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 and deep learning. 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 and deep learning architectures. 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 OpenCV and
other imaging and deep learning tools.
