Basic Concept and Models of the K-views
In this chapter, we introduce the concepts of “view” and “characteristic view”. This view concept is quite different from those of gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP). We emphasize on how to precisely describe the feature
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Texture Analysis Foundations, Models and Algorithms
Image Texture Analysis
Chih-Cheng Hung • Enmin Song Yihua Lan
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Image Texture Analysis Foundations, Models and Algorithms
123
Chih-Cheng Hung Kennesaw State University Marietta, GA, USA
Enmin Song Huazhong University of Science and Technology Wuhan, Hubei, China
Yihua Lan Nanyang Normal University Nanyang, Henan, China
ISBN 978-3-030-13772-4 ISBN 978-3-030-13773-1 https://doi.org/10.1007/978-3-030-13773-1
(eBook)
Library of Congress Control Number: 2019931824 © Springer Nature Switzerland AG 2019 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
Hello darkness, my old friend I’ve come to talk with you again because a vision softly creeping left its seeds while I was sleeping and the vision that was planted in my brain still remains within the sound of silence —Simon and Garfunkel
Research on image texture analysis has made significant progress in the past few decades. However, image texture classification (and segmentation) is still an elusive goal despite a tremendous effort devoted to work in this area. This is perhaps analogous to what Alan Turing proved, “there are many things that computers cannot do” although he also showed us that there are many things that computers can do [1]. Traditional image texture classification usually consists of texture feature extraction and texture classification. This scheme is flexible as many image and pattern classification, segmentation, and clustering algorithms in literature can be used for texture classification once texture features are available. The separation of features and classification has a limitation that texture features extractors must be well designed to provide a representa
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