Search results for: pattern-recognition-by-humans-and-machines

Pattern Recognition by Humans and Machines

Author : Eileen C. Schwab
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Pattern Recognition by Humans and Machines, Volume 2: Visual Perception provides information pertinent to the fundamental aspects of research on perception. This book focuses on several fundamental problems of pattern recognition in speech perception and visual form perception. Organized into seven chapters, this volume begins with an overview of some of the basic theoretical questions in speech perception. This text then explores the spatiotemporal orchestration of visuosensory attentional and oculomotor processes involved in active visual exploration. Other chapters consider several basic questions concerning visual form perception, including the perception of patterns and features. This book discusses as well the role of eye movements in pattern processing and models of segmentation and pattern recognition. The final chapter describes the distinctions made in perceptual processing between model-driven and data-driven processing. This book is a valuable resource for psychologists in general and specialists in the field of perception. Computer and cognitive scientists will also find this book useful.

Pattern Recognition Machine Intelligence and Biometrics

Author : Patrick S. P. Wang
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"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 security. In addition, computer modeling and simulations of human behaviors are addressed in this collection of 31 chapters by top-ranked professionals from all over the world in the field of PR/AI/Biometrics. The book is intended for researchers and graduate students in Computer and Information Science, and in Communication and Control Engineering. Dr. Patrick S. P. Wang is a Professor Emeritus at the College of Computer and Information Science, Northeastern University, USA, Zijiang Chair of ECNU, Shanghai, and NSC Visiting Chair Professor of NTUST, Taipei.

Pattern Recognition and Machine Intelligence

Author : B. Uma Shankar
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This book constitutes the proceedings of the 7th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2017,held in Kolkata, India, in December 2017. The total of 86 full papers presented in this volume were carefully reviewed and selected from 293 submissions. They were organized in topical sections named: pattern recognition and machine learning; signal and image processing; computer vision and video processing; soft and natural computing; speech and natural language processing; bioinformatics and computational biology; data mining and big data analytics; deep learning; spatial data science and engineering; and applications of pattern recognition and machine intelligence.

Computer Vision and Pattern Recognition in Environmental Informatics

Author : Zhou, Jun
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Computer Vision and Pattern Recognition (CVPR) together play an important role in the processes involved in environmental informatics due to their pervasive, non-destructive, effective, and efficient natures. As a result, CVPR has made significant contributions to the field of environmental informatics by enabling multi-modal data fusion and feature extraction, supporting fast and reliable object detection and classification, and mining the intrinsic relationship between different aspects of environmental data. Computer Vision and Pattern Recognition in Environmental Informatics describes a number of methods and tools for image interpretation and analysis, which enables observation, modelling, and understanding of environmental targets. In addition to case studies on monitoring and modeling plant, soil, insect, and aquatic animals, this publication includes discussions on innovative new ideas related to environmental monitoring, automatic fish segmentation and recognition, real-time motion tracking systems, sparse coding and decision fusion, and cell phone image-based classification and provides useful references for professionals, researchers, engineers, and students with various backgrounds within a multitude of communities.

Speechreading by Humans and Machines

Author : David G. Stork
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This book is one outcome of the NATO Advanced Studies Institute (ASI) Workshop, "Speechreading by Man and Machine," held at the Chateau de Bonas, Castera-Verduzan (near Auch, France) from August 28 to Septem ber 8, 1995 - the first interdisciplinary meeting devoted the subject of speechreading ("lipreading"). The forty-five attendees from twelve countries covered the gamut of speechreading research, from brain scans of humans processing bi-modal stimuli, to psychophysical experiments and illusions, to statistics of comprehension by the normal and deaf communities, to models of human perception, to computer vision and learning algorithms and hardware for automated speechreading machines. The first week focussed on speechreading by humans, the second week by machines, a general organization that is preserved in this volume. After the in evitable difficulties in clarifying language and terminology across disciplines as diverse as human neurophysiology, audiology, psychology, electrical en gineering, mathematics, and computer science, the participants engaged in lively discussion and debate. We think it is fair to say that there was an atmosphere of excitement and optimism for a field that is both fascinating and potentially lucrative. Of the many general results that can be taken from the workshop, two of the key ones are these: • The ways in which humans employ visual image for speech recogni tion are manifold and complex, and depend upon the talker-perceiver pair, severity and age of onset of any hearing loss, whether the topic of conversation is known or unknown, the level of noise, and so forth.

