Computer Vision Metrics: Textbook Edition
Springer | Computer Science | October 18, 2016 | ISBN-10: 3319337610 | 637 pages | pdf | 26.18 mb
Authors: Krig, Scott
Provides the most complete survey of computer vision feature description methods including local, regional, global, basis, and feature learning via deep learning and neural networks
Offers learning assignments at the end of each chapter for student or instructor use
Includes techniques for optimizing computer vision algorithm performance such as SW and HW architecture considerations
Based on the successful 2014 book published by Apress, this textbook edition is expanded to provide a comprehensive history and state-of-the-art survey for fundamental computer vision methods. With over 800 essential references, as well as chapter-by-chapter learning assignments, both students and researchers can dig deeper into core computer vision topics. The survey covers everything from feature descriptors, regional and global feature metrics, feature learning architectures, deep learning, neuroscience of vision, neural networks, and detailed example architectures to illustrate computer vision hardware and software optimization methods.
To complement the survey, the textbook includes useful analyses which provide insight into the goals of various methods, why they work, and how they may be optimized.
The text delivers an essential survey and a valuable taxonomy, thus providing a key learning tool for students, researchers and engineers, to supplement the many effective hands-on resources and open source projects, such as OpenCVand other imaging and deep learning tools.
Number of Illustrations and Tables
192 b/w illustrations, 139 illustrations in colour
Image Processing and Computer Vision
Signal, Image and Speech Processing
Document Preparation and Text Processing