Information Theory in Computer Vision and Pattern Recognition


Book Description

Information theory has proved to be effective for solving many computer vision and pattern recognition (CVPR) problems (such as image matching, clustering and segmentation, saliency detection, feature selection, optimal classifier design and many others). Nowadays, researchers are widely bringing information theory elements to the CVPR arena. Among these elements there are measures (entropy, mutual information...), principles (maximum entropy, minimax entropy...) and theories (rate distortion theory, method of types...). This book explores and introduces the latter elements through an incremental complexity approach at the same time where CVPR problems are formulated and the most representative algorithms are presented. Interesting connections between information theory principles when applied to different problems are highlighted, seeking a comprehensive research roadmap. The result is a novel tool both for CVPR and machine learning researchers, and contributes to a cross-fertilization of both areas.




Psychological Processes in Pattern Recognition


Book Description

Psychological Processes in Pattern Recognition describes information-processing models of pattern recognition. This book is organized into five parts encompassing 11 chapters that particularly focus on visual pattern recognition and the many issues relevant to a more general theory of pattern recognition. The first three parts cover the representation, temporal effects, and memory codes of pattern recognition. These parts include the features, templates, schemata, and structural descriptions of information processing models. The principles of parallel matching, iconic storage, and the components and networks of memory codes are also considered. The remaining two parts look into the perceptual classification and response selection of pattern recognition. These parts specifically tackle the development of probability, distance, and recognition models. This book is intended primarily for psychologists, graduate students, and researchers who are interested in the problems of pattern recognition and human information processing.




Elements of Pattern Theory


Book Description

"A dazzling tour de force on patterns. It is a substantial, original contribution by a leader-indeed, originator-in the field, and has the potential for significant impact on the direction of future research." -- Alan F. Karr, National Institute of Statistical Sciences




A Probabilistic Theory of Pattern Recognition


Book Description

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.




Pattern Recognition Theory and Application


Book Description

Research in the field of pattern recognition both in theo retical terms and in the area of appl ication continues to flourish. Pattern recognition is a fairly diverse field involving researchers whose primary disciplines spread over at least a half dozen fields. Possibly because of the great diversity of backgrounds but a common interest in certain broad areas of application, the field has grown so rapidly and yet seems to promise at least a similar growth rate for the future. This book is a collection containing some of the papers that were presented at the N. A. T. O. Advanced Study Institute held in Bandol, France, September 1975. The main purpose of the institute was to present material which would provide a basic background in the field. Thus, survey papers covering syntactic methods, picture processing, classification theory, and speech recognition were presented. This should have provided the listener (and we hope now, the reader) with an acquaintance with the basic tools, a look at some of the appl ications and an appraisal of how each of the particular topics will evolve. A more recent addition to the pattern recognition "family" is the work in the areas of economics and group choice. Since the process of recognizing and inter preting patterns is so fundamental, it probably is no surprise when a particular discipline is discovered to be amenable to the already developed techniques.




Handbook of Pattern Recognition and Computer Vision


Book Description

The very significant advances in computer vision and pattern recognition and their applications in the last few years reflect the strong and growing interest in the field as well as the many opportunities and challenges it offers. The second edition of this handbook represents both the latest progress and updated knowledge in this dynamic field. The applications and technological issues are particularly emphasized in this edition to reflect the wide applicability of the field in many practical problems. To keep the book in a single volume, it is not possible to retain all chapters of the first edition. However, the chapters of both editions are well written for permanent reference.




Pattern Recognition


Book Description

The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features, their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book is to clarifies the concepts so that the reader can easily understand the underlying ideas and the rationale behind the methods. For this purpose, the mathematical treatment of Pattern Recognition is pushed so far that the mechanisms of action become clear and visible, but not farther. Therefore, not all derivations are driven into the last mathematical detail, as a mathematician would expect it. Ideas of proofs are presented instead of complete proofs. From the authors’ point of view, this concept allows to teach the essential ideas of Pattern Recognition with sufficient depth within a relatively lean book. Mathematical methods explained thoroughly Extremely practical approach with many examples Based on over ten years lecture at Karlsruhe Institute of Technology For students but also for practitioners




Pattern Recognition and Neural Networks


Book Description

This 1996 book explains the statistical framework for pattern recognition and machine learning, now in paperback.




A Probabilistic Theory of Pattern Recognition


Book Description

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.