Dor, Ariane; Machkour-M'rabet, Salima; Legal, Luc; Williams, Trevor; Hénaut, Yann
Chemically mediated communication is common in spiders but has been poorly studied in burrowing tarantulas. This study aimed to determine whether chemical cues influence the behaviour of females of Brachypelma vagans, a Mexican species of tarantula, during encounters with previously inhabited burrows or with extracts from the silk of conspecific females. In laboratory choice tests, female tarantulas entered a burrow that had previously been inhabited by a conspecific female significantly more frequently than a burrow that had never been inhabited. The identity of the previous inhabitant also affected the number of spiders that chose to enter a burrow. Spiders were quicker to choose and enter a burrow previously inhabited by themselves than a burrow previously inhabited by a conspecific or a burrow that had not been previously inhabited. Hexane, methanol and dichloromethane extracts of conspecific silk elicited different responses from female tarantulas when extracts were placed on filter paper disks at one end of an experimental arena with a control filter paper disk, on to which the corresponding solvent alone had been pipetted, placed on the other end of the arena. Spiders showed the strongest responses to hexane extracts of silk, with a significant preference to move towards the hexane extract and a significantly greater period of time spent in proximity to the hexane extract compared to the control disk. Overall and in contrast to expectations, tarantulas were most strongly attracted to the cues left by other conspecific females. As encounters between B. vagans females usually lead to aggression and mortality of one of the participants, we conclude that chemical cues are not signals that are deliberately released by burrow-inhabiting females but may inadvertently escape and cannot be easily suppressed.
Sonerud, G. A.; Smedshaug, C. A.; Bråthen, O.
Communal roosting in birds may function to enhance foraging efficiency as explained by the information centre hypothesis, which predicts that successful foragers return from the roost to the rewarding food patch and that birds ignorant of this food follow knowledgeable roost-mates. We tested these predictions by exposing 34 radio-tagged, free-ranging, flock-living hooded crows (Corvus corone cornix) to a novel experimental set-up mimicking a superfluous food patch with maximum temporal and sp...
Carlo, Troy; Levy, Bruce D.
Asthma pathobiology is remarkable for chronic airway inflammation that fails to spontaneously resolve. No curative therapy is currently available. A growing body of evidence indicates that, in health, inflammation resolution is an active process orchestrated by specific chemical mediators that are elaborated to restore tissue homeostasis. Activated cell membranes release polyunsaturated fatty acids from phospholipids for enzymatic conversion to biologically active mediators with profound regu...
Chen, Hanpeng; Woods, Arcadia; Forbes, Ben; Jones, Stuart
Nanocarriers can aid therapeutic agent administration to the lung, but controlling drug delivery from these systems after deposition in the airways can be problematic. The aim of this study was to evaluate if chemically mediated shell permeabilisation could help manipulate the rate and extent of nanocarrier drug release. Rifampicin was loaded into lipid shell (loading efficiency 41.0±11.4%, size 50nm) and polymer shell nanocarriers (loading efficiency 25.9±2.3%, size 250nm). The drug release at pH 7.4 (lung epithelial pH) and 4.2 (macrophage endosomal pH) with and without the chemical permeabilisers (Pluronic L62D - lipid nanocarriers; H(+)- polymer nanocarriers) was then tested. At pH 7.4 the presence of the permeabilisers increased nanocarrier drug release rate (from 3.2μg/h to 6.8μg/h for lipid shell nanocarriers, 2.3μg/h to 3.4μg/h for polymer shell nanocarriers) and drug release extent (from 50% to 80% for lipid shell nanocarriers, from 45% to 76% for polymer shell nanocarriers). These effects were accompanied by lipid nanocarrier distension (from 50 to 240nm) and polymer shell hydrolysis. At pH 4.2 the polymer nanocarriers did not respond to the permeabiliser, but the lipid nanocarrier maintained a robust drug release enhancement response and hence they demonstrated that the manipulation of controlled drug release from lung-targeted nanocarriers was possible through chemically mediated shell permeabilisation.
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
Haugen, Liv Merete; Olavsbråten, Inge
Machine based face recognition has been a popular research area for several years, and has numerous applications. This technology has now reached a point where there already exists good algorithms for recognition for standardized still images - which have little variation in e.g. lighting, facial expression and pose. We are however in lack of good algorithms that are able to do recognition from live video. The low quality of most surveillance cameras, together with non-standardized imaging c...
Ciullini, Ilaria; Tilli, Silvia; Scozzafava, Andrea; Briganti, Fabrizio
Dyes belonging to the mono-, di-, tri- and poly-azo as well as anthraquinonic and mono-azo Cr-complexed classes, chosen among the most utilized in textile applications, were employed for a comparative enzymatic decolorization study using the extracellular crude culture extracts from the white rot fungus Funalia (Trametes) trogii grown on different culture media and activators able to trigger different levels of expression of oxidizing enzymes: laccase and cellobiose dehydrogenase. Laccase containing extracts were capable to decolorize some dyes from all the different classes analyzed, whereas the recalcitrant dyes were subjected to the combined action of laccase and the chemical mediator HBT, or laccase plus cellobiose dehydrogenase. Correlations among the decolorization degree of the various dyes and their electronic and structural diversities were rationalized and discussed. The utilization of cellobiose dehydrogenase in support to the activity of laccase for the decolorization of azo textile dyes resulted in substantial increases in decolorization for all the refractory dyes proving to be a valid alternative to more expensive and less environmentally friendly chemical treatments of textile dyes wastes.
Diefenderfer, Graig T.
The use of biometrics is an evolving component in today's society. Fingerprint recognition continues to be one of the most widely used biometric systems. This thesis explores the various steps present in a fingerprint recognition system. The study develops a working algorithm to extract fingerprint minutiae from an input fingerprint image. This stage incorporates a variety of image pre-processing steps necessary for accurate minutiae extraction and includes two different methods of ridge thin...
Shy, Oz; Stenbacka , Rune
We introduce three types of consumer recognition: identity recognition, asymmetric preference recognition, and symmetric preference recognition. We characterize price equilibria and compare profits, consumer surplus, and total welfare. Asymmetric preference recognition enhances profits compared with identity recognition, but firms have no incentive to exchange information regarding customer-specific preferences (symmetric preference recognition). Consumers would benefit from a policy panning ...
Full Text Available During their lifetime, people learn to recognize thousands of faces that they interact with. Face perception refers to an individual's understanding and interpretation of the face, particularly the human face, especially in relation to the associated information processing in the brain. The proportions and expressions of the human face are important to identify origin, emotional tendencies, health qualities, and some social information. From birth, faces are important in the individual's social interaction. Face perceptions are very complex as the recognition of facial expressions involves extensive and diverse areas in the brain. Our main goal is to put emphasis on presenting human faces specialized studies, and also to highlight the importance of attractiviness in their retention. We will see that there are many factors that influence face recognition.
Wang, Man-Ying; Ching, Chi-Le
This study adopted a change detection task to investigate whether and how recognition intent affects the construction of orthographic representation in visual word recognition. Chinese readers (Experiment 1-1) and nonreaders (Experiment 1-2) detected color changes in radical components of Chinese characters. Explicit recognition demand was imposed in Experiment 2 by an additional recognition task. When the recognition was implicit, a bias favoring the radical location informative of character identity was found in Chinese readers (Experiment 1-1), but not nonreaders (Experiment 1-2). With explicit recognition demands, the effect of radical location interacted with radical function and word frequency (Experiment 2). An estimate of identification performance under implicit recognition was derived in Experiment 3. These findings reflect the joint influence of recognition intent and orthographic regularity in shaping readers' orthographic representation. The implication for the role of visual attention in word recognition was also discussed. PMID:19036609
Questions of speaker’s recognition by voice are investigated in this dissertation. Speaker recognition systems, their evolution, problems of recognition, systems of features, questions of speaker modeling and matching used in text-independent and text-dependent speaker recognition are considered too. The text-independent speaker recognition system has been developed during this work. The Gaussian mixture model approach was used for speaker modeling and pattern matching. The automatic m...
The demand on security is increasing greatly in these years and biometric recognition gradually becomes a hot field of research. Iris recognition is a new branch of biometric recognition, which is regarded as the most stable, safe and accurate biometric recognition method. In these years, much progress in this field has been made by scholars and experts of different countries. In this paper, some successful iris recognition methods are listed and their performance are compared. Furthermore, the existing problems and challenges are discussed.
Lin, Kai-Wei; Huang, A-Mei; Tu, Huang-Yao; Weng, Jing-Ru; Hour, Tzyh-Chyuan; Wei, Bai-Luh; Yang, Shyh-Chyun; Wang, Jih-Pyang; Pu, Yeong-Shiau; Lin, Chun-Nan
Phloroglucinols, garcinielliptones HA-HE (1-5), and C (6) were studied in vitro for their inhibitory effects on chemical mediators released from mast cells, neutrophils, and macrophages. Compound 6 revealed significant inhibitory effect on release of lysozyme from rat neutrophils stimulated with formyl-Met-Leu-Phe (fMLP)/cytochalasin B (CB). Compounds 3, 4, and 6 showed significant inhibitory effects on superoxide anion generation in rat neutrophils stimulated with (fMLP)/(CB), while compounds 1 and 5 revealed inhibitory effects on tumor necrosis factor-alpha (TNF-alpha) formation in macrophages stimulated with lipopolysaccharide (LPS). Compounds 1 and 3-6 showed inhibitory effects on xanthine oxidase (XO) and could inhibit the DNA breakage caused by O2(-*). Treatment of NTUB1 with 2 to 60 microM compound 3 and 5 microM cisplatin and SV-HUC1 with 9 to 60 microM 3 and 5 microM cisplatin, respectively, resulted in an increase of viability of cells. These results indicated that compounds 1 and 3-6 showed anti-inflammatory effects and antioxidant activities. Compound 3 mediates through the suppression of XO activity and reduction of reactive oxygen species (ROS), and protection of subsequent cell death. PMID:19754119
Ali, Nanna; Ben-Ahmed, Michele; Bom, Thomas Falk; Ching, Rune Kieran; Steffensen, Lars Schmidt; Funningsstovu, Janus Hanusarson í
Contested states have existed in many decades and been on the political agenda worldwide. A small group of entities in the world are aspiring for recognition and independence, while some entities gained recognition relatively smoothly. This project accounts for UN’s recognition process and investigates entities prospects of influencing the process for obtaining recognition. Based on theories of liberalism and constructivism as well as the opposing theories of international relations, re...
Fichtner, K. -H.; Freudenberg, W.; Ohya, M.
We study a possible function of brain, in particular, we try to describe several aspects of the process of recognition. In order to understand the fundamental parts of the recognition process, the quantum teleportation scheme seems to be useful. We consider a channel expression of the teleportation process that serves for a simplified description of the recognition process in brain.
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...
Yu, Francis T. S.; Jutamulia, Suganda
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.
Zhou, Zhi; Du, Yingzi; Thomas, N. L.; Delp, Edward J., III
Multimodal biometrics use more than one means of biometric identification to achieve higher recognition accuracy, since sometimes a unimodal biometric is not good enough used to do identification and classification. In this paper, we proposed a multimodal eye recognition system, which can obtain both iris and sclera patterns from one color eye image. Gabor filter and 1-D Log-Gabor filter algorithms have been applied as the iris recognition algorithms. In sclera recognition, we introduced automatic sclera segmentation, sclera pattern enhancement, sclera pattern template generation, and sclera pattern matching. We applied kernelbased matching score fusion to improve the performance of the eye recognition system. The experimental results show that the proposed eye recognition method can achieve better performance compared to unimodal biometric identification, and the accuracy of our proposed kernel-based matching score fusion method is higher than two classic linear matching score fusion methods: Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA).
MAYANK PARASHER; SHRUTI SHARMA; A .K. SHARMA,; J.P.Gupta
Pattern Recognition is the science of recognizing patterns by machines. This is very wide research area as of today, because every newresearch tries to make machine as intelligent as human for recognizing patterns. Pattern recognition is an active research and an importanttrait of ‘artificial intelligence’. This review paper introduces pattern recognition, its fundamental definitions, and provides understanding of related research work. This paper presents different types of algorithms, their...
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.
Webb, Andrew R
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,
This thesis proposes algorithms and techniques to be used for automatic recognition of six prototypic emotion categories by computer programs, based on the recognition of facial expressions and emotion patterns in voice. Considering the applicability in real-life conditions, the research is carried
A broad range of approaches has been proposed and applied for the complex and rather difficult task of object recognition that involves the determination of object characteristics and object classification into one of many a priori object types. Our paper revises briefly the three main different paradigms in pattern recognition, namely Bayesian statistics, neural networks, and expert systems. (author)
El-Hori, Inas H.; El-Momen, Zahraa K.; Ganoun, Ali
This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. The comparative study of Facial Expression Recognition (FER) techniques namely Principal Component's analysis (PCA) and PCA with Gabor filters (GF) is done. The objective of this research is to show that PCA with Gabor filters is superior to the first technique in terms of recognition rate. To test and evaluates their performance, experiments are performed using real database by both techniques. The universally accepted five principal emotions to be recognized are: Happy, Sad, Disgust and Angry along with Neutral. The recognition rates are obtained on all the facial expressions.
Li, Stan Z
This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems
Mobile Intention Recognition addresses problems of practical relevance for mobile system engineers: how can we make mobile assistance systems more intelligent? How can we model and recognize patterns of human behavior which span more than a limited spatial context? This text provides an overview on plan and intention recognition, ranging from the late 1970s to very recent approaches. This overview is unique as it discusses approaches with respect to the specificities of mobile intention recognition. This book covers problems from research on mobile assistance systems using methods from artific
individual and collective identity formation and has led to an increase in social pathological illnesses such as stress and depression. By juxtaposing these analyses with Honneth’s theory on recognition, we conclude that the contemporary logic of work is unable to provide adequate forms of recognition......The article deals with the relationship between work and recognition, taking Axel Honneth’s social-philosophical theory of the struggle for recognition as its point of departure. In order to give sociological substance to Honneth’s theory, we turn to three contemporary social theorists - Jean......-Pierre Le Goff, Christophe Dejours and Emmanuel Renault. In spite of many differences, their work is united by a critical description of the logic of work and its consequences for individual individuation. These theorists agree that the growth of autonomy, flexibility and mobility has destabilised...
The aim of forensic speaker recognition is to establish links between individuals and criminal activities, through audio speech recordings. This field is multidisciplinary, combining predominantly phonetics, linguistics, speech signal processing, and forensic statistics. On these bases, expert-based
Full Text Available This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions (happy, sad, angry, fear, surprised, neutral etc... Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations with existing system Emotion recognition using brain activity. In this paper by using an existing simulator I have achieved 97 percent accurate results and it is easy and simplest way than Emotion recognition using brain activity system. Purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper neural network has been used for better results. In the end of paper comparisons of existing Human Emotion Recognition System has been made with new one.
Sturm, Bob L.
A fundamental problem with nearly all work in music genre recognition (MGR)is that evaluation lacks validity with respect to the principal goals of MGR. This problem also occurs in the evaluation of music emotion recognition (MER). Standard approaches to evaluation, though easy to implement, do...... not reliably differentiate between recognizing genre or emotion from music, or by virtue of confounding factors in signals (e.g., equalization). We demonstrate such problems for evaluating an MER system, and conclude with recommendations....
Ronak P Patel; Narendra M Patel; Keyur Brahmbhatt
This paper describes the Smart Vehicle Screening System, which can be installed into a tollboothfor automated recognition of vehicle license plate information using a photograph of a vehicle. An automatedsystem could then be implemented to control the payment of fees, parking areas, highways, bridges ortunnels, etc. This paper contains new algorithm for recognition number plate using Morphological operation,Thresholding operation, Edge detection, Bounding box analysis for number plate extract...
This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions (happy, sad, angry, fear, surprised, neutral etc..). Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations with existing system Emotion recognition using brain activity. In this paper by using an existing simulator I hav...
Bhawna Negi; Varun Sharma
The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae ...
Elsass, Peter; Jensen, Bodil; Mørup, Rikke;
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...
Clintin P. Davis-Stober
Full Text Available The Recognition Heuristic (Gigerenzer and Goldstein, 1996; Goldstein and Gigerenzer, 2002 makes the counter-intuitive prediction that a decision maker utilizing less information may do as well as, or outperform, an idealized decision maker utilizing more information. We lay a theoretical foundation for the use of single-variable heuristics such as the Recognition Heuristic as an optimal decision strategy within a linear modeling framework. We identify conditions under which over-weighting a single predictor is a mini-max strategy among a class of a priori chosen weights based on decision heuristics with respect to a measure of statistical lack of fit we call ``risk''. These strategies, in turn, outperform standard multiple regression as long as the amount of data available is limited. We also show that, under related conditions, weighting only one variable and ignoring all others produces the same risk as ignoring the single variable and weighting all others. This approach has the advantage of generalizing beyond the original environment of the Recognition Heuristic to situations with more than two choice options, binary or continuous representations of recognition, and to other single variable heuristics. We analyze the structure of data used in some prior recognition tasks and find that it matches the sufficient conditions for optimality in our results. Rather than being a poor or adequate substitute for a compensatory model, the Recognition Heuristic closely approximates an optimal strategy when a decision maker has finite data about the world.
Jain, Lalit Prithviraj
Real-world tasks in computer vision, pattern recognition and machine learning often touch upon the open set recognition problem: multi-class recognition with incomplete knowledge of the world and many unknown inputs. An obvious way to approach such problems is to develop a recognition system that thresholds probabilities to reject unknown classes. Traditional rejection techniques are not about the unknown; they are about the uncertain boundary and rejection around that boundary. Thus traditional techniques only represent the "known unknowns". However, a proper open set recognition algorithm is needed to reduce the risk from the "unknown unknowns". This dissertation examines this concept and finds existing probabilistic multi-class recognition approaches are ineffective for true open set recognition. We hypothesize the cause is due to weak adhoc assumptions combined with closed-world assumptions made by existing calibration techniques. Intuitively, if we could accurately model just the positive data for any known class without overfitting, we could reject the large set of unknown classes even under this assumption of incomplete class knowledge. For this, we formulate the problem as one of modeling positive training data by invoking statistical extreme value theory (EVT) near the decision boundary of positive data with respect to negative data. We provide a new algorithm called the PI-SVM for estimating the unnormalized posterior probability of class inclusion. This dissertation also introduces a new open set recognition model called Compact Abating Probability (CAP), where the probability of class membership decreases in value (abates) as points move from known data toward open space. We show that CAP models improve open set recognition for multiple algorithms. Leveraging the CAP formulation, we go on to describe the novel Weibull-calibrated SVM (W-SVM) algorithm, which combines the useful properties of statistical EVT for score calibration with one-class and binary
Genovese, Angelo; Scotti, Fabio
This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems.It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Const
Ahlmark, Nanna; Whyte, Susan Reynolds; Harting, Janneke;
meaningful benefits; these centred on reducing isolation and being met on their own terms regarding language and logistics. Most importantly, they remembered when treated with attention and respect by professionals and the mutual acknowledgement between participants. We use Axel Honneth’s notions of rights......-based and solidarity-based recognition to analyse what was at stake in these experiences, and we engage Annemarie Mol’s concept of a logic of care to show how recognition unfolded practically during the training. We propose that participants’ wider social context and experiences of misrecognition situated the training...
Hans Marius Hansteen
Full Text Available Even though “toleration” and “recognition” designate opposing attitudes (to tolerate something, implies a negative stance towards it, whereas recognition seems to imply a positive one, the concepts do not constitute mutually exclusive alternatives. However, “toleration” is often associated with liberal universalism, focusing on individual rights, whereas “recognition” often connotes communitarian perspectives, focusing on relations and identity. This paper argues that toleration may be founded on recognition, and that recognition may imply toleration. In outlining a differentiated understanding of the relationship between toleration and recognition, it seems apt to avoid an all-to-general dichotomy between universalism and particularism or, in other words, to reach beyond the debate between liberalism and communitarianism in political philosophy.The paper takes as its starting point the view that the discussion on toleration and diversity in intercultural communication is one of the contexts where it seems important to get beyond the liberal/communitarian dichotomy. Some basic features of Rainer Forst’s theory of toleration and Axel Honneth’s theory of the struggle for recognition are presented, in order to develop a more substantial understanding of the relationship between the concepts of toleration and recognition. One lesson from Forst is that toleration is a normatively dependent concept, i.e., that it is impossible to deduce principles for toleration and its limits from a theory of toleration as such. A central lesson from Honneth is that recognition – understood as a basic human need – is always conflictual and therefore dynamic.Accordingly, a main point in the paper is that the theory of struggles for and about recognition (where struggles for designates struggles within an established order of recognition, and struggles about designates struggles that challenge established orders of recognition may clarify what
Dai, Dao-Qing; Yan, Hong
Wavelets have been successfully used in image processing. Their ability to capture localized spatial-frequency information of image motivates their use for feature extraction. We give an overview of using wavelets in the face recognition technology. Due to limit of space the use of Gabor wavelets is not covered in this survey. Interested readers are referred to section 8.3 for references.
Perepelitsa, VA; Sergienko, [No Value; Kochkarov, AM
Definitions of prefractal and fractal graphs are introduced, and they are used to formulate mathematical models in different fields of knowledge. The topicality of fractal-graph recognition from the point of view, of fundamental improvement in the efficiency of the solution of algorithmic problems i
SONMEZ, Öznur Sinem; OZTAS, Oguzhan
In this study, a minutiae-based fingerprint recognition system is implemented which includes normalization, enhancement, thinning, extraction of minutiae, elimination of false minutiae, orientation estimation, core point detection, finding reference points and matching processes. Accordingly, the effects of enhancement and elimination of false minutiae processes, methods of reference point determination and low quality fingerprint images on system performance are analyzed using two different ...
de Ridder, Dick; de Ridder, Jeroen; Reinders, Marcel J T
Pattern recognition is concerned with the development of systems that learn to solve a given problem using a set of example instances, each represented by a number of features. These problems include clustering, the grouping of similar instances; classification, the task of assigning a discrete label to a given instance; and dimensionality reduction, combining or selecting features to arrive at a more useful representation. The use of statistical pattern recognition algorithms in bioinformatics is pervasive. Classification and clustering are often applied to high-throughput measurement data arising from microarray, mass spectrometry and next-generation sequencing experiments for selecting markers, predicting phenotype and grouping objects or genes. Less explicitly, classification is at the core of a wide range of tools such as predictors of genes, protein function, functional or genetic interactions, etc., and used extensively in systems biology. A course on pattern recognition (or machine learning) should therefore be at the core of any bioinformatics education program. In this review, we discuss the main elements of a pattern recognition course, based on material developed for courses taught at the BSc, MSc and PhD levels to an audience of bioinformaticians, computer scientists and life scientists. We pay attention to common problems and pitfalls encountered in applications and in interpretation of the results obtained.
Bijlstra, Gijsbert; Holland, Rob W.; Dotsch, Ron; Hugenberg, Kurt; Wigboldus, Daniel H. J.
We investigated whether stereotype associations between specific emotional expressions and social categories underlie stereotypic emotion recognition biases. Across two studies, we replicated previously documented stereotype biases in emotion recognition using both dynamic (Study 1) and static (Stud
DIMA FLORIN CONSTANTIN
Full Text Available The recognition and assessment of the component elements of the annual financial statements’ structures is crucial in order that the information released by them fulfils the qualitative characteristics and the reflected image is a “true and fair view”. Therefore, our approach takes into consideration the recognition and assessment methods for the component elements of the financial statements’ structures, as well as certain possible risks arising from the erroneous recognition or non-recognition of some of these elements.
Full Text Available The traditional role of justice is to arbitrate where the good will of people is not enough, if even present, to settle a dispute between the concerned parties. It is a procedural approach that assumes a fractured relationship between those involved. Recognition, at first glance, would not seem to mirror these aspects of justice. Yet recognition is very much a subject of justice these days. The aim of this paper is to question the applicability of justice to the practice of recognition. The methodological orientation of this paper is a Kantian-style critique of the institution of justice, highlighting the limits of its reach and the dangers of overextension. The critique unfolds in the following three steps: 1 There is an immediate appeal to justice as a practice of recognition through its commitment to universality. This allure is shown to be deceptive in providing no prescription for the actual practice of this universality. 2 The interventionist character of justice is designed to address divided relationships. If recognition is only given expression through this channel, then we can only assume division as our starting ground. 3 The outcome of justice in respect to recognition is identification. This identification is left vulnerable to misrecognition itself, creating a cycle of injustice that demands recognition from anew. It seems to be well accepted that recognition is essentjustice, but less clear how to do justice to recognition. This paper is an effort in clarification. Le rôle traditionnel de la justice est celui d’arbitrer des situations où la bonne volonté ne suffit pas à régler un différend entre les parties concernées. Il s'agit d'une approche procédurale qui suppose une relation brisée entre les personnes impliquées. La reconnaissance, à première vue, ne semble pas refléter ces caractéristiques de la justice. Pourtant, elle est souvent présentée comme rétablissant une justice entre les parties concernés. Le
Andrews, Hans A.
Value has been found in providing recognition and awards programs for excellent teachers. Research has also found a major lack of these programs in both the USA and in Australia. Teachers receiving recognition and awards for their teaching have praised recognition programs as providing motivation for them to continue high-level instruction.…
Rodrigues, Marcos; Robinson, Alan; Alboul, Lyuba; Brink, Willie
3D face recognition is an open field. In this paper we present a method for 3D facial recognition based on Principal Components Analysis. The method uses a relatively large number of facial measurements and ratios and yields reliable recognition. We also highlight our approach to sensor development for fast 3D model acquisition and automatic facial feature extraction.
Ali, Tauseef; Spreeuwers, Luuk; Veldhuis, Raymond; Quaglia, Adamo; Epifano, Calogera M.
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
Mansi Jhamb, Vinod Kumar Khera
Full Text Available The paper explores iris recognition for personal identification and verification. In this paper a newiris recognition technique is proposed using (Scale Invariant Feature Transform SIFT. Imageprocessingalgorithms have been validated on noised real iris image database. The proposedinnovative technique is computationally effective as well as reliable in terms of recognition rates.
out Galeotti's justification for recognition as a requirement of liberal justice in detail and asks in what sense the policies supported by Galeotti are policies of recognition. It is argued that Jones misrepresents Galeotti's theory, insofar as this sense of recognition actually is compatible...
Full Text Available The iris recognition is an emerging technology widely used due to various characteristics such as uniqueness,universal, stable, independent of genetics, acceptable etc. The recognition is carried out using discrete wavelet transform (DWT. It includes collection of iris database, carrying out preprocessing (includes separation ofpupil, normalization and feature extraction. Normalization includes polar to rectangular conversion. After this area of interest is selected from which features are extracted using DWT. It generates approximate, horizontal, vertical and diagonal coefficients. These are compared with the stored templates using hamming distance. If thetemplate is match with the stored one than the match ID is displayed. The unauthorized person is indicated by displaying ID equal to ‘00’
Liu, Ming; Xu, Xun; Huang, Thomas S.
Combining different modalities for pattern recognition task is a very promising field. Basically, human always fuse information from different modalities to recognize object and perform inference, etc. Audio-Visual gender recognition is one of the most common task in human social communication. Human can identify the gender by facial appearance, by speech and also by body gait. Indeed, human gender recognition is a multi-modal data acquisition and processing procedure. However, computational multimodal gender recognition has not been extensively investigated in the literature. In this paper, speech and facial image are fused to perform a mutli-modal gender recognition for exploring the improvement of combining different modalities.
Meenakshi,R. B. Dubey
Full Text Available The vehicle license plate recognition system has greater efficiency for vehicle monitoring in automatic zone access control. This Plate recognition system will avoid special tags, since all vehicles possess a unique registration number plate. A number of techniques have been used for car plate characters recognition. This system uses neural network character recognition and pattern matching of characters as two character recognition techniques. In this approach multilayer feed-forward back-propagation algorithm is used. The performance of the proposed algorithm has been tested on several car plates and provides very satisfactory results.
Lee, Colin K.
Approved for public release, distribution is unlimited This study continues a previous face recognition investigation using uncooled infrared technology. The database developed in an earlier study is further expanded to include 50 volunteers with 30 facial images from each subject. The automatic image reduction method reduces the pixel size of each image from 160 120 to 60 45 . The study reexamines two linear classification methods: the Principal Component Analysis (PCA) and Fisher ...
In this thesis the author presents a new method for the location, extraction and normalisation of discrete objects found in digital images. The extraction is by means of sub-pixcel contour following around the object. The normalisation obtains and removes the information concerning size, orientation and location of the object within an image. Analyses of the results are carried out to determine the confidence in recognition of patterns, and methods of cross correlation of object descriptions ...
Neeta Sarode; Prof. Shalini Bhatia
Facial expression analysis is rapidly becoming an area of intense interest in computer science and human-computer interaction design communities. The most expressive way humans display emotions is through facial expressions. In this paper a method is implemented using 2D appearance-based local approach for the extraction of intransient facial features and recognition of four facial expressions. The algorithm implements Radial Symmetry Transform and further uses edge projection analysis for fe...
Hammer, Dana; Piascik, Peggy; Medina, Melissa; Pittenger, Amy; Rose, Renee; Creekmore, Freddy; Soltis, Robert; Bouldin, Alicia; Schwarz, Lindsay; Scott, Steven
The 2008-2009 Task Force for the Recognition of Teaching Excellence was charged by the AACP Council of Faculties Leadership to examine teaching excellence by collecting best practices from colleges and schools of pharmacy, evaluating the literature to identify evidence-based criteria for excellent teaching, and recommending appropriate means to acknowledge and reward teaching excellence. This report defines teaching excellence and discusses a variety of ways to assess it, including student, a...
This volume is comprised of contributions from eminent leaders in the speech industry, and presents a comprehensive and in depth analysis of the progress of speech technology in the topical areas of mobile settings, healthcare and call centers. The material addresses the technical aspects of voice technology within the framework of societal needs, such as the use of speech recognition software to produce up-to-date electronic health records, not withstanding patients making changes to health plans and physicians. Included will be discussion of speech engineering, linguistics, human factors ana
It is generally known that human faces, as well as body motions and gestures, provide a wealth of information about a person, such as age, race, sex and emotional state. This monograph primarily studies the perception of facial expression of emotion, and secondarily of motion and gestures, with the purpose of developing a fully automated visual affect recognition system for use in modes of human/computer interaction. The book begins with a survey of the literature on emotion perception, followed by a description of empirical studies conducted with human participants and the construction of a '
Multi-scale window scanning has been popular in object detection but it generalizes poorly to complex features (e.g. nonlinear SVM kernel), deformable objects (e.g. animals), and finer-grained tasks (e.g. segmentation). In contrast to that, regions are appealing as image primitives for recognition because: (1) they encode object shape and scale naturally; (2) they are only mildly affected by background clutter; and (3) they significantly reduce the set of possible object locations in images.I...
Fingerprint recognition has been increasingly used to realize personal identification in civilian's daily life, such as ID card, fingerprints hard disk and so on. Great improvement has been achieved in the on-line fingerprint sensing technology and automatic fingerprint recognition algorithms. Various fingerprint recognition techniques, including fingerprint acquisition, classification, enhancement and matching, are highly improved. This paper overviews recent advances in fingerprint recognition and summarizes the algorithm proposed for every step with special focuses on the enhancement of low-quality fingerprints and the matching of the distorted fingerprint images. Both issues are believed to be significant and challenging tasks. In addition, we also discuss the common evaluation for the fingerprint recognition algorithm of the Fingerprint Verification Competition 2004 (FVC2004) and the Fingerprint Vendor Technology Evaluation 2003 (FpVTE2003), based on which we could measure the performance of the recognition algorithm objectively and uniformly.
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. .
Pillai, Sudeep; Leonard, John,
In this work, we develop a monocular SLAM-aware object recognition system that is able to achieve considerably stronger recognition performance, as compared to classical object recognition systems that function on a frame-by-frame basis. By incorporating several key ideas including multi-view object proposals and efficient feature encoding methods, our proposed system is able to detect and robustly recognize objects in its environment using a single RGB camera in near-constant time. Through e...
Wald, Mike; Bain, Keith; Basson, Sara H
The LIBERATED LEARNING PROJECT (LLP) is an applied research project studying two core questions: 1) Can speech recognition (SR) technology successfully digitize lectures to display spoken words as text in university classrooms? 2) Can speech recognition technology be used successfully as an alternative to traditional classroom notetaking for persons with disabilities? This paper addresses these intriguing questions and explores the underlying complex relationship between speech recognition te...
This thesis presents an account of an investigation into the use of information theory measures in pattern recognition problems. The objectives were firstly to determine the information content of the set of representations of an input image which are found at the output of an array of sensors; secondly to assess the information which may be used to allocate different patterns to appropriate classes in order to provide a means of recognition; and thirdly to assess the recognition capability o...
Avinash Prakash Pandhare; Umesh Balkrishna Chavan
Facial expression recognition is gaining widespread importance as the applications related to Human – Computer interactions are increasing. This paper mentions various techniques and approaches that have been used in the field of facial expression recognition. Facial expression recognition takes place in various stages and these stages have been implemented by various approaches. Viola and Jones for face detection, Gabor filters for feature extraction, SVM classifiers for classifi...
Ali, Tauseef; Veldhuis, Raymond; Spreeuwers, Luuk
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic experts‟ way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics , guidelin...
The tendency for viologen radical cations to dimerize has been harnessed to establish a recognition motif based on their ability to form extremely strong inclusion complexes with cyclobis(paraquat-p-phenylene) in its diradical dicationic redox state. This previously unreported complex involving three bipyridinium cation radicals increases the versatility of host-guest chemistry, extending its practice beyond the traditional reliance on neutral and charged guests and hosts. In particular, transporting the concept of radical dimerization into the field of mechanically interlocked molecules introduces a higher level of control within molecular switches and machines. Herein, we report that bistable and tristable rotaxanes can be switched by altering electrochemical potentials. In a tristable rotaxane composed of a cyclobis(paraquat-p-phenylene) ring and a dumbbell with tetrathiafulvalene, dioxynaphthalene and bipyridinium recognition sites, the position of the ring can be switched. On oxidation, it moves from the tetrathiafulvalene to the dioxynaphthalene, and on reduction, to the bipyridinium radical cation, provided the ring is also reduced simultaneously to the diradical dication. © 2010 Macmillan Publishers Limited. All rights reserved.
