000 | 04556nam a22005175i 4500 | ||
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001 | 978-3-030-13773-1 | ||
003 | DE-He213 | ||
005 | 20240423125011.0 | ||
007 | cr nn 008mamaa | ||
008 | 190605s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030137731 _9978-3-030-13773-1 |
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024 | 7 |
_a10.1007/978-3-030-13773-1 _2doi |
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050 | 4 | _aTA1634 | |
072 | 7 |
_aUYQV _2bicssc |
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072 | 7 |
_aCOM012000 _2bisacsh |
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072 | 7 |
_aUYQV _2thema |
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082 | 0 | 4 |
_a006.37 _223 |
100 | 1 |
_aHung, Chih-Cheng. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXII, 258 p. 142 illus., 73 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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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 |
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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 _d173095 |