MARC状态：已编 文献类型：西文图书 浏览次数：26
- 题名/责任者:
- Computer vision : models, learning, and inference Simon J.D. Prince.
- 出版发行项:
- New York : Cambridge University Press, 2012.
- ISBN:
- 9781139512251 (eISBN)
- ISBN:
- 9781107011793
- 载体形态项:
- 1 online resource (xi, 580 p.) : ill. (some col.)
- 个人责任者:
- Prince, Simon J. D. (Simon Jeremy Damion), 1972-
- 论题主题:
- Computer vision.
- 中图法分类号:
- TP391.41
- 一般附注:
- Descriptionbasedonprintversionrecord.
- 书目附注:
- Includes bibliographical references (p. 533-566) and index.
- 内容附注:
- 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.
- 摘要附注:
- "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"--
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