Tascini, Guido; Passerini, Giorgio; Puliti, Paolo; Zingaretti, Primo
The analysis of morphological and structural modifications of the retina vascular network is an interesting investigation method in the study of diabetes and hypertension. Normally this analysis is carried out by qualitative evaluations, according to standardized criteria, though medical research attaches great importance to quantitative analysis of vessel color, shape and dimensions. The paper describes a system which automatically segments and recognizes the ocular fundus circulation and micro circulation network, and extracts a set of features related to morphometric aspects of vessels. For this class of images the classical segmentation methods seem weak. We propose a computer vision system in which segmentation and recognition phases are strictly connected. The system is hierarchically organized in four modules. Firstly the Image Enhancement Module (IEM) operates a set of custom image enhancements to remove blur and to prepare data for subsequent segmentation and recognition processes. Secondly the Papilla Border Analysis Module (PBAM) automatically recognizes number, position and local diameter of blood vessels departing from optical papilla. Then the Vessel Tracking Module (VTM) analyses vessels comparing the results of body and edge tracking and detects branches and crossings. Finally the Feature Extraction Module evaluates PBAM and VTM output data and extracts some numerical indexes. Used algorithms appear to be robust and have been successfully tested on various ocular fundus images.
Since their first inception, automatic reading systems have evolved substantially, yet the recognition of handwriting remains an open research problem due to its substantial variation in appearance. With the introduction of Markovian models to the field, a promising modeling and recognition paradigm was established for automatic handwriting recognition. However, no standard procedures for building Markov model-based recognizers have yet been established. This text provides a comprehensive overview of the application of Markov models in the field of handwriting recognition, covering both hidden
Mohd Asyraf Zulkifley
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.
Aguilar, Gualberto; Sánchez, Gabriel; Toscano, Karina; Pérez, Héctor
abstract Fingerprint recognition is one of the most popular methods used for identification with greater success degree. Fingerprint has unique characteristics called minutiae, which are points where a curve track ends, intersects, or branches off. In this chapter a fingerprint recognition method is proposed in which a combination of Fast Fourier Transform (FFT) and Gabor filters is used for image enhancement. A novel recognition stage using local features for recognition is also proposed. Also a verification stage is introduced to be used when the system output has more than one person.
Full Text Available Face recognition is one of the most emerging and popular biometric authentication of a person, it presents a challenging problem in the field of image analysis and computer vision. Though there are various biometric traits such as iris, fingerprint and palm print etc., we focused on face recognition as it is socially acceptable and reliable. Here user identity plays a very important role to uniquely verify or authenticate the individual person. Many techniques were implemented in face recognition all having their respective pros and cons. In this paper, we presented an overview of face recognition techniques and its applications.
Full Text Available A study of both face recognition and detection techniques is carried out using the algorithms like Principal Component Analysis (PCA, Kernel Principal Component Analysis (KPCA, Linear Discriminant Analysis (LDA and Line Edge Map (LEM. These algorithms show different rates of accuracy under different conditions. The automatic recognition of human faces presents a challenge to the pattern recognition community. Typically, human faces are different in shapes with minor similarity from person to person. Furthermore, lighting condition changes, facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis.
GUAN Xin; HE You; YI Xiao
This paper studies the emitter recognition problem. A new recognition method based on attribute measure for emitter recognition is put forward. The steps of the method are presented. The approach to determining the weight coefficient is also discussed. Moreover, considering the temporal redundancy of emitter information detected by multi-sensor system, this new recognition method is generalized to multi-sensor system. A method based on the combination of attribute measure and D-S evidence theory is proposed. The implementation of D-S reasoning is always restricted by basic probability assignment function. Constructing basic probability assignment function based on attribute measure is presented in multi-sensor recognition system. Examples of recognizing the emitter purpose and system are selected to demonstrate the method proposed. Experimental results show that the performance of this new method is accurate and effective.
De Meulder, Maartje
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…
Wagner, J.S.; Trahan, M.W.; Nelson, W.E.; Hargis, P.H. Jr.; Tisone, G.C.
We have developed a capability to make real time concentration measurements of individual chemicals in a complex mixture using a multispectral laser remote sensing system. Our chemical recognition and analysis software consists of three parts: (1) a rigorous multivariate analysis package for quantitative concentration and uncertainty estimates, (2) a genetic optimizer which customizes and tailors the multivariate algorithm for a particular application, and (3) an intelligent neural net chemical filter which pre-selects from the chemical database to find the appropriate candidate chemicals for quantitative analyses by the multivariate algorithms, as well as providing a quick-look concentration estimate and consistency check. Detailed simulations using both laboratory fluorescence data and computer synthesized spectra indicate that our software can make accurate concentration estimates from complex multicomponent mixtures, even when the mixture is noisy and contaminated with unknowns.
Wagner, J.S.; Trahan, M.W.; Nelson, W.E.; Hargis, P.J. Jr.; Tisone, G.C.
We have developed a capability to make real time concentration measurements of individual chemicals in a complex mixture using a multispectral laser remote sensing system. Our chemical recognition and analysis software consists of three parts: (1) a rigorous multivariate analysis package for quantitative concentration and uncertainty estimates, (2) a genetic optimizer which customizes and tailors the multivariate algorithm for a particular application, and (3) an intelligent neural net chemical filter which pre-selects from the chemical database to find the appropriate candidate chemicals for quantitative analyses by the multivariate algorithms, as well as providing a quick-look concentration estimate and consistency check. Detailed simulations using both laboratory fluorescence data and computer synthesized spectra indicate that our software can make accurate concentration estimates from complex multicomponent mixtures. even when the mixture is noisy and contaminated with unknowns.
Osadchy, Margarita; Keren, Daniel; Raviv, Dolev
A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply increases with the dimension and size of the negative example set. We propose to alleviate this problem by applying a "hybrid" classifier, which replaces the negative samples by a prior, and then finds a hyperplane which separates the positive samples from this prior. The method is extended to kernel space and to an ensemble-based approach. The resulting binary classifiers achieve an identical or better classification rate than SVM, while requiring far smaller memory and lower computational complexity to train and apply. PMID:26959677
Are simple strokes unique to the artist or designer who renders them? If so, can this idea be used to identify authorship or to classify artistic drawings? Also, could training methods be devised to develop particular styles? To answer these questions, we propose the Stroke Authorship Recognition (SAR) approach, a novel method that distinguishes the authorship of 2D digitized drawings. SAR converts a drawing into a histogram of stroke attributes that is discriminative of authorship. We provide extensive classification experiments on a large variety of data sets, which validate SAR\\'s ability to distinguish unique authorship of artists and designers. We also demonstrate the usefulness of SAR in several applications including the detection of fraudulent sketches, the training and monitoring of artists in learning a particular new style and the first quantitative way to measure the quality of automatic sketch synthesis tools. © 2015 The Eurographics Association and John Wiley & Sons Ltd.
Bahreini, Kiavash; Nadolski, Rob; Westera, Wim
This paper introduces the voice emotion recognition part of our framework for improving learning through webcams and microphones (FILTWAM). This framework enables multimodal emotion recognition of learners during game-based learning. The main goal of this study is to validate the use of microphone d
Wauters, Loes N.; Knoors, Harry E. T.; Vervloed, Mathijs P. J.; Aarnoutse, Cor A. J.
This study examined whether use of sign language would facilitate reading word recognition by 16 deaf children (6- to 1 years-old) in the Netherlands. Results indicated that if words were learned through speech, accompanied by the relevant sign, accuracy of word recognition was greater than if words were learned solely through speech. (Contains…
Ali, Tauseef; Veldhuis, Raymond; Spreeuwers, Luuk
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is t
This chapter argues that recognition of emotion had a simple basis and a highly complex edifice above it. Its basis is formed by catching intent from expressive and other emotional behavior, using elementary principles of perceptual integration. In intent recognition, mirror neurons under particular
Full Text Available Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces that does not affects the recognition performance. The authors minimize the participated eigenvectors which consequently decreases the computational time. A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm. The performance of the original and the enhanced proposed algorithm is tested on face94 face database. Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm. DET Curves are used to illustrate the experimental results.
WANG Xiaohong; ZHAO Rongchun
A feature-based recognition of objectsor patterns independent of their position, size, orien-tation and other variations has been the goal of muchrecent research. The existing approaches to invarianttwo-dimensional pattern recognition are useless whenpattern is blurred. In this paper, we present a novelpattern recognition system which can solve the prob-lem by using combined invariants as image features.The classification technique we choose for our systemis weighted normalized cross correlation. The mean ofthe intraclass standard deviations of the kth featureover the total number of prototypes for each class isused as a weighting factor during the classification pro-cess to improve recognition accuracy. The feasibilityof our pattern recognition system and the invarianceof the combined features with respect to translation,scaling, rotation and blurring are approved by numer-ical experiments on head images.
Full Text Available Principle Component Analysis PCA is a classical feature extraction and data representation technique widely used in pattern recognition. It is one of the most successful techniques in face recognition. But it has drawback of high computational especially for big size database. This paper conducts a study to optimize the time complexity of PCA (eigenfaces that does not affects the recognition performance. The authorsminimize the participated eigenvectors which consequently decreases the computational time. A comparison is done to compare the differences between the recognition time in the original algorithm and in the enhanced algorithm. The performance of the original and the enhanced proposed algorithm is tested on face94 face database. Experimental results show that the recognition time is reduced by 35% by applying our proposed enhanced algorithm. DET Curves are used to illustrate the experimental results.
Li, Jun-Bao; Pan, Jeng-Shyang
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
Santemiz, Pinar; Spreeuwers, Luuk J.; Veldhuis, Raymond N. J.; Biggelaar, van den, M.
As a widely used biometrics, face recognition has many advantages such as being non-intrusive, natural and passive. On the other hand, in real-life scenarios with uncontrolled environment, pose variation up to side-view positions makes face recognition a challenging work. In this paper we discuss the use of side-view face recognition in house safety applications. Our goal is to recognize people as they pass through doors in order to estimate their location in the house. In order to preserve p...
Santemiz, Pinar; Spreeuwers, Luuk J.; Veldhuis, Raymond N. J.
Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databas...
Used by companies, organizations, and even individuals to promote recognition of their brand, logos can also act as a valuable means of identifying the source of a document. E-business applications can retrieve and catalog products according to their logos. Governmental agencies can easily inspect goods using smart mobile devices that use logo recognition techniques. However, because logos are two-dimensional shapes of varying complexity, the recognition process can be challenging. Although promising results have been found for clean logos, they have not been as robust for noisy logos. Logo Re
Rozpoznávání zoubkování poštovních známek je důležitým faktorem při posuzování pravosti poštovní známky. Typ a rozměr zoubkování mají výrazný vliv na cenu poštovní známky. Tato práce se zabývá navrhem detektoru zoubkování poštovních známek. Cílem práce je vytvořit aplikaci, která z fotografie určí zoubkování zobrazené poštovní známky. Aplikace pro práci s obrazy využívá knihovnu OpenCV. Post stamp perforation recognition is important factor in authentication of post stamps. Type and perfor...
Hashidzume, Akihito; Harada, Akira
The interaction of cyclodextrins (CD) with water soluble polymers possessing guest residues has been investigated as model systems in biological molecular recognition. The selectivity of interaction of CD with polymer-carrying guest residues is controlled by polymer chains, i.e., the steric effect of polymer main chain, the conformational effect of polymer main chain, and multi-site interaction. Macroscopic assemblies have been also realized based on molecular recognition using polyacrylamide-based gels possessing CD and guest residues.
CHEN Dapeng; WU Naiming; ZHANG Zheng
This work is aimed at developing an effective method for defect recognition in thermosonic imaging.The heat mechanism of thermosonic imaging is introduced,and the problem for defect recognition is discussed.For this purpose,defect existing in the inner wall of a metal pipeline specimen and defects embedded in a carbon fiber reinforced plastic (CFRP) laminate are tested.The experimental data are processed by pulse phase thermography (PPT) method to show the phase images at different frequencies,and the characteristic of phase angle vs frequency curve of thermal anomalies and sound area is analyzed.A binary image,which is based on the characteristic value of defects,is obtained by a new recognition algorithm to show the defects.Results demonstrate good defect recognition performance for thermosonic imaging,and the reliability of this technique can be improved by the method.
Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.
Avinash Prakash Pandhare
Full Text Available Facial expression recognition is gaining widespread importance as the applications related to Human – Computer interactions are increasing. This paper mentions various techniques and approaches that have been used in the field of facial expression recognition. Facial expression recognition takes place in various stages and these stages have been implemented by various approaches. Viola and Jones for face detection, Gabor filters for feature extraction, SVM classifiers for classification, L1 minimization for sparse representation, facial expression recognition, geometric deformation model, multiple gabor filters for robust feature extraction, parallel implementation of Viola and Jones for face detection and parallel implementation of SVM classifier for classification of expressions are discussed in this paper.
The research and development of pattern recognition have proven to be of importance in science, technology, and human activity. Many useful concepts and tools from different disciplines have been employed in pattern recognition. Among them is string matching, which receives much theoretical and practical attention. String matching is also an important topic in combinatorial optimization. This book is devoted to recent advances in pattern recognition and string matching. It consists of twenty eight chapters written by different authors, addressing a broad range of topics such as those from classifica tion, matching, mining, feature selection, and applications. Each chapter is self-contained, and presents either novel methodological approaches or applications of existing theories and techniques. The aim, intent, and motivation for publishing this book is to pro vide a reference tool for the increasing number of readers who depend upon pattern recognition or string matching in some way. This includes student...
Hanhela, T. (Teemu)
Abstract The starting point for the research is to examine the educational perspectives of Axel Honneth’s recognition theory to find useful contents for educational institutions. The method of the thesis is conceptual analysis which gets a dual role: chapters two and three of the treatise define and analyse Honneth’s concept of recognition and its historic-philosophical context and with help of critical analyses, the articles (I, II and III) and chapter four of the dissertation connects t...
James V Stone
The sequence of images generated by motion between observer and object specifies a spatiotemporal signature for that object. Evidence is presented that such spatiotemporal signatures are used in object recognition. Subjects learned novel, three-dimensional, rotating objects from image sequences in a continuous recognition task. During learning, the temporal order of images of a given object was constant. During testing, the order of images in each sequence was reversed, relative to its order ...
As the Tangut studies have made progress, a considerable number of Tangut historical documents' copies have been published. It is of great importance to carry out digitalization and domestication of these copies. The paper firstly makes an initial processing of images by global threshold, then dissect the photocopies by scanning. Finally adopts the recognition approach of principal component analysis. The experiment shows that a better recognition can be achieved by calculation without extra time.
Full Text Available Preliminary data are reported from experiments in which Warrington's (1984 Recognition Memory Tests were given to patients with misidentification delusions including the Capgras type and to psychotic patients. The results showed a profound impairment on face recognition for most groups, especially those with the Capgras delusion. It was rare to find a patent whose score on the word test was anything but normal.
Rajshree Dhruw; Dharmendra Roy
Automatic Number Plate Recognition (ANPR) is a mass surveillance system that captures the image of vehicles and recognizes their license number. The objective is to design an efficient automatic authorized vehicle identification system by using the Indian vehicle number plate. In this paper we discus different methodology for number plate localization, character segmentation & recognition of the number plate. The system is mainly applicable for non standard Indian number plates by recognizing...
Senthil Ragavan Valayapalayam Kittusamy; Venkatesh Chakrapani
A challenging research topic is to make the Computer Systems to recognize facial expressions from the face image. A method of facial expression recognition, based on Eigenspaces is presented in this study. Here, the authors recognize the userâs facial expressions from the input images, using a method that was customized from eigenface recognition. Evaluation was done for this method in terms of identification correctness using two different Facial Expressions databases, Cohn-Kanade facial exp...
Muhammad Sharif; Sajjad Mohsin; Muhammad Younas Javed
In this study, the existing techniques of face recognition are to be encountered along with their pros and cons to conduct a brief survey. The most general methods include Eigenface (Eigenfeatures), Hidden Markov Model (HMM), geometric based and template matching approaches. This survey actually performs analysis on these approaches in order to constitute face representations which will be discussed as under. In the second phase of the survey, factors affecting the recognition rates and proce...
Face recognition is one of the most emerging and popular biometric authentication of a person, it presents a challenging problem in the field of image analysis and computer vision. Though there are various biometric traits such as iris, fingerprint and palm print etc., we focused on face recognition as it is socially acceptable and reliable. Here user identity plays a very important role to uniquely verify or authenticate the individual person. Many techniques were implemented in face recogni...
Harmat, Veronika; Náray-Szabó, Gábor
Molecular recognition is a key process in non-covalent interactions, which determines, among others, host-guest complexation, drug action and protein-protein interaction. A simple and attractive formulation is the lock-and-key analogy defining the host as a lock accommodating the guest as a key. We stress three major aspects of molecular recognition, determining both complementarity between host and guest and similarity within a group of guest molecules. These aspects are: steric, i.e. maximi...
Full Text Available Behavioral studies of spoken word memory have shown that context congruency facilitates both word and source recognition, though the level at which context exerts its influence remains equivocal. We measured event-related potentials (ERPs while participants performed both types of recognition task with words spoken in four voices. Two voice parameters (i.e., gender and accent varied between speakers, with the possibility that none, one or two of these parameters was congruent between study and test. Results indicated that reinstating the study voice at test facilitated both word and source recognition, compared to similar or no context congruency at test. Behavioral effects were paralleled by two ERP modulations. First, in the word recognition test, the left parietal old/new effect showed a positive deflection reflective of context congruency between study and test words. Namely, the same speaker condition provided the most positive deflection of all correctly identified old words. In the source recognition test, a right frontal positivity was found for the same speaker condition compared to the different speaker conditions, regardless of response success. Taken together, the results of this study suggest that the benefit of context congruency is reflected behaviorally and in ERP modulations traditionally associated with recognition memory.
Full Text Available Face Recognition has been identified as one of the attracting research areas and it has drawn the attention of many researchers due to its varying applications such as security systems, medical systems,entertainment, etc. Face recognition is the preferred mode of identification by humans: it is natural,robust and non-intrusive. A wide variety of systems requires reliable personal recognition schemes to either confirm or determine the identity of an individual requesting their services. The purpose of such schemes is to ensure that the rendered services are accessed only by a legitimate user and no one else.Examples of such applications include secure access to buildings, computer systems, laptops, cellular phones, and ATMs. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor.In this paper we have developed and illustrated a recognition system for human faces using a novel Kohonen self-organizing map (SOM or Self-Organizing Feature Map (SOFM based retrieval system.SOM has good feature extracting property due to its topological ordering. The Facial Analytics results for the 400 images of AT&T database reflects that the face recognition rate using one of the neural network algorithm SOM is 85.5% for 40 persons.
Richler, Jennifer J; Cheung, Olivia S; Gauthier, Isabel
The concept of holistic processing is a cornerstone of face-recognition research. In the study reported here, we demonstrated that holistic processing predicts face-recognition abilities on the Cambridge Face Memory Test and on a perceptual face-identification task. Our findings validate a large body of work that relies on the assumption that holistic processing is related to face recognition. These findings also reconcile the study of face recognition with the perceptual-expertise work it inspired; such work links holistic processing of objects with people's ability to individuate them. Our results differ from those of a recent study showing no link between holistic processing and face recognition. This discrepancy can be attributed to the use in prior research of a popular but flawed measure of holistic processing. Our findings salvage the central role of holistic processing in face recognition and cast doubt on a subset of the face-perception literature that relies on a problematic measure of holistic processing.
Different mathematical models of recognition processes are known. In the present paper we consider a pattern recognition algorithm as an oracle computation on a Turing machine. Such point of view seems to be useful in pattern recognition as well as in recursion theory. Use of recursion theory in pattern recognition shows connection between a recognition algorithm comparison problem and complexity problems of oracle computation. That is because in many cases we can take into account only the n...
Jacobsen, Michael Hviid; Kristiansen, Søren
an interpretation of Goffman as a major recognition theorist on the micro-level of social analysis by way of his ritual metaphor. Erving Goffman's sociology is conventionally approached and appreciated primarily through his famous dramaturgical metaphor that describes and comprehends social life through...... that human everyday encounters are characterised as much by rituals and ritual care than by cynical or shallow self-presentations. The main content of this ritual metaphor therefore consists of the notion that a variety of almost unnoticed minor courtesies and microscopic civilities such as facework......-structural or macro-sociological recognition claims and all contain a certain political or moral edge - Goffman rather provides a much more descriptive and micro-sociological account of the workings and necessity of recognition. In his ritual metaphorical perspective, social interaction to a large extent consists...
Han, Yi; Wang, Guoyin; Yang, Yong; He, Kun
Human emotions could be expressed by many bio-symbols. Speech and facial expression are two of them. They are both regarded as emotional information which is playing an important role in human-computer interaction. Based on our previous studies on emotion recognition, an audiovisual emotion recognition system is developed and represented in this paper. The system is designed for real-time practice, and is guaranteed by some integrated modules. These modules include speech enhancement for eliminating noises, rapid face detection for locating face from background image, example based shape learning for facial feature alignment, and optical flow based tracking algorithm for facial feature tracking. It is known that irrelevant features and high dimensionality of the data can hurt the performance of classifier. Rough set-based feature selection is a good method for dimension reduction. So 13 speech features out of 37 ones and 10 facial features out of 33 ones are selected to represent emotional information, and 52 audiovisual features are selected due to the synchronization when speech and video fused together. The experiment results have demonstrated that this system performs well in real-time practice and has high recognition rate. Our results also show that the work in multimodules fused recognition will become the trend of emotion recognition in the future.
Raju, Chaitanya; Varadarajan, Karthik Mahesh; Krishnamurthi, Niyant; Xu, Shuli; Biederman, Irving; Kelley, Troy
We have developed a framework, Cognitive Object Recognition System (CORS), inspired by current neurocomputational models and psychophysical research in which multiple recognition algorithms (shape based geometric primitives, 'geons,' and non-geometric feature-based algorithms) are integrated to provide a comprehensive solution to object recognition and landmarking. Objects are defined as a combination of geons, corresponding to their simple parts, and the relations among the parts. However, those objects that are not easily decomposable into geons, such as bushes and trees, are recognized by CORS using "feature-based" algorithms. The unique interaction between these algorithms is a novel approach that combines the effectiveness of both algorithms and takes us closer to a generalized approach to object recognition. CORS allows recognition of objects through a larger range of poses using geometric primitives and performs well under heavy occlusion - about 35% of object surface is sufficient. Furthermore, geon composition of an object allows image understanding and reasoning even with novel objects. With reliable landmarking capability, the system improves vision-based robot navigation in GPS-denied environments. Feasibility of the CORS system was demonstrated with real stereo images captured from a Pioneer robot. The system can currently identify doors, door handles, staircases, trashcans and other relevant landmarks in the indoor environment.
De Silva, Liyanage C.; Esther, Kho G. P.
Current face recognition techniques tend to work well when recognizing faces under small variations in lighting, facial expression and pose, but deteriorate under more extreme conditions. In this paper, a face recognition system to recognize faces of known individuals, despite variations in facial expression due to different emotions, is developed. The eigenface approach is used for feature extraction. Classification methods include Euclidean distance, back propagation neural network and generalized regression neural network. These methods yield 100% recognition accuracy when the training database is representative, containing one image representing the peak expression for each emotion of each person apart from the neutral expression. The feature vectors used for comparison in the Euclidean distance method and for training the neural network must be all the feature vectors of the training set. These results are obtained for a face database consisting of only four persons.
Full Text Available To achieve high speed in data processing it is necessary to convert the analog data into digital data. Storage of hard copy of any document occupies large space and retrieving of information from that document is time consuming. Optical character recognition system is an effective way in recognition of printed character. It provides an easy way to recognize and convert the printed text on image into the editable text. It also increases the speed of data retrieval from the image. The image which contains characters can be scanned through scanner and then recognition engine of the OCR system interpret the images and convert images of printed characters into machine-readable characters .It improving the interface between man and machine in many applications
Monnig, Nathan D.; Sakla, Wesam
The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.
Mandarin Chinese is the official language in China and Taiwan, it is the native language of a quarter of the world population. As the services enabled by speech recognition technology (e.g. telephone voice dialing, information query) become more popular in English, we would like to extend this capability to other languages. Mandarin is one of the major languages under research in our laboratory. This paper describes how we extend our work in English speech recognition into Mandarin. We will described the corpus: Voice Across Taiwan, the training of a complete set of Mandarin syllable models, preliminary performance results and error analysis. A fast prototyping system was built, where a user can write any context free grammar with no restriction of vocabulary, then the grammar can be compiled into recognition models. It enables user to quickly test the performance of a new vocabulary.
LI Zhan; XU Ji-sheng; XU Min; SUN Hong
By utilizing artificial intelligence and pattern recognition techniques, we propose an integrated mobile-customer identity recognition approach in this paper, based on customer's behavior characteristics extracted from the customer information database. To verify the effectiveness of this approach, a test has been run on the dataset consisting of 1 000 customers in 3 consecutive months. The result is compared with the real dataset in the fourth month consisting of 162 customers, which has been set as the customers for recognition. The high correct rate of the test (96.30%), together with 1.87% of the judge-by-mistake rate and 7.82% of the leaving-out rate, demonstrates the effectiveness of this approach.
Barsics, Catherine; Brédart, Serge
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.
This work presents a full generic approach to the detection and recognition of traffic signs. The approach is based on the latest computer vision methods for object detection, and on powerful methods for multiclass classification. The challenge was to robustly detect a set of different sign classes in real time, and to classify each detected sign into a large, extensible set of classes. To address this challenge, several state-of-the-art methods were developed that can be used for different recognition problems. Following an introduction to the problems of traffic sign detection and categoriza
Anne, Koteswara Rao; Vankayalapati, Hima Deepthi
This book presents state of art research in speech emotion recognition. Readers are first presented with basic research and applications – gradually more advance information is provided, giving readers comprehensive guidance for classify emotions through speech. Simulated databases are used and results extensively compared, with the features and the algorithms implemented using MATLAB. Various emotion recognition models like Linear Discriminant Analysis (LDA), Regularized Discriminant Analysis (RDA), Support Vector Machines (SVM) and K-Nearest neighbor (KNN) and are explored in detail using prosody and spectral features, and feature fusion techniques.
In this book, we introduce the background and mainstream methods of probabilistic modeling and discriminative parameter optimization for speech recognition. The specific models treated in depth include the widely used exponential-family distributions and the hidden Markov model. A detailed study is presented on unifying the common objective functions for discriminative learning in speech recognition, namely maximum mutual information (MMI), minimum classification error, and minimum phone/word error. The unification is presented, with rigorous mathematical analysis, in a common rational-functio
Full Text Available The objective of this work describes a method for Traffic sign detection and recognition from the traffic panel board(signage. It detect the traffic signs especially for Indian conditions. Images are acquired through the camera and it is invariant to size then it is scaled. It consist of the following steps, first, it detect the traffic sign, if it has sufficient contrast from the background then we use sobel edge detection technique and morphological dilation. Second, extract the detected traffic sign from the board using row count and column count. Third, to extract the feature using DCT, DWT and Hybrid DWT-DCT. In training phase, DCT 20 highest energy coefficients are extracted, In DWT 300 features extracted from each traffic sign and in Hybrid DWT-DCT 20 features are extracted. Finally recognition are performed through SVM. The application is to improve the efficiency of transportation networks through applications of communication visually impaired person wear the camera to identify the traffic destination board. Experimental results show that state-of-the-art algorithms obtains highly competitive performance and is especially efficient to different levels of corruptions. The performance of Traffic Sign recognition is evaluated for Traffic Sign board image and the system achieves a recognition rate of 86% using DCT, 90% using DWT and 96% using Hybrid DWT-DCT and SVM.
Minimum Classification Error (MSE) Approach in Pattern Recognition, Wu ChouMinimum Bayes-Risk Methods in Automatic Speech Recognition, Vaibhava Goel and William ByrneA Decision Theoretic Formulation for Adaptive and Robust Automatic Speech Recognition, Qiang HuoSpeech Pattern Recognition Using Neural Networks, Shigeru KatagiriLarge Vocabulary Speech Recognition Based on Statistical Methods, Jean-Luc GauvainToward Spontaneous Speech Recognition and Understanding, Sadaoki FuruiSpeaker Authentication, Qi Li and Biing-Hwang JuangHMMs for Language Processing Problems, Ri
Full Text Available This paper addresses the recognition of large-scale outdoor scenes on smartphones by fusing outputs of inertial sensors and computer vision techniques. The main contributions can be summarized as follows. Firstly, we propose an ORD (overlap region divide method to plot image position area, which is fast enough to find the nearest visiting area and can also reduce the search range compared with the traditional approaches. Secondly, the vocabulary tree-based approach is improved by introducing GAGCC (gravity-aligned geometric consistency constraint. Our method involves no operation in the high-dimensional feature space and does not assume a global transform between a pair of images. Thus, it substantially reduces the computational complexity and memory usage, which makes the city scale image recognition feasible on the smartphone. Experiments on a collected database including 0.16 million images show that the proposed method demonstrates excellent recognition performance, while maintaining the average recognition time about 1 s.
Kuypers, K P C; Steenbergen, L; Theunissen, E L; Toennes, S W; Ramaekers, J G
Chronic or repeated cocaine use has been linked to impairments in social skills. It is not clear whether cocaine is responsible for this impairment or whether other factors, like polydrug use, distort the observed relation. We aimed to investigate this relation by means of a placebo-controlled experimental study. Additionally, associations between stressor-related activity (cortisol, cardiovascular parameters) induced by the biological stressor cocaine, and potential cocaine effects on emotion recognition were studied. Twenty-four healthy recreational cocaine users participated in this placebo-controlled within-subject study. Participants were tested between 1 and 2 h after treatment with oral cocaine (300 mg) or placebo. Emotion recognition of low and high intensity expressions of basic emotions (fear, anger, disgust, sadness, and happiness) was tested. Findings show that cocaine impaired recognition of negative emotions; this was mediated by the intensity of the presented emotions. When high intensity expressions of Anger and Disgust were shown, performance under influence of cocaine 'normalized' to placebo-like levels while it made identification of Sadness more difficult. The normalization of performance was most notable for participants with the largest cortisol responses in the cocaine condition compared to placebo. It was demonstrated that cocaine impairs recognition of negative emotions, depending on the intensity of emotion expression and cortisol response.
Manfredotti, Cristina Elena; Fleet, David J.; Hamilton, Howard J.;
recognition, our framework performs these two tasks simultaneously. In particular, we adopt a Bayesian standpoint where the system maintains a joint distribution of the positions, the interactions and the possible activities. This turns out to be advantegeous, as information about the ongoing activities can...
Hoogsteen, G; Krist, J.O.; Bakker, V.; Smit, G.J.M.
Energy conservation becomes more important nowadays. The use of smart meters and, in the near future, smart appliances, are the key to achieve reduction in energy consumption. This research proposes a non-intrusive appliance monitor and recognition system for implementation on an embedded system. Th
Ali, Tauseef; Spreeuwers, Luuk; Veldhuis, Raymond
In this paper we present a methodology and experimental results for evidence evaluation in the context of forensic face recognition. In forensic applications, the matching score (hereafter referred to as similarity score) from a biometric system must be represented as a Likelihood Ratio (LR). In our
Podilchuk, Christine; Hulbert, William; Flachsbart, Ralph; Barinov, Lev
A new face recognition algorithm has been proposed which is robust to variations in pose, expression, illumination and occlusions such as sunglasses. The algorithm is motivated by the Edit Distance used to determine the similarity between strings of one dimensional data such as DNA and text. The key to this approach is how to extend the concept of an Edit Distance on one-dimensional data to two-dimensional image data. The algorithm is based on mapping one image into another and using the characteristics of the mapping to determine a two-dimensional Pictorial-Edit Distance or P-Edit Distance. We show how the properties of the mapping are similar to insertion, deletion and substitution errors defined in an Edit Distance. This algorithm is particularly well suited for face recognition in uncontrolled environments such as stand-off and other surveillance applications. We will describe an entire system designed for face recognition at a distance including face detection, pose estimation, multi-sample fusion of video frames and identification. Here we describe how the algorithm is used for face recognition at a distance, present some initial results and describe future research directions.(
We prove that the three-sphere recognition problem lies in the complexity class NP. Our work relies on Thompson's original proof that the problem is decidable [Math. Res. Let., 1994], Casson's version of her algorithm, and recent results of Agol, Hass, and Thurston [ArXiv, 2002].
Fihl, Preben; Holte, Michael Boelstoft; Moeslund, Thomas B.
The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent t...
Joswig, Michael; Lutz, Frank H.; Tsuruga, Mimi
Heuristic techniques for recognizing PL spheres using the topological software polymake are presented. These methods have been successful very often despite sphere recognition being known to be hard (for dimensions $d \\ge 3$) or even undecidable (for $d \\ge 5$). A deeper look into the simplicial complexes for which the heuristics failed uncovered a trove of examples having interesting topological and combinatorial properties.
D'Ettorre, Patrizia; Heinze, Jürgen
recognize each other's unique facial color patterns  . Individual recognition is advantageous when dominance hierarchies control the partitioning of work and reproduction 2 and 4 . Here, we show that unrelated founding queens of the ant Pachycondyla villosa use chemical cues to recognize each other...
Prof.D.D.Patil; Prof.N.A.Nemade; K.M.Attarde
By using biometric system automatic recognition of an individual is provided. Biometric systems include fingerprints, facial features, voice, hand geometry, handwriting, the retina and the one presented inthis thesis, the iris. Working of this system is simple. It captures the image and compare with exiting image. If match is found then access is granted.
Criss, Amy H.; Malmberg, Kenneth J.; Shiffrin, Richard M.
Dennis and Humphreys (2001) proposed that interference in recognition memory arises solely from the prior contexts of the test word: Interference does not arise from memory traces of other words (from events prior to the study list or on the study list, and regardless of similarity to the test item). We evaluate this model using output…
Kam Ho Tin
Machines capable of automatic pattern recognition have many fascinating uses. Algorithms for supervised classification, where one infers a decision boundary from a set of training examples, are at the core of this capability. This book looks at data complexity and its role in shaping the theories and techniques in different disciplines
Tan, Zheng-Hua; Lindberg, Børge
The enthusiasm of deploying automatic speech recognition (ASR) on mobile devices is driven both by remarkable advances in ASR technology and by the demand for efficient user interfaces on such devices as mobile phones and personal digital assistants (PDAs). This chapter presents an overview of ASR...
