activated assessed muscles, whereas the chew manipulation activated muscles only intermittently. Further, expressing happiness generated most facial action. Experiment 2 found that the bite manipulation interfered most with recognition of happiness. These findings suggest that facial mimicry differentially contributes to recognition of specific facial expressions, thus allowing for more refined predictions from embodied cognition theories.

Multimodal emotional state recognition using sequence-dependent deep hierarchical features.

Science.gov (United States)

Barros, Pablo; Jirak, Doreen; Weber, Cornelius; Wermter, Stefan

2015-12-01

Emotional state recognition has become an important topic for human-robot interaction in the past years. By determining emotion expressions, robots can identify important variables of human behavior and use these to communicate in a more human-like fashion and thereby extend the interaction possibilities. Human emotions are multimodal and spontaneous, which makes them hard to be recognized by robots. Each modality has its own restrictions and constraints which, together with the non-structured behavior of spontaneous expressions, create several difficulties for the approaches present in the literature, which are based on several explicit feature extraction techniques and manual modality fusion. Our model uses a hierarchical feature representation to deal with spontaneous emotions, and learns how to integrate multiple modalities for non-verbal emotion recognition, making it suitable to be used in an HRI scenario. Our experiments show that a significant improvement of recognition accuracy is achieved when we use hierarchical features and multimodal information, and our model improves the accuracy of state-of-the-art approaches from 82.5% reported in the literature to 91.3% for a benchmark dataset on spontaneous emotion expressions. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auditory orientation in crickets: Pattern recognition controls reactive steering

Science.gov (United States)

Poulet, James F. A.; Hedwig, Berthold

2005-10-01

Many groups of insects are specialists in exploiting sensory cues to locate food resources or conspecifics. To achieve orientation, bees and ants analyze the polarization pattern of the sky, male moths orient along the females' odor plume, and cicadas, grasshoppers, and crickets use acoustic signals to locate singing conspecifics. In comparison with olfactory and visual orientation, where learning is involved, auditory processing underlying orientation in insects appears to be more hardwired and genetically determined. In each of these examples, however, orientation requires a recognition process identifying the crucial sensory pattern to interact with a localization process directing the animal's locomotor activity. Here, we characterize this interaction. Using a sensitive trackball system, we show that, during cricket auditory behavior, the recognition process that is tuned toward the species-specific song pattern controls the amplitude of auditory evoked steering responses. Females perform small reactive steering movements toward any sound patterns. Hearing the male's calling song increases the gain of auditory steering within 2-5 s, and the animals even steer toward nonattractive sound patterns inserted into the speciesspecific pattern. This gain control mechanism in the auditory-to-motor pathway allows crickets to pursue species-specific sound patterns temporarily corrupted by environmental factors and may reflect the organization of recognition and localization networks in insects. localization | phonotaxis

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.

Pathways to Medical Home Recognition: A Qualitative Comparative Analysis of the PCMH Transformation Process.

Science.gov (United States)

Mendel, Peter; Chen, Emily K; Green, Harold D; Armstrong, Courtney; Timbie, Justin W; Kress, Amii M; Friedberg, Mark W; Kahn, Katherine L

2017-12-15

To understand the process of practice transformation by identifying pathways for attaining patient-centered medical home (PCMH) recognition. The CMS Federally Qualified Health Center (FQHC) Advanced Primary Care Practice Demonstration was designed to help FQHCs achieve NCQA Level 3 PCMH recognition and improve patient outcomes. We used a stratified random sample of 20 (out of 503) participating sites for this analysis. We developed a conceptual model of structural, cultural, and implementation factors affecting PCMH transformation based on literature and initial qualitative interview themes. We then used conventional cross-case analysis, followed by qualitative comparative analysis (QCA), a cross-case method based on Boolean logic algorithms, to systematically identify pathways (i.e., combinations of factors) associated with attaining-or not attaining-Level 3 recognition. Site-level indicators were derived from semistructured interviews with site leaders at two points in time (mid- and late-implementation) and administrative data collected prior to and during the demonstration period. The QCA results identified five distinct pathways to attaining PCMH recognition and four distinct pathways to not attaining recognition by the end of the demonstration. Across these pathways, one condition (change leader capacity) was common to all pathways for attaining recognition, and another (previous improvement or recognition experience) was absent in all pathways for not attaining recognition. In general, sites could compensate for deficiencies in one factor with capacity in others, but they needed a threshold of strengths in cultural and implementation factors to attain PCMH recognition. Future efforts at primary care transformation should take into account multiple pathways sites may pursue. Sites should be assessed on key cultural and implementation factors, in addition to structural components, in order to differentiate interventions and technical assistance. © Health

