01983cam a2200205 a 4500 17224176 20171226091245.0 120323s2012 nyua b 001 0 eng 9781107011793 (hardback) PK-LaUMT 006.37 23 PRI-C Prince, Simon J. D. Computer vision : models, learning, and inference / Simon J.D. Prince New York : Cambridge University Press, 2012 Reprint in 2017 xi, 580 p. : ill. (some col.) ; 26 cm. "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"-- Computer vision. COMPUTERS / Computer Graphics. BK 100962 100962 0 0 0 0 01 01 2017-12-26 0 006.37 PRI-C 116801 2017-12-26 00:00:00 C. 2 2017-12-26 BK 0 0 0 0 01 01 2017-12-26 1 006.37 PRI-C 116800 2018-12-14 00:00:00 2018-08-17 2017-12-26 BK