Image Texture Analysis - Foundations, Models and Algorithms (Hardcover, 1st ed. 2019)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Release date:	June 2019
First published:	2019
Authors:	Chih-Cheng Hung • Enmin Song • Yihua Lan
Dimensions:	235 x 155 x 20mm (L x W x T)
Format:	Hardcover
Pages:	258
Edition:	1st ed. 2019
ISBN-13:	978-3-03-013772-4
Categories:	Books > Computing & IT > Applications of computing > Image processing > General Promotions
LSN:	3-03-013772-4
Barcode:	9783030137724

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