Optical coherence tomography used for internal biometrics

Science.gov (United States)

Chang, Shoude; Sherif, Sherif; Mao, Youxin; Flueraru, Costel

2007-06-01

Traditional biometric technologies used for security and person identification essentially deal with fingerprints, hand geometry and face images. However, because all these technologies use external features of human body, they can be easily fooled and tampered with by distorting, modifying or counterfeiting these features. Nowadays, internal biometrics which detects the internal ID features of an object is becoming increasingly important. Being capable of exploring under-skin structure, optical coherence tomography (OCT) system can be used as a powerful tool for internal biometrics. We have applied fiber-optic and full-field OCT systems to detect the multiple-layer 2D images and 3D profile of the fingerprints, which eventually result in a higher discrimination than the traditional 2D recognition methods. More importantly, the OCT based fingerprint recognition has the ability to easily distinguish artificial fingerprint dummies by analyzing the extracted layered surfaces. Experiments show that our OCT systems successfully detected the dummy, which was made of plasticene and was used to bypass the commercially available fingerprint scanning system with a false accept rate (FAR) of 100%.

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.

Eye-movement strategies in developmental prosopagnosia and "super" face recognition.

Science.gov (United States)

Bobak, Anna K; Parris, Benjamin A; Gregory, Nicola J; Bennetts, Rachel J; Bate, Sarah

2017-02-01

Developmental prosopagnosia (DP) is a cognitive condition characterized by a severe deficit in face recognition. Few investigations have examined whether impairments at the early stages of processing may underpin the condition, and it is also unknown whether DP is simply the "bottom end" of the typical face-processing spectrum. To address these issues, we monitored the eye-movements of DPs, typical perceivers, and "super recognizers" (SRs) while they viewed a set of static images displaying people engaged in naturalistic social scenarios. Three key findings emerged: (a) Individuals with more severe prosopagnosia spent less time examining the internal facial region, (b) as observed in acquired prosopagnosia, some DPs spent less time examining the eyes and more time examining the mouth than controls, and (c) SRs spent more time examining the nose-a measure that also correlated with face recognition ability in controls. These findings support previous suggestions that DP is a heterogeneous condition, but suggest that at least the most severe cases represent a group of individuals that qualitatively differ from the typical population. While SRs seem to merely be those at the "top end" of normal, this work identifies the nose as a critical region for successful face recognition.

A Vocal-Based Analytical Method for Goose Behaviour Recognition

Directory of Open Access Journals (Sweden)

Henrik Karstoft

2012-03-01

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

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.

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 ...

Optical music recognition on the International Music Score Library Project

Science.gov (United States)

Raphael, Christopher; Jin, Rong

2013-12-01

A system is presented for optical recognition of music scores. The system processes a document page in three main phases. First it performs a hierarchical decomposition of the page, identifying systems, staves and measures. The second phase, which forms the heart of the system, interprets each measure found in the previous phase as a collection of non-overlapping symbols including both primitive symbols (clefs, rests, etc.) with fixed templates, and composite symbols (chords, beamed groups, etc.) constructed through grammatical composition of primitives (note heads, ledger lines, beams, etc.). This phase proceeds by first building separate top-down recognizers for the symbols of interest. Then, it resolves the inevitable overlap between the recognized symbols by exploring the possible assignment of overlapping regions, seeking globally optimal and grammatically consistent explanations. The third phase interprets the recognized symbols in terms of pitch and rhythm, focusing on the main challenge of rhythm. We present results that compare our system to the leading commercial OMR system using MIDI ground truth for piano music.

Achievements and challenges of the World Bank Loan/Department for International Development grant-assisted Tuberculosis Control Project in China.

