机读格式显示(MARC)

• 000 03277cam a2200313 a 4500

• 001 0000605390

• 003 CAL

• 005 20220223162900.0

• 006 m_ d

• 007 cr cn

• 008 120323s2012 nyua sb 001 0 eng

• 010 __ |a 2012008187

• 020 __ |a 9781139512251 (eISBN)

• 020 __ |a 9781107011793

• 040 __ |a DLC |c DLC |d DLC |d ECN

• 042 __ |a pcc

• 050 00 |a TA1634 |b .P75 2012

• 082 00 |a 006.3/7 |2 23

• 093 __ |a TP391.41 |2 4

• 100 1_ |a Prince, Simon J. D. |q (Simon Jeremy Damion), |d 1972-

• 245 10 |a Computer vision : |b models, learning, and inference |h [electronicresource] / |c Simon J.D. Prince.

• 260 __ |a New York : |b Cambridge University Press, |c 2012.

• 300 __ |a 1 online resource (xi, 580 p.) : |b ill. (some col.)

• 500 __ |a Descriptionbasedonprintversionrecord.

• 504 __ |a Includes bibliographical references (p. 533-566) and index.

• 505 8_ |a 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 __ |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 |a Computer vision.

• 856 4_ |u http://www.itextbook.cn/f/book/bookDetail?bookId=cd5e0c41169845ef922e48f43e6de934 |z An electronic book accessible through the World Wide Web; click to view