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.
This paper highlights 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, it illustrates 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.
- ISBN13 9781601983145
- Publish Date 1 February 2010
- Publish Status Active
- Publish Country US
- Imprint now publishers Inc
- Format Paperback
- Pages 166
- Language English
- URL https://nowpublishers.com/article/Details/SIG-007