000			nam a22 4500
999			_c120779 _d120779
008			200625b ||||| |||| 00| 0 eng d
020			_a9781107011793
082			_a006.37 _bPRI
100			_aPrince, Simon J. D. _957861
245			_aComputer Vision: Models, Learning, and Inference
260			_bCambridge University Press _aUSA _c2012
300			_a580p
500			_aPart 1. Probability: 1. Introduction to probability 2. Common probability distributions 3. Fitting probability models 4. The normal distribution Part 2. Machine Learning for Machine Vision: 5. Learning and inference in vision 6. Modeling complex data densities 7. Regression models 8. Classification models Part 3. Connecting Local Models: 9. Graphical models 10. Models for chains and trees 11. Models for grids Part 4. Preprocessing: 12. Image preprocessing and feature extraction Part 5. Models for Geometry: 13. The pinhole camera 14. Models for transformations 15. Multiple cameras Part 6. Models for Vision: 16. Models for style and identity 17. Temporal models 18. Models for visual words Part 7. Appendices: A. Optimization B. Linear algebra C. Algorithms.
600			_aElectronics and Communication _948726
942			_2ddc _cLB