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Handbook of Face Recognition, Li Stan Z., Jain Anil K.



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Цена: 8657р.
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Автор: Li Stan Z., Jain Anil K.
Название:  Handbook of Face Recognition
ISBN: 9780387405957
Издательство: Springer
Классификация:
ISBN-10: 038740595X
Обложка/Формат: Hardback
Страницы: 408
Вес: 0.971 кг.
Дата издания: 13.04.2005
Язык: English
Иллюстрации: 210 black & white illustrations, 38 black & white tables
Размер: 24.33 x 17.88 x 2.31 cm
Читательская аудитория: Professional & vocational
Ссылка на Издательство: Link
Рейтинг:
Поставляется из: Германии
Дополнительное описание: Формат: 235x178
Илюстрации: 210
Круг читателей: Professionals, researchers
Ключевые слова:
Язык: eng
Оглавление: * Introduction * Face Detection * Modeling Facial Shape and Appearance * Parametric Face Modeling and Tracking * Illumination Modeling for Face Recognition * Facial Skin Color Modeling * Face recognition in Subspaces * Face Tracking and Recognition from Video * Face Recognition Across Pose and Illumination * Morphable Models of Faces * Facial Expression Analysis * Face Synthesis * Face Databases * Evalution Methods in Face Recognition * Psychological and Neural Perpsectives in Human Face Recognition * Face Recognition Applications * Index





Pattern Recognition and Machine Learning

Автор: Christopher M. Bishop
Название: Pattern Recognition and Machine Learning
ISBN: 0387310738 ISBN-13(EAN): 9780387310732
Издательство: Springer
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Цена: 9816 р.
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Описание: The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.A forthcoming companion volume will deal with practical aspects of pattern recognition and machine learning, and will include free software implementations of the key algorithms along with example data sets and demonstration programs.Christopher Bishop is Assistant Director at Microsoft Research Cambridge, and also holds a Chair in Computer Science at the University of Edinburgh. He is a Fellow of Darwin College Cambridge, and was recently elected Fellow of the Royal Academy of Engineering. The author's previous textbook "Neural Networks for Pattern Recognition" has been widely adopted.Coming soon:*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)*For instructors, worked solutions to remaining exercises from the Springer web site*Lecture slides to accompany each chapter*Data sets available for download

Neural Networks for Pattern Recognition

Автор: Bishop, Christopher M.
Название: Neural Networks for Pattern Recognition
ISBN: 0198538642 ISBN-13(EAN): 9780198538646
Издательство: Oxford Academ
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Цена: 9313 р.
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Описание: Providing a comprehensive account of neural networks from a statistical perspective, this book emphasizes on pattern recognition, which represents the area of greatest applicability for neural networks in contemporary times.

Error Estimation for Pattern Recognition

Автор: Braga-Neto
Название: Error Estimation for Pattern Recognition
ISBN: 1118999738 ISBN-13(EAN): 9781118999738
Издательство: Wiley
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Цена: 14713 р.
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Описание:

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributional and Bayesian theory, it covers important topics and essential issues pertaining to the scientific validity of pattern classification.

Error Estimation for Pattern Recognition focuses on error estimation, which is a broad and poorly understood topic that reaches all research areas using pattern classification. It includes model-based approaches and discussions of newer error estimators such as bolstered and Bayesian estimators. This book was motivated by the application of pattern recognition to high-throughput data with limited replicates, which is a basic problem now appearing in many areas. The first two chapters cover basic issues in classification error estimation, such as definitions, test-set error estimation, and training-set error estimation. The remaining chapters in this book cover results on the performance and representation of training-set error estimators for various pattern classifiers.

Additional features of the book include:

- The latest results on the accuracy of error estimation
- Performance analysis of re-substitution, cross-validation, and bootstrap error estimators using analytical and simulation approaches
- Highly interactive computer-based exercises and end-of-chapter problems

This is the first book exclusively about error estimation for pattern recognition.

Ulisses M. Braga Neto is an Associate Professor in the Department of Electrical and Computer Engineering at Texas A&M University, USA. He received his PhD in Electrical and Computer Engineering from The Johns Hopkins University. Dr. Braga Neto received an NSF CAREER Award for his work on error estimation for pattern recognition with applications in genomic signal processing. He is an IEEE Senior Member.

