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Author: Julius T. Tou Publisher: Addison Wesley Publishing Company ISBN: Category : Computers Languages : en Pages : 410
Book Description
The information-handling problem; Basic concepts of pattern recognition; Fundamental problems in pattern recognition system design; Design concepts and methodologies; Decision functions; Pattern classification by distance functions; Pattern classification by likelihood functions; Trainable pattern classifiers - the deterministic approach; Trainable pattern classifiers - the statistical approach; Pattern preprocessing and feature selection; Syntactic pattern recognition.
Author: Julius T. Tou Publisher: Addison Wesley Publishing Company ISBN: Category : Computers Languages : en Pages : 410
Book Description
The information-handling problem; Basic concepts of pattern recognition; Fundamental problems in pattern recognition system design; Design concepts and methodologies; Decision functions; Pattern classification by distance functions; Pattern classification by likelihood functions; Trainable pattern classifiers - the deterministic approach; Trainable pattern classifiers - the statistical approach; Pattern preprocessing and feature selection; Syntactic pattern recognition.
Author: Jürgen Beyerer Publisher: Walter de Gruyter GmbH & Co KG ISBN: 3110537966 Category : Computers Languages : en Pages : 330
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
Author: Francisco Escolano Ruiz Publisher: Springer Science & Business Media ISBN: 1848822979 Category : Computers Languages : en Pages : 375
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.
Author: A. Ardeshir Goshtasby Publisher: Springer Science & Business Media ISBN: 1447124588 Category : Computers Languages : en Pages : 454
Book Description
This book presents a thorough and detailed guide to image registration, outlining the principles and reviewing state-of-the-art tools and methods. The book begins by identifying the components of a general image registration system, and then describes the design of each component using various image analysis tools. The text reviews a vast array of tools and methods, not only describing the principles behind each tool and method, but also measuring and comparing their performances using synthetic and real data. Features: discusses similarity/dissimilarity measures, point detectors, feature extraction/selection and homogeneous/heterogeneous descriptors; examines robust estimators, point pattern matching algorithms, transformation functions, and image resampling and blending; covers principal axes methods, hierarchical methods, optimization-based methods, edge-based methods, model-based methods, and adaptive methods; includes a glossary, an extensive list of references, and an appendix on PCA.
Author: Luis Enrique Sucar Publisher: Springer Nature ISBN: 3030619435 Category : Computers Languages : en Pages : 370
Book Description
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.
Author: Frank Y. Shih Publisher: John Wiley & Sons ISBN: 0470404612 Category : Technology & Engineering Languages : en Pages : 564
Book Description
A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.
Author: Stephen K. Reed Publisher: Academic Press ISBN: 1483263347 Category : Psychology Languages : en Pages : 261
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.
Author: Laszlo Györfi Publisher: Springer ISBN: 3709125685 Category : Technology & Engineering Languages : en Pages : 344
Book Description
This volume provides a systematic in-depth analysis of nonparametric learning. It covers the theoretical limits and the asymptotical optimal algorithms and estimates, such as pattern recognition, nonparametric regression estimation, universal prediction, vector quantization, distribution and density estimation, and genetic programming.
Author: Julius T. Tou
Author: Julius T. Tou
Author: Jürgen Beyerer
Author: Julius Tsu Lieh Tou
Author: Francisco Escolano Ruiz
Author: A. Ardeshir Goshtasby
Author: Luis Enrique Sucar
Author: Frank Y. Shih
Author: Brian D. Ripley
Author: Stephen K. Reed
Author: Laszlo Györfi