Pattern Recognition and Machine Vision

Author : Patrick Shen-Pei Wang
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In recent years, there has been a growing interest in the fields of pattern recognition and machine vision in academia and industries. New theories have been developed, with new design of technology and systems in both hardware and software. They are widely applied to our daily life to solve real problems in such diverse areas as science, engineering, agriculture, e-commerce, education, robotics, government, medicine, games and animation, medical imaging analysis and diagnosis, military, and national security. The foundation of all this field can be traced back to the late Professor King-Sun Fu, one of the founding fathers of pattern recognition, who, with visionary insight founded the probably true to say that most people are affected by, or use applications of pattern recognition in daily life. Today, on the eve of 25th anniversary of the unfortunate and untimely passing of Professor Fu, we are proud to produce this volume of collected works from world renowned professionals and experts in pattern recognition and machine vision, in honor and memory of the late Professor King-Sun Fu. We hope this book will help promote further the course, not only of fundamental principles, systems and technologies, but also its vast range of applications to help in solving problems in daily life.

Pattern Recognition and Machine Learning

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Pattern recognition is persistent to be one of the imperative research fields in computer science and electrical engineering. Plenty of new applications are rising, and consequently pattern analysis and synthesis turn into significant subfields in pattern recognition. In these days, giving a computer to carry out any task involve a set of specific instructions or the accomplishment of an algorithm that defines the rules that need to be followed. The present day computer system has no ability to learn from past experiences and hence cannot readily recover on the basis of past mistakes. Subsequently, giving a computer or instructing a computer controlled program to execute a task entail one to define an absolute and accurate algorithm for task and then program the algorithm into the computer. Research in machine learning is now converging from several sources and from artificial intelligent field.This book as the name suggests Pattern Recognition and Machine Learning is packed with the benefits of machine learning and pattern recognition techniques and research in machine learning. The book covers chapters that aim to realize the future abilities by presenting a variety of integrated research in various scientific and engineering fields such as perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and also will be of helpful for graduate students, researchers, and practicing engineers working in the field of machine vision and computer science and engineering.

Pattern Recognition and Machine Intelligence

Author : Ashish Ghosh
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institute's motto "Unity in Diversity." As evidence and justi'cation of the int- disciplinary research comprising statistics and computer science, one may note thatstatistics providesone ofthe bestparadigmsfor learning,andit hasbecome an integralpart of the theories/paradigmsof machine learning, e.g., arti'cial - telligence, neural networks, brain mapping, data mining, and search machines on the Internet. Zadeh, the founder of fuzzy set theory, has observed that there are three essential ingredients for dramatic success in computer applications, namely, a fuzzy model of data, Bayesian inference and genetic algorithms for optimization. Similarly, statistical science will be a part, in many ways, of the validation of the tentative model of the human brain, its functions and prop- ties, including consciousness. As a mark of the signi'cant achievements in these activities in ISI, special mention may be made of the DOE-sponsored KBCS Nodal Center of ISI in the 1980s and the Center for Soft Computing Research of ISI recently established in 2004 by the DST, Government of India. The soft computing center is the ?rst national initiative in the country in this domain, and has many imp- tant objectives like providing a six-month value addition certi'cate course for post-graduates, enriching national institutes, e.g., NITs through funding for - search in soft computing, establishing linkage to premier institutes/industries, organizing specialized courses, apart from conducting fundamental research.

Recognition of Humans and Their Activities Using Video

Author : Rama Chellappa
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The recognition of humans and their activities from video sequences is a very important and active area of research because of direct applications in video surveillance, design of realistic entertainment systems, multimedia communications, and medical diagnosis. In this lecture, the authors discuss the use of face and gait signatures for human identification and recognition of human activities from video sequences.Face Recognition* methods for image-based analysis.* developments related to the use of video sequences, 3D models, and techniques for representing variations of illumination.* challenges of recognition strategies that are robust to changes due to pose, illumination, disguise, and aging.Gait Recognition* techniques to automatically extract the parameters for representation of human gait, most of which are appearance based.* challenges involved in dealing with changes in viewpoint.* methods based on image synthesis, visual hull, and 3D models.Human Activity Recognition* methods that have been developed in different disciplines like artificial intelligence, image processing, pattern recognition, and computer vision.* complex activities using 2D and 3D deformable shape theory.

Data Complexity in Pattern Recognition

Author : Mitra Basu
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Automatic pattern recognition has uses in science and engineering, social sciences and finance. This book examines data complexity and its role in shaping theory and techniques across many disciplines, probing strengths and deficiencies of current classification techniques, and the algorithms that drive them. The book offers guidance on choosing pattern recognition classification techniques, and helps the reader set expectations for classification performance.

