01789nam a2200253 i 4500991002056149707536130327s2012 nyua b 001 0 eng d9781107011793b14113909-39ule_instDip.to Ingegneria dell'Innovazioneeng006.3723Prince, Simon Jeremy Damion,1972-478480Computer vision :models, learning, and inference /Simon J.D. PrinceNew York :Cambridge University Press,2012xi, 580 p. :ill. (some col.) ;26 cmIncludes bibliographical references and indexPart 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. AlgorithmsComputer vision.b1411390928-01-1415-05-13991002056149707536LE026 006.37 PRI 01.01 201212026000064437le026Prof. Reina / BibliotecapE63.66-l- 44040.i1551300227-05-13Computer vision264133UNISALENTOle02627-03-13ma -engnyu00