Image Texture Analysis: Foundations, Models and Algorithms

Bys: Hung, Chih-Cheng, Song, Enmin, Lan, Yihua

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.

• Format: Paperback
• Language: English
• ISBN: 3030137759
• ISBN13: 9783030137755
• Release Date: October 2020
• Publisher: Springer
• Length: 258 Pages
• Weight: 0.85 lbs.
• Dimensions: 0.6" x 6.1" x 9.2"