Science.gov (United States)

Kong, Peng; Jiang, Xu; Zhang, Ben; Jiang, Shi-wen; Liu, Bo

2011-07-01

In March 2002, the government of China launched the World Bank Loan/ Department for International Development-supported Tuberculosis (TB) Control Project to reduce the prevalence and mortality of TB. The project generated promising results in policy development, strengthening of TB control systems, patient treatment success, funds management, and the introduction of legislation. In light of the global TB epidemic and control environment, it is useful to review the TB control priorities of the project, summarize the achievements and experiences around its implementation.

Recognition and assessment of pain in animals

Directory of Open Access Journals (Sweden)

Aleksić Jelena

2010-01-01

Full Text Available Pain is a complex physiological phenomenon, it is hard to define in a satisfactory manner in human beings, and it is extremely difficult to recognize and interpret in animals. According to the International Association for the Study of Pain (IASP, pain is defined as an unpleasant sensory or emotional experience associated with actual or potential tissue damage. Pain is an important aspect of life and its prevention and decrease are important as a goal to achieve the well-being of animals. The task of scientists is to recognize the language of pain interpretation which animals use to seek help. For an objective evaluation of pain, it is essential to possess a good knowledge of physiology, etiology and clinical diagnosis. We are obliged to do this also because of the ethic principles to defend the well-being of animals and to eliminate any factor which can cause feelings of pain or suffering. The recognition of pain and its manifestation is especially important in cases of animal abuse, when it could be the only symptom. Animals can be quiet and instinctively hide the presence of pain, which makes the symptoms more subtle, but does not make their injuries any less painful. It is also important to have knowledge of manifestations of pain that appear during different surgical procedures performed by the veterinarinarian in spite of the applied dose of analgetic. Pain significantly contributes to the suffering of animals and in such cases it is important to collect relevant documents, in the form of video recordings or in photodocumentation form, because it is important information in the processing of cases of animal abuse. Veterinary experts have the responsibility to recognize, evaluate, and prevent pain and to relieve animals from the pain, which should be the fourth vital sign, following temperature, pulse and breathing, and participate in the evaluation of the condition of the animal during an examination. Due to all the above mentioned, it is

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

Science.gov (United States)

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

2018-03-01

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

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.

Evolving expectations from international organisations

International Nuclear Information System (INIS)

Ruiz Lopez, C.

2008-01-01

The author stated that implementation of the geological disposal concept requires a strategy that provides national decision makers with sufficient confidence in the level of long-term safety and protection ultimately achieved. The concept of protection against harm has a broader meaning than radiological protection in terms of risk and dose. It includes the protection of the environment and socio-economic interests of communities. She recognised that a number of countries have established regulatory criteria already, and others are now discussing what constitutes a proper regulatory test and suitable time frame for judging the safety of long-term disposal. Each regulatory programme seeks to define reasonable tests of repository performance, using protection criteria and safety approaches consistent with the culture, values and expectations of the citizens of the country concerned. This means that there are differences in how protection and safety are addressed in national approaches to regulation and in the bases used for that. However, as was recognised in the Cordoba Workshop, it would be important to reach a minimum level of consistency and be able to explain the differences. C. Ruiz-Lopez presented an overview of the development of international guidance from ICRP, IAEA and NEA from the Cordoba workshop up to now, and positions of independent National Advisory Bodies. The evolution of these guidelines over time demonstrates an evolving understanding of long-term implications, with the recognition that dose and risk constraints should not be seen as measures of detriment beyond a few hundred years, the emphasis on sound engineering practices, and the introduction of new concepts and approaches which take into account social and economical aspects (e.g. constrained optimisation, BAT, managerial principles). In its new recommendations, ICRP (draft 2006) recognizes. in particular, that decision making processes may depend on other societal concerns and considers

2D-3D Face Recognition Method Basedon a Modified CCA-PCA Algorithm

Directory of Open Access Journals (Sweden)

Patrik Kamencay

2014-03-01

Full Text Available This paper presents a proposed methodology for face recognition based on an information theory approach to coding and decoding face images. In this paper, we propose a 2D-3D face-matching method based on a principal component analysis (PCA algorithm using canonical correlation analysis (CCA to learn the mapping between a 2D face image and 3D face data. This method makes it possible to match a 2D face image with enrolled 3D face data. Our proposed fusion algorithm is based on the PCA method, which is applied to extract base features. PCA feature-level fusion requires the extraction of different features from the source data before features are merged together. Experimental results on the TEXAS face image database have shown that the classification and recognition results based on the modified CCA-PCA method are superior to those based on the CCA method. Testing the 2D-3D face match results gave a recognition rate for the CCA method of a quite poor 55% while the modified CCA method based on PCA-level fusion achieved a very good recognition score of 85%.