Hirsch, Anna K.H.; Fischer, Felix R.; Diederich, François
Drug-discovery research in the past decade has seen an increased selection of targets with phosphate recognition sites, such as protein kinases and phosphatases, in the past decade. This review attempts, with the help of database-mining tools, to give an overview of the most important principles in
Garris, Michael D.; Blue, James L.; Candela, Gerald T.; Dimmick, Darrin L.; Geist, Jon C.; Grother, Patrick J.; Janet, Stanley A.; Wilson, Charles L.
A public domain document processing system has been developed by the National Institute of Standards and Technology (NIST). The system is a standard reference form-based handprint recognition system for evaluating optical character recognition (OCR), and it is intended to provide a baseline of performance on an open application. The system's source code, training data, performance assessment tools, and type of forms processed are all publicly available. The system recognizes the handprint entered on handwriting sample forms like the ones distributed with NIST Special Database 1. From these forms, the system reads hand-printed numeric fields, upper and lowercase alphabetic fields, and unconstrained text paragraphs comprised of words from a limited-size dictionary. The modular design of the system makes it useful for component evaluation and comparison, training and testing set validation, and multiple system voting schemes. The system contains a number of significant contributions to OCR technology, including an optimized probabilistic neural network (PNN) classifier that operates a factor of 20 times faster than traditional software implementations of the algorithm. The source code for the recognition system is written in C and is organized into 11 libraries. In all, there are approximately 19,000 lines of code supporting more than 550 subroutines. Source code is provided for form registration, form removal, field isolation, field segmentation, character normalization, feature extraction, character classification, and dictionary-based postprocessing. The recognition system has been successfully compiled and tested on a host of UNIX workstations. This paper gives an overview of the recognition system's software architecture, including descriptions of the various system components along with timing and accuracy statistics.
Full Text Available Extraction and expression of features are critical to improving the recognition rate of ear image recognition. This paper proposes a new ear recognition method based on SIFT(Scale-invariant feature transform and Forstner corner detection technology. Firstly, Forstner corner points and SIFT keypoints are detected respectively. Then taking Forstner corner into the SIFT algorithm to calculate their descriptor as the image feature vectors. Finally ear recognition based on these feature is carried out with Euclidean distance as similarity measurement. A bi-directional matching algorithm is utilized for improving recognition rate. Experiments on USTB database show that the recognition rate reaches more 94%. The Experimental results prove the effectiveness of the proposed method in term of recognition accuracy in comparison with previous methods. It is robust to rigid changes of ear image and provides a new approach to the research for ear recognition.
Hlaing Htake Khaung Tin
Face recognition is the most successful form of human surveillance. Face recognition technology, is being used to improve human efficiency when recognition faces, is one of the fastest growing fields in the biometric industry. In the first stage, the age is classified into eleven categories which distinguish the person oldness in terms of age. In the second stage of the process is face recognition based on the predicted age. Age prediction has considerable potential applications in human comp...
Ma Chi; Zhu Yongyong; Tian Ying
Extraction and expression of features are critical to improving the recognition rate of ear image recognition. This paper proposes a new ear recognition method based on SIFT(Scale-invariant feature transform) and Forstner corner detection technology. Firstly, Forstner corner points and SIFT keypoints are detected respectively. Then taking Forstner corner into the SIFT algorithm to calculate their descriptor as the image feature vectors. Finally ear recognition based on these feature is carrie...
Anderson, J R; Bower, G H
This paper modifies the Anderson and Bower (1972) theory of recognition memory for words. A propositional representation is outlined for the contextual information underlying word recognition. Logical arguments are offered for preferring this representation over the undifferentiated associative representation used earlier. The propositional representation is used to interpret effects of verbal context upon recognition memory. The implications of these context effects are considered for two-process models of recall and recognition. PMID:21274765
Wang, Patrick S P
""Pattern Recognition, Machine Intelligence and Biometrics"" covers the most recent developments in Pattern Recognition and its applications, using artificial intelligence technologies within an increasingly critical field. It covers topics such as: image analysis and fingerprint recognition; facial expressions and emotions; handwriting and signatures; iris recognition; hand-palm gestures; and multimodal based research. The applications span many fields, from engineering, scientific studies and experiments, to biomedical and diagnostic applications, to personal identification and homeland secu
Bradler, C.; Dur, R.; Neckermann, S.; Non, J.A.
This paper reports the results from a controlled field experiment designed to investigate the causal effect of public recognition on employee performance. We hired more than 300 employees to work on a three-hour data-entry task. In a random sample of work groups, workers unexpectedly received recognition after two hours of work. We find that recognition increases subsequent performance substantially, and particularly so when recognition is exclusively provided to the best performers. Remarkab...
Olsen, Søren I.
This paper presents an approach of visual shape recognition based on exemplars of attributed keypoints. Training is performed by storing exemplars of keypoints detected in labeled training images. Recognition is made by keypoint matching and voting according to the labels for the matched keypoint....... The matching is insensitive to rotations, limited scalings and small deformations. The recognition is robust to noise, background clutter and partial occlusion. Recognition is possible from few training images and improve with the number of training images....
Bjerre, Henrik Jøker
Hegel's concept in the Phenomenology of the Spirit of the "recognition of an independent self-consciousness" is investigated as a point of separation for contemporary philosophy of recognition. I claim that multiculturalism and the theories of recognition (such as Axel Honneth's) based on empiric...... psychology neglect or deny crucial metaphysical aspects of the Hegelian legacy. Instead, I seek to point at an additional, "spiritual", level of recognition, based on the concept of the subject in Lacanian psychoanalysis....
Dr.Asmahan M Altaher
Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in th...
Klinger, A; Kunii, T L
Data Structures, Computer Graphics, and Pattern Recognition focuses on the computer graphics and pattern recognition applications of data structures methodology.This book presents design related principles and research aspects of the computer graphics, system design, data management, and pattern recognition tasks. The topics include the data structure design, concise structuring of geometric data for computer aided design, and data structures for pattern recognition algorithms. The survey of data structures for computer graphics systems, application of relational data structures in computer gr
Muhammad Hameed Siddiqi
Full Text Available Over the last decade, human facial expressions recognition (FER has emerged as an important research area. Several factors make FER a challenging research problem. These include varying light conditions in training and test images; need for automatic and accurate face detection before feature extraction; and high similarity among different expressions that makes it difﬁcult to distinguish these expressions with a high accuracy. This work implements a hierarchical linear discriminant analysis-based facial expressions recognition (HL-FER system to tackle these problems. Unlike the previous systems, the HL-FER uses a pre-processing step to eliminate light effects, incorporates a new automatic face detection scheme, employs methods to extract both global and local features, and utilizes a HL-FER to overcome the problem of high similarity among different expressions. Unlike most of the previous works that were evaluated using a single dataset, the performance of the HL-FER is assessed using three publicly available datasets under three different experimental settings: n-fold cross validation based on subjects for each dataset separately; n-fold cross validation rule based on datasets; and, ﬁnally, a last set of experiments to assess the effectiveness of each module of the HL-FER separately. Weighted average recognition accuracy of 98.7% across three different datasets, using three classifiers, indicates the success of employing the HL-FER for human FER.
Full Text Available The need to increase security in open or public spaces has in turn given rise to the requirement to monitor these spaces and analyse those images on‐site and on‐time. At this point, the use of smart cameras ‐ of which the popularity has been increasing ‐ is one step ahead. With sensors and Digital Signal Processors (DSPs, smart cameras generate ad hoc results by analysing the numeric images transmitted from the sensor by means of a variety of image‐processing algorithms. Since the images are not transmitted to a distance processing unit but rather are processed inside the camera, it does not necessitate high‐ bandwidth networks or high processor powered systems; it can instantaneously decide on the required access. Nonetheless, on account of restricted memory, processing power and overall power, image processing algorithms need to be developed and optimized for embedded processors. Among these algorithms, one of the most important is for face detection and recognition. A number of face detection and recognition methods have been proposed recently and many of these methods have been tested on general‐purpose processors. In smart cameras ‐ which are real‐life applications of such methods ‐ the widest use is on DSPs. In the present study, the Viola‐Jones face detection method ‐ which was reported to run faster on PCs ‐ was optimized for DSPs; the face recognition method was combined with the developed sub‐region and mask‐based DCT (Discrete Cosine Transform. As the employed DSP is a fixed‐point processor, the processes were performed with integers insofar as it was possible. To enable face recognition, the image was divided into sub‐ regions and from each sub‐region the robust coefficients against disruptive elements ‐ like face expression, illumination, etc. ‐ were selected as the features. The discrimination of the selected features was enhanced via LDA (Linear Discriminant Analysis and then employed for
Rao, K Sreenivasa
“Emotion Recognition Using Speech Features” covers emotion-specific features present in speech and discussion of suitable models for capturing emotion-specific information for distinguishing different emotions. The content of this book is important for designing and developing natural and sophisticated speech systems. Drs. Rao and Koolagudi lead a discussion of how emotion-specific information is embedded in speech and how to acquire emotion-specific knowledge using appropriate statistical models. Additionally, the authors provide information about using evidence derived from various features and models. The acquired emotion-specific knowledge is useful for synthesizing emotions. Discussion includes global and local prosodic features at syllable, word and phrase levels, helpful for capturing emotion-discriminative information; use of complementary evidences obtained from excitation sources, vocal tract systems and prosodic features in order to enhance the emotion recognition performance; and pro...
H. D. Ellis
Full Text Available An experiment is reported where subjects were presented with familiar or unfamiliar faces for supraliminal durations or for durations individually assessed as being below the threshold for recognition. Their electrodermal responses to each stimulus were measured and the results showed higher peak amplitude skin conductance responses for familiar than for unfamiliar faces, regardless of whether they had been displayed supraliminally or subliminally. A parallel is drawn between elevated skin conductance responses to subliminal stimuli and findings of covert recognition of familiar faces in prosopagnosic patients, some of whom show increased electrodermal activity (EDA to previously familiar faces. The supraliminal presentation data also served to replicate similar work by Tranel et al (1985. The results are considered alongside other data indicating the relation between non-conscious, “automatic” aspects of normal visual information processing and abilities which can be found to be preserved without awareness after brain injury.
Full Text Available As the Person‟s/Organization‟s Private information‟s are becoming very easy to access, the demand for a Simple, Convenient, Efficient, and a highly Securable Authentication System has been increased. In considering these requirements for data Protection, Biometrics, which uses human physiological or behavioral system for personal Identification has been found as a solution for these difficulties. However most of the biometric systems have high complexity in both time and space. So we are going to use a Real time Finger-Vein recognition System for authentication purposes. In this paper we had implemented the Finger Vein Recognition concept using MATLAB R2013a. The features used are Lacunarity Distance, Blanket Dimension distance. This has more accuracy when compared to conventional methods.
Physics of Automatic Target Recognition addresses the fundamental physical bases of sensing, and information extraction in the state-of-the art automatic target recognition field. It explores both passive and active multispectral sensing, polarimetric diversity, complex signature exploitation, sensor and processing adaptation, transformation of electromagnetic and acoustic waves in their interactions with targets, background clutter, transmission media, and sensing elements. The general inverse scattering, and advanced signal processing techniques and scientific evaluation methodologies being used in this multi disciplinary field will be part of this exposition. The issues of modeling of target signatures in various spectral modalities, LADAR, IR, SAR, high resolution radar, acoustic, seismic, visible, hyperspectral, in diverse geometric aspects will be addressed. The methods for signal processing and classification will cover concepts such as sensor adaptive and artificial neural networks, time reversal filt...
Full Text Available This paper presents the maneuver of mouse pointer a nd performs various mouse operations such as left click, right click, double click, drag etc using ge stures recognition technique. Recognizing gestures is a complex task which involves many aspects such as mo tion modeling, motion analysis, pattern recognition and machine learning. Keeping all the essential factors in mind a system has been created which recognizes the movement of fingers and various patterns formed by them. Color caps have been used for fingers to distinguish it f rom the background color such as skin color. Thus recog nizing the gestures various mouse events have been performed. The application has been created on MATL AB environment with operating system as windows 7.
Face recognition has been studied extensively for more than 20 years now. Since the beginning of 90s the subject has became a major issue. This technology is used in many important real-world applications, such as video surveillance, smart cards, database security, internet and intranet access. This report reviews recent two algorithms for face recognition which take advantage of a relatively new multiscale geometric analysis tool - Curvelet transform, for facial processing and feature extraction. This transform proves to be efficient especially due to its good ability to detect curves and lines, which characterize the human's face. An algorithm which is based on the two algorithms mentioned above is proposed, and its performance is evaluated on three data bases of faces: AT&T (ORL), Essex Grimace and Georgia-Tech. k-nearest neighbour (k-NN) and Support vector machine (SVM) classifiers are used, along with Principal Component Analysis (PCA) for dimensionality reduction. This algorithm shows good results, ...
Saurabh D. Parmar,
Full Text Available Face Recognition (FR under various illuminations is very challenging. Normalization technique is useful for removing the dimness and shadow from the facial image which reduces the effect of illumination variations still retaining the necessary information of the face. The robust local feature extractor which is the gray-scale invariant texture called Local Binary Pattern (LBP is helpful for feature extraction. K-Nearest Neighbor classifier is utilized for the purpose of classification and to match the face images from the database. Experimental results were based on Yale-B database with three different sub categories. The proposed method has been tested to robust face recognition in various illumination conditions. Extensive experiment shows that the proposed system can achieve very encouraging performance in various illumination environments.
Ved Prakash Agnihotri
Full Text Available Handwritten Devanagari script recognition system using neural network is presented in this paper. Diagonal based feature extraction is used for extracting features of the handwritten Devanagari script. After that these feature of each character image is converted into chromosome bit string of length 378. More than 1000 sample is used for training and testing purpose in this proposed work. It is attempted to use the power of genetic algorithm to recognize the character. In step-I preprocessing on the character image, then image suitable for feature extraction as here is used. Diagonal based feature extraction method to extract 54 features to each character. In the next step character recognize image in which extracted feature in converted into Chromosome bit string of size 378. In recognition step using fitness function in which find the Chromosome difference between unknown character and Chromosome which are store in data base.
J Sheeba Rani; D Devaraj
Feature extraction is one of the important tasks in face recognition. Moments are widely used feature extractor due to their superior discriminatory power and geometrical invariance. Moments generally capture the global features of the image. This paper proposes Krawtchouk moment for feature extraction in face recognition system, which has the ability to extract local features from any region of interest. Krawtchouk moment is used to extract both local features and global features of the face. The extracted features are fused using summed normalized distance strategy. Nearest neighbour classiﬁer is employed to classify the faces. The proposed method is tested using ORL and Yale databases. Experimental results show that the proposed method is able to recognize images correctly, even if the images are corrupted with noise and possess change in facial expression and tilt.
Craciunescu, Razvan; Mihovska, Albena Dimitrova; Kyriazakos, Sofoklis;
Gesture recognition is a topic in computer science and language technology with the goal of interpreting human gestures via mathematical algorithms. Gestures can originate from any bodily motion or state but commonly originate from the face or hand. Current research focus includes on the emotion...... recognition from the face and hand gesture recognition. Gesture recognition enables humans to communicate with the machine and interact naturally without any mechanical devices. This paper investigates the possibility to use non-audio/video sensors in order to design a low-cost gesture recognition device...
In order to solve emitter recognition problems in a practical reconnaissance environment, attribute mathematics is introduced. The basic concepts and theory of attribute set and attribute measure are described in detail. A new attribute recognition method based on attribute measure is presented in this paper. Application example is given, which demonstrates this new method is accurate and effective. Moreover, computer simulation for recognizing the emitter purpose is selected, and compared with classical statistical pattern recognition through simulation. The excellent experimental results demonstrate that this is a brand-new attribute recognition method as compared to existing statistical pattern recognition techniques.
Rafiqul Zaman Khan
Full Text Available Hand gesture recognition system received great attention in the recent few years because of itsmanifoldness applications and the ability to interact with machine efficiently through human computerinteraction. In this paper a survey of recent hand gesture recognition systems is presented. Key issues ofhand gesture recognition system are presented with challenges of gesture system. Review methods of recentpostures and gestures recognition system presented as well. Summary of research results of hand gesturemethods, databases, and comparison between main gesture recognition phases are also given. Advantagesand drawbacks of the discussed systems are explained finally
The detector algorithms in use at date rely on negative decision logic: based on a model of the ambient noise process they detect all deviations, but many of them are false alarms. The principal alternative to this approach is pattern recognition, which tests on positive correlation with some known signal patterns. The Sonogram-detector realizes this scheme for single seismogram traces. Sonograms display spectral energy versus time. Suitably scaled, these images display only information wh...
Gurung, Deepak; Jiang, Cansen; Deray, Jeremie; Sidibé, Désiré
In this project we develop a system that uses low cost web cameras to recognise gestures and track 2D orientations of the hand. This report is organized as such. First in section 2 we introduce various methods we undertook for hand detection. This is the most important step in hand gesture recognition. Results of various skin detection algorithms are discussed in length. This is followed by region extraction step (section 3). In this section approaches like contours and convex hull to extract...
Nguyen, Hai; James, Eddie A
Conversion of arginine into citrulline is a post-translational modification that is observed in normal physiological processes. However, abnormal citrullination can provoke autoimmunity by generating altered self-epitopes that are specifically targeted by autoantibodies and T cells. In this review we discuss the recognition of citrullinated antigens in human autoimmune diseases and the role that this modification plays in increasing antigenic diversity and circumventing tolerance mechanisms. Early published work demonstrated that citrullinated proteins are specifically targeted by autoantibodies in rheumatoid arthritis and that citrullinated peptides are more readily presented to T cells by arthritis-susceptible HLA class II 'shared epitope' proteins. Emerging data support the relevance of citrullinated epitopes in other autoimmune diseases, including type 1 diabetes and multiple sclerosis, whose susceptible HLA haplotypes also preferentially present citrullinated peptides. In these settings, autoimmune patients have been shown to have elevated responses to citrullinated epitopes derived from tissue-specific antigens. Contrasting evidence implicates autophagy or perforin and complement-mediated membrane attack as inducers of ectopic citrullination. In either case, the peptidyl deiminases responsible for citrullination are activated in response to inflammation or insult, providing a mechanistic link between this post-translational modification and interactions with the environment and infection. As such, it is likely that immune recognition of citrullinated epitopes also plays a role in pathogen clearance. Indeed, our recent data suggest that responses to citrullinated peptides facilitate recognition of novel influenza strains. Therefore, increased understanding of responses to citrullinated epitopes may provide important insights about the initiation of autoimmunity and recognition of heterologous viruses. PMID:27531825
Chappelier, Jean-Cédric; Rajman, Martin; Aragües, Ramon; Rozenknop, Antoine
A lot of work remains to be done in the domain of a better integration of speech recognition and language processing systems. This paper gives an overview of several strategies for integrating linguistic models into speech understanding systems and investigates several ways of producing sets of hypotheses that include more "semantic" variability than usual language models. The main goal is to present and demonstrate by actual experiments that sequential couplingmay be efficiently achieved byw...
Several languages use the Arabic alphabets and arabic scripts present challenges because the letter shape is context sensitive. For the past three decades, there has been a mounting interest among researchers in this problem. In this paper we present an Arabic Character Recognition system and quence steps of recognizing Arabic text. These steps are separately discussed, and previous research work on each step is reviewed. Also in this paper we give some samples of Arabic fonts.
Baldi, Pierre; Chauvin, Yves
After collecting a data base of fingerprint images, we design a neural network algorithm for fingerprint recognition. When presented with a pair of fingerprint images, the algorithm outputs an estimate of the probability that the two images originate from the same finger. In one experiment, the neural network is trained using a few hundred pairs of images and its performance is subsequently tested using several thousand pairs of images originated from a subset of the database corresponding to...
Yoon, Soweon; Anil K Jain
Fingerprint recognition, which is considered to be a reliable means for human identification, has been used in many applications ranging from law enforcement and forensics to unlocking mobile phones. Despite its successful deployment, the fundamental premise of fingerprint-based identification—persistence and uniqueness of fingerprints—has not yet been well studied, resulting in challenges to the admissibility of friction ridge evidence in courts of law. This study investigates the tendency o...
This PhD thesis in human-computer interfaces (HCI, informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia record, but also with newer electronic versions, in particular ergonomic issues and the fact that anaesthesiologists tend to postpone the registration of the medications and other events during b...
Rashid, Sheikh Faisal; Shafait, Faisal; Breuel, Thomas M.
In current study we examine how letter permutation affects in visual recognition of words for two orthographically dissimilar languages, Urdu and German. We present the hypothesis that recognition or reading of permuted and non-permuted words are two distinct mental level processes, and that people use different strategies in handling permuted words as compared to normal words. A comparison between reading behavior of people in these languages is also presented. We present our study in context of dual route theories of reading and it is observed that the dual-route theory is consistent with explanation of our hypothesis of distinction in underlying cognitive behavior for reading permuted and non-permuted words. We conducted three experiments in lexical decision tasks to analyze how reading is degraded or affected by letter permutation. We performed analysis of variance (ANOVA), distribution free rank test, and t-test to determine the significance differences in response time latencies for two classes of data. Results showed that the recognition accuracy for permuted words is decreased 31% in case of Urdu and 11% in case of German language. We also found a considerable difference in reading behavior for cursive and alphabetic languages and it is observed that reading of Urdu is comparatively slower than reading of German due to characteristics of cursive script.
Full Text Available Gesture Recognition Technology has evolved greatly over the years. The past has seen the contemporary Human – Computer Interface techniques and their drawbacks, which limit the speed and naturalness of the human brain and body. As a result gesture recognition technology has developed since the early 1900s with a view to achieving ease and lessening the dependence on devices like keyboards, mice and touchscreens. Attempts have been made to combine natural gestures to operate with the technology around us to enable us to make optimum use of our body gestures making our work faster and more human friendly. The present has seen huge development in this field ranging from devices like virtual keyboards, video game controllers to advanced security systems which work on face, hand and body recognition techniques. The goal is to make full use of themovements of the body and every angle made by the parts of the body in order to supplement technology to become human friendly and understand natural human behavior and gestures. The future of this technology is very bright with prototypes of amazing devices in research and development to make the world equipped with digital information at hand whenever and wherever required.
Wenndt, Stanley J
Recognizing familiar voices is something we do every day. In quiet environments, it is usually easy to recognize a familiar voice. In noisier environments, this can become a difficult task. This paper examines how robust listeners are at identifying familiar voices in noisy, changing environments and what factors may affect their recognition rates. While there is previous research addressing familiar speaker recognition, the research is limited due to the difficulty in obtaining appropriate data that eliminates speaker dependent traits, such as word choice, along with having corresponding listeners who are familiar with the speakers. The data used in this study were collected in such a fashion to mimic conversational, free-flow dialogue, but in a way to eliminate many variables such as word choice, intonation, or non-verbal cues. These data provide some of the most realistic test scenarios to-date for familiar speaker identification. A pure-tone hearing test was used to separate listeners into normal hearing and hearing impaired groups. It is hypothesized that the results of the Normal Hearing Group will be statistically better. Additionally, the aspect of familiar speaker recognition is addressed by having each listener rate his or her familiarity with each speaker. Two statistical approaches showed that the more familiar a listener is with a speaker, the more likely the listener will recognize the speaker. PMID:27586746
This PhD thesis in human-computer interfaces (informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia ...... accuracy. Finally, the last part of the thesis looks at the acceptance and success of a speech recognition system introduced in a Danish hospital to produce patient records.......This PhD thesis in human-computer interfaces (informatics) studies the case of the anaesthesia record used during medical operations and the possibility to supplement it with speech recognition facilities. Problems and limitations have been identified with the traditional paper-based anaesthesia...... inaccuracies in the anaesthesia record. Supplementing the electronic anaesthesia record interface with speech input facilities is proposed as one possible solution to a part of the problem. The testing of the various hypotheses has involved the development of a prototype of an electronic anaesthesia record...
Reynolds, Greg D.
This paper explores the role visual attention plays in the recognition of objects in infancy. Research and theory on the development of infant attention and recognition memory are reviewed in three major sections. The first section reviews some of the major findings and theory emerging from a rich tradition of behavioral research utilizing preferential looking tasks to examine visual attention and recognition memory in infancy. The second section examines research utilizing neural measures of attention and object recognition in infancy as well as research on brain-behavior relations in the early development of attention and recognition memory. The third section addresses potential areas of the brain involved in infant object recognition and visual attention. An integrated synthesis of some of the existing models of the development of visual attention is presented which may account for the observed changes in behavioral and neural measures of visual attention and object recognition that occur across infancy. PMID:25596333
Full Text Available Face recognition from images is a sub-area of the general object recognition problem. It is of particular interest in a wide variety of applications. Here, the face recognition is based on the new proposed modified PCA algorithm by using some components of the LDA algorithm of the face recognition. The proposed algorithm is based on the measure of the principal components of the faces and also to find the shortest distance between them. The experimental results demonstrate that this arithmetic can improve the face recognition rate. . Experimental results on ORL face database show that the method has higher correct recognition rate and higher recognition speeds than traditional PCA algorithm.
Susilo, Tirta; Wright, Victoria; Tree, Jeremy J; Duchaine, Bradley
It has long been suggested that face recognition relies on specialized mechanisms that are not involved in visual recognition of other object categories, including those that require expert, fine-grained discrimination at the exemplar level such as written words. But according to the recently proposed many-to-many theory of object recognition (MTMT), visual recognition of faces and words are carried out by common mechanisms [Behrmann, M., & Plaut, D. C. ( 2013 ). Distributed circuits, not circumscribed centers, mediate visual recognition. Trends in Cognitive Sciences, 17, 210-219]. MTMT acknowledges that face and word recognition are lateralized, but posits that the mechanisms that predominantly carry out face recognition still contribute to word recognition and vice versa. MTMT makes a key prediction, namely that acquired prosopagnosics should exhibit some measure of word recognition deficits. We tested this prediction by assessing written word recognition in five acquired prosopagnosic patients. Four patients had lesions limited to the right hemisphere while one had bilateral lesions with more pronounced lesions in the right hemisphere. The patients completed a total of seven word recognition tasks: two lexical decision tasks and five reading aloud tasks totalling more than 1200 trials. The performances of the four older patients (3 female, age range 50-64 years) were compared to those of 12 older controls (8 female, age range 56-66 years), while the performances of the younger prosopagnosic (male, 31 years) were compared to those of 14 younger controls (9 female, age range 20-33 years). We analysed all results at the single-patient level using Crawford's t-test. Across seven tasks, four prosopagnosics performed as quickly and accurately as controls. Our results demonstrate that acquired prosopagnosia can exist without word recognition deficits. These findings are inconsistent with a key prediction of MTMT. They instead support the hypothesis that face
Gribble Kristin E
Full Text Available Abstract Background Chemically mediated prezygotic barriers to reproduction likely play an important role in speciation. In facultatively sexual monogonont rotifers from the Brachionus plicatilis cryptic species complex, mate recognition of females by males is mediated by the Mate Recognition Protein (MRP, a globular glycoprotein on the surface of females, encoded by the mmr-b gene family. In this study, we sequenced mmr-b copies from 27 isolates representing 11 phylotypes of the B. plicatilis species complex, examined the mode of evolution and selection of mmr-b, and determined the relationship between mmr-b genetic distance and mate recognition among isolates. Results Isolates of the B. plicatilis species complex have 1–4 copies of mmr-b, each composed of 2–9 nearly identical tandem repeats. The repeats within a gene copy are generally more similar than are gene copies among phylotypes, suggesting concerted evolution. Compared to housekeeping genes from the same isolates, mmr-b has accumulated only half as many synonymous differences but twice as many non-synonymous differences. Most of the amino acid differences between repeats appear to occur on the outer face of the protein, and these often result in changes in predicted patterns of phosphorylation. However, we found no evidence of positive selection driving these differences. Isolates with the most divergent copies were unable to mate with other isolates and rarely self-crossed. Overall the degree of mate recognition was significantly correlated with the genetic distance of mmr-b. Conclusions Discrimination of compatible mates in the B. plicatilis species complex is determined by proteins encoded by closely related copies of a single gene, mmr-b. While concerted evolution of the tandem repeats in mmr-b may function to maintain identity, it can also lead to the rapid spread of a mutation through all copies in the genome and thus to reproductive isolation. The mmr-b gene is evolving
Full Text Available Problem statement: In any real time biometric system processing speed and recognition time are crucial parameters. Reducing processing time involves many parameters like normalization, FAR, FRR, management of eyelid and eyelash occlusions, size of signature etc. Normalization consumes substantial amount of time of the system. This study contributes for improved iris recognition system with reduced processing time, False Acceptance Rate (FAR and False Rejection Rate (FRR. Approach: To improve system performance and reliability of a biometric system. It avoided the iris normalization process used traditionally in iris recognition systems. The technique proposed here used different masks to filter out iris image from an eye. Comparative study of different masks was done and optimized mask is proposed. The experiment was carried on CASIA database consisting of 756 iris images of 108 persons. Each person contributes seven images of eye (108×7 = 756 images in the database. Results: In the proposed method: (1 Normalization step is avoided; (2 Computational time is reduced by 0.3342 sec; (3 Iris signature size is reduced; (4 Improved performance parameters. (With reduced feature size, proposed method achieves 99.4866% accuracy, 0.0069% FAR, 1.0198% FRR and significant increase in speed of the system. Conclusion: Iris signature proposed was comparatively small just of 1×24 size. Though Daugmans method gives best accuracy of 99.90% but the iris signature length used by that algorithm is comparatively very high that is 1×2048 phase vector. Also Daugman has used phase information in signature formation. Our method gives a accuracy of 99.474% with a signature of comparatively very small length. This has definitely contributed to improve the speed.
Turk, Matthew A.; Pentland, Alexander P.
The human ability to process faces is remarkable. We can identify perhaps thousands of faces learned throughout our lifetime and read facial expression to understand such subtle qualities as emotion. These skills are quite robust, despite sometimes large changes in the visual stimulus due to expression, aging, and distractions such as glasses or changes in hairstyle or facial hair. Computers which model and recognize faces will be useful in a variety of applications, including criminal identification, human-computer interface, and animation. We discuss models for representing faces and their applicability to the task of recognition, and present techniques for identifying faces and detecting eye blinks.
Full Text Available The importance of the immaterial investments within companies nowadays urges the specialists in accounting to find the ways to present more in the elements. In their studies researchers face the controversy reinvestments, as an asset in the balance sheet or an expense in the profit or loss account. The main goal of this paper is to analyze the difficulties in commercial fund. In the first part we will analyze various definitions of the problems concerning the commercial fund’s recognition and assessment. The paper also suggests that investments are really social and economic problems.
This new text provides an overview of the radar target recognition process and covers the key techniques being developed for operational systems. It is based on the fundamental scientific principles of high resolution radar, and explains how the techniques can be used in real systems, taking into account the characteristics of practical radar system designs and component limitations. It also addresses operational aspects, such as how high resolution modes would fit in with other functions such as detection and tracking. Mathematics is kept to a minimum and the complex techniques and issues are
Full Text Available This paper presents a novel method of facial image representation for face recognition in uncontrolled environment. It is named as augmented local binary patterns (A-LBP that works on both, uniform and non-uniform patterns. It replaces the central non-uniform pattern with a majority value of the neighbouring uniform patterns obtained after processing all neighbouring non-uniform patterns. These patterns are finally combined with the neighbouring uniform patterns, in order to extract discriminatory information from the local descriptors. The experimental results indicate the vitality of the proposed method on particular face datasets, where the images are prone to extreme variations of illumination.
Full Text Available An Elastic Bunch Graph Map (EBGM algorithm is being proposed in this research paper that successfully implements face recognition using Gabor filters. The proposed system applies 40 different Gabor filters on an image. As aresult of which 40 images with different angles and orientation are received. Next, maximum intensity points in each filtered image are calculated and mark them as Fiducial points. The system reduces these points in accordance to distance between them. The next step is calculating the distances between the reduced points using distance formula. At last, the distances are compared with database. If match occurs, it means that the image is recognized.
Wu, Bo-Wen; Fang, Yi-Chin; Chang, Lin-Song
This paper proposes a new method to improve human recognition by artificial intelligence, specifically of images without the interference of high frequencies. The human eye is the most delicate optical system. Notwithstanding the dramatic progression of its structure and functions through a long evolution, the capability of visual recognition is not yet close to perfection. This paper is a study, based on the limitations of recognition by the human eye, of image recognition through the application of artificial intelligence. Those aspects which have been explored focus on human eye modeling, including aberration analysis, creative models of the human eye, human vision recognition characteristics and various mathematical models for verification. By using images consisting of four black and white bands and modulation transfer function (MTF) curve evaluation recognition capability on all the studied models, the optimum model most compatible with the physiology of the human eye is found.
Dr.Asmahan M Altaher
Full Text Available Face recognition is the ability of categorize a set of images based on certain discriminatory features. Classification of the recognition patterns can be difficult problem and it is still very active field of research. The paper introduces conceptual framework for descriptive study on techniques of face recognition systems. It aims to describe the previous researches have been study the face recognition system, in order scope on the algorithms, usages, benefits , challenges and problems in this felids, the paper proposed the face recognition as sensitive learning task experiments on a large face databases demonstrate of the new feature. The researcher recommends that there's a needs to evaluate the previous studies and researches, especially on face recognition field and 3D, hopeful for advanced techniques and methods in the near future.
GAOJun; DONGHuoming; SHAOJing; ZHAOJing
Synergetic pattern recognition is a novel and effective pattern recognition method, and has some advantages in image recognition. Researches have shown that attention parameters λ and parameters B， C directly influence on the recognition results, but there is no general research theory to control these parameters in the recognition process. We abstractly analyze these parameters in this paper, and purpose a novel parameters optimization method based on simulated annealing algorithm. SA algorithm has good optimization performance and is used to search the global optimized solution of these parameters. Theoretic analysis and experimental results both show that the proposed parameters optimization method is effective, which can fully improve the performance of synergetic recognition approach, and the algorithm realization is simple and fast.
BAI BoFeng; ZHANG ShaoJun; ZHAO Liang; ZHANG XiMin; GUO LieJin
The key reasons that the present method cannot be used to solve the industrial multi-phase flow pattern recognition are clarified firstly. The prerequisite to realize the online recognition is proposed and recognition rules for partial flow pattern are obtained based on the massive experimental data. The standard templates for every flow regime feature are calculated with self-organization cluster algorithm. The multi-sensor data fusion method is proposed to realize the online recognition of multiphase flow regime with the pressure and differential pressure signals, which overcomes the severe influence of fluid flow velocity and the oil fraction on the recognition. The online recognition method is tested in the practice, which has less than 10 percent measurement error. The method takes advantages of high confidence, good fault tolerance and less requirement of single sensor performance.