Edward R. Dougherty is a Distinguished Professor, Robert F. Kennedy '26 Chair, and Scientific Director at the Center for Bioinformatics and Genomic Systems Engineering at Texas A&M University, USA. He is a fellow of both the IEEE and SPIE, and he has received the SPIE Presidents Award. Dr. Dougherty has authored several books including Epistemology of the Cell: A Systems Perspective on Biological Knowledge and Random Processes for Image and Signal Processing (Wiley-IEEE Press).

Human Recognition in Unconstrained Environments

Автор: De Marsico, Maria
Название: Human Recognition in Unconstrained Environments
ISBN: 0081007051 ISBN-13(EAN): 9780081007051
Издательство: Elsevier Science
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Цена: 12012 р.
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Описание: Human Recognition in Unconstrained Environments provides a unique picture of the complete ‘in-the-wild’ biometric recognition processing chain; from data acquisition through to detection, segmentation, encoding, and matching reactions against security incidents. . Coverage includes:. . . Data hardware architecture fundamentals. Background subtraction of humans in outdoor scenes . Camera synchronization. Biometric traits: Real-time detection and data segmentation. Biometric traits: Feature encoding / matching . Fusion at different levels. Reaction against security incidents. Ethical issues in non-cooperative biometric recognition in public spaces. With this book readers will learn how to:. . . Use computer vision, pattern recognition and machine learning methods for biometric recognition in real-world, real-time settings, especially those related to forensics and security. Choose the most suited biometric traits and recognition methods for uncontrolled settings. Evaluate the performance of a biometric system on real world data

Handbook of Fingerprint Recognition

Автор: Davide Maltoni; Dario Maio; Anil K. Jain; Salil Pr
Название: Handbook of Fingerprint Recognition
ISBN: 1848822537 ISBN-13(EAN): 9781848822535
Издательство: Springer
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Цена: 20789 р.
Наличие на складе: Поставка под заказ.

Описание: Provides coverage of the major concepts, topics, and security methods associated with fingerprint recognition. This work features chapters covering sensor technology, performance evaluation, standards, and securing fingerprint systems. It is suitable for biometric security professionals, researchers, developers and systems administrators.

Handbook of Geometric Computing / Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics

Автор: Bayro Corrochano Eduardo
Название: Handbook of Geometric Computing / Applications in Pattern Recognition, Computer Vision, Neuralcomputing, and Robotics
ISBN: 3540205950 ISBN-13(EAN): 9783540205951
Издательство: Springer
Рейтинг:
Цена: 26450 р.
Наличие на складе: Поставка под заказ.

Описание: Many computer scientists, engineers, applied mathematicians, and physicists use geometry theory and geometric computing methods in the design of perception-action systems, intelligent autonomous systems, and man-machine interfaces. This handbook brings together the most recent advances in the application of geometric computing for building such systems, with contributions from leading experts in the important fields of neuroscience, neural networks, image processing, pattern recognition, computer vision, uncertainty in geometric computations, conformal computational geometry, computer graphics and visualization, medical imagery, geometry and robotics, and reaching and motion planning. For the first time, the various methods are presented in a comprehensive, unified manner.This handbook is highly recommended for postgraduate students and researchers working on applications such as automated learning; geometric and fuzzy reasoning; human-like artificial vision; tele-operation; space maneuvering; haptics; rescue robots; man-machine interfaces; tele-immersion; computer- and robotics-aided neurosurgery or orthopedics; the assembly and design of humanoids; and systems for metalevel reasoning.

Pattern Recognition,

Автор: Sergios Theodoridis
Название: Pattern Recognition,
ISBN: 1597492728 ISBN-13(EAN): 9781597492720
Издательство: Elsevier Science
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Цена: 10389 р.
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Описание: This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. . · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques. . · Many more diagrams included--now in two color--to provide greater insight through visual presentation. . . · Matlab code of the most common methods are given at the end of each chapter. . . . . . · More Matlab code is available, together with an accompanying manual, via this site . . . . · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms. . · An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).

