Image Texture Analysis: Foundations, Models And Algorithms
Springer
ISBN13:
9783030137724
$76.99
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.
- | Author: Chih-Cheng Hung, Enmin Song, Yihua Lan
- | Publisher: Springer
- | Publication Date: Jun 17, 2019
- | Number of Pages: 270 pages
- | Language: English
- | Binding: Hardcover
- | ISBN-10: 3030137724
- | ISBN-13: 9783030137724
- Author:
- Chih-Cheng Hung, Enmin Song, Yihua Lan
- Publisher:
- Springer
- Publication Date:
- Jun 17, 2019
- Number of pages:
- 270 pages
- Language:
- English
- Binding:
- Hardcover
- ISBN-10:
- 3030137724
- ISBN-13:
- 9783030137724