Overview
Reviews the state of the art in models and algorithms for texture analysis, including deep learning and image texture analysis
Introduces the K-View model and its advanced models, highlighting the benefits offered by these models
Discusses the theory, explains the necessary mathematics, and describes the implementation of the algorithms
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Table of contents (10 chapters)
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Front Matter
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Existing Models and Algorithms for Image Texture
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Front Matter
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The K-Views Models and Algorithms
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Front Matter
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Deep Machine Learning Models for Image Texture Analysis
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Front Matter
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Back Matter
Keywords
About this book
This useful textbook/reference presents an accessible primer on the fundamentals of image texture analysis, as well as an introduction to the K-views model for extracting and classifying image textures. Divided into three parts, the book opens with a review of existing models and algorithms for image texture analysis, before delving into the details of the K-views model. The work then concludes with a discussion of popular deep learning methods for image texture analysis.
Topics and features: provides self-test exercises in every chapter; describes the basics of image texture, texture features, and image texture classification and segmentation; examines a selection of widely-used methods for measuring and extracting texture features, and various algorithms for texture classification; explains the concepts of dimensionality reduction and sparse representation; discusses view-based approaches to classifying images; introduces the template for the K-views algorithm, as well as a range of variants of this algorithm; reviews several neural network models for deep machine learning, and presents a specific focus on convolutional neural networks.This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year graduate students of computer science, who will benefit from the numerous clarifying examples provided throughout the work.
Authors and Affiliations
About the authors
Dr. Chih-Cheng Hung is a Tenured Professor of Computer Science in the College of Computing and Software Engineering at Kennesaw State University, where he serves as the Director of the Center for Machine Vision and Security Research. He also holds the position of YinDu Scholar at Anyang Normal University, China.
Dr. Enmin Song is a Professor and Director of the Department of Computer Science and Application at Huazhong University of Science and Technology, Wuhan, China.
Dr. Yihua Lan is an Associate Professor of Computer Science in the School of Computer and Information Technology at Nanyang Normal University, China.
Bibliographic Information
Book Title: Image Texture Analysis
Book Subtitle: Foundations, Models and Algorithms
Authors: Chih-Cheng Hung, Enmin Song, Yihua Lan
DOI: https://doi.org/10.1007/978-3-030-13773-1
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-13772-4Published: 17 June 2019
Softcover ISBN: 978-3-030-13775-5Published: 06 October 2020
eBook ISBN: 978-3-030-13773-1Published: 05 June 2019
Edition Number: 1
Number of Pages: XII, 258
Number of Illustrations: 69 b/w illustrations, 73 illustrations in colour
Topics: Image Processing and Computer Vision, Artificial Intelligence