Из серии Foundations and Trends in Signal Processing издательства NOWPress, 2009, -154 pp.
Computer vision systems attempt to understand a scene and its components from mostly visual information. The geometry exhibited by the real world, the influence of material properties on scattering of incident light, and the process of imaging introduce constraints and properties that are key to solving some of these tasks. In the presence of noisy observations and other uncertainties, the algorithms make use of statistical methods for robust inference. In this monograph, we highlight the role of geometric constraints in statistical estimation methods, and how the interplay of geometry and statistics leads to the choice and design of algorithms. In particular, we illustrate the role of imaging, illumination, and motion constraints in classical vision problems such as tracking, structure from motion, metrology, activity analysis and recognition, and appropriate statistical methods used in each of these problems.
Introduction
Geometric Models for Imaging
Statistical Estimation Techniques
Detection, Tracking, and Recognition in Video
Statistical Analysis of Structure and Motion Algorithms
Shape, Identity, and Activity Recognition
Future Trends