000			03233 am a2200325 i 4500
001			42783
008			120323s2012 nyua b 001 0 eng
010			_a 2012008187
020			_a9781107011793 _qhardback
035			_a(OCoLC)
040			_aDLC _cDLC _dDLC _dBAUN _erda _beng
049			_aBAUN_MERKEZ
050	0	0	_aTA1634 _b.P75 2012
082	0	0	_223
100	1		_aPrince, Simon J. D. _q(Simon Jeremy Damion), _d1972-
245	1	0	_aComputer vision : _bmodels, learning, and inference / _cSimon J.D. Prince.
264		1	_aNew York : _bCambridge University Press, _c[2012]
264		4	_c©2012
300			_axi, 580 pages : _billustrations (some color) ; _c26 cm.
336			_2rdacontent _atext _btxt
337			_2rdamedia _aunmediated _bn
338			_2rdacarrier _avolume _bnc
504			_aIncludes bibliographical references (pages 533-566) and index.
505	8		_tMachine generated contents note _tPart I. Probability _t1. Introduction to probability; 2. Common probability distributions; 3. Fitting probability models; 4. The normal distribution; Part II. Machine Learning for Machine Vision _t5. Learning and inference in vision; 6. Modeling complex data densities; 7. Regression models; 8. Classification models; Part III. Connecting Local Models _t9. Graphical models; 10. Models for chains and trees; 11. Models for grids; Part IV. Preprocessing _t12. Image preprocessing and feature extraction; Part V. Models for Geometry _t13. The pinhole camera; 14. Models for transformations; 15. Multiple cameras; Part VI. Models for Vision _t16. Models for style and identity; 17. Temporal models; 18. Models for visual words; Part VII. Appendices _tA. Optimization; B. Linear algebra; C. Algorithms.
520			_a"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"--
650		0	_aComputer vision.
650		7	_aCOMPUTERS / Computer Graphics. _2bisacsh
710	2		_972911 _aCambridge University Press.
942			_2lcc _cKT
999			_c41310 _d41310