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Image Texture Analysis : Foundations, Models and Algorithms by Chih-Cheng Hung, Yihua Lan and Enmin Song (2019, Hardcover)

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About this product

Product Identifiers

PublisherSpringer International Publishing A&G

ISBN-103030137724

ISBN-139783030137724

eBay Product ID (ePID)19038566629

Product Key Features

Number of PagesXii, 258 Pages

LanguageEnglish

Publication NameImage Texture Analysis : Foundations, Models and Algorithms

SubjectIntelligence (Ai) & Semantics, Computer Vision & Pattern Recognition

Publication Year2019

TypeTextbook

Subject AreaComputers

AuthorChih-Cheng Hung, Yihua Lan, Enmin Song

FormatHardcover

Dimensions

Item Weight20.3 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Number of Volumes1 vol.

IllustratedYes

Table Of ContentPart 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.

SynopsisThis 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.

LC Classification NumberTA1634