The key reasons that the present method cannot be used to solve the industrial multi- phase flow pattern recognition are clarified firstly. The prerequisite to realize the online recognition is proposed and recognition rules for partial flow pattern are obtained based on the massive experimental data. The standard templates for every flow regime feature are calculated with self-organization cluster algorithm. The multi-sensor data fusion method is proposed to realize the online recognition of multiphase flow regime with the pressure and differential pressure signals, which overcomes the severe influence of fluid flow velocity and the oil fraction on the recognition. The online recognition method is tested in the practice, which has less than 10 percent measurement error. The method takes advantages of high confidence, good fault tolerance and less requirement of single sensor performance.
Fruchterman, James R.
Document recognition advances have improved the lives of people with print disabilities, by providing accessible documents. This invited paper provides perspectives on the author's career progression from document recognition professional to social entrepreneur applying this technology to help people with disabilities. Starting with initial thoughts about optical character recognition in college, it continues with the creation of accurate omnifont character recognition that did not require training. It was difficult to make a reading machine for the blind in a commercial setting, which led to the creation of a nonprofit social enterprise to deliver these devices around the world. This network of people with disabilities scanning books drove the creation of Bookshare.org, an online library of scanned books. Looking forward, the needs for improved document recognition technology to further lower the barriers to reading are discussed. Document recognition professionals should be proud of the positive impact their work has had on some of society's most disadvantaged communities.
Li, Yangyang; Wu, Yunhui; Jiao, Lc; Wu, Jianshe
With the development of society, handwritten digits recognition technique has been widely applied to production and daily life. It is a very difficult task to solve these problems in the field of pattern recognition. In this paper, a new method is presented for handwritten digit recognition. The digit samples firstly are processed and features extraction. Based on these features, a novel immune network classification algorithm is designed and implemented to the handwritten digits recognition. The proposed algorithm is developed by Jerne's immune network model for feature selection and KNN method for classification. Its characteristic is the novel network with parallel commutating and learning. The performance of the proposed method is experimented to the handwritten number datasets MNIST and compared with some other recognition algorithms-KNN, ANN and SVM algorithm. The result shows that the novel classification algorithm based on immune network gives promising performance and stable behavior for handwritten digits recognition.
Wenjuan Gong; Weishan Zhang; Jordi Gonzàlez; Yan Ren; Zhen Li
Bilinear models have been successfully applied to separate two factors, for example, pose variances and different identities in face recognition problems. Asymmetric model is a type of bilinear model which models a system in the most concise way. But seldom there are works exploring the applications of asymmetric bilinear model on face recognition problem with illumination changes. In this work, we propose enhanced asymmetric model for illumination-robust face recognition. Instead of initiali...
Minaee, Shervin; Wang, Yao
Fingerprint recognition has drawn a lot of attention during last decades. Different features and algorithms have been used for fingerprint recognition in the past. In this paper, a powerful image representation called scattering transform/network, is used for recognition. Scattering network is a convolutional network where its architecture and filters are predefined wavelet transforms. The first layer of scattering representation is similar to sift descriptors and the higher layers capture hi...
Richard Mo; Adnan Shaout
A portable real-time facial recognition system that is able to play personalized music based on the identified person’s preferences was developed. The system is called Portable Facial Recognition Jukebox Using Fisherfaces (FRJ). Raspberry Pi was used as the hardware platform for its relatively low cost and ease of use. This system uses the OpenCV open source library to implement the computer vision Fisherfaces facial recognition algorithms, and uses the Simple DirectMedia Layer (SDL) library ...
Ghosh, Madhumala; Das, Devkumar; Chakraborty, Chandan; Ray, Ajoy K
This paper aims at introducing an automated approach to leukocyte recognition using fuzzy divergence and modified thresholding techniques. The recognition is done through the segmentation of nuclei where Gamma, Gaussian and Cauchy type of fuzzy membership functions are studied for the image pixels. It is in fact found that Cauchy leads better segmentation as compared to others. In addition, image thresholding is modified for better recognition. Results are studied and discussed.
González-Díaz, Iván; Boujut, Hugo; Buso, Vincent; Benois-Pineau, Jenny; Domenger, Jean-Philippe
10 pages In this paper we study the problem of object recognition in egocentric video recorded with cameras worn by persons. This task hasgained much attention during the last years, since it has turned tobe a main building block for action recognition systems in applications involving wearable cameras, such as tele-medicine or lifelogging. Under these scenarios, an action can be effectively deﬁnedas a sequence of manipulated or observed objects, so that recognition becomes a relevant stag...
Jamal Ahmad Dargham; Ali Chekima; Ervin Gubin Moung
Face recognition is an important biometric method because of its potential applications in many fields, such as access control, surveillance, and human-computer interaction. In this paper, a face recognition system that fuses the outputs of three face recognition systems based on Gabor jets is presented. The first system uses the magnitude, the second uses the phase, and the third uses the phase-weighted magnitude of the jets. The jets are generated from facial landmarks selected using three ...
Chen, Xiaoming; Cheng, Wushan
Relational Over the last two decades, the advances in computer vision and pattern recognition power have opened the door to new opportunity of automatic facial expression recognition system. This paper use Canny edge detection method for facial expression recognition. Image color space transformation in the first place and then to identify and locate human face .Next pick up the edge of eyes and mouth's features extraction. Last we judge the facial expressions after compared wi...
Information is carried in changes of a signal. The paper starts with revisiting Dudley’s concept of the carrier nature of speech. It points to its close connection to modulation spectra of speech and argues against short-term spectral envelopes as dominant carriers of the linguistic information in speech. The history of spectral representations of speech is brieﬂy discussed. Some of the history of gradual infusion of the modulation spectrum concept into Automatic recognition of speech (ASR) comes next, pointing to the relationship of modulation spectrum processing to wellaccepted ASR techniques such as dynamic speech features or RelAtive SpecTrAl (RASTA) ﬁltering. Next, the frequency domain perceptual linear prediction technique for deriving autoregressive models of temporal trajectories of spectral power in individual frequency bands is reviewed. Finally, posterior-based features, which allow for straightforward application of modulation frequency domain information, are described. The paper is tutorial in nature, aims at a historical global overview of attempts for using spectral dynamics in machine recognition of speech, and does not always provide enough detail of the described techniques. However, extensive references to earlier work are provided to compensate for the lack of detail in the paper.
Theodoridis, Sergios; Koutroumbas, Konstantinos; Cavouras, Dionisis
An accompanying manual to Theodoridis/Koutroumbas, Pattern Recognition, that includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. *Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition 4e.*Solved examples in Matlab, including real-life data sets in imaging and audio recognition*Available separately or at a special package price with the mai
Full Text Available Using current neurological and neuropsychological literature, and the analysis of different cultural and historical conditions, people recognition is analyzed. Different “subsystems” or “modules” could be involved in individuals' recognition: living versus non-living, own species versus other species, familiar versus non-familiar, males versus females, and individual identification versus emotional identification. Not only visual, but also auditory and even olfactory information may be involved in people recognition. Visual information involved in people recognition is proposed to include not only the perception of faces, but also the perception of whole body and gait, clothes, emotional expressions, and individual marks.
Olsen, Søren I.
An approach to exemplar based recognition of visual shapes is presented. The shape information is described by attributed interest points (keys) detected by an end-stop operator. The attributes describe the statistics of lines and edges local to the interest point, the position of neighboring int...... interest points, and (in the training phase) a list of recognition names. Recognition is made by a simple voting procedure. Preliminary experiments indicate that the recognition is robust to noise, small deformations, background clutter and partial occlusion....
Claudia Sassenrath; Kai Sassenberg; Devin G Ray; Katharina Scheiter; Halszka Jarodzka
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...
Filis, Dimitrios P.; Renios, Christos I.
The technology of aircraft recognition and tracking has various applications in all areas of air navigation, be they civil or military, spanning from air traffic control and regulation at civilian airports to anti-aircraft weapon handling and guidance for military purposes.1, 18 The system presented in this thesis is an alternative implementation of identifying and tracking flying objects, which benefits from the optical spectrum by using an optical camera built into a servo motor (pan-tilt unit). More specifically, through the purpose-developed software, when a target (aircraft) enters the field of view of the camera18, it is both detected and identified.5, 22 Then the servo motor, being provided with data on target position and velocity, tracks the aircraft while it is in constant communication with the camera (Fig. 1). All the features are so designed as to operate under real time conditions.
Vanderelst, Dieter; Steckel, Jan; Boen, Andre; Peremans, Herbert; Holderied, Marc W
Echolocating bats have excellent spatial memory and are able to navigate to salient locations using bio-sonar. Navigating and route-following require animals to recognize places. Currently, it is mostly unknown how bats recognize places using echolocation. In this paper, we propose template based place recognition might underlie sonar-based navigation in bats. Under this hypothesis, bats recognize places by remembering their echo signature - rather than their 3D layout. Using a large body of ensonification data collected in three different habitats, we test the viability of this hypothesis assessing two critical properties of the proposed echo signatures: (1) they can be uniquely classified and (2) they vary continuously across space. Based on the results presented, we conclude that the proposed echo signatures satisfy both criteria. We discuss how these two properties of the echo signatures can support navigation and building a cognitive map.
Moeslund, Thomas B.; Fihl, Preben; Holte, Michael Boelstoft
The number of potential applications has made automatic recognition of human actions a very active research area. Different approaches have been followed based on trajectories through some state space. In this paper we also model an action as a trajectory through a state space, but we represent...... the actions as a sequence of temporal isolated instances, denoted primitives. These primitives are each defined by four features extracted from motion images. The primitives are recognized in each frame based on a trained classifier resulting in a sequence of primitives. From this sequence we recognize...... different temporal actions using a probabilistic Edit Distance method. The method is tested on different actions with and without noise and the results show recognizing rates of 88.7% and 85.5%, respectively....
Global methods of track finding and of parameter estimation require sufficient information to be available at a certain point on a particle trajectory. Then vector functions can be looked for such that in the space spanned by these functions each track forms a cluster of points which is well separated from the clusters of the other tracks. A clever detector lay-out can simplify this task. However, current wire chamber detectors do not provide either enough, or precise enough, measurements at points on curved trajectories to allow the application of global pattern recognition methods - improved detectors should not only provide precise coordinate measurements but also sufficiently precise measurements of the track direction. (orig.)
Bauer, Alexander; Fischer, Yvonne
From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams, new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect critical situations early enough to enable an operator to take preventive actions, instead of using video material merely for forensic investigations. However, problems such as limited computational resources, privacy regulations and a constant change in potential threads have to be addressed by a practical automated video surveillance system. In this paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the system are presented and illustrated for the surveillance of guests inside a video-monitored building.
Mainy, Nelly; Jung, Julien; Baciu, Monica; Kahane, Philippe; Schoendorff, Benjamin; Minotti, Lorella; Hoffmann, Dominique; Bertrand, Olivier; Lachaux, Jean-Philippe
While functional neuroimaging studies have helped elucidate major regions implicated in word recognition, much less is known about the dynamics of the associated activations or the actual neural processes of their functional network. We used intracerebral electroencephalography recordings in 10 patients with epilepsy to directly measure neural activity in the temporal and frontal lobes during written words' recognition, predominantly in the left hemisphere. The patients were presented visually with consonant strings, pseudo-words, and words and performed a hierarchical paradigm contrasting semantic processes (living vs. nonliving word categorization task), phonological processes (rhyme decision task on pseudo-words), and visual processes (visual analysis of consonant strings). Stimuli triggered a cascade of modulations in the gamma-band (>40 Hz) with reproducible timing and task-sensitivity throughout the functional reading network: the earliest gamma-band activations were observed for all stimuli in the mesial basal temporal lobe at 150 ms, reaching the word form area in the mid fusiform gyrus at 200 ms, evidencing a superiority effect for word-like stimuli. Peaks of gamma-band activations were then observed for word-like stimuli after 400 ms in the anterior and middle portion of the superior temporal gyrus (BA 38 and BA 22 respectively), in the pars triangularis of Broca's area for the semantic task (BAs 45 and 47), and in the pars opercularis for the phonological task (BA 44). Concurrently, we observed a two-pronged effect in the prefrontal cortex (BAs 9 and 46), with nonspecific sustained dorsal activation related to sustained attention and, more ventrally, a strong reflex deactivation around 500 ms, possibly due to semantic working memory reset. PMID:17712785
Lee, Chang H.; Kwon, Youan; Kim, Kyungil; Rastle, Kathleen
Research on the impact of letter transpositions in visual word recognition has yielded important clues about the nature of orthographic representations. This study investigated the impact of syllable transpositions on the recognition of Korean multisyllabic words. Results showed that rejection latencies in visual lexical decision for…
Full Text Available This is a case study of goldsmith craft apprenticeship learning and recognition. The study includes 13 participants in a goldsmith's workshop. The theoretical approach to recognition and learning is inspired by sociocultural theory. In this article recognition is defined with reference to Hegel’s understanding of the concept as a transformed struggle of granting acknowledgement to another person plus receiving acknowledgement as a person. It is argued that the notion of recognition can enhance sociocultural notions of learning. In analysing the case study of apprenticeship learning, the article suggests that recognition is expressed in the act of participants staking their lives to prove their autonomy, in work activity in terms of the role of artefacts and in the form of abstract and concrete recognition. Finally recognition is discussed in relation to learning and development. The study concludes that recognition is an important category not only to explain apprenticeship learning but also to give a sociocultural explanation of learning in general.
Gunes, Hatice; Pantic, Maja; Vallverdú, J.
Recognition and analysis of human emotions have attracted a lot of interest in the past two decades and have been researched extensively in neuroscience, psychology, cognitive sciences, and computer sciences. Most of the past research in machine analysis of human emotion has focused on recognition o
Pang, Chao-Yang; Ding, Cong-Bao; Hu, Ben-Qiong
It's the key research topic of signal processing that recognizing genuine targets real time from the disturbed signal which has giant amount of data. A quantum algorithm for pattern recognition of classical signal which has time complexity O(sqrt(N)) is presented in this paper. Key Words: Pattern recognition, Grover's algorithm, Rotation on subspace
In optical pattern recognition system with self-amplification, no reference beam used in addressing mode. Polarization of laser beam and orientation of photorefractive crystal chosen to maximize photorefractive effect. Intensity of recognition signal is orders of magnitude greater than other optical correlators. Apparatus regarded as real-time or quasi-real-time optical pattern recognizer with memory and reprogrammability.
Segelke, Brent W.; Toppani, Dominique
A robotic CCD microscope and procedures to automate crystal recognition. The robotic CCD microscope and procedures enables more accurate crystal recognition, leading to fewer false negative and fewer false positives, and enable detection of smaller crystals compared to other methods available today.
The results of a word recognition study are compared to those of a sign recognition study in order to determine which aspects of lexical access are comparable in speech and sign, and which are specific to each of the two language modalities. The "gating paradigm" was used in both studies. (Author/AMH)
Full Text Available The recognition heuristic (RH; Goldstein and Gigerenzer, 2002 suggests that, when applicable, probabilistic inferences are based on a noncompensatory examination of whether an object is recognized or not. The overall findings on the processes that underlie this fast and frugal heuristic are somewhat mixed, and many studies have expressed the need for considering a more compensatory integration of recognition information. Regardless of the mechanism involved, it is clear that recognition has a strong influence on choices, and this finding might be explained by the fact that recognition cues arouse affect and thus receive more attention than cognitive cues. To test this assumption, we investigated whether recognition results in a direct affective signal by measuring physiological arousal (i.e., peripheral arterial tone in the established city-size task. We found that recognition of cities does not directly result in increased physiological arousal. Moreover, the results show that physiological arousal increased with increasing inconsistency between recognition information and additional cue information. These findings support predictions derived by a compensatory Parallel Constraint Satisfaction model rather than predictions of noncompensatory models. Additional results concerning confidence ratings, response times, and choice proportions further demonstrated that recognition information and other cognitive cues are integrated in a compensatory manner.
Herzog, Dennis; Krüger, Volker
A common problem in human movement recognition is the recognition of movements of a particular type (semantic). E.g., grasping movements have a particular semantic (grasping) but the actual movements usually have very different appearances due to, e.g., different grasping directions. In this paper...
LI Xungao; FENG Xinxin; GE Yi
On the basis of envelope spectrum analysis of ship-radiated noise, the features and the extracting methods are discussed, and then a new method of dynamic recognition has been proposed. Using this method, accurate recognition rate for different types of ship targets can be effectively improved, and some of the important parameters of target's movement can be obtained at the same time.
A reasonable explanation is given for possibility distribution descriptor that defined by Hall and Kandel and used in target recognition. Based on this explanation, possibility distribution entropy is defined. Some properties of possibility distribution entropy are discussed and a experiment for recognition of two fighters is shown.
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
Kasahara, Yusuke; Shimono, Tomoyuki; Kuwahara, Hiroaki; Sato, Masataka; Ohnishi, Kouhei
Motion recognition based on sensory information is important for providing assistance to human using robots. Several studies have been carried out on motion recognition based on image information. However, in the motion of humans contact with an object can not be evaluated precisely by image-based recognition. This is because the considering force information is very important for describing contact motion. In this paper, a modal-power-based haptic motion recognition is proposed; modal power is considered to reveal information on both position and force. Modal power is considered to be one of the defining features of human motion. A motion recognition algorithm based on linear discriminant analysis is proposed to distinguish between similar motions. Haptic information is extracted using a bilateral master-slave system. Then, the observed motion is decomposed in terms of primitive functions in a modal space. The experimental results show the effectiveness of the proposed method.
Rawan I. Zaghloul
Full Text Available Recognition of handwritten numerals has been one of the most challenging topics in image processing. This is due to its contributions in the automation process in several applications. The aim of this study was to build a classifier that can easily recognize offline handwritten Arabic numerals to support those applications that are deal with Hindi (Arabic numerals. A new algorithm for Hindi (Arabic Numeral Recognition is proposed. The proposed algorithm was developed using MATLAB and tested with a large sample of handwritten numeral datasets for different writers in different ages. Pattern recognition techniques are used to identify Hindi (Arabic handwritten numerals. After testing, high recognition rates were achieved, their ranges from 95% for some numerals and up to 99% for others. The proposed algorithm used a powerful set of features which proved to be effective in the recognition of Hindi (Arabic numerals.
Nadir Farah; Labiba Souici; Mokhtar Sellami
Given the number and variety of methods used for handwriting recognition, it has been shown that there is no single method that can be called the "best". In recent years, the combination of different classifiers and the use of contextual information have become major areas of interest in improving recognition results. This paper addresses a case study on the combination of multiple classifiers and the integration of syntactic level information for the recognition of handwritten Arabic literal amounts. To the best of our knowledge, this is the first time either of these methods has been applied to Arabic word recognition. Using three individual classifiers with high level global features, we performed word recognition experiments. A parallel combination method was tested for all possible configuration cases of the three chosen classifiers. A syntactic analyzer makes a final decision on the candidate words generated by the best configuration scheme.The effectiveness of contextual knowledge integration in our application is confirmed by the obtained results.
Mohammad, Khader; Agaian, Sos; Saleh, Hani
Since 1970's, the need of an automatic license plate recognition system, sometimes referred as Automatic License Plate Recognition system, has been increasing. A license plate recognition system is an automatic system that is able to recognize a license plate number, extracted from image sensors. In specific, Automatic License Plate Recognition systems are being used in conjunction with various transportation systems in application areas such as law enforcement (e.g. speed limit enforcement) and commercial usages such as parking enforcement and automatic toll payment private and public entrances, border control, theft and vandalism control. Vehicle license plate recognition has been intensively studied in many countries. Due to the different types of license plates being used, the requirement of an automatic license plate recognition system is different for each country. [License plate detection using cluster run length smoothing algorithm ].Generally, an automatic license plate localization and recognition system is made up of three modules; license plate localization, character segmentation and optical character recognition modules. This paper presents an Arabic license plate recognition system that is insensitive to character size, font, shape and orientation with extremely high accuracy rate. The proposed system is based on a combination of enhancement, license plate localization, morphological processing, and feature vector extraction using the Haar transform. The performance of the system is fast due to classification of alphabet and numerals based on the license plate organization. Experimental results for license plates of two different Arab countries show an average of 99 % successful license plate localization and recognition in a total of more than 20 different images captured from a complex outdoor environment. The results run times takes less time compared to conventional and many states of art methods.
Pham, Viet-Khoi; Nguyen, Hai-Dang; Nguyen-Khac, Tung-Anh; Tran, Minh-Triet
The problems of digitalization and transformation of musical scores into machine-readable format are necessary to be solved since they help people to enjoy music, to learn music, to conserve music sheets, and even to assist music composers. However, the results of existing methods still require improvements for higher accuracy. Therefore, the authors propose lightweight algorithms for Optical Music Recognition to help people to recognize and automatically play musical scores. In our proposal, after removing staff lines and extracting symbols, each music symbol is represented as a grid of identical M ∗ N cells, and the features are extracted and classified with multiple lightweight SVM classifiers. Through experiments, the authors find that the size of 10 ∗ 12 cells yields the highest precision value. Experimental results on the dataset consisting of 4929 music symbols taken from 18 modern music sheets in the Synthetic Score Database show that our proposed method is able to classify printed musical scores with accuracy up to 99.56%.
Full Text Available Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities.
Quan, Changqin; Ren, Fuji
The growing interest in affective computing (AC) brings a lot of valuable research topics that can meet different application demands in enterprise systems. The present study explores a sub area of AC techniques - textual emotion recognition for enhancing enterprise computing. Multi-label emotion recognition in text is able to provide a more comprehensive understanding of emotions than single label emotion recognition. A representation of 'emotion state in text' is proposed to encompass the multidimensional emotions in text. It ensures the description in a formal way of the configurations of basic emotions as well as of the relations between them. Our method allows recognition of the emotions for the words bear indirect emotions, emotion ambiguity and multiple emotions. We further investigate the effect of word order for emotional expression by comparing the performances of bag-of-words model and sequence model for multi-label sentence emotion recognition. The experiments show that the classification results under sequence model are better than under bag-of-words model. And homogeneous Markov model showed promising results of multi-label sentence emotion recognition. This emotion recognition system is able to provide a convenient way to acquire valuable emotion information and to improve enterprise competitive ability in many aspects.
ROH Yong-Wan; KIM Dong-Ju; LEE Woo-Seok; HONG Kwang-Seok
This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech. The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform (FFT) spectral entropy, delta FFT spectral entropy, Mel-frequency filter bank (MFB)spectral entropy, and Delta MFB spectral entropy. Spectral-based entropy features are simple. They reflect frequency characteristic and changing characteristic in frequency of speech. We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance. Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results, respectively.These scores are first obtained from a pattern recognition procedure. The pattern recognition phase uses the Gaussian mixture model (GMM). We classify the four emotional states as anger, sadness,happiness and neutrality. The proposed method is evaluated using 45 sentences in each emotion for 30 subjects, 15 males and 15 females. Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy, Zero Crossing Rate (ZCR),linear prediction coefficient (LPC), and pitch parameters. We demonstrate the effectiveness of the proposed approach. One of the proposed features, combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods. We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.
Smead, Rory; Forber, Patrick
Recognition of behavioral types can facilitate the evolution of cooperation by enabling altruistic behavior to be directed at other cooperators and withheld from defectors. While much is known about the tendency for recognition to promote cooperation, relatively little is known about whether such a capacity can coevolve with the social behavior it supports. Here we use evolutionary game theory and multi-population dynamics to model the coevolution of social behavior and recognition. We show that conditional harming behavior enables the evolution and stability of social recognition, whereas conditional helping leads to a deterioration of recognition ability. Expanding the model to include a complex game where both helping and harming interactions are possible, we find that conditional harming behavior can stabilize recognition, and thereby lead to the evolution of conditional helping. Our model identifies a novel hypothesis for the evolution of cooperation: conditional harm may have coevolved with recognition first, thereby helping to establish the mechanisms necessary for the evolution of cooperation. PMID:27225673
Face recognition provides a natural visual interface for human computer interaction (HCI) applications.The process of face recognition,however,is inhibited by variations in the appearance of face images caused by changes in lighting,expression,viewpoint,aging and introduction of occlusion.Although various algorithms have been presented for face recognition,face recognition is still a very challenging topic.A novel approach of real time face recognition for HCI is proposed in the paper.In view of the limits of the popular approaches to foreground segmentation,wavelet multi-scale transform based background subtraction is developed to extract foreground objects.The optimal selection of the threshold is automatically determined,which does not require any complex supervised training or manual experimental calibration.A robust real time face recognition algorithm is presented,which combines the projection matrixes without iteration and kernel Fisher discriminant analysis (KFDA) to overcome some difficulties existing in the real face recognition.Superior performance of the proposed algorithm is demonstrated by comparing with other algorithms through experiments.The proposed algorithm can also be applied to the video image sequences of natural HCI.
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.
Pattern recognition has become one of the fastest growing research topics in the fields of computer science and electrical and electronic engineering in the recent years.Advanced research and development in pattern recognition have found numerous applications in such areas as artificial intelligence,information security,biometrics,military science and technology,finance and economics,weather forecast,image processing,communication,biomedical engineering,document processing,robot vision,transportation,and endless other areas,with many encouraging results.The achievement of pattern recognition is most likely to benefit from some new developments of theoretical mathematics including wavelet analysis.This paper aims at a brief survey of pattern recognition with the wavelet theory.It contains the following respects:analysis and detection of singularities with wavelets;wavelet descriptors for shapes of the objects;invariant representation of patterns;handwritten and printed character recognition;texture analysis and classification;image indexing and retrieval;classification and clustering;document analysis with wavelets;iris pattern recognition;face recognition using wavelet transform;hand gestures classification;character processing with B-spline wavelet transform;wavelet-based image fusion,and others.
Virginia J Emery
Full Text Available Social organisms rank among the most abundant and ecologically dominant species on Earth, in part due to exclusive recognition systems that allow cooperators to be distinguished from exploiters. Exploiters, such as social parasites, manipulate their hosts' recognition systems, whereas cooperators are expected to minimize interference with their partner's recognition abilities. Despite our wealth of knowledge about recognition in single-species social nests, less is known of the recognition systems in multi-species nests, particularly involving cooperators. One uncommon type of nesting symbiosis, called parabiosis, involves two species of ants sharing a nest and foraging trails in ostensible cooperation. Here, we investigated recognition cues (cuticular hydrocarbons and recognition behaviors in the parabiotic mixed-species ant nests of Camponotus femoratus and Crematogaster levior in North-Eastern Amazonia. We found two sympatric, cryptic Cr. levior chemotypes in the population, with one type in each parabiotic colony. Although they share a nest, very few hydrocarbons were shared between Ca. femoratus and either Cr. levior chemotype. The Ca. femoratus hydrocarbons were also unusually long-chained branched alkenes and dienes, compounds not commonly found amongst ants. Despite minimal overlap in hydrocarbon profile, there was evidence of potential interspecific nestmate recognition -Cr. levior ants were more aggressive toward Ca. femoratus non-nestmates than Ca. femoratus nestmates. In contrast to the prediction that sharing a nest could weaken conspecific recognition, each parabiotic species also maintains its own aggressive recognition behaviors to exclude conspecific non-nestmates. This suggests that, despite cohabitation, parabiotic ants maintain their own species-specific colony odors and recognition mechanisms. It is possible that such social symbioses are enabled by the two species each using their own separate recognition cues, and that
Full Text Available Feature Selection is a optimization technique used in face recognition technology. Feature selection removes the irrelevant, noisy and redundant data thus leading to the more accurate recognition of face from the database.Cuckko Algorithm is one of the recent optimization algorithm in the league of nature based algorithm. Its optimization results are better than the PSO and ACO optimization algorithms. The proposal of applying the Cuckoo algorithm for feature selection in the process of face recognition is presented in this paper.
Full Text Available In this paper, a novel algorithm is proposed for fingertip detection and finger type recognition. The algorithm is applied for locating fingertips in hand region extracted by Bayesian rule based skincolor segmentation. Morphological operations are performed in the segmented hand region by observing key geometric features. A probabilistic modeling of the geometric features of finger movement has made the finger type recognition process significantly robust. Proposed method can be employed in a variety of applications like sign language recognition and human robot interactions.
He, Ran; Yuan, Xiaotong; Wang, Liang
This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.The?authors?resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency,?the brief?introduces the additive and multip
Rao, K Sreenivasa
This book discusses speaker recognition methods to deal with realistic variable noisy environments. The text covers authentication systems for; robust noisy background environments, functions in real time and incorporated in mobile devices. The book focuses on different approaches to enhance the accuracy of speaker recognition in presence of varying background environments. The authors examine: (a) Feature compensation using multiple background models, (b) Feature mapping using data-driven stochastic models, (c) Design of super vector- based GMM-SVM framework for robust speaker recognition, (d) Total variability modeling (i-vectors) in a discriminative framework and (e) Boosting method to fuse evidences from multiple SVM models.
Full Text Available This document is devoted to the task of object detection and recognition in digital images by using genetic programming. The goal was to improve and simplify existing approaches. The detection and recognition are achieved by means of extracting the features. A genetic program is used to extract and classify features of objects. Simple features and primitive operators are processed in genetic programming operations. We are trying to detect and to recognize objects in SAR images. Due to the new approach described in this article, five and seven types of objects were recognized with good recognition results.
The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in image and signal processing and analysis, and in computer visi
Linear Discrimiant Analysis (LDA) has demonstrated their success in face recognition. But LDA is difficult to handle the high nonlinear problems, such as changes of large viewpoint and illumination in face recognition. In order to overcome these problems, we investigate Kernel Discriminant Analysis (KDA) for face recognition. This approach adopts the kernel functions to replace the dot products of nonlinear mapping in the high dimensional feature space, and then the nonlinear problem can be solved in the input space conveniently without explicit mapping. Two face databases are used to test KDA approach. The results show that our approach outperforms the conventional PCA(Eigenface) and LDA(Fisherface) approaches.
Rein, R.; Garduno, R.; Colombano, S.; Nir, S.; Haydock, K.; Macelroy, R. D.
Computer model-building procedures using stereochemical principles together with theoretical energy calculations appear to be, at this stage, the most promising route toward the elucidation of DNA-protein binding schemes and recognition principles. A review of models and bonding principles is conducted and approaches to modeling are considered, taking into account possible di-hydrogen-bonding schemes between a peptide and a base (or a base pair) of a double-stranded nucleic acid in the major groove, aspects of computer graphic modeling, and a search for isogeometric helices. The energetics of recognition complexes is discussed and several models for peptide DNA recognition are presented.
Riis, Søren Kamaric
We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...... (HNNs) with much fewer parameters than conventional HMMs and other hybrids can obtain comparable performance, and for the broad class task it is illustrated how the HNN can be applied as a purely transition based system, where acoustic context dependent transition probabilities are estimated by neural...... networks...
This book summarizes the recent advancement in the field of automatic speech recognition with a focus on discriminative and hierarchical models. This will be the first automatic speech recognition book to include a comprehensive coverage of recent developments such as conditional random field and deep learning techniques. It presents insights and theoretical foundation of a series of recent models such as conditional random field, semi-Markov and hidden conditional random field, deep neural network, deep belief network, and deep stacking models for sequential learning. It also discusses practical considerations of using these models in both acoustic and language modeling for continuous speech recognition.
Arunalatha J S
Full Text Available The recognition of a person based on biological features are efficient compared with traditional knowledge based recognition system. In this paper we propose Wrapping Curvelet Transform based Face Recognition (WCTFR. The Wrapping Curvelet Transform (WCT is applied on face images of database and test images to derive coefficients. The obtained coefficient matrix is rearranged to form WCT features of each image. The test image WCT features are compared with database images using Euclidean Distance (ED to compute Equal Error Rate (EER and True Success Rate (TSR. The proposed algorithm with WCT performs better than Curvelet Transform algorithms used in ,  and .
Møller, Maiken Bjerg; Gaarsdal, Jesper; Steen, Kim Arild;
an Android application developed for acoustic pattern recognition of bird species. The acoustic data is recorded using a built-in microphone, and pattern recognition is performed on the device, requiring no network connection. The algorithm is implemented in C++ as a native Android module and the Open......CV library is used for signal processing. We conclude that the approach presented here is a viable solution to pattern recognition problems. Since it requires no network connection, it shows promise in fields such as wildlife research....
1 - Description of program or function: PATTER is an interactive program with extensive facilities for modeling analytical processes and solving complex data analysis problems using statistical methods, spectral analysis, and pattern recognition techniques. PATTER addresses the type of problem generally stated as follows: given a set of objects and a list of measurements made on these objects, is it possible to find or predict a property of the objects which is not directly measurable but is known to define some unknown relationship? When employed intelligently, PATTER will act upon a data set in such a way it becomes apparent if useful information, beyond that already discerned, is contained in the data. 2 - Method of solution: In order to solve the general problem, PATTER contains preprocessing techniques to produce new variables that are related to the values of the measurements which may reduce the number of variables and/or reveal useful information about the 'obscure' property; display techniques to represent the variable space in some way that can be easily projected onto a two- or three-dimensional plot for human observation to see if any significant clustering of points occurs; and learning techniques based on both unsupervised and supervised methods, to extract as much information from the data as possible so that the optimum solution can be found
Full Text Available The common denominator in the careers of two contemporaries and great men, citizens of Austria-Hungary - Leoš Janáček and Sigmund Freud - was that, in spite of their status as outsiders, they managed to achieve well-deserved recognition. Both non-Germans, they had to surmount a number of obstacles in order to attain their professional goals. The Slavophile Janáček dreamed for a long time of success in Prague, which came at last in 1916, two years before a triumph in Vienna. Freud had serious difficulties in his academic career because of the strengthening of racial prejudices and national hatred which were especially marked at the end of the 19th century. After the dissolution of the Empire things changed for the better for the composer, whose works got an excellent reception in Austria and Germany, whereas the psychiatrist had to leave Vienna after the Anschluss. [Projekat Ministarstva nauke Republike Srbije, br. ON 177004: Serbian Musical Identities within Local and Global Frameworks: Traditions, Changes, Challenges
Getzoff, Elizabeth D.; Roberts, Victoria A.
