TY  - BOOK
AU  - Prince,Simon J.D
TI  - Computer vision: models, learning, and inference 
SN  - 1107011795
U1  - 006.37 
PY  - 2012///, c2017
CY  - Cambridge
PB  - Cambridge University Press
KW  - Computadores
KW  - Visión por computador
N1  - Varían los años de reimpresión; Incluye bibliografía; Machine generated contents note: 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
ER  -