Computer Vision Metrics: Survey, Taxonomy, and Analysis
by Scott Krig
Publisher: Springer 2014
Number of pages: 498
Provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications.
Home page url
Download or read it online for free here:
by David Marshall - Cardiff School of Computer Science
From the table of contents: Image Acquisition: 2D Image Input, 3D imaging; Image processing: Fourier Methods, Smoothing Noise; Edge Detection; Edge Linking; Segmentation; Line Labelling; Relaxation Labelling; Optical Flow; Object Recognition.
by Simon J.D. Prince - Cambridge University Press
This modern treatment of computer vision focuses on learning and inference in probabilistic models as a unifying theme. It shows how to use data to learn the relationships between the observed image data and the aspects that we wish to estimate.
by Andrew Blake, Andrew Zisserman - The MIT Press
Visual Reconstruction presents a unified and highly original approach to the treatment of continuity in vision. The book introduces two new concepts: the weak continuity constraint and the graduated nonconvexity algorithm.
by Rustam Stolkin - InTech
This book reports recent advances in the use of pattern recognition techniques for computer and robot vision. The areas of low level vision such as segmentation, edge detection, and region identification, are the focus of this book.