The objective is to elucidate the nature of electrostatic forces controlling protein recognition processes by using a tightly coupled computational and interactive computer graphics approach. The TURNIP program was developed to determine the most favorable precollision orientations for two molecules by systematic search of all orientations and evaluation of the resulting electrostatic interactions. TURNIP was applied to the transient interaction between two electron transfer metalloproteins, plastocyanin and cytochrome c. The results suggest that the productive electron-transfer complex involves interaction of the positive region of cytochrome c with the negative patch of plastocyanin, consistent with experimental data. Application of TURNIP to the formation of the stable complex between the HyHEL-5 antibody and its protein antigen lysozyme showed that long-distance electrostatic forces guide lysozyme toward the HyHEL-5 binding site, but do not fine tune its orientation. Determination of docked antigen/antibody complexes requires including steric as well as electrostatic interactions, as was done for the U10 mutant of the anti-phosphorylcholine antibody S107. The graphics program Flex, a convenient desktop workstation program for visualizing molecular dynamics and normal mode motions, was enhanced. Flex now has a user interface and was rewritten to use standard graphics libraries, so as to run on most desktop workstations.
The first merit of this algorithm is the high-speed image processing, instead of using images it does operate on vectors. The second merit is the precise recognition of known geometries shapes even for arbitrary or complexes ones.
At a ceremony at CERN, Mr W. Cleon Anderson, President of the Institute of Electrical and Electronics Engineers (IEEE) formally a Milestone plaque in recognition of the invention of electronic particle detectors at CERN
Azzouz, Elsayed Elsayed
Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a cr...
Biyik, Utku; Keskin, Duygu; Oguz, Kaya; Akdeniz, Fisun; Gonul, Ali Saffet
Although major depressive disorder (MDD) is primarily characterized by mood symptoms, depressed patients have impairments in facial emotion recognition in many of the basic emotions (anger, fear, happiness, surprise, disgust and sadness). On the other hand, the data in remitted MDD (rMDD) patients is inconsistent and it is not clear that if those impairments persist in remission. To extend the current findings, we applied facial emotion recognition test to a group of remitted depressed women and compared to those of controls. Analyses of variance results showed a significant emotion and group interaction, and in the post hoc analyses, rMDD patients had higher accuracy rate for recognition of sadness compared to those of controls. There were no differences in the reaction time among the patients and controls across the all the basic emotions. The higher recognition rates for sad faces in rMDD patients might contribute to the impairments in social communication and the prognosis of the disease.
Full Text Available In this paper, we present a named entity recognition model for Korean Language. Named entity recognition is an essential and important process of Question Answering and Information Extraction system. This paper proposes a HMM based named entity recognition using compound word construction principles. In Korean, above 60% of NE (Named-Entity is a compound word. This compound word may be consisted of proper noun, common noun, or bound noun, etc. There is an intercontextual relationship among nouns which consists NE. NE and surrounding words of NE have a contextual relationship. For considering these relationships, we classified nouns into 4 word classes (Independent Entity, Constituent Entity, Adjacent Entity, Not an Entity. With this classification, our system gets contextual and lexical information by stochastic based machine leaning method from a NE labeled training data. Experimental result shows that this approach is better approach than rulebased in the Korean named-entity recognition.
张永越; 彭振云; 等
In many automatic face recognition systems,posture constraining is a key factor preventing them from application.In this paper a series of strategies will be described to achieve a system which enables face recognition under varying pose.These approaches include the multi-view face modeling,the threschold image based face feature detection,the affine transformation based face posture normalization and the template matching based face identification.Combining all of these strategies,a face recognition system with the pose invariance is designed successfully,Using a 75MHZ Pentium PC and with a database of 75 individuals,15 images for each person,and 225 test images with various postures,a very good recognition rate of 96.89% is obtained.
Ji Eun eOh
Full Text Available Autophagy is an ancient biological process for maintaining cellular homeostasis by degradation of long-lived cytosolic proteins and organelles. Recent studies demonstrated that autophagy is availed by immune cells to regulate innate immunity. On the one hand, cells exert direct effector function by degrading intracellular pathogens; on the other hand, autophagy modulates pathogen recognition and downstream signaling for innate immune responses. Pathogen recognition via pattern recognition receptors induces autophagy. The function of phagocytic cells is enhanced by recruitment of autophagy-related proteins. Moreover, autophagy acts as a delivery system for viral replication complexes to migrate to the endosomal compartments where virus sensing occurs. In another case, key molecules of the autophagic pathway have been found to negatively regulate immune signaling, thus preventing aberrant activation of cytokine production and consequent immune responses. In this review, we focus on the recent advances in the role of autophagy in pathogen recognition and modulation of innate immune responses.
Elmahdy, Mohamed; Minker, Wolfgang
Novel Techniques for Dialectal Arabic Speech describes approaches to improve automatic speech recognition for dialectal Arabic. Since speech resources for dialectal Arabic speech recognition are very sparse, the authors describe how existing Modern Standard Arabic (MSA) speech data can be applied to dialectal Arabic speech recognition, while assuming that MSA is always a second language for all Arabic speakers. In this book, Egyptian Colloquial Arabic (ECA) has been chosen as a typical Arabic dialect. ECA is the first ranked Arabic dialect in terms of number of speakers, and a high quality ECA speech corpus with accurate phonetic transcription has been collected. MSA acoustic models were trained using news broadcast speech. In order to cross-lingually use MSA in dialectal Arabic speech recognition, the authors have normalized the phoneme sets for MSA and ECA. After this normalization, they have applied state-of-the-art acoustic model adaptation techniques like Maximum Likelihood Linear Regression (MLLR) and M...
The output of most medical imaging systems is a display for interpretation by human observers. This paper provides a general summary of recent work on shape recognition by humans. Two broad modes of visual image processing executed by different cortical loci can be distinguished: a) a mode for motor interaction which is sensitive to quantitative variation in image parameters and b) a mode for basic-level object recognition which is based on a small set of qualitative contrasts in viewpoint invariant properties of images edges. Many medical image classifications pose inherently difficult problems for the recognition system in that they are based on quantitative and surface patch variations--rather than qualitative--variations. But when recognition can be achieved quickly and accurately it is possible that a small viewpoint invariant contrast has been discovered and is being exploited by the interpreter.
Gacem, Brahim; Vergouw, Robert; Verbiest, Harm; Cicek, Emrullah; Van Oosterhout, Tim; Bakkes, Sander; Kröse, Ben
We will demonstrate a prototype exergame aimed at the serious domain of elderly fitness. The exergame incorporates straightforward means to gesture recognition, and utilises a Kinect camera to obtain 2.5D sensory data of the human user.
Rafiqul Zaman Khan
Full Text Available Human imitation for his surrounding environment makes him interfere in every details of this great environment, hear impaired people are gesturing with each other for delivering a specific message, this method of communication also attracts human imitation attention to cast it on human-computer interaction. The faculty of vision based gesture recognition to be a natural, powerful, and friendly tool for supporting efficient interaction between human and machine. In this paper a review of recent hand gesture recognition systems is presented with description of hand gestures modelling, analysis and recognition. A comparative study included in this paper with focusing on different segmentation, features extraction and recognition tools, research advantages and drawbacks are provided as well.
Nehring, Volker; Evison, Sophie E F; Santorelli, Lorenzo A;
behaviour is thought to be rare in one of the classic examples of cooperation--social insect colonies--because the colony-level costs of individual selfishness select against cues that would allow workers to recognize their closest relatives. In accord with this, previous studies of wasps and ants have...... found little or no kin information in recognition cues. Here, we test the hypothesis that social insects do not have kin-informative recognition cues by investigating the recognition cues and relatedness of workers from four colonies of the ant Acromyrmex octospinosus. Contrary to the theoretical...... prediction, we show that the cuticular hydrocarbons of ant workers in all four colonies are informative enough to allow full-sisters to be distinguished from half-sisters with a high accuracy. These results contradict the hypothesis of non-heritable recognition cues and suggest that there is more potential...
Full Text Available Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4 wavelet and Coiflet (1 wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database.
Monwar, Md Maruf; Rezaei, Siamak
Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.
Full Text Available Many varieties of systems require some reliable recognition system that gives the identity of a person. There are many applications that need the identity of a person to operate such as ATM, Laptops, Access to buildings, cellular Phones & some sensitive security locations. In the absence of robust personal recognition schemes, these systems are vulnerable to the wiles of an impostor. Biometric recognition, or simply biometrics, refers to the automatic recognition of individuals based on their physiological and/or behavioral characteristics. By using biometrics it is possible to confirm or establish an ndividual’s identity based on “who she is”, rather than by “what she possesses” (e.g., an ID card or “what she remembers” (e.g., a password. In this paper, we give a brief overview of the field of biometrics and summarize some of its advantages, disadvantages, strengths, limitations, and related privacy concerns.
Full Text Available The purpose of the proposed research work is to develop a computer system that can recognize a person by comparing the characteristics of face to those of known individuals. The main focus is on frontal two dimensional images that are taken in a controlled environment i.e. the illumination and the background will be constant. All the other methods of person’s identification and verification like iris scan or finger print scan require high quality and costly equipment’s but in face recognition we only require a normal camera giving us a 2-D frontal image of the person that will be used for the process of the person’s recognition. Principal Component Analysis technique has been used in the proposed system of face recognition. The purpose is to compare the results of the technique under the different conditions and to find the most efficient approach for developing a facial recognition system
JIA Chun-yu; SHAN Xiu-ying; LIU Hong-min; NIU Zhao-ping
For the problems occurring in a least square method model,a fuzzy model,and a neural network model for flatness pattern recognition,a fuzzy neural network model for flatness pattern recognition with only three-input and three-output signals was proposed with Legendre orthodoxy polynomial as basic pattern,based on fuzzy logic expert experiential knowledge and genetic-BP hybrid optimization algorithm.The model not only had definite physical meanings in its inner nodes,but also had strong self-adaptability,anti-interference ability,high recognition precision,and high velocity,thereby meeting the demand of high-precision flatness control for cold strip mill and providing a convenient,practical,and novel method for flatness pattern recognition.
Intensive research has been done on optical character recognition ocr and a large number of articles have been published on this topic during the last few decades. Many commercial OCR systems are now available in the market, but most of these systems work for Roman, Chinese, Japanese and Arabic characters. There are no sufficient number of works on Indian language character recognition especially Kannada script among 12 major scripts in India. This paper presents a review of existing work on printed Kannada script and their results. The characteristics of Kannada script and Kannada Character Recognition System kcr are discussed in detail. Finally fusion at the classifier level is proposed to increase the recognition accuracy.
Lee, Heesung; Hong, Sungjun; Kim, Euntai
Gait-based human identification is a paradigm to recognize individuals using visual cues that characterize their walking motion. An important requirement for successful gait recognition is robustness to variations including different lighting conditions, poses, and walking speed. Deformation of the gait silhouette caused by objects carried by subjects also has a significant effect on the performance of gait recognition systems; a backpack is the most common of these objects. This paper proposes methods for eliminating the effect of a carried backpack for efficient gait recognition. We apply simple, recursive principal component analysis (PCA) reconstructions and error compensation to remove the backpack from the gait representation and then conduct gait recognition. Experiments performed with the CASIA database illustrate the performance of the proposed algorithm.
Larsen, Anders Boesen Lindbo
This thesis addresses the problem of extracting image structures for representing images effectively in order to solve visual recognition tasks. Problems from diverse research areas (medical imaging, material science and food processing) have motivated large parts of the methodological development...
Muharrem Mercimek; Kayhan Gulez; Tarik Veli Mumcu
Moments and functions of moments have been extensively employed as invariant global features of images in pattern recognition. In this study, a flexible recognition system that can compute the good features for high classiﬁcation of 3-D real objects is investigated. For object recognition, regardless of orientation, size and position, feature vectors are computed with the help of nonlinear moment invariant functions. Representations of objects using two-dimensional images that are taken from different angles of view are the main features leading us to our objective. After efﬁcient feature extraction, the main focus of this study, the recognition performance of classiﬁers in conjunction with moment–based feature sets, is introduced.
Bordea, Prashant; Varpeb, Amarsinh; Manzac, Ramesh; Yannawara, Pravin
Automatic Speech Recognition (ASR) by machine is an attractive research topic in signal processing domain and has attracted many researchers to contribute in this area. In recent year, there have been many advances in automatic speech reading system with the inclusion of audio and visual speech features to recognize words under noisy conditions. The objective of audio-visual speech recognition system is to improve recognition accuracy. In this paper we computed visual features using Zernike m...
Feature Selection is a optimization technique used in face recognition technology. Feature selection removes the irrelevant, noisy and redundant data thus leading to the more accurate recognition of face from the database.Cuckko Algorithm is one of the recent optimization algorithm in the league of nature based algorithm. Its optimization results are better than the PSO and ACO optimization algorithms. The proposal of applying the Cuckoo algorithm for feature selection in the process of face ...
Aruni Singh; Sanjay Kumar Singh
Modern face recognition systems are vulnerable to spoofing attack. Spoofing attack occurs when a persontries to cheat the system by presenting fake biometric data gaining unlawful access. A lot of researchershave originated novel techniques to fascinate these types of face tampering attack. It seems that nocomparative studies of different face recognition algorithms on same protocols and fake data have beenincorporated. The motivation behind this paper is to present the effect of face tamperi...
A novel Discrete Wavelet Transform (DWT) based Hidden Markov Module (HMM) for face recognition is presented in this letter. To improve the accuracy of HMM based face recognition algorithm, DWT is used to replace Discrete Cosine Transform (DCT) for observation sequence extraction. Extensive experiments are conducted on two public databases and the results show that the proposed method can improve the accuracy significantly, especially when the face database is large and only few training images are available.
A methodology is proposed to address the recognition of competences acquired online. It is derived from the project VIRQUAL (http://virqual.up.pt) outputs. It addresses current issues of the working world like Virtual Mobility, Learning Outcomes, e-Assessment, Qualification Frameworks and Recognition of Prior Learning. The approach is based on the application of proper methods of assessment for each type of learning outcomes. A matrix is proposed to align the different types of competences (b...
Dr. Raman Chadha; Iram Shah; Priya Thakur
This paper assesses the effects of prolonged exposure of fingers to water on the performance of existing fingerprint recognition systems.Many fingers wrinkle or dwindle when immersed in water .When used for biometric identification; recognition rate for wrinkled fingers degrades. Apart from this the existing techniques used are not rotation invariant and fail when enrolled image of a person is matched with a rotated test image .The influence of wrinkling and rotated finger prints has so far n...
Labiba Souici Meslati; Mokhtar Sellami
Since humans are the best readers, one of the most promising trends in automatic handwriting recognition is to get inspiration from psychological reading models. The underlying idea is to derive benefits from studies of human reading, in order to build efficient automatic reading systems. In this context, we propose a human reading inspired system for the recognition of Arabic handwritten literalamounts. Our approach is based on the McClelland and Rumelhart's neural model called IAM, which is...
Computer vision research enables machines to understand the world. Humans usually interpret and analyze the world through what they see - the objects they capture with their eyes. Similarly, machines can better understand the world by recognizing objects in images. Object recognition is therefore a major branch of computer vision. To achieve the highest accuracy, state-of-the-art object recognition systems must extract features from hundreds to millions of images, train models with enormous d...
James, Alex Pappachen
This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision. We note that edge detection is one of the most fundamental process within the low level vision and provides the basis for the higher level visual intelligence in primates. The recognition of the patterns within the images relate closely to the spatiotemporal processes of edge formations, and its implementation needs a crossdisciplanry ap...
An image matching system for object recognition in scenes of varying complexity was constructed in Matlab for evaluating the recognition quality of two types of image features: SURF (Speeded-Up Robust Features) and SIFT (Scale Invariant Feature Transform) using the affine Hough matching algorithm for finding matches between training and test images. In the experimental part of the thesis, the matching was algorithm tested for varying number of image features extracted from the train and test ...
Shiqing Zhang; Xiaoming Zhao; Bicheng Lei
Recently, compressive sensing (CS) has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC). The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, ...
K.Meena; A. Suruliandi; R. Reena Rose
Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE), Gamma Intensity Correction (GIC), Normalization chain and Modified Homomorphic Filteri...
Suvarnsing Bhable; Sangramsing Kayte,
This paper presents an efficient ear recognition technique which derives benefits from the local features of the ear and attempt to handle the problems due to pose, poor contrast, change in illumination and lack of registration. Recognizing humans by their ear have recently received significant attention in the field of research. Ear is the rich in characteristics. This paper provides a detailed survey of research done in ear detection and recognition. This survey paper is very us...
The problem of pattern recognition has been solved with numerous methods in the Artificial Intelligence field. We present an unconventional method based on Lingustic Fuzzy Logic Forecaster which is primarily used for the task of time series analysis and prediction through logical deduction wtih linguistic variables. This method should be used not only to the time series prediction itself, but also for recognition of patterns in a signal with seasonal component.
The Study covers the research of conditions for recognition of inventories in the governmental sector accounting. The comparative analysis has been performed as to the inventories recognition criteria provided by the National Accounting Provisions (Standards) 123 in the public sector - 'Inventories', Accounting Provision (Standard) 9 - 'Inventories', International Accounting Standard 16 - 'Inventories' in the public sector. It has been found that the common characteristic, in accordance with ...
Bedregal, Benjamín C.; Costa, Antônio Carlos da Rocha; Dimuro, Graçaliz P.
This paper introduces a fuzzy rule-based method for the recognition of hand gestures acquired from a data glove, with an application to the recognition of some sample hand gestures of LIBRAS, the Brazilian Sign Language. The method uses the set of angles of finger joints for the classification of hand configurations, and classifications of segments of hand gestures for recognizing gestures. The segmentation of gestures is based on the concept of monotonic gesture segment, sequences of hand co...
M. Senthil Kumar,; Gayathri, R.
This paper presents a novel approach to improve the recognition percentage of palm-vascular-based authentication systems presented in the literature. The proposed method efficiently accommodates the rotational, translational changes and potential deformations by encoding the orientation preserving features. The proposed palm-vein approach is compared with other existing methods and obtained an improved performance in both verification and recognition scenarios. The experimenta...
Atul Chinchamalatpure; Prof. Anurag Jain
Facial expression recognition plays an important and effective role in the interaction in Human. There are seven basic facial expressions namely fear, surprise, happy, sad, disgust, Neutral and anger. Facial Expression Recognition is process performed by computers which consist of detect the face in the image and pre-process the face region, extracting facial expression features from image by analyzing the motion of facial features or change in the appearance of facial features and classifyin...
Schwartz, Barbara L.; Marvel, Cherie L.; Drapalski, Amy; Rosse, Richard B.; Deutsch, Stephen I.
Introduction. There is currently substantial literature to suggest that patients with schizophrenia are impaired on many face-processing tasks. This study investigated the specific effects of configural changes on face recognition in groups of schizophrenia patients. Methods. In Experiment 1, participants identified facial expressions in upright faces and in faces inverted from their upright orientation. Experiments 2 and 3 examined recognition memory for faces and other non-face objects pres...
Haval A. Ahmed; Tarik A. Rashid; Ahmed T. Sadiq
Communication between computers and humans has grown to be a major field of research. Facial Behavior Recognition through computer algorithms is a motivating and difficult field of research for establishing emotional interactions between humans and computers. Although researchers have suggested numerous methods of emotion recognition within the literature of this field, as yet, these research works have mainly focused on one method for their system output i.e. used one facial database for ass...
Vasanth P.C.; Nataraj. K. R
Facial feature tracking and facial actions recognition from image sequence attracted great attention in computer vision field. Computational facial expression analysis is a challenging research topic in computer vision. It is required by many applications such as human-computer interaction, computer graphic animation and automatic facial expression recognition. In recent years, plenty of computer vision techniques have been developed to track or recognize the facial activities in three levels...
Mur Escartín, Olga
The thesis consist in the study and evaluation of different methods for face recognition. The final objective is to select the most suitable techniques for face detection and recognition. Some of these techniques will be intergrated in a real time demontrator which will be a preliminary prototype that will have to work in controlled conditions (for ilumination and pose) and with reduced databases. The demonstrator will be done in Matlab and the main image acquisition rotines and face detectio...
Connaghan, Damien; Ó Conaire, Ciarán; Kelly, Philip; O''Connor, Noel E.
In this paper we describe an approach for automatic recognition of tennis strokes using a single low cost camera. Professional tennis is played at high speed so the ability to classify tennis strokes on camera is hindered by the rapid movement of the players. We have developed an accurate recognition system which can automatically index tennis strokes from video footage. We aim to evolve this system so that meta data, such as time codes and descriptions of the strokes played, can be automatic...
Bodade, Rajesh M
The book presents three most significant areas in Biometrics and Pattern Recognition. A step-by-step approach for design and implementation of Dual Tree Complex Wavelet Transform (DTCWT) plus Rotated Complex Wavelet Filters (RCWF) is discussed in detail. In addition to the above, the book provides detailed analysis of iris images and two methods of iris segmentation. It also discusses simplified study of some subspace-based methods and distance measures for iris recognition backed by empirical studies and statistical success verifications.
Konstantinos N. Plataniotis; Dimitrios Hatzinakos; Foteini Agrafioti; Yongjin Wang
Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact loca...
Ravi. J; K B Raja; Venugopal K R2
The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person’s life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae ...
Le Hoang Thai; Ha Nhat Tam
Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transf...
Stiller, Peter F.; Huber, Birkett
We discuss a new geometric hashing method for searching large databases of 2D images (or 3D objects) to match a query built from geometric information presented by a single 3D object (or single 2D image). The goal is to rapidly determine a small subset of the images that potentially contain a view of the given object (or a small set of objects that potentially match the item in the image). Since this must be accomplished independent of the pose of the object, the objects and images, which are characterized by configurations of geometric features such as points, lines and/or conics, must be treated using a viewpoint invariant formulation. We are therefore forced to characterize these configurations in terms of their 3D and 2D geometric invariants. The crucial relationship between the 3D geometry and its 'residual' in 2D is expressible as a correspondence (in the sense of algebraic geometry). Computing a set of generating equations for the ideal of this correspondence gives a complete characterization of the view of independent relationships between an object and all of its possible images. Once a set of generators is in hand, it can be used to devise efficient recognition algorithms and to give an efficient geometric hashing scheme. This requires exploiting the form and symmetry of the equations. The result is a multidimensional access scheme whose efficiency we examine. Several potential directions for improving this scheme are also discussed. Finally, in a brief appendix, we discuss an alternative approach to invariants for generalized perspective that replaces the standard invariants by a subvariety of a Grassmannian. The advantage of this is that one can circumvent many annoying general position assumptions and arrive at invariant equations (in the Plucker coordinates) that are more numerically robust in applications.
Maly, R C
Chemical dependence is a leading cause of morbidity and death in the United States. At least 20% of patients seen by primary care physicians in both the outpatient and inpatient setting are chemically dependent. Up to 90% of these patients go undiagnosed by their primary physicians. Chemical dependence is defined as a chronic, progressive illness characterized by the repeated and persistent use of alcohol or drugs despite negative health, family, work, financial, or legal consequences. Primary care physicians are in an ideal position to detect chemical dependence at its earliest stages, when irreversible medical consequences and death are most likely preventable. Alcohol is the most common drug of abuse. Improving the rate of recognition of chemical dependence depends on being familiar with the constellation of physical, mental, and social indicators. Early medical manifestations of alcoholism common in the primary care setting include: gastric complaints, elevated blood pressure, palpitations, traumatic injuries, headaches, impotence, and gout. Early psychosocial manifestations common in both alcohol and drug dependence include anxiety, depression, insomnia, persistent relationship conflicts, work or school problems, and financial or legal problems. Particularly useful laboratory indicators of alcoholism include elevated levels of GGT and MCV, both displaying high specificity, with the GGT level being the most sensitive. Similarly specific laboratory tests for drug dependence are not available. Any patient presenting with any of the above medical, psychosocial, or laboratory manifestations should be screened for chemical dependence. The CAGE questionnaire for alcoholism, a four-question test, is particularly well suited to the primary care setting, where it can be administered in fewer than 60 seconds. The CAGE has demonstrated high sensitivity (in the 80% range) and specificity (approximately 85%) for alcoholism. Comparably convenient instruments do not yet exist
Campbell, Anna; Ruffman, Ted; Murray, Janice E; Glue, Paul
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.
Campbell, Anna; Ruffman, Ted; Murray, Janice E; Glue, Paul
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. PMID:24856057
Full Text Available Recently face recognition is attracting much attention in the society of network multimedia information access. Areas such as network security, content indexing and retrieval, and video compression benefits from face recognition technology because "people" are the center of attention in a lot of video. Network access control via face recognition not only makes hackers virtually impossible to steal one's "password", but also increases the user-friendliness in human-computer interaction. Indexing and/or retrieving video data based on the appearances of particular persons will be useful for users such as news reporters, political scientists, and moviegoers. For the applications of videophone and teleconferencing, the assistance of face recognition also provides a more efficient coding scheme. In this paper, we give an introductory course of this new information processing technology. The paper shows the readers the generic framework for the face recognition system, and the variants that are frequently encountered by the face recognizer. Several famous face recognition algorithms, such as eigenfaces and neural networks, will also be explained.
Plebe, Alessio; Domenella, Rosaria Grazia
Object recognition is one of the most important functions of the human visual system, yet one of the least understood, this despite the fact that vision is certainly the most studied function of the brain. We understand relatively well how several processes in the cortical visual areas that support recognition capabilities take place, such as orientation discrimination and color constancy. This paper proposes a model of the development of object recognition capability, based on two main theoretical principles. The first is that recognition does not imply any sort of geometrical reconstruction, it is instead fully driven by the two dimensional view captured by the retina. The second assumption is that all the processing functions involved in recognition are not genetically determined or hardwired in neural circuits, but are the result of interactions between epigenetic influences and basic neural plasticity mechanisms. The model is organized in modules roughly related to the main visual biological areas, and is implemented mainly using the LISSOM architecture, a recent neural self-organizing map model that simulates the effects of intercortical lateral connections. This paper shows how recognition capabilities, similar to those found in brain ventral visual areas, can develop spontaneously by exposure to natural images in an artificial cortical model.
Hlaing Htake Khaung Tin
Full Text Available Face recognition is the most successful form of human surveillance. Face recognition technology, is being used to improve human efficiency when recognition faces, is one of the fastest growing fields in the biometric industry. In the first stage, the age is classified into eleven categories which distinguish the person oldness in terms of age. In the second stage of the process is face recognition based on the predicted age. Age prediction has considerable potential applications in human computer interaction and multimedia communication. In this paper proposes an Eigen based age estimation algorithm for estimate an image from the database. Eigenface has proven to be a useful and robust cue for age prediction, age simulation, face recognition, localization and tracking. The scheme is based on an information theory approach that decomposes face images into a small set of characteristic feature images called eigenfaces, which may be thought of as the principal components of the initial training set of face images. The eigenface approach used in this scheme has advantages over other face recognition methods in its speed, simplicity, learning capability and robustness to small changes in the face image.
M. S. Abual-Rub
Full Text Available Problem statement: Predicting the tertiary structure of proteins from their linear sequence is really a big challenge in biology. This challenge is related to the fact that the traditional computational methods are not powerful enough to search for the correct structure in the huge conformational space. This inadequate capability of the computational methods, however, is a major obstacle in facing this problem. Trying to solve the problem of the protein fold recognition, most of the researchers have examined the use of the protein threading technique. This problem is known as NP-hard; researchers have used various methods such as neural networks, Monte Carlo, support vector machine and genetic algorithms to solve it. Some researchers tried the use of the parallel evolutionary methods for protein fold recognition but it is less well known. Approach: We reviewed various algorithms that have been developed for protein structure prediction by threading and fold recognition. Moreover, we provided a survey of parallel evolutionary methods for protein fold recognition. Results: The findings of this survey showed that evolutionary methods can be used to resolve the protein fold recognition problem. Conclusion: There are two aspects of protein fold recognition problem: First is the computational difficulty and second is that current energy functions are still not accurate enough to calculate the free energy of a given conformation.
I. A. Jannoud
Full Text Available Despite of the decent development of the pattern recognition science applications in the last decade of the twentieth century and this century, text recognition remains one of the most important problems in pattern recognition. To the best of our knowledge, little work has been done in the area of Arabic text recognition compared with those for Latin, Chins and Japanese text. The main difficulty encountered when dealing with Arabic text is the cursive nature of Arabic writing in both printed and handwritten forms. An Automatic Arabic Hand-Written Text Recognition (AHTR System is proposed. An efficient segmentation stage is required in order to divide a cursive word or sub-word into its constituting characters. After a word has been extracted from the scanned image, it is thinned and its base line is calculated by analysis of horizontal density histogram. The pattern is then followed through the base line and the segmentation points are detected. Thus after the segmentation stage, the cursive word is represented by a sequence of isolated characters. The recognition problem thus reduces to that of classifying each character. A set of features extracted from each individual characters. A minimum distance classifier is used. Some approaches are used for processing the characters and post processing added to enhance the results. Recognized characters will be appended directly to a word file which is editable form.
DAI Ruwei; LIU Chenglin; XIAO Baihua
Chinese character recognition (CCR) is an important branch of pattern recognition.It was considered as an extremely difficult problem due to the very large number of categories,complicated structures,similarity between characters,and the variability of fonts or writing styles.Because of its unique technical challenges and great social needs,the last four decades witnessed the intensive research in this field and a rapid increase of successful applications.However,higher recognition performance is continuously needed to improve the existing applications and to exploit new applications.This paper first provides an overview of Chinese character recognition and the properties of Chinese characters.Some important methods and successful results in the history of Chinese character recognition are then summarized.As for classification methods,this article pays special attention to the syntactic-semantic approach for online Chinese character recognition,as well as the metasynthesis approach for discipline crossing.Finally,the remaining problems and the possible solutions are discussed.
Iliana M. Vargas
Full Text Available In some circumstances, accurate recognition of repeated images in an explicit memory test is driven by implicit memory. We propose that this “implicit recognition” results from perceptual fluency that influences responding without awareness of memory retrieval. Here we examined whether recognition would vary if images appeared in the same or different visual hemifield during learning and testing. Kaleidoscope images were briefly presented left or right of fixation during divided-attention encoding. Presentation in the same visual hemifield at test produced higher recognition accuracy than presentation in the opposite visual hemifield, but only for guess responses. These correct guesses likely reflect a contribution from implicit recognition, given that when the stimulated visual hemifield was the same at study and test, recognition accuracy was higher for guess responses than for responses with any level of confidence. The dramatic difference in guessing accuracy as a function of lateralized perceptual overlap between study and test suggests that implicit recognition arises from memory storage in visual cortical networks that mediate repetition-induced fluency increments.
Corbalan-Fuertes, Montserrat; Millan Garcia-Verela, Maria S.; Yzuel, Maria J.
A color pattern recognition system must identify a target by its shape and color distribution. In real situations, however, the color information is affected by changes of the light source (e.g., from indoor illumination to outdoor daylight), often making recognition impossible. In this work, we propose a color pattern recognition technique with tolerance for illumination changes within the common sources of white light. This can be accomplished using the coordinates of the CIELAB system, luminance (L*), chroma (C*), and hue (h*) instead of the conventional RGB system. The proposal has some additional advantages: there is no need to store a matched filters base to analyze scenes captured under different light sources (one set of filters for each illuminant light source) and therefore the recognition process can be simplified; and in most cases, the contribution of only two channels (C* and h*) is enough to avoid false alarms in color pattern recognition. From the results, we show that the recognition system is improved when CIELAB coordinates are used.
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%.
Face recognition from video requires dealing with uncertainty both in tracking and recognition. This paper proposed an effective method for face recognition from video. In order to realize simultaneous tracking and recognition, fisherface-based recognition is combined with tracking into one model. This model is then embedded into particle filter to perform face recognition from video. In order to improve the robustness of tracking, an expectation maximization (EM) algorithm was adopted to update the appearance model. The experimental results show that the proposed method can perform well in tracking and recognition even in poor conditions such as occlusion and remarkable change in lighting.
Timson David J
Full Text Available Abstract Background Galactokinase catalyses the first committed step of galactose catabolism in which the sugar is phosphorylated at the expense of MgATP. Recent structural studies suggest that the enzyme makes several contacts with galactose – five side chain and two main chain hydrogen bonds. Furthermore, it has been suggested that inhibition of galactokinase may help sufferers of the genetic disease classical galactosemia which is caused by defects in another enzyme of the pathway galactose-1-phosphate uridyl transferase. Galactokinases from different sources have a range of substrate specificities and a diversity of kinetic mechanisms. Therefore only studies on the human enzyme are likely to be of value in the design of therapeutically useful inhibitors. Results Using recombinant human galactokinase expressed in and purified from E. coli we have investigated the sugar specificity of the enzyme and the kinetic consequences of mutating residues in the sugar-binding site in order to improve our understanding of substrate recognition by this enzyme. D-galactose and 2-deoxy-D-galactose are substrates for the enzyme, but N-acetyl-D-galactosamine, L-arabinose, D-fucose and D-glucose are all not phosphorylated. Mutation of glutamate-43 (which forms a hydrogen bond to the hydroxyl group attached to carbon 6 of galactose to alanine results in only minor changes in the kinetic parameters of the enzyme. Mutation of this residue to glycine causes a ten-fold drop in the turnover number. In contrast, mutation of histidine 44 to either alanine or isoleucine results in insoluble protein following expression in E. coli. Alteration of the residue that makes hydrogen bonds to the hydroxyl attached to carbons 3 and 4 (aspartate 46 results in an enzyme that although soluble is essentially inactive. Conclusions The enzyme is tolerant to small changes at position 2 of the sugar ring, but not at positions 4 and 6. The results from site directed mutagenesis could
Kavelar, Albert; Zambanini, Sebastian; Kampel, Martin
Standard OCR is a well-researched topic of computer vision and can be considered solved for machine-printed text. However, when applied to unconstrained images, the recognition rates drop drastically. Therefore, the employment of object recognition-based techniques has become state of the art in scene text recognition applications. This paper presents a scene text recognition method tailored to ancient coin legends and compares the results achieved in character and word recognition experiment...
