000			01293cam a2200193 a 4500
003			OSt
005			20241010161943.0
008			120323s2012 nyua b 001 0 eng
020			_a9781107011793
082	0	0	_a006.37
100	1		_aPrince, Simon J. D.
245	1	0	_aComputer vision : models, learning, and inference
260			_aNew York : _bCambridge University Press, _c2012.
300			_axi, 580 p.
505	8		_a Part I. Probability: 1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision: 5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models: 9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing: 12. Image preprocessing and feature extraction; Part V. Models for Geometry: 13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision: 16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices: A. Optimization; B. Linear algebra; C. Algorithms.
650		0	_aComputer vision
650		7	_aComputer Graphics
942			_cBK
999			_c6616 _d6616