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.

The importance of internal facial features in learning new faces.

Science.gov (United States)

Longmore, Christopher A; Liu, Chang Hong; Young, Andrew W

2015-01-01

For familiar faces, the internal features (eyes, nose, and mouth) are known to be differentially salient for recognition compared to external features such as hairstyle. Two experiments are reported that investigate how this internal feature advantage accrues as a face becomes familiar. In Experiment 1, we tested the contribution of internal and external features to the ability to generalize from a single studied photograph to different views of the same face. A recognition advantage for the internal features over the external features was found after a change of viewpoint, whereas there was no internal feature advantage when the same image was used at study and test. In Experiment 2, we removed the most salient external feature (hairstyle) from studied photographs and looked at how this affected generalization to a novel viewpoint. Removing the hair from images of the face assisted generalization to novel viewpoints, and this was especially the case when photographs showing more than one viewpoint were studied. The results suggest that the internal features play an important role in the generalization between different images of an individual's face by enabling the viewer to detect the common identity-diagnostic elements across non-identical instances of the face.

Is international junior success a reliable predictor for international senior success in elite combat sports?

Science.gov (United States)

Li, Pingwei; De Bosscher, Veerle; Pion, Johan; Weissensteiner, Juanita R; Vertonghen, Jikkemien

2018-05-01

Currently in the literature, there is a dearth of empirical research that confirms whether international junior success is a reliable predictor for future international senior success. Despite the uncertainty of the junior-senior relationship, federations and coaches still tend to use junior success as a predictor for long-term senior success. A range of former investigations utilising a retrospective lens has merely focused on success that athletes attained at junior level competitions. Success that was achieved at senior-level competitions but at a junior age was relatively ignored. This study explored to what extent international senior success can be predicted based on success that athletes achieved in either international junior level competitions (i.e. junior medalists) or senior competitions at a junior age (i.e. early achievers). The sample contains 4011 international male and female athletes from three combat sports (taekwondo, wrestling and boxing), who were born between 1974 and 1990 and participated in both international junior and senior-level competitions between 1990 and 2016. Gender and sport differences were compared. The results revealed that 61.4% of the junior medalists and 90.4% of the early achievers went on to win international medals at a senior age. Among the early achievers, 92.2% of the taekwondo athletes, 68.4% of the wrestling athletes and 37.9% of the boxing athletes could be reliably "predicted" to win international senior medals. The findings demonstrate that specific to the three combat sports examined, international junior success appears to be an important predictor to long-term international senior success.

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.

International recognition of the Chronic Otitis Media Questionnaire 12.

Science.gov (United States)

Kosyakov, S I; Minavnina, J V; Phillips, J S; Yung, M W

2017-06-01

The Chronic Otitis Media Questionnaire 12 was developed initially in the UK to assess patient-reported health-related quality of life associated with chronic otitis media. This study aimed to determine whether this tool is applicable to the Russian population, which has a materially different healthcare system. A total of 108 patients with different forms of chronic otitis media completed the Russian Chronic Otitis Media Questionnaire 12. The average Russian Chronic Otitis Media Questionnaire 12 score was 19.4 (standard deviation = 8.3). The internal consistency of the Russian Chronic Otitis Media Questionnaire 12 was high, with a Cronbach's alpha value of 0.860. The Russian version of the Chronic Otitis Media Questionnaire 12 was found to be a reliable tool for the assessment of health-related quality of life in patients with chronic otitis media. This sets the scene for international collaboration, using this tool to assess the effectiveness of surgical treatments even amongst countries with different healthcare systems.

