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020 _a9783030137731
_9978-3-030-13773-1
024 7 _a10.1007/978-3-030-13773-1
_2doi
050 4 _aTA1634
072 7 _aUYQV
_2bicssc
072 7 _aCOM012000
_2bisacsh
072 7 _aUYQV
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082 0 4 _a006.37
_223
100 1 _aHung, Chih-Cheng.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aImage Texture Analysis
_h[electronic resource] :
_bFoundations, Models and Algorithms /
_cby Chih-Cheng Hung, Enmin Song, Yihua Lan.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXII, 258 p. 142 illus., 73 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aPart I: Existing Models and Algorithms for Image Texture -- Image Texture, Texture Features, and Image Texture Classification and Segmentation -- Texture Features and Image Texture Models -- Algorithms for Image Texture Classification -- Dimensionality Reduction and Sparse Representation -- Part II: The K-Views Models and Algorithms -- Basic Concept and Models of the K-Views -- Using Datagram in the K-Views Model -- Features-Based K-Views Model -- Advanced K-Views Algorithms -- Part III: Deep Machine Learning Models for Image Texture Analysis -- Foundations of Deep Machine Learning in Neural Networks -- Convolutional Neural Networks and Texture Classification.
520 _aThis 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. 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.
650 0 _aComputer vision.
650 0 _aArtificial intelligence.
650 1 4 _aComputer Vision.
650 2 4 _aArtificial Intelligence.
700 1 _aSong, Enmin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aLan, Yihua.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030137724
776 0 8 _iPrinted edition:
_z9783030137748
776 0 8 _iPrinted edition:
_z9783030137755
856 4 0 _uhttps://doi.org/10.1007/978-3-030-13773-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c173095
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