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Feature Selection for Data and Pattern Recognition (Studies in Computational Intelligence Book 584)
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Pattern Recognition
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Computational Intelligence in Multi-Feature Visual Pattern Recognition: Hand Posture and Face Recognition using Biological…
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Supervised and Unsupervised Pattern Recognition: Feature Extraction and Computational Intelligence (Industrial Electronics)
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Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
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Pattern Recognition for Feature-based and Comparative Visualization: Moment Invariants for Pattern Recognition in Flows
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Pattern Recognition: Introduction, Features, Classifiers and Principles (De Gruyter Textbook)
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ge Registration: Principles, Tools and Methods (Advances in Computer Vision and Pattern Recognition)
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Advances in Feature Selection for Data and Pattern Recognition (Intelligent Systems Reference Library Book 138)
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Fundamentals of Pattern Recognition and Machine Learning
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Computer Vision for Microscopy Image Analysis (Computer Vision and Pattern Recognition)
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The Art of Feature Engineering: Essentials for Machine Learning
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Face Recognition Using Independent Component Analysis (Paperback)
The Independent Component Analysis (ICA) plays very important role in blind source separation and has many more applications in pattern recognition. The ICA is new area for researchers in the last decade for face recognition. There is much more scope for research using ICA for face recognition with different methods of feature extractions and needs to be addressed. As the promising applications of ICA is feature extraction, where it extracts independent image bases which are not necessarily orthogonal and it is sensitive to high order statistics. In the task of face recognition, important information may be contained in the high order relationship among pixels. Independent Component Analysis (ICA) minimizes both second order and higher-order dependencies in the input data and attempts to find the basis along with the data when projected onto them are statistically independent. So ICA seems to be a promising face feature extraction method. Face Recognition Using Independent Component Analysis (Paperback)
De Gruyter Textbook: Pattern Recognition: Introduction, Features, Classifiers and Principles (Paperback)
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: J\\u0026uuml;rgen Beyerer,Matthias Richter,Matthias Nagel • ISBN:9783110537932 • Format:Paperback • Publication Date:2017-12-11
Handbook of Pattern Recognition and Computer Vision (4th Edition) (Edition 4) (Hardcover)
Both pattern recognition and computer vision have experienced rapid progress in the last twenty-five years. This book provides the latest advances on pattern recognition and computer vision along with their many applications. It features articles written by renowned leaders in the field while topics are presented in readable form to a wide range of readers. The book is divided into five parts: basic methods in pattern recognition, basic methods in computer
vision and image processing, recognition applications, life science and human identification, and systems and technology. There are eight new chapters on the latest developments in life sciences using pattern recognition as well as two new chapters on pattern recognition in remote sensing. • ISBN:9789814273381 • Format:Hardcover • Publication Date:2010-02-01
Studies in Computational Intelligence: Computational Intelligence in Multi-Feature Visual Pattern Recognition : Hand Posture and Face Recognition Using Biologically Inspired Approaches (Series #556) (Hardcover)
Part I Computational Intelligence in Visual Pattern Recognition.- 1 Visual Pattern Recognition.- 2 Computational Intelligence Techniques.- 3 Multi-Feature Pattern Recognition.- Part II Feature Selection and Classification.- 4 Fuzzy-Rough Discriminative Feature Selection and Classification.- 5 Hand Posture and Face Recognition using Fuzzy-Rough Approach.- 6 Boosting based Fuzzy-Rough Pattern Classifier.- Part III Biologically Inspired Approaches in Hand Posture Recognition.- 7 Hand Posture Recognition using Neurobiologically Inspired Features.- 8 Attention based Segmentation and Recognition (ASR) Algorithm for Hand Postures Against Complex Backgrounds.- Appendices.- Index. • Author: Pramod Kumar Pisharady,Prahlad Vadakkepat,Loh Ai Poh • ISBN:9789812870551 • Format:Hardcover • Publication Date:2014-06-25
Studies in Computational Intelligence: Feature Selection for Data and Pattern Recognition (Paperback)
This research book provides the reader with a selection of high-quality texts dedicated to current progress, new developments and research trends in feature selection for data and pattern recognition. Even though it has been the subject of interest for some time, feature selection remains one of actively pursued avenues of investigations due to its importance and bearing upon other problems and tasks. This volume points to a number of advances topically subdivided into four parts: estimation of importance of characteristic features, their relevance, dependencies, weighting and ranking; rough set approach to attribute reduction with focus on relative reduces; construction of rules and their evaluation; and data and domain oriented methodologies. • ISBN:9783662508459 • Format:Paperback • Publication Date:2016-09-24
Pattern Recognition and Classification : An Introduction (Hardcover)
This volume, both comprehensive and accessible, introduces all the key concepts in pattern recognition, and includes many examples and exercises that make it an ideal guide to an important methodology widely deployed in today’s ubiquitous automated systems. Introduction.- Classification.- Nonmetric Methods.- Statistical Pattern Recognition.- Supervised Learning.- Nonparametric Learning.- Feature Extraction and Selection.- Unsupervised Learning.- Estimating and Comparing Classifiers.- Projects • Author: Geoff Dougherty • ISBN:9781461453222 • Format:Hardcover • Publication Date:2012-10-29
Communications in Computer and Information Science: Pattern Recognition : 6th Chinese Conference, Ccpr 2014, Changsha, China, November 17-19, 2014. Proceedings, Part I (Series #483) (Paperback)
Fundamentals of pattern recognition.- Feature extraction and classification.- Computer vision.- Image processing and analysis.- Video processing and analysis.- Biometric and action recognition.- Biomedical image analysis.- Document and speech analysis.- Pattern recognition applications. • ISBN:9783662456453 • Format:Paperback • Publication Date:2014-11-20