Exploring Spatio-temporal Dynamics of Cellular Automata for Pattern Recognition in Networks

Science.gov (United States)

Miranda, Gisele Helena Barboni; Machicao, Jeaneth; Bruno, Odemir Martinez

2016-11-01

Network science is an interdisciplinary field which provides an integrative approach for the study of complex systems. In recent years, network modeling has been used for the study of emergent phenomena in many real-world applications. Pattern recognition in networks has been drawing attention to the importance of network characterization, which may lead to understanding the topological properties that are related to the network model. In this paper, the Life-Like Network Automata (LLNA) method is introduced, which was designed for pattern recognition in networks. LLNA uses the network topology as a tessellation of Cellular Automata (CA), whose dynamics produces a spatio-temporal pattern used to extract the feature vector for network characterization. The method was evaluated using synthetic and real-world networks. In the latter, three pattern recognition applications were used: (i) identifying organisms from distinct domains of life through their metabolic networks, (ii) identifying online social networks and (iii) classifying stomata distribution patterns varying according to different lighting conditions. LLNA was compared to structural measurements and surpasses them in real-world applications, achieving improvement in the classification rate as high as 23%, 4% and 7% respectively. Therefore, the proposed method is a good choice for pattern recognition applications using networks and demonstrates potential for general applicability.

Recognition and characterization of unstructured environmental sounds

Science.gov (United States)

Chu, Selina

2011-12-01

Environmental sounds are what we hear everyday, or more generally sounds that surround us ambient or background audio. Humans utilize both vision and hearing to respond to their surroundings, a capability still quite limited in machine processing. The first step toward achieving multimodal input applications is the ability to process unstructured audio and recognize audio scenes (or environments). Such ability would have applications in content analysis and mining of multimedia data or improving robustness in context aware applications through multi-modality, such as in assistive robotics, surveillances, or mobile device-based services. The goal of this thesis is on the characterization of unstructured environmental sounds for understanding and predicting the context surrounding of an agent or device. Most research on audio recognition has focused primarily on speech and music. Less attention has been paid to the challenges and opportunities for using audio to characterize unstructured audio. My research focuses on investigating challenging issues in characterizing unstructured environmental audio and to develop novel algorithms for modeling the variations of the environment. The first step in building a recognition system for unstructured auditory environment was to investigate on techniques and audio features for working with such audio data. We begin by performing a study that explore suitable features and the feasibility of designing an automatic environment recognition system using audio information. In my initial investigation to explore the feasibility of designing an automatic environment recognition system using audio information, I have found that traditional recognition and feature extraction for audio were not suitable for environmental sound, as they lack any type of structures, unlike those of speech and music which contain formantic and harmonic structures, thus dispelling the notion that traditional speech and music recognition techniques can simply

Externalizing and Internalizing Symptoms Moderate Longitudinal Patterns of Facial Emotion Recognition in Autism Spectrum Disorder

Science.gov (United States)

Rosen, Tamara E.; Lerner, Matthew D.

2016-01-01

Facial emotion recognition (FER) is thought to be a key deficit domain in autism spectrum disorder (ASD). However, the extant literature is based solely on cross-sectional studies; thus, little is known about even short-term intra-individual dynamics of FER in ASD over time. The present study sought to examine trajectories of FER in ASD youth over…

Assessment of Homomorphic Analysis for Human Activity Recognition from Acceleration Signals.

Science.gov (United States)

Vanrell, Sebastian Rodrigo; Milone, Diego Humberto; Rufiner, Hugo Leonardo

2017-07-03

Unobtrusive activity monitoring can provide valuable information for medical and sports applications. In recent years, human activity recognition has moved to wearable sensors to deal with unconstrained scenarios. Accelerometers are the preferred sensors due to their simplicity and availability. Previous studies have examined several \\azul{classic} techniques for extracting features from acceleration signals, including time-domain, time-frequency, frequency-domain, and other heuristic features. Spectral and temporal features are the preferred ones and they are generally computed from acceleration components, leaving the acceleration magnitude potential unexplored. In this study, based on homomorphic analysis, a new type of feature extraction stage is proposed in order to exploit discriminative activity information present in acceleration signals. Homomorphic analysis can isolate the information about whole body dynamics and translate it into a compact representation, called cepstral coefficients. Experiments have explored several configurations of the proposed features, including size of representation, signals to be used, and fusion with other features. Cepstral features computed from acceleration magnitude obtained one of the highest recognition rates. In addition, a beneficial contribution was found when time-domain and moving pace information was included in the feature vector. Overall, the proposed system achieved a recognition rate of 91.21% on the publicly available SCUT-NAA dataset. To the best of our knowledge, this is the highest recognition rate on this dataset.