Fatima Maria Felisberti
Full Text Available BACKGROUND: The ability to recognize the faces of potential cooperators and cheaters is fundamental to social exchanges, given that cooperation for mutual benefit is expected. Studies addressing biases in face recognition have so far proved inconclusive, with reports of biases towards faces of cheaters, biases towards faces of cooperators, or no biases at all. This study attempts to uncover possible causes underlying such discrepancies. METHODOLOGY AND FINDINGS: Four experiments were designed to investigate biases in face recognition during social exchanges when behavioral descriptors (prosocial, antisocial or neutral embedded in different scenarios were tagged to faces during memorization. Face recognition, measured as accuracy and response latency, was tested with modified yes-no, forced-choice and recall tasks (N = 174. An enhanced recognition of faces tagged with prosocial descriptors was observed when the encoding scenario involved financial transactions and the rules of the social contract were not explicit (experiments 1 and 2. Such bias was eliminated or attenuated by making participants explicitly aware of "cooperative", "cheating" and "neutral/indifferent" behaviors via a pre-test questionnaire and then adding such tags to behavioral descriptors (experiment 3. Further, in a social judgment scenario with descriptors of salient moral behaviors, recognition of antisocial and prosocial faces was similar, but significantly better than neutral faces (experiment 4. CONCLUSION: The results highlight the relevance of descriptors and scenarios of social exchange in face recognition, when the frequency of prosocial and antisocial individuals in a group is similar. Recognition biases towards prosocial faces emerged when descriptors did not state the rules of a social contract or the moral status of a behavior, and they point to the existence of broad and flexible cognitive abilities finely tuned to minor changes in social context.
ROH; Yong-Wan; KIM; Dong-Ju; LEE; Woo-Seok; HONG; Kwang-Seok
This paper focuses on acoustic features that effectively improve the recognition of emotion in human speech.The novel features in this paper are based on spectral-based entropy parameters such as fast Fourier transform(FFT) spectral entropy,delta FFT spectral entropy,Mel-frequency filter bank(MFB) spectral entropy,and Delta MFB spectral entropy.Spectral-based entropy features are simple.They reflect frequency characteristic and changing characteristic in frequency of speech.We implement an emotion rejection module using the probability distribution of recognized-scores and rejected-scores.This reduces the false recognition rate to improve overall performance.Recognized-scores and rejected-scores refer to probabilities of recognized and rejected emotion recognition results,respectively.These scores are first obtained from a pattern recognition procedure.The pattern recognition phase uses the Gaussian mixture model(GMM).We classify the four emotional states as anger,sadness,happiness and neutrality.The proposed method is evaluated using 45 sentences in each emotion for 30 subjects,15 males and 15 females.Experimental results show that the proposed method is superior to the existing emotion recognition methods based on GMM using energy,Zero Crossing Rate(ZCR),linear prediction coefficient(LPC),and pitch parameters.We demonstrate the effectiveness of the proposed approach.One of the proposed features,combined MFB and delta MFB spectral entropy improves performance approximately 10% compared to the existing feature parameters for speech emotion recognition methods.We demonstrate a 4% performance improvement in the applied emotion rejection with low confidence score.
Dewar, Michaela; Hoefeijzers, Serge; Zeman, Adam; Butler, Christopher; Della Sala, Sergio
Transient epileptic amnesia (TEA) is an epileptic syndrome characterized by recurrent, brief episodes of amnesia. Transient epileptic amnesia is often associated with the rapid decline in recall of new information over hours to days (accelerated long-term forgetting - 'ALF'). It remains unknown how recognition memory is affected in TEA over time. Here, we report a systematic study of picture recognition in patients with TEA over the course of one week. Sixteen patients with TEA and 16 matched controls were presented with 300 photos of everyday life scenes. Yes/no picture recognition was tested 5min, 2.5h, 7.5h, 24h, and 1week after picture presentation using a subset of target pictures as well as similar and different foils. Picture recognition was impaired in the patient group at all test times, including the 5-minute test, but it declined normally over the course of 1week. This impairment was associated predominantly with an increased false alarm rate, especially for similar foils. High performance on a control test indicates that this impairment was not associated with perceptual or discrimination deficits. Our findings suggest that, at least in some TEA patients with ALF in verbal recall, picture recognition does not decline more rapidly than in controls over 1week. However, our findings of an early picture recognition deficit suggest that new visual memories are impoverished after minutes in TEA. This could be the result of deficient encoding or impaired early consolidation. The early picture recognition deficit observed could reflect either the early stages of the process that leads to ALF or a separable deficit of anterograde memory in TEA. Lastly, our study suggests that at least some patients with TEA are prone to falsely recognizing new everyday visual information that they have not in fact seen previously. This deficit, alongside their ALF in free recall, likely affects everyday memory performance. PMID:25506793
Sassenrath, Claudia; Sassenberg, Kai; Ray, Devin G; Scheiter, Katharina; Jarodzka, Halszka
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.
Brooks, Brian E.; Cooper, Eric E.
Three divided visual field experiments tested current hypotheses about the types of visual shape representation tasks that recruit the cognitive and neural mechanisms underlying face recognition. Experiment 1 found a right hemisphere advantage for subordinate but not basic-level face recognition. Experiment 2 found a right hemisphere advantage for…
Full Text Available 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.
Full Text Available This paper is a sincere attempt to recognize english alphabets as part of hand gesture recognition, using the SIFT algorithm. The novelty of this approach is, it is a space, size, illumination and rotation invariant approach. The approach has evolved to work well with both the standard American Sign Language (ASL database and home-made database. The problem of alphabet recognition may seem to sound small but the intricacies involved in it cannot be solved using a single algorithm. Hand gesture recognition is a complicated task. A one stop solution is still not evolved for any recognition process. This paper has tried to approach this in a simple but efficient manner using the basic SIFT algorithm for recognition. The efficacy of the approach is proved well through the results obtained, invariably on both the datasets.
Full Text Available This paper is a sincere attempt to recognize english alphabets as part of hand gesture recognition, usingthe SIFT algorithm. The novelty of this approach is, it is a space, size, illumination and rotation invariantapproach. The approach has evolved to work well with both the standard American Sign Language (ASLdatabase and home-made database. The problem of alphabet recognition may seem to sound small but theintricacies involved in it cannot be solved using a single algorithm. Hand gesture recognition is acomplicated task. A one stop solution is still not evolved for any recognition process. This paper has triedto approach this in a simple but efficient manner using the basic SIFT algorithm for recognition. Theefficacy of the approach is proved well through the results obtained, invariably on both the datasets.
Mill, Ravi D; O'Connor, Akira R; Dobbins, Ian G
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. PMID:27253862
du Castel, Bertrand
In his 2012 book How to Create a Mind, Ray Kurzweil defines a "Pattern Recognition Theory of Mind" that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call "Pattern Activation/Recognition Theory of Mind." While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.
Bertrand edu Castel
Full Text Available In his 2012 book How to Create a Mind, Ray Kurzweil defines a Pattern Recognition Theory of Mind that states that the brain uses millions of pattern recognizers, plus modules to check, organize, and augment them. In this article, I further the theory to go beyond pattern recognition and include also pattern activation, thus encompassing both sensory and motor functions. In addition, I treat checking, organizing, and augmentation as patterns of patterns instead of separate modules, therefore handling them the same as patterns in general. Henceforth I put forward a unified theory I call Pattern Activation/Recognition Theory of Mind. While the original theory was based on hierarchical hidden Markov models, this evolution is based on their precursor: stochastic grammars. I demonstrate that a class of self-describing stochastic grammars allows for unifying pattern activation, recognition, organization, consistency checking, metaphor, and learning, into a single theory that expresses patterns throughout. I have implemented the model as a probabilistic programming language specialized in activation/recognition grammatical and neural operations. I use this prototype to compute and present diagrams for each stochastic grammar and corresponding neural circuit. I then discuss the theory as it relates to artificial network developments, common coding, neural reuse, and unity of mind, concluding by proposing potential paths to validation.
Mateo, Jill M
Despite extensive research on the functions of kin recognition, little is known about ontogenetic changes in the cues mediating such recognition. In Belding's ground squirrels, Spermophilus beldingi, secretions from oral glands are both individually distinct and kin distinct, and function in social recognition across many contexts. Behavioral studies of recognition and kin preferences suggest that these cues may change across development, particularly around the time of weaning and emergence from natal burrows (around 25 days of age). I used an habituation-discrimination task with captive S. beldingi, presenting subjects with odors collected from a pair of pups at several ages across early development. I found that at 21 days of age, but not at 7 or 14, young produce detectable odors. Odors are not individually distinct, however, until 28 days of age, after young have emerged from their burrows and begun foraging. In addition, an individual's odor continues to develop after emergence: odors produced by an individual at 20 and 40 days of age are perceived as dissimilar, yet odors produced at 28 and 40 days are treated as similar. Developmental changes in odors provide a proximate explanation for why S. beldingi littermate preferences are not consolidated until after natal emergence, and demonstrate that conspecifics must update their recognition templates as young develop. PMID:17016836
Mumenthaler, Christian; Sander, David
The notion of social appraisal emphasizes the importance of a social dimension in appraisal theories of emotion by proposing that the way an individual appraises an event is influenced by the way other individuals appraise and feel about the same event. This study directly tested this proposal by asking participants to recognize dynamic facial expressions of emotion (fear, happiness, or anger in Experiment 1; fear, happiness, anger, or neutral in Experiment 2) in a target face presented at the center of a screen while a contextual face, which appeared simultaneously in the periphery of the screen, expressed an emotion (fear, happiness, anger) or not (neutral) and either looked at the target face or not. We manipulated gaze direction to be able to distinguish between a mere contextual effect (gaze away from both the target face and the participant) and a specific social appraisal effect (gaze toward the target face). Results of both experiments provided evidence for a social appraisal effect in emotion recognition, which differed from the mere effect of contextual information: Whereas facial expressions were identical in both conditions, the direction of the gaze of the contextual face influenced emotion recognition. Social appraisal facilitated the recognition of anger, happiness, and fear when the contextual face expressed the same emotion. This facilitation was stronger than the mere contextual effect. Social appraisal also allowed better recognition of fear when the contextual face expressed anger and better recognition of anger when the contextual face expressed fear. PMID:22288528
Full Text Available Automatic face recognition remains an interesting but challenging computer vision open problem. Poor illumination is considered as one of the major issue, since illumination changes cause large variation in the facial features. To resolve this, illumination normalization preprocessing techniques are employed in this paper to enhance the face recognition rate. The methods such as Histogram Equalization (HE, Gamma Intensity Correction (GIC, Normalization chain and Modified Homomorphic Filtering (MHF are used for preprocessing. Owing to great success, the texture features are commonly used for face recognition. But these features are severely affected by lighting changes. Hence texture based models Local Binary Pattern (LBP, Local Derivative Pattern (LDP, Local Texture Pattern (LTP and Local Tetra Patterns (LTrPs are experimented under different lighting conditions. In this paper, illumination invariant face recognition technique is developed based on the fusion of illumination preprocessing with local texture descriptors. The performance has been evaluated using YALE B and CMU-PIE databases containing more than 1500 images. The results demonstrate that MHF based normalization gives significant improvement in recognition rate for the face images with large illumination conditions.
Zue, V. W.
Our long-term research goal is the development and implementation of speaker-independent continuous speech recognition systems. It is our conviction that proper utilization of speech-specific knowledge is essential for advanced speech recognition systems. With this in mind, we have continued to make progress on the acquisition of acoustic-phonetic and lexical knowledge. We have completed the development of a continuous digit recognition system. The system was constructed to investigate the utilization of acoustic phonetic knowledge in a speech recognition system. Some of the significant development of this study includes a soft-failure procedure for lexical access, and the discovery of a set of acoustic-phonetic features for verification. We have completed a study of the constraints provided by lexical stress on word recognition. We found that lexical stress information alone can, on the average, reduce the number of word candidates from a large dictionary by more than 80%. In conjunction with this study, we successfully developed a system that automatically determines the stress pattern of a word from the acoustic signal.
Sacramone, Anthony; Scola, Joseph; Shazeer, Dov J.
Architectures for the development of image recognition algorithms must support the implementation of systematic procedures for solving image recognition problems. All too often, designers develop image recognition architectures in an ad hoc fashion which lacks the structure to meet long term needs. Vendors typically supply customers with standard image processing libraries and display tools. Combining these tools and formulating development strategies have remained stumbling blocks in the design of complete image recognition algorithm development environments. In this paper, an architecture is presented which provides a well defined framework, and at the same time is sufficiently flexible to accommodate images of multiple sensor and data types. The primary components of the architecture are: ground-truthing, preprocessing (which includes image processing and segmentation), feature extraction, classification, and performance analysis. Powerful and well defined data structures are exploited for each of the primary components. Groups of programs called tasks manipulate one or more of these data structures, each task belonging to one of the primary components. Multiple tasks can be executed in an unsupervised mode over an entire database of images. Results are then subjected to performance analysis and feedback. A description of the primary components and how they are integrated to facilitate the rapid prototyping and development of image recognition algorithms is presented.
Full Text Available This study presents a Text Signage Recognition (TSR model in Android mobile devices for Visually Impaired People (VIP. Independence navigation is always a challenge to VIP for indoor navigation in unfamiliar surroundings. Assistive Technology such as Android smart devices has great potential to assist VIPs in indoor navigation using built-in speech synthesizer. In contrast to previous TSR research which was deployed in standalone personal computer system using Otsu’s algorithm, we have developed an affordable Text Signage Recognition in Android Mobile Devices using Tesseract OCR engine. The proposed TSR model used the input images from the International Conference on Document Analysis and Recognition (ICDAR 2003 dataset for system training and testing. The TSR model was tested by four volunteers who were blind-folded. The system performance of the TSR model was assessed using different metrics (i.e., Precision, Recall, F-Score and Recognition Formulas to determine its accuracy. Experimental results show that the proposed TSR model has achieved recognition rate satisfactorily.
Full Text Available The paper presents a multi‐modal emotion recognition system exploiting audio and video (i.e., facial expression information. The system first processes both sources of information individually to produce corresponding matching scores and then combines the computed matching scores to obtain a classification decision. For the video part of the system, a novel approach to emotion recognition, relying on image‐set matching, is developed. The proposed approach avoids the need for detecting and tracking specific facial landmarks throughout the given video sequence, which represents a common source of error in video‐based emotion recognition systems, and, therefore, adds robustness to the video processing chain. The audio part of the system, on the other hand, relies on utterance‐specific Gaussian Mixture Models (GMMs adapted from a Universal Background Model (UBM via the maximum a posteriori probability (MAP estimation. It improves upon the standard UBM‐MAP procedure by exploiting gender information when building the utterance‐specific GMMs, thus ensuring enhanced emotion recognition performance. Both the uni‐modal parts as well as the combined system are assessed on the challenging multi‐modal eNTERFACEʹ05 corpus with highly encouraging results. The developed system represents a feasible solution to emotion recognition that can easily be integrated into various systems, such as humanoid robots, smart surveillance systems and alike.
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.
Full Text Available A portable real-time facial recognition system that is able to play personalized music based on the identified person’s preferences was developed. The system is called Portable Facial Recognition Jukebox Using Fisherfaces (FRJ. Raspberry Pi was used as the hardware platform for its relatively low cost and ease of use. This system uses the OpenCV open source library to implement the computer vision Fisherfaces facial recognition algorithms, and uses the Simple DirectMedia Layer (SDL library for playing the sound files. FRJ is cross-platform and can run on both Windows and Linux operating systems. The source code was written in C++. The accuracy of the recognition program can reach up to 90% under controlled lighting and distance conditions. The user is able to train up to 6 different people (as many as will fit in the GUI. When implemented on a Raspberry Pi, the system is able to go from image capture to facial recognition in an average time of 200ms.
Zhou, Xiang; Ross, Lars; Lehn-Schiøler, Tue;
BACKGROUND: It is well known that under noisy conditions, viewing a speaker's articulatory movement aids the recognition of spoken words. Conventionally it is thought that the visual input disambiguates otherwise confusing auditory input. HYPOTHESIS: In contrast we hypothesize that it is the temp......BACKGROUND: It is well known that under noisy conditions, viewing a speaker's articulatory movement aids the recognition of spoken words. Conventionally it is thought that the visual input disambiguates otherwise confusing auditory input. HYPOTHESIS: In contrast we hypothesize...... that it is the temporal synchronicity of the visual input that aids parsing of the auditory stream. More specifically, we expected that purely temporal information, which does not convey information such as place of articulation may facility word recognition. METHODS: To test this prediction we used temporal features...... of audio to generate an artificial talking-face video and measured word recognition performance on simple monosyllabic words. RESULTS: When presenting words together with the artificial video we find that word recognition is improved over purely auditory presentation. The effect is significant (p...
Automatic speech recognition under conditions of a noisy environment remains a challenging problem. Traditionally, methods focused on noise structure, such as spectral subtraction, have been employed to address this problem, and thus the performance of such methods depends on the accuracy in noise estimation. In this paper, an alternative method, using a harmonic-based spectral reconstruction algorithm, is proposed for the enhancement of robust automatic speech recognition. Neither noise estimation nor noise-model training are required in the proposed approach. A spectral subtraction integrated autocorrelation function is proposed to determine the pitch for the harmonic model. Recognition results show that the harmonic-based spectral reconstruction approach outperforms spectral subtraction in the middle- and low-signal noise ratio (SNR) ranges. The advantage of the proposed method is more manifest for non-stationary noise, as the algorithm does not require an assumption of stationary noise.
Full Text Available A fast pedestrian recognition algorithm based on multisensor fusion is presented in this paper. Firstly, potential pedestrian locations are estimated by laser radar scanning in the world coordinates, and then their corresponding candidate regions in the image are located by camera calibration and the perspective mapping model. For avoiding time consuming in the training and recognition process caused by large numbers of feature vector dimensions, region of interest-based integral histograms of oriented gradients (ROI-IHOG feature extraction method is proposed later. A support vector machine (SVM classifier is trained by a novel pedestrian sample dataset which adapt to the urban road environment for online recognition. Finally, we test the validity of the proposed approach with several video sequences from realistic urban road scenarios. Reliable and timewise performances are shown based on our multisensor fusing method.
Li, Jingguang; Tian, Moqian; Fang, Huizhen; Xu, Miao; Li, He; Liu, Jia
In daily life, one of the most common social tasks we perform is to recognize faces. However, the relation between face recognition ability and social activities is largely unknown. Here we ask whether individuals with better social skills are also better at recognizing faces. We found that extraverts who have better social skills correctly recognized more faces than introverts. However, this advantage was absent when extraverts were asked to recognize non-social stimuli (e.g., flowers). In particular, the underlying facet that makes extraverts better face recognizers is the gregariousness facet that measures the degree of inter-personal interaction. In addition, the link between extraversion and face recognition ability was independent of general cognitive abilities. These findings provide the first evidence that links face recognition ability to our daily activity in social communication, supporting the hypothesis that extraverts are better at decoding social information than introverts.
Chen Tianlu; Que Peiwen
Evidence theory is widely used in the field of target recognition. The invalidation problem of this theory when dealing with highly conflict evidences is a research hotspot. Several alternatives of the combination rule are analyzed and compared. A new combination approach is proposed. Calculate the reliabilities of evidence sources using existing evidences. Construct reliabilities judge matrixes and get the weights of each evidence source. Weight average all inputted evidences. Combine processed evidences with D-S combination rule repeatedly to identify a target. The application in multi-sensor target recognition as well as the comparison with typical alternatives all validated that this approach can dispose highly conflict evidences efficiently and get reasonable recognition results rapidly.
Full Text Available The paper focuses on the potential of principal directions based approaches in signal classification and recognition. In probabilistic models, the classes are represented in terms of multivariate density functions, and an object coming from a certain class is modeled as a random vector whose repartition has the density function corresponding to this class. In cases when there is no statistical information concerning the set of density functions corresponding to the classes involved in the recognition process, usually estimates based on the information extracted from available data are used instead. In the proposed methodology, the characteristics of a class are given by a set of eigen vectors of the sample covariance matrix. The overall dissimilarity of an object X with a given class C is computed as the disturbance of the structure of C, when X is allotted to C. A series of tests concerning the behavior of the proposed recognition algorithm are reported in the final section of the paper.
Full Text Available In ecology, the patterns usually refer to all kinds of nonrandom spatial and temporal structures of ecosystems driving by multiple ecological processes. Pattern recognition is an important step to reveal the complicated relationship between ecological patterns and processes. To review and present some advances about ecological modeling, patterns recognition, and computer simulation, an international workshop on Mathematical and Numerical Ecology with the theme "Pattern recognition and simulation in ecology" was held in in October 2014 in Guangzhou, China, and the International Society of Computational Ecology was the co-sponsor. Eight peer-reviewed papers those were originally presented at this workshop covering three themes: patterns in phylogeny, patterns in communities and ecosystems, and spatial pattern analysis are included in this special issue.
Cell-cell recognition is the key for multicellular organisms to survive. This recognition critically depends on protein-protein interactions from opposing cell surfaces. Recent structural investigations reveal unique features of these cell surface receptors and how they interact. These interactions are specific, but usually relatively weak, with more hydrophilic forces involved in binding. The receptors appear to have specialized ways to present their key interacting elements for ligand-binding from the cell surface. Cell-cell contacts are multivalent. A large group of cell surface molecules are engaged in interactions. Characteristic weak interactions make possible for each individual molecule pair within the group to constantly associate-dissociate-reassociate, such that the cell-cell recognition becomes a dynamic process. The immunological synapse is a good example for immune receptors to be orchestrated in performing immunological function in a collective fashion.
Zhou, Saohua; Krüger, Volker; Chellappa, Rama
Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework. In still-to-video recognition, where the gallery consists of still images, a time series state space model is proposed to fuse temporal...... instant. Marginalization over the motion vector yields a robust estimate of the posterior distribution of the identity variable. A computationally efficient sequential importance sampling algorithm is developed to provide numerical solution to the model. Theoretical derivations under minor assumptions...... of the identity variable produces the recognition result. The model formulation is very general and it allows a variety of image representations and transformations. Experimental results using videos collected by NIST/USF and CMU illustrate the effectiveness of this approach for both still-to-video and video...
Full Text Available Abstract Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.
Caponetti, Laura; Buscicchio, Cosimo Alessandro; Castellano, Giovanna
Emotion recognition has become a fundamental task in human-computer interaction systems. In this article, we propose an emotion recognition approach based on biologically inspired methods. Specifically, emotion classification is performed using a long short-term memory (LSTM) recurrent neural network which is able to recognize long-range dependencies between successive temporal patterns. We propose to represent data using features derived from two different models: mel-frequency cepstral coefficients (MFCC) and the Lyon cochlear model. In the experimental phase, results obtained from the LSTM network and the two different feature sets are compared, showing that features derived from the Lyon cochlear model give better recognition results in comparison with those obtained with the traditional MFCC representation.
Harrison, Amy; Sullivan, Sarah; Tchanturia, Kate; Treasure, Janet
It is recognized that emotional problems lie at the core of eating disorders (EDs) but scant attention has been paid to specific aspects such as emotional recognition, regulation and expression. This study aimed to investigate emotion recognition using the Reading the Mind in the Eyes (RME) task and emotion regulation using the Difficulties in Emotion Regulation Scale (DERS) in 20 women with anorexia nervosa (AN) and 20 female healthy controls (HCs). Women with AN had significantly lower scores on RME and reported significantly more difficulties with emotion regulation than HCs. There was a significant negative correlation between total DERS score and correct answers from the RME. These results suggest that women with AN have difficulties with emotional recognition and regulation. It is uncertain whether these deficits result from starvation and to what extent they might be reversed by weight gain alone. These deficits may need to be targeted in treatment.
Full Text Available A technique of object recognition which can detect absence or presence of objects of interest without making explicit use of their underlying geometric structure is deemed suitable for many practical applications. In this work, a method of recognising unstructured objects has been presented, wherein several gray patterns are input as examples to a morphological rule-based learning algorithm. The output of the algorithm are the corresponding gray structuring elements capable of recognising patterns in query images. The learning is carried out offline before recognition of the queries. The technique has been tested to identify fuel pellet surface imperfections. Robustness wrt intensity, orientation, and shape variations of the query patterns is built into the method. Moreover, simplicity of the recognition process leading to reduced computational time makes the method attractive to solve many practical problems.
Rohner, Jean-Christophe; Rasmussen, Anders
Previous studies have found a recognition bias for information consistent with the physical attractiveness stereotype (PAS), in which participants believe that they remember that attractive individuals have positive qualities and that unattractive individuals have negative qualities, regardless of what information actually occurred. The purpose of this research was to examine whether recognition bias for PAS congruent information is replicable and invariant across a variety of conditions (i.e. generalizable). The effects of nine different moderator variables were examined in two experiments. With a few exceptions, the effect of PAS congruence on recognition bias was independent of the moderator variables. The results suggest that the tendency to believe that one remembers information consistent with the physical attractiveness stereotype is a robust phenomenon. PMID:22416805
WuYuanqing; HaoJie; 等
Based on the analysis of the physiological and psychological characteristics of human auditory system,we can classify human auditory process into two hearing modes:active one and passive one.A novel approach of robust speech recognition,Auditory-spectrum Quantization Based Speech Recognition(AQBSR),is proposed.In this method,we intend to simulate human active hearing mode and locate the effective areas of speech signals in temporal domain and in frequency domain.Adaptive filter banks are used in place of fixed-band filters to extract feature parameters.The effective speech components and their corresponding frequency areas of each word in the vocabulary can be found out during training.In recognition stage,comparison between the unknown sound and the current template is maintained only in the effective areas of the template word.The control experiments show that the AQ BSR method is more robust than traditional systems.
TENG Xiao-long; WANG Xiang-yang; LIU Chong-qing
The trained Gaussian mixture model is used to make skincolour segmentation for the input image sequences. The hand gesture region is extracted, and the relative normalization images are obtained by interpolation operation. To solve the problem of hand gesture recognition, Fuzzy-Rough based nearest neighbour (FRNN) algorithm is applied for classification. For avoiding the costly compute, an improved nearest neighbour classification algorithm based on fuzzy-rough set theory (FRNNC) is proposed. The algorithm employs the represented cluster points instead of the whole training samples, and takes the hand gesture data's fuzziness and the roughness into account, so the compute spending is decreased and the recognition rate is increased. The 30 gestures in Chinese sign language alphabet are used for approving the effectiveness of the proposed algorithm. The recognition rate is 94.96%, which is better than that of KNN (K nearest neighbor)and Fuzzy-KNN (Fuzzy K nearest neighbor).
Mahmoud Zaki Abdo
Full Text Available This paper introduces an Arabic Alphabet and Numbers Sign Language Recognition (ArANSLR. It facilitates the communication between the deaf and normal people by recognizing the alphabet and numbers signs of Arabic sign language to text or speech. To achieve this target, the system able to visually recognize gestures from hand image input. The proposed algorithm uses hand geometry and the different shape of a hand in each sign for classifying letters shape by using Hidden Markov Model (HMM. Experiments on real-world datasets showed that the proposed algorithm for Arabic alphabet and numbers sign language recognition is suitability and reliability compared with other competitive algorithms. The experiment results show that the increasing of the gesture recognition rate depends on the increasing of the number of zones by dividing the rectangle surrounding the hand.
This book contains the extended and revised versions of a set of selected papers from the 2nd International Conference on Pattern Recognition (ICPRAM 2013), held in Barcelona, Spain, from 15 to 18 February, 2013. ICPRAM was organized by the Institute for Systems and Technologies of Information, Control and Communication (INSTICC) and was held in cooperation with the Association for the Advancement of Artificial Intelligence (AAAI). The hallmark of this conference was to encourage theory and practice to meet in a single venue. The focus of the book is on contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods.
Full Text Available Recently, compressive sensing (CS has attracted increasing attention in the areas of signal processing, computer vision and pattern recognition. In this paper, a new method based on the CS theory is presented for robust facial expression recognition. The CS theory is used to construct a sparse representation classifier (SRC. The effectiveness and robustness of the SRC method is investigated on clean and occluded facial expression images. Three typical facial features, i.e., the raw pixels, Gabor wavelets representation and local binary patterns (LBP, are extracted to evaluate the performance of the SRC method. Compared with the nearest neighbor (NN, linear support vector machines (SVM and the nearest subspace (NS, experimental results on the popular Cohn-Kanade facial expression database demonstrate that the SRC method obtains better performance and stronger robustness to corruption and occlusion on robust facial expression recognition tasks.
Rohner, Jean-Christophe; Rasmussen, Anders
Previous studies have found a recognition bias for information consistent with the physical attractiveness stereotype (PAS), in which participants believe that they remember that attractive individuals have positive qualities and that unattractive individuals have negative qualities, regardless of what information actually occurred. The purpose of this research was to examine whether recognition bias for PAS congruent information is replicable and invariant across a variety of conditions (i.e. generalizable). The effects of nine different moderator variables were examined in two experiments. With a few exceptions, the effect of PAS congruence on recognition bias was independent of the moderator variables. The results suggest that the tendency to believe that one remembers information consistent with the physical attractiveness stereotype is a robust phenomenon.
The Institute for Scientific Computing Research (ISCR) at Lawrence Livermore National Laboratory recently developed the real-time, face recognition technology KEN. KEN uses novel imaging devices such as silicon retinas developed at Caltech or off-the-shelf CCD cameras to acquire images of a face and to compare them to a database of known faces in a robust fashion. The KEN-Online project makes that recognition technology accessible through the World Wide Web (WWW), an internet service that has recently seen explosive growth. A WWW client can submit face images, add them to the database of known faces and submit other pictures that the system tries to recognize. KEN-Online serves to evaluate the recognition technology and grow a large face database. KEN-Online includes the use of public domain tools such as mSQL for its name-database and perl scripts to assist the uploading of images.
Rutt, Rebecca Leigh
Abstract This thesis concerns the role of local institutions in fostering development including natural resource management, and how this role is shaped by relations with higher scale institutions such as development agencies and national governments. Specifically, it examines the choice of local...... objective of this thesis was to contribute to understanding processes and outcomes of institutional choice and recognition. It employed mixed methods but primarily semi structured interviews in multiple sites across Nepal. In responding to specific objectives, namely to better understand: i) the rationales...... behind choices of local institutional counterparts, ii) the belonging and citizenship available with local institutions, iii) the dynamics and mutuality of recognition between higher and lower scale institutions, and iv) the social outcomes of choice and recognition, this thesis shows that the way choice...
Bos, Nick; d'Ettorre, Patrizia
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...
Li Ning; Chen Dan
Ancient Chinese characters, typically the ideographic characters on bones and bronze before Shang Dynasty (16th—11th century B.C.), are valuable culture legacy of history. However the recognition of Ancient Chinese characters has been the task of paleography experts for long. With the help of modern computer technique, everyone can expect to be able to recognize the characters and understand the ancient inscriptions. This research is aimed to help people recognize and understand those ancient Chinese characters by combining Chinese paleography theory and computer information processing technology. Based on the analysis of ancient character features, a method for structural character recognition is proposed. The important characteristics of strokes and basic components or radicals used in recognition are introduced in detail. A system was implemented based on above method to show the effectiveness of the method.
Luo, Yuan; Cui, Ye; Zhang, Yi
For the cumulative error problem because of randomization sequence of traditional DAGSVM(Directed Acyclic Graph Support Vector Machine) classification, this paper presents an improved DAGSVM expression recognition method. The method uses the distance of class and the standard deviation as the measure of the classer, which minimize the error rate of the upper structure of the classification. At the same time, this paper uses the method which combines discrete cosine transform (Discrete Cosine Transform, DCT) with Local Binary Pattern(Local Binary Pattern - LBP) ,to extract expression feature and be the input to improve the DAGSVM classifier for recognition. Experimental results show that compared with other multi-class support vector machine method, improved DAGSVM classifier can achieve higher recognition rate. And when it's used at the platform of the intelligent wheelchair, experiments show that the method has a better robustness.
Robila, Stefan A.; Chang, Marco; D'Amico, Nisha B.
We present a novel unsupervised method for facial recognition using hyperspectral imaging and decision fusion. In previous work we have separately investigated the use of spectra matching and image based matching. In spectra matching, face spectra are being classified based on spectral similarities. In image based matching, we investigated various approaches based on orthogonal subspaces (such as PCA and OSP). In the current work we provide an automated unsupervised method that starts by detecting the face in the image and then proceeds to performs both spectral and image based matching. The results are fused in a single classification decision. The algorithm is tested on an experimental hyperspectral image database of 17 subjects each with five different facial expressions and viewing angles. Our results show that the decision fusion leads to improvement of recognition accuracy when compared to the individual approaches as well as to recognition based on regular imaging.
Snehal Charjan, R. V. Mante, P. N. Chatur
Full Text Available Smart Phones have Internet access anywhere. The automatic text localization and recognition of text within a natural image is very useful for many problems. Once identified, the text can be used for many purposes. User can get current information about the product, place or boards. More exciting applications can be developed over the text extraction method with a high performance while also being computationally inexpensive. There are various methods proposed for Text Localization, text area segmentation, sign recognition and translation, Optical Character Recognition. In this paper we have described these methods. We have also compared all methods on the basis of performance and accuracy. Finally we concluded some good methods for Smartphone OCR application.
Konstantinos N. Plataniotis
Full Text Available Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC in conjunction with discrete cosine transform (DCT is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.
Wang, Yongjin; Agrafioti, Foteini; Hatzinakos, Dimitrios; Plataniotis, Konstantinos N.
Security concerns increase as the technology for falsification advances. There are strong evidences that a difficult to falsify biometric trait, the human heartbeat, can be used for identity recognition. Existing solutions for biometric recognition from electrocardiogram (ECG) signals are based on temporal and amplitude distances between detected fiducial points. Such methods rely heavily on the accuracy of fiducial detection, which is still an open problem due to the difficulty in exact localization of wave boundaries. This paper presents a systematic analysis for human identification from ECG data. A fiducial-detection-based framework that incorporates analytic and appearance attributes is first introduced. The appearance-based approach needs detection of one fiducial point only. Further, to completely relax the detection of fiducial points, a new approach based on autocorrelation (AC) in conjunction with discrete cosine transform (DCT) is proposed. Experimentation demonstrates that the AC/DCT method produces comparable recognition accuracy with the fiducial-detection-based approach.
Warburton, E.C.; Brown, M.W.
Information concerning the roles of different brain regions in recognition memory processes is reviewed. The review concentrates on findings from spontaneous recognition memory tasks performed by rats, including memory for single objects, locations, object–location associations and temporal order. Particular emphasis is given to the potential roles of different regions in the circuit of interacting structures involving the perirhinal cortex, hippocampus, medial prefrontal cortex and medial dorsal thalamus in recognition memory for the association of objects and places. It is concluded that while all structures in this circuit play roles critical to such memory, these roles can potentially be differentiated and differences in the underlying synaptic and biochemical processes involved in each region are beginning to be uncovered. PMID:25315129
Pattern recognition is essential for translating complex olfactory sensor responses into simple outputs that are relevant to users. Many approaches to pattern recognition have been applied in this field, including multivariate statistics (e.g. discriminant analysis), artificial neural networks (ANNs) and support vector machines (SVMs). Reviewing our experience of using these techniques with many different sensor systems reveals some useful insights. Most importantly, it is clear beyond any doubt that the quantity and selection of samples used to train and test a pattern recognition system are by far the most important factors in ensuring it performs as accurately and reliably as possible. Here we present evidence for this assertion and make suggestions for best practice based on these findings.
