Image Texture Analysis : Foundations, Models and Algorithms

個数:1
紙書籍版価格
¥14,583
  • 電子書籍

Image Texture Analysis : Foundations, Models and Algorithms

  • 著者名:Hung, Chih-Cheng/Song, Enmin/Lan, Yihua
  • 価格 ¥8,890 (本体¥8,082)
  • Springer(2019/06/05発売)
  • ポイント 80pt (実際に付与されるポイントはご注文内容確認画面でご確認下さい)
  • 言語:ENG
  • ISBN:9783030137724
  • eISBN:9783030137731

ファイル: /

Description

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.

Table of Contents

Part 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.

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