ANOVA Based Approch for Efficient Customer Recognition: Dealing with Common Names

OpenAIRE

Saberi , Morteza; Saberi , Zahra

2015-01-01

Part 2: Artificial Intelligence for Knowledge Management; International audience; This study proposes an Analysis of Variance (ANOVA) technique that focuses on the efficient recognition of customers with common names. The continuous improvement of Information and communications technologies (ICT) has led customers to have new expectations and concerns from their related organization. These new expectations bring various difficulties for organizations’ help desk to meet their customers’ needs....

Mutual Recognition of Financial Penalties between the EU Member States. Critical Observations

Directory of Open Access Journals (Sweden)

Minodora Ioana Rusu

2010-07-01

Full Text Available According to the special Romanian law, one of the forms of judicial assistance in criminal matters recognized in the relations between the EU member states is, among others, the one referringto the cooperation in applying the principle of mutual recognition of financial penalties. The European normative act that establishes the general cooperation norms in this matter is the Council’sDecision Frame 2005/214/JAI on February 24, 2005 on the application of the principle of mutual recognition of financial penalties. This European normative act has been transposed in the internallegislation through Law no.302/2004, according to the international judicial cooperation in criminal matters, with the subsequent amendments and completions, the latter being represented by Lawno.222/2008. The amendments and completions instituted by the abovementioned normative act establish the procedure of transmitting the decision, the procedures for recognition and execution ofsuch a decision by the competent Romanian judicial authorities, the grounds of non recognition and non execution, the definition of used terms, as well as other aspects referring to the recognition andexecution of such decisions. Commenting refers to a number of provisions in the law under both European and domestic in the special law, comments aimed in particular the replacement of terms ofrecognition or non-performance reasons, the procedure of identification of persons convicted when they are evade the enforcement of financial obligations and failure to transpose into national law of subsequent changes to European law.

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.

A multi-environment dataset for activity of daily living recognition in video streams.

Science.gov (United States)

Borreo, Alessandro; Onofri, Leonardo; Soda, Paolo

2015-08-01

Public datasets played a key role in the increasing level of interest that vision-based human action recognition has attracted in last years. While the production of such datasets has been influenced by the variability introduced by various actors performing the actions, the different modalities of interactions with the environment introduced by the variation of the scenes around the actors has been scarcely took into account. As a consequence, public datasets do not provide a proper test-bed for recognition algorithms that aim at achieving high accuracy, irrespective of the environment where actions are performed. This is all the more so, when systems are designed to recognize activities of daily living (ADL), which are characterized by a high level of human-environment interaction. For that reason, we present in this manuscript the MEA dataset, a new multi-environment ADL dataset, which permitted us to show how the change of scenario can affect the performances of state-of-the-art approaches for action recognition.

An international waste convention: measures for achieving sustainable development.

Science.gov (United States)

Meyers, Gary D; McLeod, Glen; Anbarci, Melanie A

2006-12-01

Waste is a by-product of economic growth. Consequently, economic growth presents challenges for sustainable resource management and development because continued economic growth implies continued growth in waste outputs. Poor management of waste results in the inappropriate depletion of natural resources and potentially adverse effects on the environment, health and the economy. It is unsustainable. This paper begins by outlining the magnitude of and the current response to the growth in the quantity of waste outputs. This is followed by a consideration of why the international response to date, including the Rio Declaration and Agenda 21, fails to address the issue adequately. The paper concludes with a discussion on why and how an international treaty or other measure could advance sustainable development by providing an appropriate framework within which to address the problem.

Component Pin Recognition Using Algorithms Based on Machine Learning

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Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

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...

Processing Speed and Intelligence as Predictors of School Achievement: Mediation or Unique Contribution?

Science.gov (United States)

Dodonova, Yulia A.; Dodonov, Yury S.

2012-01-01

The relationships between processing speed, intelligence, and school achievement were analyzed on a sample of 184 Russian 16-year-old students. Two speeded tasks required the discrimination of simple geometrical shapes and the recognition of the presented meaningless figures. Raven's Advanced Progressive Matrices and the verbal subtests of…

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

Voice Recognition in Face-Blind Patients

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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

Optical character recognition: an illustrated guide to the frontier

Science.gov (United States)

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

1999-12-01

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

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