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LEADER 00000cam  2200301 a 4500 
005    20120817161044.0 
008    120323s2012    nyua     b    001 0 eng   
010    2012008187 
019    17224176 
020    9781107011793 (hardback) 
040    DLC|cDLC|dDLC 
042    pcc 
050 00 TA1634|b.P75 2012 
100 1  Prince, Simon J. D.|q(Simon Jeremy Damion),|d1972- 
245 10 Computer vision :|bmodels, learning, and inference /
       |cSimon J.D. Prince. 
260    New York :|bCambridge University Press,|c2012. 
300    xviii, 580 p. :|bill. (some col.) ;|c26 cm. 
504    Includes bibliographical references (p. 533-566) and 
       index. 
505 8  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. 
520    "This modern treatment of computer vision focuses on 
       learning and inference in probabilistic models as a 
       unifying theme. It shows how to use training data to learn
       the relationships between the observed image data and the 
       aspects of the world that we wish to estimate, such as the
       3D structure or the object class, and how to exploit these
       relationships to make new inferences about the world from 
       new image data. With minimal prerequisites, the book 
       starts from the basics of probability and model fitting 
       and works up to real examples that the reader can 
       implement and modify to build useful vision systems. 
       Primarily meant for advanced undergraduate and graduate 
       students, the detailed methodological presentation will 
       also be useful for practitioners of computer vision. 
       [bullet] Covers cutting-edge techniques, including graph 
       cuts, machine learning and multiple view geometry [bullet]
       A unified approach shows the common basis for solutions of
       important computer vision problems, such as camera 
       calibration, face recognition and object tracking [bullet]
       More than 70 algorithms are described in sufficient detail
       to implement [bullet] More than 350 full-color 
       illustrations amplify the text [bullet] The treatment is 
       self-contained, including all of the background 
       mathematics [bullet] Additional resources at 
       www.computervisionmodels.com"--|cProvided by publisher. 
650  0 Computer vision 
856 42 |3Cover image|uhttp://assets.cambridge.org/97811070/11793/
       cover/9781107011793.jpg