Pattern Recognition and Neural Networks

Автор: Brian D. Ripley
Название: Pattern Recognition and Neural Networks
ISBN: 0521717701 ISBN-13(EAN): 9780521717700
Издательство: Cambridge Academ
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Цена: 5751 р.
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Описание: Now in paperback: the most reliable account of the statistical framework for pattern recognition and machine learning. With unparalleled coverage and a wealth of case-studies this book gives valuable insight into both the theory and the enormously diverse applications (which can be found in remote sensing, astrophysics, engineering and medicine, for example). So that readers can develop their skills and understanding, many of the real data sets used in the book are available from the author’s website: www.stats.ox.ac.uk/~ripley/PRbook/. For the same reason, many examples are included to illustrate real problems in pattern recognition. Unifying principles are highlighted, and the author gives an overview of the state of the subject, making the book valuable to experienced researchers in statistics, machine learning/artificial intelligence and engineering. The clear writing style means that the book is also a superb introduction for non-specialists.

Introduction to Pattern Recognition: A Matlab Approach,

Автор: Sergios Theodoridis
Название: Introduction to Pattern Recognition: A Matlab Approach,
ISBN: 0123744865 ISBN-13(EAN): 9780123744869
Издательство: Elsevier Science
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Цена: 3690 р.
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Описание: Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It 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. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.

Reliable Face Recognition Methods / System Design, Implementation and Evaluation

Автор: Wechsler Harry
Название: Reliable Face Recognition Methods / System Design, Implementation and Evaluation
ISBN: 038722372X ISBN-13(EAN): 9780387223728
Издательство: Springer
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Цена: 13859 р.
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Описание: Addresses the face recognition problem while gaining insights from complementary fields of endeavor such as neurosciences, signal and image processing, computer vision, machine learning and data mining. This book examines the evolution of research surrounding biometrics, explores directions, and offers guidance for future research and development.

Face Detection and Gesture Recognition for Human-Computer Interaction

Автор: Ming-Hsuan Yang, Ahuja Narendra
Название: Face Detection and Gesture Recognition for Human-Computer Interaction
ISBN: 0792374096 ISBN-13(EAN): 9780792374091
Издательство: Springer
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Цена: 27605 р.
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Описание: With the ubiquity of new information technology and media, more effective and friendly methods for human computer interaction (HCI) are being developed which do not rely on traditional devices such as keyboards, mice and displays. The first step for any intelligent HCI system is face detection, and one of most friendly HCI systems is hand gesture. Face Detection and Gesture Recognition for Human-Computer Interaction introduces the frontiers of vision-based interfaces for intelligent human computer interaction with focus on two main issues: face detection and gesture recognition. The first part of the book reviews and discusses existing face detection methods, followed by a discussion on future research. Performance evaluation issues on the face detection methods are also addressed. The second part discusses an interesting hand gesture recognition method based on a generic motion segmentation algorithm. The system has been tested with gestures from American Sign Language with promising results. We conclude this book with comments on future work in face detection and hand gesture recognition. Face Detection and Gesture Recognition for Human-Computer Interaction will interest those working in vision-based interfaces for intelligent human computer interaction. It also contains a comprehensive survey on existing face detection methods, which will serve as the entry point for new researchers embarking on such topics. Furthermore, this book also covers in-depth discussion on motion segmentation algorithms and applications, which will benefit more seasoned graduate students or researchers interested in motion pattern recognition.

Unconstrained Face Recognition

Автор: Zhou Shaohua Kevin, Chellappa Rama, Zhao Wenyi
Название: Unconstrained Face Recognition
ISBN: 0387264078 ISBN-13(EAN): 9780387264073
Издательство: Springer
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Цена: 16169 р.
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Описание: Face recognition has been actively studied over the past decade and continues to be a big research challenge. Just recently, researchers have begun to investigate face recognition under unconstrained conditions. Unconstrained Face Recognition provides a comprehensive review of this biometric, especially face recognition from video, assembling a collection of novel approaches that are able to recognize human faces under various unconstrained situations. The underlying basis of these approaches is that, unlike conventional face recognition algorithms, they exploit the inherent characteristics of the unconstrained situation and thus improve the recognition performance when compared with conventional algorithms.Unconstrained Face Recognition is structured to meet the needs of a professional audience of researchers and practitioners in industry. This volume is also suitable for advanced-level students in computer science.


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