Larsen, Anders Boesen Lindbo; Hviid, Marchen Sonja; Larsen, Rasmus;
Bag-of-words (BoW) image description has shown good performance for a large variety of image recognition scenarios. We investigate approaches to alleviating a standard BoW image description pipeline representations for the specific task of recognizing pork loins. Specifically, we extend the BoW d......W description to include depth maps, perform non-rigid image registration to align the images, and apply PCA dimensionality reduction on the BoW descriptors. Our results show that the combination of image registration and PCA yields a more distinctive recognition.......Bag-of-words (BoW) image description has shown good performance for a large variety of image recognition scenarios. We investigate approaches to alleviating a standard BoW image description pipeline representations for the specific task of recognizing pork loins. Specifically, we extend the Bo...
Lenaghan, Andrew P.; Malyan, Ron
Both cognitive processes and artificial recognition systems may be characterized by the forms of representation they build and manipulate. This paper looks at how handwriting is represented in current recognition systems and the psychological evidence for its representation in the cognitive processes responsible for reading. Empirical psychological work on feature extraction in early visual processing is surveyed to show that a sound psychological basis for feature extraction exists and to describe the features this approach leads to. The first stage of the development of an architecture for a handwriting recognition system which has been strongly influenced by the psychological evidence for the cognitive processes and representations used in early visual processing, is reported. This architecture builds a number of parallel low level feature maps from raw data. These feature maps are thresholded and a region labeling algorithm is used to generate sets of features. Fuzzy logic is used to quantify the uncertainty in the presence of individual features.
Quentin R. Johnson
Full Text Available Carbohydrate recognition by proteins, such as lectins and other (biomolecules, can be essential for many biological functions. Recently, interest has arisen due to potential protein and drug design and future bioengineering applications. A quantitative measurement of carbohydrate-protein interaction is thus important for the full characterization of sugar recognition. We focus on the aspect of utilizing computer simulations and biophysical models to evaluate the strength and specificity of carbohydrate recognition in this review. With increasing computational resources, better algorithms and refined modeling parameters, using state-of-the-art supercomputers to calculate the strength of the interaction between molecules has become increasingly mainstream. We review the current state of this technique and its successful applications for studying protein-sugar interactions in recent years.
Wang Ai Qiang
Full Text Available The recognition rate of single recognition method is inefficiency in computer facial recognition. We proposed a new confluent facial recognition method using data fusion technology, a variety of recognition algorithm are combined to form the fusion-based face recognition system to improve the recognition rate in many ways. Data fusion considers three levels of data fusion, feature level fusion and decision level fusion. And the data layer uses a simple weighted average algorithm, which is easy to implement. Artificial neural network algorithm was selected in feature layer and fuzzy reasoning algorithm was used in decision layer. Finally, we compared with the BP neural network algorithm in the MATLAB experimental platform. The result shows that the recognition rate has been greatly improved after adopting data fusion technology in computer facial recognition.
Zhou, Zhi; Du, Eliza Y.; Lin, Yong; Thomas, N. Luke; Belcher, Craig; Delp, Edward J.
Iris recognition has been tested to the most accurate biometrics using high resolution near infrared images. However, it does not work well under visible wavelength illumination. Sclera recognition, however, has been shown to achieve reasonable recognition accuracy under visible wavelengths. Combining iris and sclera recognition together can achieve better recognition accuracy. However, image quality can significantly affect the recognition accuracy. Moreover, in unconstrained situations, the acquired eye images may not be frontally facing. In this research, we proposed a feature quality-based multimodal unconstrained eye recognition method that combine the respective strengths of iris recognition and sclera recognition for human identification and can work with frontal and off-angle eye images. The research results show that the proposed method is very promising.
Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.
Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the
When we think about the relations of recognition that structure a social order, exclusion is usually regarded as the problem and inclusion as the solution. In the following I will argue that in some cases inclusion—or rather the specific mode of inclusion—may very well constitute the problem. This is the case when we are dealing with ideological forms of recognition. System Justification Theory—a relatively novel approach in social and political psychology—offers an analysis of those cases in...
Zeng, Wei; Wang, Cong
Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. Human gait recognition is a typical difficulty in the area of dynamical pattern recognition. It classifies and identifies individuals by their time-varying gait signature data. Recently, a new dynamical pattern recognition method based on deterministic learning theory was presented, in which a time-varying dynamical pattern can be effectively represented in a time-invariant manner and can be rapidly recognized. In this paper, we present a new model-based approach for human gait recognition via the aforementioned method, specifically for recognizing people by gait. The approach consists of two phases: a training (learning) phase and a test (recognition) phase. In the training phase, side silhouette lower limb joint angles and angular velocities are selected as gait features. A five-link biped model for human gait locomotion is employed to demonstrate that functions containing joint angle and angular velocity state vectors characterize the gait system dynamics. Due to the quasi-periodic and symmetrical characteristics of human gait, the gait system dynamics can be simplified to be described by functions of joint angles and angular velocities of one side of the human body, thus the feature dimension is effectively reduced. Locally-accurate identification of the gait system dynamics is achieved by using radial basis function (RBF) neural networks (NNs) through deterministic learning. The obtained knowledge of the approximated gait system dynamics is stored in constant RBF networks. A gait signature is then derived from the extracted gait system dynamics along the phase portrait of joint angles versus angular velocities. A bank of estimators is constructed using constant RBF networks to represent the training gait patterns. In the test phase, by comparing the set of estimators with the test gait pattern, a set of recognition errors are generated, and the average L(1) norms
Kim, Soo-Hyun; Kang, Suk-Bum; Yang, Tae-Kyu
Robot vision technology is needed for the stable walking, object recognition and the movement to the target spot. By some sensors which use infrared rays and ultrasonic, robot can overcome the urgent state or dangerous time. But stereo vision of three dimensional space would make robot have powerful artificial intelligence. In this paper we consider about the stereo vision for stable and correct movement of a biped robot. When a robot confront with an inclination plane or steps, particular algorithms are needed to go on without failure. This study developed the recognition algorithm of distance and gradient of environment by stereo matching process.
Rainey, Katie; Reeder, John D.; Corelli, Alexander G.
Algorithms to automatically recognize ship type from satellite imagery are desired for numerous maritime applications. This task is difficult, and example imagery accurately labeled with ship type is hard to obtain. Convolutional neural networks (CNNs) have shown promise in image recognition settings, but many of these applications rely on the availability of thousands of example images for training. This work attempts to under- stand for which types of ship recognition tasks CNNs might be well suited. We report the results of baseline experiments applying a CNN to several ship type classification tasks, and discuss many of the considerations that must be made in approaching this problem.
Wang, Jiang; Wu, Ying
Action recognition technology has many real-world applications in human-computer interaction, surveillance, video retrieval, retirement home monitoring, and robotics. The commoditization of depth sensors has also opened up further applications that were not feasible before. This text focuses on feature representation and machine learning algorithms for action recognition from depth sensors. After presenting a comprehensive overview of the state of the art, the authors then provide in-depth descriptions of their recently developed feature representations and machine learning techniques, includi
A fundamental question in DNA repair is how a lesion is detected when embedded in millions to billions of normal base pairs. Extensive structural and functional studies reveal atomic details of DNA repair protein and nucleic acid interactions. This review summarizes seemingly diverse structural motifs used in lesion recognition and suggests a general mechanism to recognize DNA lesion by the poor base stacking. After initial recognition of this shared struc-tural feature of lesions, different DNA repair pathways use unique verification mechanisms to ensure correct lesion identification and removal.
Gender differences favoring women have been found in face recognition, and in addition to this, it has been shown that women remember more female than male faces. This own-gender effect may be a result of women directing more attention towards female faces, resulting in a better memory. The aim of this study was to assess the role of attention for gender differences in face recognition and women’s own-gender bias by dividing attention at encoding of faces. Thirty-two participants completed tw...
Full Text Available This paper presents an efficient ear recognition technique which derives benefits from the local features of the ear and attempt to handle the problems due to pose, poor contrast, change in illumination and lack of registration. Recognizing humans by their ear have recently received significant attention in the field of research. Ear is the rich in characteristics. This paper provides a detailed survey of research done in ear detection and recognition. This survey paper is very useful in the current state-of- art for those who are working in this area and also for those who might exploit this new approach.
Călin Enăchescu; Cristian-Dumitru Miron
In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classiﬁcation task is performed using a Convolutional Neural Network (CNN). CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recogni...
Visual Recognition is one of the fundamental challenges in AI, where the goal is to understand the semantics of visual data. Employing mid-level representation, in particular, shifted the paradigm in visual recognition. The mid-level image/video representation involves discovering and training a set of mid-level visual patterns (e.g., parts and attributes) and represent a given image/video utilizing them. The mid-level patterns can be extracted from images and videos using the motion and appe...
Iwamura, Masakazu; Horimatsu, Akira; Niwa, Ryo; Kise, Koichi; Uchida, Seiichi; Omachi, Shinichiro
Recognizing characters in scene images suffering from perspective distortion is a challenge. Although there are some methods to overcome this difficulty, they are time-consuming. In this paper, we propose a set of affine invariant features and a new recognition scheme called “progressive removing” that can help reduce the processing time. Progressive removing gradually removes less feasible categories and skew angles by using multiple classifiers. We observed that progressive removing and the use of the affine invariant features reduced the processing time by about 60% in comparison to a trivial one without decreasing the recognition rate.
The purpose of this article is to argue for recognition as a normative ideal for social work and to confront the ideal with the reality found in the social institutions. I shall use the concept "judgement" to describe institutional routines and ways of thinking which constitute barriers...... to recognition. In the first part, I outline the normative ideal and show its relevance for practical social work on the basis of social clients' experiences of disrespect. In the second, I expalin the concept of judgement and criticise the prevailing forms of judgement to be found in the social institutions...
ZHANG Yu-hua; ZENG Xiao-ming
In this paper, we propose a face recognition approach-Structed Sparse Representation-based classification when the measurement of the test sample is less than the number training samples of each subject. When this condition is not satisfied, we exploit Nearest Subspace approach to classify the test sample. In order to adapt all the cases, we combine the two approaches to an adaptive classification method-Adaptive approach. The adaptive approach yields greater recognition accuracy than the SRC approach and CRC_RLS approach with low sample rate on the Extend Yale B dataset. And it is more efficient than other two approaches.
This letter presents a face normalization algorithm based on 2-D face model to rec-ognize faces with variant postures from front-view face.A 2-D face mesh model can be extracted from faces with rotation to left or right and the corresponding front-view mesh model can be estimated according to facial symmetry.Then based on the relationship between the two mesh models,the nrmalized front-view face is formed by gray level mapping.Finally,the face recognition will be finished based on Principal Component Analysis(PCA).Experiments show that better face recognition performance is achieved in this way.
Wu Lifang; Shen Lansun
This letter presents a face normalization algorithm based on 2-D face model to recognize faces with variant postures from front-view face. A 2-D face mesh model can be extracted from faces with rotation to left or right and the corresponding front-view mesh model can be estimated according to the facial symmetry. Then based on the inner relationship between the two mesh models, the normalized front-view face is formed by gray level mapping. Finally, the face recognition will be finished based on Principal Component Analysis (PCA). Experiments show that better face recognition performance is achieved in this way.
This account deacribes our research progress in recent years in the areas of the molecular recognition studies on modified cy clodextrins, including positively charged cyclodextrins, cy clodextrin derivatives with hydrophobic substituent, and dimeric cyclodextrins. Calorimetric titration and various spec trometric techniques were employed to determine the complex stability constants, as well as the thermodynamic parameters, for their inclusion complexation with diverse guest molecules. The results obtained have heen discussed from the viewpoint of size/shape-matching, induced-fit, geometric compensation, and multiple recognition. Thermodynamically, the compen satory relationship between △H and T△S was found to be ex hibited in the inclusion complexation of modified cyclodextrin.
Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world environmental conditions. Gait and face data are the two biometrics that can be most easily captured from a distance using a video camera. This comprehensive and logically organized text/reference addresses the fundamental problems associated with gait and face-based human recognition, from color and infrared video data that are
The 2007 performance appraisal and merit recognition exercise will start in the usual way with annual interviews between staff and their supervisors. This year, these interviews should be held in the period from 8 January 2007 to 16 April 2007. Interconnection with the 5-yearly review, a number of modifications to the procedures relating to the performance appraisal and merit recognition are currently under study. Administrative Circular No. 26 (Procedures governing the Career Development of Staff Members) and the electronic MAPS form in EDH are being reviewed and will be available from January onwards. HR Department will shortly provide further information on this subject. Human Resources Department Tel. 73566
J, Ravi; R, Venugopal K
The popular Biometric used to authenticate a person is Fingerprint which is unique and permanent throughout a person's life. A minutia matching is widely used for fingerprint recognition and can be classified as ridge ending and ridge bifurcation. In this paper we projected Fingerprint Recognition using Minutia Score Matching method (FRMSM). For Fingerprint thinning, the Block Filter is used, which scans the image at the boundary to preserves the quality of the image and extract the minutiae from the thinned image. The false matching ratio is better compared to the existing algorithm.
Thai, Le Hoang
Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models...are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transformation between templates, adjust parameters, synthesize fingerprint, and reduce noises. Then, we use the final fingerprint to match with others in FVC2004 fingerprint database (DB4) to show the capability of the model.
Full Text Available This paper describes a mobile device which tries to give the blind or visually impaired access to text information. Three key technologies are required for this system: text detection, optical character recognition, and speech synthesis. Blind users and the mobile environment imply two strong constraints. First, pictures will be taken without control on camera settings and a priori information on text (font or size and background. The second issue is to link several techniques together with an optimal compromise between computational constraints and recognition efficiency. We will present the overall description of the system from text detection to OCR error correction.
Le Hoang Thai
Full Text Available Fingerprint recognition is one of most popular and accuracy Biometric technologies. Nowadays, it is used in many real applications. However, recognizing fingerprints in poor quality images is still a very complex problem. In recent years, many algorithms, models are given to improve the accuracy of recognition system. This paper discusses on the standardized fingerprint model which is used to synthesize the template of fingerprints. In this model, after pre-processing step, we find the transformation between templates, adjust parameters, synthesize fingerprint, and reduce noises. Then, we use the final fingerprint to match with others in FVC2004 fingerprint database (DB4 to show the capability of the model.
Faces constitute one of the most important stimuli for humans. Studies show that women recognize more faces than men, and that females are particularly able to recognize female faces, thus exhibiting an own-sex bias. In the present thesis, three empirical studies investigated the generality of sex differences in face recognition and the female own-sex bias. Study I explored men’s and women’s face recognition performance for Bangladeshi and Swedish female and male faces of adults and children....
贲俊; 万旺根; 余小清
In this paper we propose a new algorithm of phoneme based speaker-independent English command recognition and develop a speaker-independent English command recognition system. It accelerates the whole system development by using HTK (hide Markov toolkits) and Visual C + + based on the character' istics of speaker-independent speech recognition. In recognition phase we combine the confidence measures with incomplete matching, which considerably improve the quality of recognition. The recognition accuracyis increased by 4.8 % over complete matching without back-end processing when the sige of vocabulary is more than 10.
Full Text Available In this paper I am proposing a “processual” account of struggles for recognition. I read Hegel in the section of “Evil and Its Forgiveness,” in his Phenomenology of Spirit, as arguing for a transition from basically action-theoretic, relational or institutional conceptions of recognition to a processual conception. Furthermore, I will be claiming that John Dewey’s experimentalist social philosophy can be understood as an attempt at working out the implications of this move in Hegel’s thought. Finally, I will try to indicate how to integrate central concepts of the recognition-theoretical vocabulary into such a processual account
Zheng, Nan-Xin; Wang, Yan; Hu, Dan-Dan; Yan, Lan; Jiang, Yuan-Ying
Candida albicans is both a commensal microorganism in healthy individuals and a major fungal pathogen causing high mortality in immunocompromised patients. Yeast-hypha morphological transition is a well known virulence trait of C. albicans. Host innate immunity to C. albicans critically requires pattern recognition receptors (PRRs). In this review, we summarize the PRRs involved in the recognition of C. albicans in epithelial cells, endothelial cells, and phagocytic cells separately. We figure out the differential recognition of yeasts and hyphae, the findings on PRR-deficient mice, and the discoveries on human PRR-related single nucleotide polymorphisms (SNPs).
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
C. Bradler (Christiane); A.J. Dur (Robert); S. Neckermann (Susanne); J.A. Non (Arjan)
textabstractThis paper reports the results from a controlled field experiment designed to investigate the causal effect of public recognition on employee performance. We hired more than 300 employees to work on a three-hour data-entry task. In a random sample of work groups, workers unexpectedly rec
The orbit of an equivariant bifurcation problem with multiparameter is characterized under the action of the group of unipotent equivalences. When the unipotent tangent space is invariant under unipotent equivalences, the recognition problem can be solved by just using linear algebra. Sufficient conditions for a subspace to be intrinsic subspace under unipotent equivalences are given.
S. Katrenko; P. Adriaans; M. van Someren
This paper discusses the problem of marrying structural similarity with semantic relatedness for Information Extraction from text. Aiming at accurate recognition of relations, we introduce local alignment kernels and explore various possibilities of using them for this task. We give a definition of
Automated target recognition (ATR) based on high resolution radar (HRR) features can be used to increase the confidence in aircraft class. Standard radar systems are not designed for performing classification and uses additional identification systems. It is shown that with the use of features the a
Agne, Hans; Bartelson, Jens; Erman, Eva; Lindemann, Thomas; Herborth, Benjamin; Kessler, Oliver; Chwaszcza, Christine; Fabry, Mikulas; Krasner, Stephen D.
Recognition plays a multifaceted role in international theory. In rarely communicating literatures, the term is invoked to explain creation of new states and international structures; policy choices by state and non-state actors; and normative justifiability, or lack thereof, of foreign and internat
Napa Valley Unified School District, Napa, CA.
THE FOLLOWING IS THE FULL TEXT OF THIS DOCUMENT: Recognition of good citizenship is one component of the Vintage High School Student Incentive Program which could be easily adapted for any school. The only direct cost is for postage to mail congratulatory letters home and a small initial cost for printing award certificates. On a rotating basis,…
One of the open problems in content-based Image Retrieval is the recognition of material present in an image. Knowledge about the set of materials present gives important semantic information about the scene under consideration. For example, detecting sand, sky, and water certainly classifies the im
Chen, L.; van der Aa, N.P.; Tan, R.T.; Veltkamp, R.C.
In the field of action recognition, the design of features has been explored extensively, but the choice of action classification methods is limited. Commonly used classification methods like k-Nearest Neighbors and Support Vector Machines assume conditional independency between features. In contras
Miguel, Marta C.; Ornelas, José H.; Maroco, João P.
The current narrative on lifelong learning goes beyond formal education and training, including learning at work, in the family and in the community. Recognition of prior learning is a process of evaluation of those skills and knowledge acquired through life experience, allowing them to be formally recognized by the qualification systems. It is a…
Bradler, C.; Dur, R.; Neckermann, S.; Non, J.A.
This paper reports the results from a controlled field experiment designed to investigate the causal effect of public recognition on employee performance. We hired more than 300 employees to work on a three-hour data-entry task. In a random sample of work groups, workers unexpectedly received recogn
Zue, V. W.
During this reporting period we continued to make progress on the acquisition of acoustic-phonetic and lexical knowledge. We completed development of a continuous digit recognition system. The system was constructed to investigate the use of acoustic-phonetic knowledge in a speech recognition system. The significant achievements of this study include the development of a soft-failure procedure for lexical access and the discovery of a set of acoustic-phonetic features for verification. We completed a study of the constraints that lexical stress imposes on word recognition. We found that lexical stress information alone can, on the average, reduce the number of word candidates from a large dictionary by more than 80 percent. In conjunction with this study, we successfully developed a system that automatically determines the stress pattern of a word from the acoustic signal. We performed an acoustic study on the characteristics of nasal consonants and nasalized vowels. We have also developed recognition algorithms for nasal murmurs and nasalized vowels in continuous speech. We finished the preliminary development of a system that aligns a speech waveform with the corresponding phonetic transcription.
Kauffman, J.A.; Bazen, A.M.; Gerez, S.H.; Veldhuis, R.N.J.
This paper describes the design, implementation and evaluation of a user-verification system for a smart gun, which is based on grip-pattern recognition. An existing pressure sensor consisting of an array of 44 x 44 piezoresistive elements has been used. An interface has been developed to acquire pr
Offers both foundations and advances on emotion recognition in a single volumeProvides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domainsInspires young researchers to prepare themselves for their own researchDemonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.
Three general optical approaches to multiple degree of freedom object pattern recognition (where no stable object rest position exists) are advanced. These techniques include: feature extraction, correlation, and artificial intelligence. The details of the various processors are advanced together with initial results.
Biyik, Utku; Keskin, Duygu; Oguz, Kaya; Akdeniz, Fisun; Gonul, Ali Saffet
Although major depressive disorder (MDD) is primarily characterized by mood symptoms, depressed patients have impairments in facial emotion recognition in many of the basic emotions (anger, fear, happiness, surprise, disgust and sadness). On the other hand, the data in remitted MDD (rMDD) patients is inconsistent and it is not clear that if those impairments persist in remission. To extend the current findings, we applied facial emotion recognition test to a group of remitted depressed women and compared to those of controls. Analyses of variance results showed a significant emotion and group interaction, and in the post hoc analyses, rMDD patients had higher accuracy rate for recognition of sadness compared to those of controls. There were no differences in the reaction time among the patients and controls across the all the basic emotions. The higher recognition rates for sad faces in rMDD patients might contribute to the impairments in social communication and the prognosis of the disease. PMID:26321673
Full Text Available Face perception is integral to human perception system as it underlies social interactions. Saccadic eye movements are frequently made to bring interesting visual information, such as faces, onto the fovea for detailed processing. Just before eye movement onset, the processing of some basic features, such as the orientation, of an object improves at the saccade landing point. Interestingly, there is also evidence that indicates faces are processed in early visual processing stages similar to basic features. However, it is not known whether this early enhancement of processing includes face recognition. In this study, three experiments were performed to map the timing of face presentation to the beginning of the eye movement in order to evaluate pre-saccadic face recognition. Faces were found to be similarly processed as simple objects immediately prior to saccadic movements. Starting ∼ 120 ms before a saccade to a target face, independent of whether or not the face was surrounded by other faces, the face recognition gradually improved and the critical spacing of the crowding decreased as saccade onset was approaching. These results suggest that an upcoming saccade prepares the visual system for new information about faces at the saccade landing site and may reduce the background in a crowd to target the intended face. This indicates an important role of pre-saccadic eye movement signals in human face recognition.
Moeslund, Thomas B.; Nørgaard, Lau
-invasive handgesture recognition system aimed at deictic gestures. Our system is based on the powerful Sequential Monte Carlo framework which is enhanced with respect to increased robustness. This is achieved by using ratios in the likelihood function together with two image cues: edges and skin color. The system...
Plato, Anthony; Hardison, Sarah E; Brown, Gordon D
Receptors of the innate immune system are the first line of defence against infection, being able to recognise and initiate an inflammatory response to invading microorganisms. The Toll-like (TLR), NOD-like (NLR), RIG-I-like (RLR) and C-type lectin-like receptors (CLR) are four receptor families that contribute to the recognition of a vast range of species, including fungi. Many of these pattern recognition receptors (PRRs) are able to initiate innate immunity and polarise adaptive responses upon the recognition of fungal cell wall components and other conserved molecular patterns, including fungal nucleic acids. These receptors induce effective mechanisms of fungal clearance in normal hosts, but medical interventions, immunosuppression or genetic predisposition can lead to susceptibility to fungal infections. In this review, we highlight the importance of PRRs in fungal infection, specifically CLRs, which are the major PRR involved. We will describe specific PRRs in detail, the importance of receptor collaboration in fungal recognition and clearance, and describe how genetic aberrations in PRRs can contribute to disease pathology.
Bartsch, A.; Fitzek, F.; Rasshofer, R. H.
The application of modern series production automotive radar sensors to pedestrian recognition is an important topic in research on future driver assistance systems. The aim of this paper is to understand the potential and limits of such sensors in pedestrian recognition. This knowledge could be used to develop next generation radar sensors with improved pedestrian recognition capabilities. A new raw radar data signal processing algorithm is proposed that allows deep insights into the object classification process. The impact of raw radar data properties can be directly observed in every layer of the classification system by avoiding machine learning and tracking. This gives information on the limiting factors of raw radar data in terms of classification decision making. To accomplish the very challenging distinction between pedestrians and static objects, five significant and stable object features from the spatial distribution and Doppler information are found. Experimental results with data from a 77 GHz automotive radar sensor show that over 95% of pedestrians can be classified correctly under optimal conditions, which is compareable to modern machine learning systems. The impact of the pedestrian's direction of movement, occlusion, antenna beam elevation angle, linear vehicle movement, and other factors are investigated and discussed. The results show that under real life conditions, radar only based pedestrian recognition is limited due to insufficient Doppler frequency and spatial resolution as well as antenna side lobe effects.
Nikisins, Olegs; Nasrollahi, Kamal; Greitans, Modris;
Facial images are of critical importance in many real-world applications from gaming to surveillance. The current literature on facial image analysis, from face detection to face and facial expression recognition, are mainly performed in either RGB, Depth (D), or both of these modalities. But...
Fereczkowski, Michal; Bothe, Hans-Heinrich
The main idea of the project is to build a widely speaker-independent, biologically motivated automatic speech recognition (ASR) system. The two main differences between our approach and current state-of-the-art ASRs are that i) the features used here are based on the responses of neuronlike spec...
Ren, Huamin; Moeslund, Thomas B.
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...
Mendoza-Morales, América I.; Miramontes-Jaramillo, Daniel; Kober, Vitaly
In recent years, human-computer interaction (HCI) has received a lot of interest in industry and science because it provides new ways to interact with modern devices through voice, body, and facial/hand gestures. The application range of the HCI is from easy control of home appliances to entertainment. Hand gesture recognition is a particularly interesting problem because the shape and movement of hands usually are complex and flexible to be able to codify many different signs. In this work we propose a three step algorithm: first, detection of hands in the current frame is carried out; second, hand tracking across the video sequence is performed; finally, robust recognition of gestures across subsequent frames is made. Recognition rate highly depends on non-uniform illumination of the scene and occlusion of hands. In order to overcome these issues we use two Microsoft Kinect devices utilizing combined information from RGB and infrared sensors. The algorithm performance is tested in terms of recognition rate and processing time.
Popa, M.C.; Rothkrantz, L.J.M.; Wiggers, P.; Braspenning, R.A.C.; Shan, C.
Many approaches to facial expression recognition focus on assessing the six basic emotions (anger, disgust, happiness, fear, sadness, and surprise). Real-life situations proved to produce many more subtle facial expressions. A reliable way of analyzing the facial behavior is the Facial Action Coding
Tang, Mingchuan; Wu, Jianhong
Vander-Lugt correlator plays an important role in optical pattern recognition due to the characteristics of accurate positioning and high signal-to-noise ratio. The ideal Vander-Lugt correlator should have the ability of outputting strong and sharp correlation peak in allusion to the true target, in the existing Spatial Light Modulators, Liquid Crystal On Silicon(LCOS) has been the most competitive candidate for the matched filter owing to the continuous phase modulation peculiarity. Allowing for the distortions of the target to be identified including rotations, scaling changes, perspective changes, which can severely impact the correlation recognition results, herein, we present a modified Vander-Lugt correlator based on the LCOS by means of applying an iterative algorithm to the design of the filter so that the correlator can invariant to the distortions while maintaining good performance. The results of numerical simulation demonstrate that the filter could get the similar recognition results for all the training images. And the experiment shows that the modified correlator achieves the 180° rotating tolerance significantly improving the recognition efficiency of the correlator.
B. El Kessab
In this context we propose a data set for handwritten Tifinagh regions composed of 1600 image (100 Image for each region. The dataset can be used in one hand to test the efficiency of the Tifinagh region recognition system in extraction of characteristics significatives and the correct identification of each region in classification phase in the other hand.
Antu Annam Thomas
Full Text Available Face recognition has become one of the most active research areas of pattern recognition since the early1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP(SFAM. For extracting features to be used for classification, combination of Principal ComponentAnalysis (PCA and Linear Discriminant Analysis (LDA is used. This is for improving the capability ofLDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extractsthe features that help the classifier to classify the input face images. The classifier employed was SFAM.Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’sefficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage thatthe network is adaptive, that is, during testing phase if the network comes across a new face that it is nottrained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can beused in applications where database needs to be updated frequently. SFAM thus proves itself to be anefficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
Gelsema, E S
As a part of a collaboration with NEMCH aimed at the automation of the differential white blood cell count, studies have been made of the different possibilities for using colour to help in the recognition process. Results are presented comparing data obtained with a microspectrophotometer and with a simulated three-colour scanner.
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.
Shoaib, Muhammad; Scholten, Hans; Havinga, P.J.M.
In recent years, the use of a smartphone accelerometer in physical activity recognition has been well studied. However, the role of a gyroscope and a magnetometer is yet to be explored, both when used alone as well as in combination with an accelerometer. For this purpose, we investigate the role of
Dan, Zhiping; Sun, Shuifa; Chen, Yanfei; Gan, Haitao
Biometrics recognition aims to identify and predict new personal identities based on their existing knowledge. As the use of multiple biometric traits of the individual may enables more information to be used for recognition, it has been proved that multi-biometrics can produce higher accuracy than single biometrics. However, a common problem with traditional machine learning is that the training and test data should be in the same feature space, and have the same underlying distribution. If the distributions and features are different between training and future data, the model performance often drops. In this paper, we propose a transfer learning method for face recognition on bimodal biometrics. The training and test samples of bimodal biometric images are composed of the visible light face images and the infrared face images. Our algorithm transfers the knowledge across feature spaces, relaxing the assumption of same feature space as well as same underlying distribution by automatically learning a mapping between two different but somewhat similar face images. According to the experiments in the face images, the results show that the accuracy of face recognition has been greatly improved by the proposed method compared with the other previous methods. It demonstrates the effectiveness and robustness of our method.
Mualem, Orit; Lavidor, Michal
The current study is an interdisciplinary examination of the interplay among music, language, and emotions. It consisted of two experiments designed to investigate the relationship between musical abilities and vocal emotional recognition. In experiment 1 (N = 24), we compared the influence of two short-term intervention programs--music and…
Full Text Available Speech recognition applications are known to require a significant amount of resources. However, embedded speech recognition only authorizes few KB of memory, few MIPS, and small amount of training data. In order to fit the resource constraints of embedded applications, an approach based on a semicontinuous HMM system using state-independent acoustic modelling is proposed. A transformation is computed and applied to the global model in order to obtain each HMM state-dependent probability density functions, authorizing to store only the transformation parameters. This approach is evaluated on two tasks: digit and voice-command recognition. A fast adaptation technique of acoustic models is also proposed. In order to significantly reduce computational costs, the adaptation is performed only on the global model (using related speaker recognition adaptation techniques with no need for state-dependent data. The whole approach results in a relative gain of more than 20% compared to a basic HMM-based system fitting the constraints.
Li, Shunshan; Wei, Min; Tang, Haiying; Zhuang, Tiange; Buonocore, Michael
Image enhancement plays an important role in Fingerprint Recognition System. In this paper fingerprint image enhancement method, a refined Gabor filter, is presented. This enhancement method can connect the ridge breaks, ensures the maximal gray values located at the ridge center and has the ability to compensate for the nonlinear deformations. The result shows it can improve the performance of image enhancement.
Solomon, Joshua A; Cavanagh, Patrick; Gorea, Andrei
In distinct experiments we examined memories for orientation and size. After viewing a randomly oriented Gabor patch (or a plain white disk of random size), observers were given unlimited time to reproduce as faithfully as possible the orientation (or size) of that standard stimulus with an adjustable Gabor patch (or disk). Then, with this match stimulus still in view, a recognition probe was presented. On half the trials, this probe was identical to the standard. We expected observers to classify the probe (a same/different task) on the basis of its difference from the match, which should have served as an explicit memory of the standard. Observers did better than that. Larger differences were classified as "same" when probe and standard were indeed identical. In some cases, recognition performance exceeded that of a simulated observer subject to the same matching errors, but forced to adopt the single most advantageous criterion difference between the probe and match. Recognition must have used information that was not or could not be exploited in the reproduction phase. One possible source for that information is observers' confidence in their reproduction (e.g., in their memory of the standard). Simulations confirm the enhancement of recognition performance when decision criteria are adjusted trial-by-trial, on the basis of the observer's estimated reproduction error.
Thomas, N. L.; Du, Yingzi; Zhou, Zhi
The vein structure in the sclera is stable over time, unique to each person, and well suited for human identification. A few researchers have performed sclera vein pattern recognition and reported promising initial results. Sclera recognition poses several challenges: the vein structure moves and deforms with the movement of the eye; images of sclera patterns are often defocused and/or saturated; and, most importantly, the vein structure in the sclera is multi-layered and has complex non-linear deformation. In this paper, we proposed a new method for sclera recognition: First, we developed a color-based sclera region estimation scheme for sclera segmentation. Second, we designed a Gabor wavelet-based sclera pattern enhancement method, and an adaptive thresholding method to emphasize and binarize the sclera vein patterns. Third, we proposed a line descriptor-based feature extraction, registration, and matching method that is illumination-, scale-, orientation-, and deformation-invariant, and can mitigate the multi-layered deformation effects exhibited in the sclera and tolerate segmentation error. It is empirically verified using the UBIRIS database that the proposed method can perform accurate sclera recognition.
Full Text Available Iris recognition for human identification is one of the most accurate biometrics, and its employment is expanding globally. The use of portable iris systems, particularly in law enforcement applications, is growing. In many of these applications, the portable device may be required to transmit an iris image or template over a narrow-bandwidth communication channel. Typically, a full resolution image (e.g., VGA is desired to ensure sufficient pixels across the iris to be confident of accurate recognition results. To minimize the time to transmit a large amount of data over a narrow-bandwidth communication channel, image compression can be used to reduce the file size of the iris image. In other applications, such as the Registered Traveler program, an entire iris image is stored on a smart card, but only 4 kB is allowed for the iris image. For this type of application, image compression is also the solution. This paper investigates the effects of image compression on recognition system performance using a commercial version of the Daugman iris2pi algorithm along with JPEG-2000 compression, and links these to image quality. Using the ICE 2005 iris database, we find that even in the face of significant compression, recognition performance is minimally affected.