Pattern Recognition and Machine Intelligence

Author : Sankar K. Pal
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This book constitutes the refereed proceedings of the First International Conference on Pattern Recognition and Machine Intelligence, PReMI 2005, held in Kolkata, India in December 2005. The 108 revised papers presented together with 6 keynote talks and 14 invited papers were carefully reviewed and selected from 250 submissions. The papers are organized in topical sections on clustering, feature selection and learning, classification, neural networks and applications, fuzzy logic and applications, optimization and representation, image processing and analysis, video processing and computer vision, image retrieval and data mining, bioinformatics application, Web intelligence and genetic algorithms, as well as rough sets, case-based reasoning and knowledge discovery.

Pattern Recognition

Author : Gerhard Rigoll
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This book constitutes the refereed proceedings of the 30th Symposium of the German Association for Pattern Recognition, DAGM 2008, held in Munich, Germany, in June 2008. The 53 revised full papers were carefully reviewed and selected from 136 submissions. The papers are organized in topical sections on learning and classification, tracking, medical image processing and segmentation, audio, speech and handwriting recognition, multiview geometry and 3D-reconstruction, motion and matching, and image analysis.

Pattern Recognition with Support Vector Machines

Author : Seong-Whan Lee
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Presents a collection of papers from the SVM 2002 workshop.

Internet Scale Pattern Recognition

Author : Anang Hudaya Muhamad Amin
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For machine intelligence applications to work successfully, machines must perform reliably under variations of data and must be able to keep up with data streams. Internet-Scale Pattern Recognition: New Techniques for Voluminous Data Sets and Data Clouds unveils computational models that address performance and scalability to achieve higher levels of reliability. It explores different ways of implementing pattern recognition using machine intelligence. Based on the authors’ research from the past 10 years, the text draws on concepts from pattern recognition, parallel processing, distributed systems, and data networks. It describes fundamental research on the scalability and performance of pattern recognition, addressing issues with existing pattern recognition schemes for Internet-scale data deployment. The authors review numerous approaches and introduce possible solutions to the scalability problem. By presenting the concise body of knowledge required for reliable and scalable pattern recognition, this book shortens the learning curve and gives you valuable insight to make further innovations. It offers an extendable template for Internet-scale pattern recognition applications as well as guidance on the programming of large networks of devices.

Correlation Pattern Recognition

Author : B. V. K. Vijaya Kumar
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Correlation is a robust and general technique for pattern recognition and is used in many applications, such as automatic target recognition, biometric recognition and optical character recognition. The design, analysis and use of correlation pattern recognition algorithms requires background information, including linear systems theory, random variables and processes, matrix/vector methods, detection and estimation theory, digital signal processing and optical processing. This 2005 book provides a needed review of this diverse background material and develops the signal processing theory, the pattern recognition metrics, and the practical application know-how from basic premises. It shows both digital and optical implementations. It also contains technology presented by the team that developed it and includes case studies of significant interest, such as face and fingerprint recognition. Suitable for graduate students taking courses in pattern recognition theory, whilst reaching technical levels of interest to the professional practitioner.

Machine Learning and Data Mining in Pattern Recognition

Author : Petra Perner
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The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.

Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering

Author : Larisa Angstenberger
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Dynamic Fuzzy Pattern Recognition with Applications to Finance and Engineering focuses on fuzzy clustering methods which have proven to be very powerful in pattern recognition and considers the entire process of dynamic pattern recognition. This book sets a general framework for Dynamic Pattern Recognition, describing in detail the monitoring process using fuzzy tools and the adaptation process in which the classifiers have to be adapted, using the observations of the dynamic process. It then focuses on the problem of a changing cluster structure (new clusters, merging of clusters, splitting of clusters and the detection of gradual changes in the cluster structure). Finally, the book integrates these parts into a complete algorithm for dynamic fuzzy classifier design and classification.

Artificial Neural Networks in Pattern Recognition

Author : Friedhelm Schwenker
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Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing.

Multimodal Pattern Recognition of Social Signals in Human Computer Interaction

Author : Friedhelm Schwenker
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This book constitutes the refereed post-workshop proceedings of the 5th IAPR TC9 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction, MPRSS 2018, held in Beijing, China, in August 2018. The 10 revised papers presented in this book focus on pattern recognition, machine learning and information fusion methods with applications in social signal processing, including multimodal emotion recognition and pain intensity estimation, especially the question how to distinguish between human emotions from pain or stress induced by pain is discussed.

Speech Separation by Humans and Machines

Author : Pierre Divenyi
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This book is appropriate for those specializing in speech science, hearing science, neuroscience, or computer science and engineers working on applications such as automatic speech recognition, cochlear implants, hands-free telephones, sound recording, multimedia indexing and retrieval.