Full Text Available Micro-expressions are often embedded in a flow of expressions including both neutral and other facial expressions. However, it remains unclear whether the types of facial expressions appearing before and after the micro-expression, i.e., the emotional context, influence micro-expression recognition. To address this question, the present study used a modified METT (Micro-Expression Training Tool paradigm that required participants to recognize the target micro-expressions presented briefly between two identical emotional faces. The results of Experiments 1 and 2 showed that negative context impaired the recognition of micro-expressions regardless of the duration of the target micro-expression. Stimulus-difference between the context and target micro-expression was accounted for in Experiment 3. Results showed that a context effect on micro-expression recognition persists even when the stimulus similarity between the context and target micro-expressions was controlled. Therefore, our results not only provided evidence for the context effect on micro-expression recognition but also suggested that the context effect might result from both the stimulus and valence differences.
Sandbach, G.; Zafeiriou, S.; Pantic, Maja; Rueckert, D.
In this paper we propose a method that exploits 3D motion-based features between frames of 3D facial geometry sequences for dynamic facial expression recognition. An expressive sequence is modelled to contain an onset followed by an apex and an offset. Feature selection methods are applied in order
Baby, Sanmohan; Krüger, Volker
Imitation learning is considered to be an effective way of teaching humanoid robots and action recognition is the key step to imitation learning. In this paper an online algorithm to recognize parametric actions with object context is presented. Objects are key instruments in understanding an ac...
Knight, G. William; And Others
First year dental students (n=86) in a conservative restorations course were taught a discrimination learning paradigm to improve production quality. Evaluation of Class 1 amalgam preparations indicates the improved recognition skills corresponded with improved cavity preparation, supporting the use of this teaching model. (Author/MSE)
Zhou, J.; Kryzak, A.; Suen, C.Y.
In the field of financial document processing, recognition of touching handwritten numerals has been limited by lack of good benchmarking databases and low reliability of algorithms. This paper addresses the efforts toward solving the two problems. Two databases IRIS-Bell\\\\\\'98 and TNIST are built/o
Wulf, Christoph; Bittner, Martin; Clemens, Iris; Kellermann, Ingrid
The article focuses on pedagogical practices of recognition and esteem (Wertschatzung) and on the question of how those practices can be appropriately studied and epistemologically grasped. The investigation involves an inner-city elementary school in a socio-economically problematic district. With regard to the communication forms in this…
Mattys, Sven L.; Wiget, Lukas
The effect of cognitive load (CL) on speech recognition has received little attention despite the prevalence of CL in everyday life, e.g., dual-tasking. To assess the effect of CL on the interaction between lexically-mediated and acoustically-mediated processes, we measured the magnitude of the "Ganong effect" (i.e., lexical bias on phoneme…
Degn, S E; Thiel, S
In the context of immunity, pattern recognition is the art of discriminating friend from foe and innocuous from noxious. The basis of discrimination is the existence of evolutionarily conserved patterns on microorganisms, which are intrinsic to these microorganisms and necessary for their function and existence. Such immutable or slowly evolving patterns are ideal handles for recognition and have been targeted by early cellular immune defence mechanisms such as Toll-like receptors, NOD-like receptors, RIG-I-like receptors, C-type lectin receptors and by humoral defence mechanisms such as the complement system. Complement is a proteolytic cascade system comprising around 35 different soluble and membrane-bound proteins. It constitutes a central part of the innate immune system, mediating several major innate effector functions and modulating adaptive immune responses. The complement cascade proceeds via controlled, limited proteolysis and conformational changes of constituent proteins through three activation pathways: the classical pathway, the alternative pathway and the lectin pathway, which converge in common effector functions. Here, we review the nature of the pattern recognition molecules involved in complement activation, as well as their close relatives with no or unknown capacity for activating complement. We proceed to examine the composition of the pattern recognition complexes involved in complement activation, focusing on those of the lectin pathway, and arrive at a new model for their mechanism of operation, supported by recently emerging evidence.
Nallure Balasubramanian, Vineeth
The fields of pattern recognition and machine learning are on a fundamental quest to design systems that can learn the way humans do. One important aspect of human intelligence that has so far not been given sufficient attention is the capability of humans to express when they are certain about a decision, or when they are not. Machine learning…
An algebraic semantic approach for the description and recognition of patterns is presented.Specifically,patterns are assumed as algebraic structures,and semantic constraints are given in the form of equational specifications.By such an idea,to recogniz a pattern is to check the validity of an equational conjecture by term rewriting.Such an approach is demonstrated through examples.
Veenstra-Knol, HE; Scheewe, JH; van der Vlist, GJ; van Doorn, ME; Ausems, MGEM
The basal cell naevus syndrome is an autosomal dominant syndrome characterised by major manifestations such as basal cell carcinomas, jaw cysts, palmar or plantar pits, and intracranial calcifications. Early recognition is important in order to reduce morbidity due to cutaneous and cerebral malignan
N. Hu; G. Englebienne; Z. Lou; B. Kröse
We present a novel latent discriminative model for human activity recognition. Unlike the approaches that require conditional independence assumptions, our model is very flexible in encoding the full connectivity among observations, latent states, and activity states. The model is able to capture ri
Han, Sunhyoung; Vasconcelos, Nuno
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
Khosla, Deepak; Chen, Yang; Kim, Kyungnam
Automated video object recognition is a topic of emerging importance in both defense and civilian applications. This work describes an accurate and low-power neuromorphic architecture and system for real-time automated video object recognition. Our system, Neuormorphic Visual Understanding of Scenes (NEOVUS), is inspired by computational neuroscience models of feed-forward object detection and classification pipelines for processing visual data. The NEOVUS architecture is inspired by the ventral (what) and dorsal (where) streams of the mammalian visual pathway and integrates retinal processing, object detection based on form and motion modeling, and object classification based on convolutional neural networks. The object recognition performance and energy use of the NEOVUS was evaluated by the Defense Advanced Research Projects Agency (DARPA) under the Neovision2 program using three urban area video datasets collected from a mix of stationary and moving platforms. These datasets are challenging and include a large number of objects of different types in cluttered scenes, with varying illumination and occlusion conditions. In a systematic evaluation of five different teams by DARPA on these datasets, the NEOVUS demonstrated the best performance with high object recognition accuracy and the lowest energy consumption. Its energy use was three orders of magnitude lower than two independent state of the art baseline computer vision systems. The dynamic power requirement for the complete system mapped to commercial off-the-shelf (COTS) hardware that includes a 5.6 Megapixel color camera processed by object detection and classification algorithms at 30 frames per second was measured at 21.7 Watts (W), for an effective energy consumption of 5.45 nanoJoules (nJ) per bit of incoming video. These unprecedented results show that the NEOVUS has the potential to revolutionize automated video object recognition toward enabling practical low-power and mobile video processing
Beritelli, Francesco; Borrometi, Luca; Cuce, Antonino
The acoustic approach to speech recognition has an important advantage compared with pattern recognition approach: it presents a lower complexity because it doesn't require explicit structures such as the hidden Markov model. In this work, we show how to characterize some phonetic classes of the Italian language in order to obtain a speaker and vocabulary independent speech recognition system. A phonetic data base is carried out with 200 continuous speech sentences of 12 speakers, 6 females and 6 males. The sentences are sampled at 8000 Hz and manual labelled with Asystem Sound Impression Software to obtain about 1600 units. We analyzed several speech parameters such as formants, LPC and reflection coefficients, energy, normal/differential zero crossing rate, cepstral and autocorrelation coefficients. The aim is the achievement of a phonetic recognizer to facilitate the so- called lexical access problem, that is to decode phonetic units into complete sense word strings. The knowledge is supplied to the recognizer in terms of fuzzy systems. The utilized software is called adaptive fuzzy modeler and it belongs to the rule generator family. A procedure has been implemented to integrate in the fuzzy system an 'expert' knowledge in order to obtain significant improvements in the recognition accuracy. Up to this point the tests show a recognition rate of 92% for the vocal class, 89% for the fricatives class and 94% for the nasal class, utilizing 1000 phonemes in phase of learning and 600 phonemes in phase of testing. Our intention is to complete the fuzzy recognizer extending this work to the other phonetic classes.
Moro, V; Pernigo, S; Avesani, R; Bulgarelli, C; Urgesi, C; Candidi, M; Aglioti, S M
Conspicuous deficits in face recognition characterize prosopagnosia. Information on whether agnosic deficits may extend to non-facial body parts is lacking. Here we report the neuropsychological description of FM, a patient affected by a complete deficit in face recognition in the presence of mild clinical signs of visual object agnosia. His deficit involves both overt and covert recognition of faces (i.e. recognition of familiar faces, but also categorization of faces for gender or age) as well as the visual mental imagery of faces. By means of a series of matching-to-sample tasks we investigated: (i) a possible association between prosopagnosia and disorders in visual body perception; (ii) the effect of the emotional content of stimuli on the visual discrimination of faces, bodies and objects; (iii) the existence of a dissociation between identity recognition and the emotional discrimination of faces and bodies. Our results document, for the first time, the co-occurrence of body agnosia, i.e. the visual inability to discriminate body forms and body actions, and prosopagnosia. Moreover, the results show better performance in the discrimination of emotional face and body expressions with respect to body identity and neutral actions. Since FM's lesions involve bilateral fusiform areas, it is unlikely that the amygdala-temporal projections explain the relative sparing of emotion discrimination performance. Indeed, the emotional content of the stimuli did not improve the discrimination of their identity. The results hint at the existence of two segregated brain networks involved in identity and emotional discrimination that are at least partially shared by face and body processing.
Hannagan, Thomas; Magnuson, James S; Grainger, Jonathan
How do we map the rapid input of spoken language onto phonological and lexical representations over time? Attempts at psychologically-tractable computational models of spoken word recognition tend either to ignore time or to transform the temporal input into a spatial representation. TRACE, a connectionist model with broad and deep coverage of speech perception and spoken word recognition phenomena, takes the latter approach, using exclusively time-specific units at every level of representation. TRACE reduplicates featural, phonemic, and lexical inputs at every time step in a large memory trace, with rich interconnections (excitatory forward and backward connections between levels and inhibitory links within levels). As the length of the memory trace is increased, or as the phoneme and lexical inventory of the model is increased to a realistic size, this reduplication of time- (temporal position) specific units leads to a dramatic proliferation of units and connections, begging the question of whether a more efficient approach is possible. Our starting point is the observation that models of visual object recognition-including visual word recognition-have grappled with the problem of spatial invariance, and arrived at solutions other than a fully-reduplicative strategy like that of TRACE. This inspires a new model of spoken word recognition that combines time-specific phoneme representations similar to those in TRACE with higher-level representations based on string kernels: temporally independent (time invariant) diphone and lexical units. This reduces the number of necessary units and connections by several orders of magnitude relative to TRACE. Critically, we compare the new model to TRACE on a set of key phenomena, demonstrating that the new model inherits much of the behavior of TRACE and that the drastic computational savings do not come at the cost of explanatory power. PMID:24058349
Full Text Available How do we map the rapid input of spoken language onto phonological and lexical representations over time? Attempts at psychologically-tractable computational models of spoken word recognition tend either to ignore time or to transform the temporal input into a spatial representation. TRACE, a connectionist model with broad and deep coverage of speech perception and spoken word recognition phenomena, takes the latter approach, using exclusively time-specific units at every level of representation. TRACE reduplicates featural, phonemic, and lexical inputs at every time step in a large memory trace, with rich interconnections (excitatory forward and backward connections between levels and inhibitory links within levels. As the length of the memory trace is increased, or as the phoneme and lexical inventory of the model is increased to a realistic size, this reduplication of time- (temporal position specific units leads to a dramatic proliferation of units and connections, begging the question of whether a more efficient approach is possible. Our starting point is the observation that models of visual object recognition - including visual word recognition - have grappled with the problem of spatial invariance, and arrived at solutions other than a fully-reduplicative strategy like that of TRACE. This inspires a new model of spoken word recognition that combines time-specific phoneme representations similar to those in TRACE with higher-level representations based on string kernels: temporally independent (time invariant diphone and lexical units. This reduces the number of necessary units and connections by several orders of magnitude relative to TRACE. Critically, we compare the new model to TRACE on a set of key phenomena, demonstrating that the new model inherits much of the behavior of TRACE and that the drastic computational savings do not come at the cost of explanatory power.
Full Text Available This Paper presents a new hybrid technique for digit recognition applied to the speed limit sign recognition task. The complete recognition system consists in the detection and recognition of the speed signs in RGB images. A pretreatment is applied to extract the pictogram from a detected circular road sign, and then the task discussed in this work is employed to recognize digit candidates. To realize a compromise between performances, reduced execution time and optimized memory resources, the developed method is based on a conjoint use of a Neural Network and a Decision Tree. A simple Network is employed firstly to classify the extracted candidates into three classes and secondly a small Decision Tree is charged to determine the exact information. This combination is used to reduce the size of the Network as well as the memory resources utilization. The evaluation of the technique and the comparison with existent methods show the effectiveness.
Full Text Available This essay aims to extend Paul Ricœur’s account of recognition to address some of the concerns of globalization, especially those voiced by Enrique Dussel. The extension is accomplished in two parts. First, Dussel’s account of spatial existence as dwelling is reviewed as it is pertinent to the concerns of globalization. Next, it is demonstrated that each of the aspects of Ricœur’s account of recognition may be given a spatial re-articulation. The results thus establish an outline of how recognition theory might address some of the concerns of globalization. The essay concludes with several consequences for the modification of recognition politics as one finds it in the late work of Ricœur and in Axel Honneth’s ongoing inquiries.
Full Text Available Traffic signs provide the driver various information for safe and efficient navigation. Automatic recognition of traffic signs is, therefore, important for automated driving or driver assistance systems.In this paper, a new and efficient traffic sign recognition system based on extracting diverse feature set, and applying mixture of experts'architecture on the extracted featuresis proposed.In the result part, the proposed approach is evaluated on the German traffic sign recognition and Grigorescu traffic signsbenchmark and high recognition rate is achieved.Comparison with some of the most related methods indicates that the proposed novel model yields excellent recognition rate in traffic sign recognition that is the recognition rate of 99.94% for the training set and 98.50% for the test set.In addition, experimental results have demonstrated our method robust in successful recognition of traffic signs even with variant lighting.
Describes the goals, the selection criteria, and the selection process of the Elementary Private School Recognition Program. Includes a listing, by states, of the 60 private elementary schools selected for 1985-86 recognition. (IW)
S.J. Gervais; T.K. Vescio; J. Förster; A. Maass; C. Suitner
Objectification theory suggests that the bodies of women are sometimes reduced to their sexual body parts. As well, an extensive literature in cognitive psychology suggests that global processing underlies person recognition, whereas local processing underlies object recognition. Integrating these l
Grether, GF; Drury, JP; Berlin, E.; Anderson, CN
© 2015 Blackwell Verlag GmbH. The decision rules that animals use for distinguishing between conspecifics of different age and sex classes are relevant for understanding how closely related species interact in sympatry. In rubyspot damselflies (Hetaerina spp.), the red wing coloration of mature males is hypothesized to be a key trait for sex recognition and competitor recognition within species and the proximate trigger for interspecific male-male aggression. We tested this hypothesis by mani...
Teresa Souto; Alexandre Baptista; Diana Tavares; Cristina Queirós; Marques António
BACKGROUND: Significant deficits in emotional recognition and social perception characterize patients with schizophrenia and have direct negative impact both in inter-personal relationships and in social functioning. Virtual reality, as a methodological resource, might have a high potential for assessment and training skills in people suffering from mental illness. OBJECTIVES: To present preliminary results of a facial emotional recognition assessment designed for patients with schizophrenia,...
Full Text Available Abstract Transparent conductive films (TCFs are critical components of a myriad of technologies including flat panel displays, light-emitting diodes, and solar cells. Graphene-based TCFs have attracted a lot of attention because of their high electrical conductivity, transparency, and low cost. Carrier doping of graphene would potentially improve the properties of graphene-based TCFs for practical industrial applications. However, controlling the carrier type and concentration of dopants in graphene films is challenging, especially for the synthesis of p-type films. In this article, a new method for doping graphene using the conjugated organic molecule, tetracyanoquinodimethane (TCNQ, is described. Notably, TCNQ is well known as a powerful electron accepter and is expected to favor electron transfer from graphene into TCNQ molecules, thereby leading to p-type doping of graphene films. Small amounts of TCNQ drastically improved the resistivity without degradation of optical transparency. Our carrier doping method based on charge transfer has a huge potential for graphene-based TCFs.
Hoffman, K. L.; Logothetis, N.K.
Learning about the world through our senses constrains our ability to recognise our surroundings. Experience shapes perception. What is the neural basis for object recognition and how are learning-induced changes in recognition manifested in neural populations? We consider first the location of neurons that appear to be critical for object recognition, before describing what is known about their function. Two complementary processes of object recognition are considered: discrimination among d...
The recognition heuristic models the adaptive use and dominant role of recognition knowledge in judgment under uncertainty. Of the several predictions that the heuristic makes, empirical tests have predominantly focused on the proposed noncompensatory processing of recognition. Some authors have emphasized that the heuristic needs to be scrutinized based on precise tests of the exclusive use of recognition. Although precise tests have clear merits, I critically evaluate the value of such test...
Ismail, Ismail A; danaf, Talaat S El; Samak, Ahmed H
This paper present a novel off-line signature recognition method based on multi scale Fourier Descriptor and wavelet transform . The main steps of constructing a signature recognition system are discussed and experiments on real data sets show that the average error rate can reach 1%. Finally we compare 8 distance measures between feature vectors with respect to the recognition performance. Key words: signature recognition; Fourier Descriptor; Wavelet transform; personal verification
Gašpar, Tina; Labor, Marina; Jurić, Iva; Dumančić, Dijana; Ilakovac, Vesna; Heffer, Marija
The recognition of basic emotions in everyday communication involves interpretation of different visual and auditory clues. The ability to recognize emotions is not clearly determined as their presentation is usually very short (micro expressions), whereas the recognition itself does not have to be a conscious process. We assumed that the recognition from facial expressions is selected over the recognition of emotions communicated through music. In order to compare the success rate in recogni...
Bhawna Chouhan; Dr.(Mrs) Shailja Shukla
biometric system provides automatic identification of an individual based on a unique feature or characteristic possessed by the individual. Iris recognition is regarded as the most reliable and accurate biometric identification system available. Most commercial iris recognition systems use patented algorithms developed by Daugman, and these algorithms are able to produce perfect recognition rates. Especially it focuses on image segmentation and feature extraction for iris recognition process...
Barnes, Bonnie; Barnes, Mark; Sweeney, Cynthia D
What is meaningful recognition? As a nurse leader, are you prepared to answer that question? Understanding the implications and impact of recognition for nursing staff is a powerful tool for nursing leaders. The DAISY Award is used in more than 2,100 organizations around the globe to give meaning to recognition. Here is a glimpse of the power that recognition can bring to an organization, to its leaders, and most importantly to staff. PMID:27011149
Zumalt, Joseph R.
Voice recognition software allows computer users to bypass their keyboards and use their voices to enter text. While the library literature is somewhat silent about voice recognition technology, the medical and legal communities have reported some success using it. Voice recognition software was tested for dictation accuracy and usability within an agriculture library at the University of Illinois. Dragon NaturallySpeaking 8.0 was found to be more accurate than speech recognition within Micro...
According to the fundamental theory of visual cognition mechanism and cognitive psychology,the visual pattern recognition model is introduced briefly.Three pattern recognition models,i.e.template-based matching model,prototype-based matching model and feature-based matching model are built and discussed separately.In addition,the influence of object background information and visual focus point to the result of pattern recognition is also discussed with the example of recognition for fuzzy letters and figures.
Yu, Hua; Zhou, Peng; Deng, Maolin; Shang, Zhicai
Protein-peptide interactions are prevalent and play essential roles in many living activities. Peptides recognize their protein partners by direct nonbonded interactions and indirect adjustment of conformations. Although processes of protein-peptide recognition have been comprehensively studied in both sequences and structures recently, flexibility of peptides and the configuration entropy penalty in recognition did not get enough attention. In this study, 20 protein-peptide complexes and their corresponding unbound peptides were investigated by molecular dynamics simulations. Energy analysis revealed that configurational entropy penalty introduced by restriction of the degrees of freedom of peptides in indirect readout process of protein-peptide recognition is significant. Configurational entropy penalty has become the main content of the indirect readout energy in protein-peptide recognition instead of deformation energy which is the main source of the indirect readout energy in classical biomolecular recognition phenomena, such as protein-DNA binding. These results provide us a better understanding of protein-peptide recognition and give us some implications in peptide ligand design.
... 46 Shipping 1 2010-10-01 2010-10-01 false Recognition of a classification society. 8.220 Section 8... INSPECTION ALTERNATIVES Recognition of a Classification Society § 8.220 Recognition of a classification society. (a) A classification society must be recognized by the Commandant before it may receive...
Dijck, van Hendrikus Alfonsus Ludovicus
The subject of this thesis is the automatic recognition of objects from digital images. The discussion is restricted to recognition of man made objects that can be described by deterministic, structural models. Applications of this kind of recognition tasks can be found in industry. Object recogniti
This report describes how to use an automatic target recognition system (version 14). In separate volumes are a general description of the ATR system, Automatic TLI Recognition System, General Description, and a programmer`s manual, Automatic TLI Recognition System, Programmer`s Guide.
Hook, Genine A.
This paper aims to examine some of ways sole parents sought recognition as postgraduate students in Australian universities. Judith Butler's theory of recognition notes that recognition is always partial and any account we give of ourselves must be given to another. Participants articulated that supervisors were critical in the process of…
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…
Shoujia Wang; Wenhui Li; Ying Wang; Yuanyuan Jiang; Shan Jiang; Ruilin Zhao
In this paper, we analyze an improved DOG filter with different parameters in horizontal and vertical directions and the improved filter uses oval recognition domain instead of round recognition domain of classic DOG filter. The improved filter is used to compare with major illumination pretreatment methods. The experiment result shows the improved method has better recognition and false detection rate.
Full Text Available The recognition heuristic is a prime example of how, by exploiting a match between mind and environment, a simple mental strategy can lead to efficient decision making. The proposal of the heuristic initiated a debate about the processes underlying the use of recognition in decision making. We review research addressing four key aspects of the recognition heuristic: (a that recognition is often an ecologically valid cue; (b that people often follow recognition when making inferences; (c that recognition supersedes further cue knowledge; (d that its use can produce the less-is-more effect—the phenomenon that lesser states of recognition knowledge can lead to more accurate inferences than more complete states. After we contrast the recognition heuristic to other related concepts, including availability and fluency, we carve out, from the existing findings, some boundary conditions of the use of the recognition heuristic as well as key questions for future research. Moreover, we summarize developments concerning the connection of the recognition heuristic with memory models. We suggest that the recognition heuristic is used adaptively and that, compared to other cues, recognition seems to have a special status in decision making. Finally, we discuss how systematic ignorance is exploited in other cognitive mechanisms (e.g., estimation and preference.
Muhammed Tayyib Kadak; Burak Dogangn; Turkay Demir
Autism is a genetically transferred neurodevelopmental disorder characterized by severe and permanent deficits in many interpersonal relation areas like communication, social interaction and emotional responsiveness. Patients with autism have deficits in face recognition, eye contact and recognition of emotional expression. Both recognition of face and expression of facial emotion carried on face processing. Structural and functional impairment in fusiform gyrus, amygdala, superior temporal s...
Kadak, Muhammed Tayyib; Demir, Türkay; Doğangün, Burak
Autism is a genetically transferred neurodevelopmental disorder characterized by severe and permanent deficits in many interpersonal relation areas like communication, social interaction and emotional responsiveness. Patients with autism have deficits in face recognition, eye contact and recognition of emotional expression. Both recognition of face and expression of facial emotion carried on face processing. Structural and functional impairment in fusiform gyrus, amygdala, superior temporal...
Trupti R. Zalke
Full Text Available Optical Character Recognition is process of recognition of character from scanned document and lots of OCR now available in the market. But most of these systems work for Roman, Chinese, Japanese and Arabic characters . There are no sufficient number of work on Indian language script like Devanagari so this paper present a review on optical character recognition on handwritten Devanagari script.
Avery, Cynthia M.
According to many psychologists, the connections between motivation and rewards and recognition are crucial to employee satisfaction. A plan for developing a multi-layered recognition program within a division of student affairs is described. These recognitions programs are designed taking into account the differences in perceptions of awards by…
Biometrics - recognition of persons based on how they look or behave, is the main subject of research at the Chair of Biometric Pattern Recognition (BPR) of the Services, Cyber Security and Safety Group (SCS) of the EEMCS Faculty at the University of Twente. Examples are finger print recognition, ir
Many current and future NASA missions make extreme demands on mission personnel both in terms of work load and in performing under difficult environmental conditions. In situations where hands are impeded or needed for other tasks, eyes are busy attending to the environment, or tasks are sufficiently complex that ease of use of the interface becomes critical, spoken natural language dialog systems offer unique input and output modalities that can improve efficiency and safety. They also offer new capabilities that would not otherwise be available. For example, many NASA applications require astronauts to use computers in micro-gravity or while wearing space suits. Under these circumstances, command and control systems that allow users to issue commands or enter data in hands-and eyes-busy situations become critical. Speech recognition technology designed for current commercial applications limits the performance of the open-ended state-of-the-art dialog systems being developed at NASA. For example, today's recognition systems typically listen to user input only during short segments of the dialog, and user input outside of these short time windows is lost. Mistakes detecting the start and end times of user utterances can lead to mistakes in the recognition output, and the dialog system as a whole has no way to recover from this, or any other, recognition error. Systems also often require the user to signal when that user is going to speak, which is impractical in a hands-free environment, or only allow a system-initiated dialog requiring the user to speak immediately following a system prompt. In this project, SRI has developed software to enable speech recognition in a hands-free, open-microphone environment, eliminating the need for a push-to-talk button or other signaling mechanism. The software continuously captures a user's speech and makes it available to one or more recognizers. By constantly monitoring and storing the audio stream, it provides the spoken
Productivity--why it's low and how to enhance it--is on everyone's mind these days. A major component of productivity is employee satisfaction. If an employee is dissatisfied, feels unappreciated or under-compensated, that employee will not perform to the best of his or her ability. How is the personnel administrator to address this pressing problem? One answer that emerges is employee recognition programs. In many cases, properly run recognition programs can boost awareness of the organization, build employee pride, raise morale and, ultimately, increase productivity. As some of our respondents observed, higher salary is not the best answer. While a larger paycheck is always appreciated, everyone's pride is boosted by a public demonstration of appreciation.
Full Text Available In this paper we scrutinize the influence of fusion on the face recognition performance. In pattern recognition task, benefiting from different uncorrelated observations and performing fusion at feature and/or decision levels improves the overall performance. In features fusion approach, we fuse (concatenate the feature vectors obtained using different feature extractors for the same image. Classification is then performed using different similarity measures. In decisions fusion approach, the fusion is performed at decisions level, where decisions from different algorithms are fused using majority voting. The proposed method was tested using face images having different facial expressions and conditions obtained from ORL and FRAV2D databases. Simulations results show that the performance of both feature and decision fusion approaches outperforms the single performances of the fused algorithms significantly.
Bhowmik, Mrinal Kanti; Nasipuri, Mita; Basu, Dipak Kumar; Kundu, Mahantapas
In this paper we present a technique for fusion of optical and thermal face images based on image pixel fusion approach. Out of several factors, which affect face recognition performance in case of visual images, illumination changes are a significant factor that needs to be addressed. Thermal images are better in handling illumination conditions but not very consistent in capturing texture details of the faces. Other factors like sunglasses, beard, moustache etc also play active role in adding complicacies to the recognition process. Fusion of thermal and visual images is a solution to overcome the drawbacks present in the individual thermal and visual face images. Here fused images are projected into an eigenspace and the projected images are classified using a radial basis function (RBF) neural network and also by a multi-layer perceptron (MLP). In the experiments Object Tracking and Classification Beyond Visible Spectrum (OTCBVS) database benchmark for thermal and visual face images have been used. Compar...
Henry, E. Michael
Since 1989, Martin Marietta has invested in the development of an innovative concept for robust real-time pattern recognition for any two-dimensioanal sensor. This concept has been tested in simulation, and in laboratory and field hardware, for a number of DOD and commercial uses from automatic target recognition to manufacturing inspection. We have now joined Rose Health Care Systems in developing its use for medical diagnostics. The concept is based on determining regions of interest by using optical Fourier bandpassing as a scene segmentation technique, enhancing those regions using wavelet filters, passing the enhanced regions to a neural network for analysis and initial pattern identification, and following this initial identification with confirmation by optical correlation. The optical scene segmentation and pattern confirmation are performed by the same optical module. The neural network is a recursive error minimization network with a small number of connections and nodes that rapidly converges to a global minimum.
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.
Full Text Available A novel face recognition method is proposed, in which face images are represented by a set of local labeled graphs, each containing information about the appearance and geometry of a 3-tuple of face feature points, extracted using Local Feature Analysis (LFA technique. Our method automatically learns a model set and builds a graph space for each individual. A two-stage method for optimal matching between the graphs extracted from a probe image and the trained model graphs is proposed. The recognition of each probe face image is performed by assigning it to the trained individual with the maximum number of references. Our approach achieves perfect result on the ORL face set and an accuracy rate of 98.4% on the FERET face set, which shows the superiority of our method over all considered state-of-the-art methods. I
Zheng-wei HUANG; Wen-tao XUE; Qi-rong MAO
Emotion-based features are critical for achieving high performance in a speech emotion recognition (SER) system. In general, it is diﬃcult to develop these features due to the ambiguity of the ground-truth. In this paper, we apply several unsupervised feature learning algorithms (including K-means clustering, the sparse auto-encoder, and sparse restricted Boltzmann machines), which have promise for learning task-related features by using unlabeled data, to speech emotion recognition. We then evaluate the performance of the proposed approach and present a detailed analysis of the effect of two important factors in the model setup, the content window size and the number of hidden layer nodes. Experimental results show that larger content windows and more hidden nodes contribute to higher performance. We also show that the two-layer network cannot explicitly improve performance compared to a single-layer network.
This paper addresses the application of hand gesture recognition in monocular image sequences using Active Appearance Model (AAM). For this work, the proposed algorithm is conposed of constructing AAMs and fitting the models to the interest region. In training stage, according to the manual labeled feature points, the relative AAM is constructed and the corresponding average feature is obtained. In recognition stage, the interesting hand gesture region is firstly segmented by skin and movement cues.Secondly, the models are fitted to the image that includes the hand gesture, and the relative features are extracted.Thirdly, the classification is done by comparing the extracted features and average features. 30 different gestures of Chinese sign language are applied for testing the effectiveness of the method. The Experimental results are given indicating good performance of the algorithm.
Bartusevicius, E; Vanagas, V; Radil-Weiss, T
Rectangular geometrical patterns with equal number of lines, identical relations between their vertical and horizontal components, identical angles, line crossing knots and free ends of lines were presented tachistoscopically to human subjects with a restricted recognition time caused by backward masking. Symmetrical linear transformation with respect to the Y or X axes and rotations of the patterns were performed and the correctness of their reproduction was measured in psychophysical experiments. A mental pattern transformation was a fast operation (under 100 ms) not directly linked to a graphic or a verbal expression of the results of reproduction. Mental transformation is probably determined by the recognition process. Symmetrical transformations are easier than rotational, whereas the most difficult is a detection of a pattern differing in its form from those considered within a predetermined group of samples.
Murynin, Alexander B.; Matveev, Ivan A.; Kuznetsov, Victor D.
A biometric access control system based on identification of human face is presented. The system developed performs remote measurements of the necessary face features. Two different scenarios of the system behavior are implemented. The first one assumes the verification of personal data entered by visitor from console using keyboard or card reader. The system functions as an automatic checkpoint, that strictly controls access of different visitors. The other scenario makes it possible to identify visitors without any person identifier or pass. Only person biometrics are used to identify the visitor. The recognition system automatically finds necessary identification information preliminary stored in the database. Two laboratory models of recognition system were developed. The models are designed to use different information types and sources. In addition to stereoscopic images inputted to computer from cameras the models can use voice data and some person physical characteristics such as person's height, measured by imaging system.
Sheila Esmeralda Gonzalez-Reyna
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.
Chen Aijun; Li Jinzong; Zhu Bing
To recognize circular objects rapidly in satellite remote sensing imagery, an approach using their geometry properties is presented.The original image is segmented to be a binary one by one dimension maximum entropy threshold algorithm and the binary image is labeled with an algorithm based on recursion technique.Then, shape parameters of all labeled regions are calculated and those regions with shape parameters satisfying certain conditions are recognized as circular objects.The algorithm is described in detail, and comparison experiments with the randomized Hough transformation (RHT) are also provided.The experimental results on synthetic images and real images show that the proposed method has the merits of fast recognition rate, high recognition efficiency and the ability of anti-noise and anti-jamming.In addition, the method performs well when some circular objects are little deformed and partly misshapen.
Nguyen, Phuong Giang; Andersen, Hans Jørgen
features (Multi-scale Oriented Patches) in , which extract features of patches around interest points. To speed up the searching process, we employ the vocabulary tree based search technique in . Our final system shows high performance in recognizing buildings under significant temporal variations......In literature, existing researches on building recognition mainly concentrate on scales, rotations, and viewpoints variance. In urban environment, large temporal variations of weather and lighting conditions should also be considered as major challenges for robust recognition. For instances......, there are differences between images captured during daytime and nighttime, especially significant changes in building appearances between seasons because of the differences in light setting. To date, these large temporal variation issues have not been fully investigated. In this paper, we therefore focus...
Full Text Available The image of the license plate is located and segmented by some digital-image processing technologies such as gray-scale processing, gray-scale stretching and filtering, edge detection, morphological processing, Hough transformation etc. According to the characteristics of the license plate, binary matrix of character image sets the fuzzy matrix, and based on the principle of proximity computing space of closeness to get the fuzzy pattern recognition of characters. On the basis of the data of image pixels, the samples which are randomly selected under noisy condition and which are treated by morphological processing are randomly selected, and then the samples are used to test the simulation and identification of Back Propagation (BP Neural Networks. With the mathematical software-Matlab programming, the license plates are recognized. The aim is to develop the Visualization of user interface in License Plate Recognition System.