| 000 | 02254camuu2200265 a 4500 | |
| 001 | 000045793932 | |
| 005 | 20140326180653 | |
| 008 | 140326s2012 nyua b 001 0 eng | |
| 010 | ▼a 2012008187 | |
| 020 | ▼a 9781107011793 (hardback) | |
| 035 | ▼a (KERIS)REF000016756987 | |
| 040 | ▼a DLC ▼c DLC ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a TA1634 ▼b .P75 2012 |
| 082 | 0 0 | ▼a 006.3/7 ▼2 23 |
| 084 | ▼a 006.37 ▼2 DDCK | |
| 090 | ▼a 006.37 ▼b P957c | |
| 100 | 1 | ▼a Prince, Simon J. D. ▼q (Simon Jeremy Damion), ▼d 1972-. |
| 245 | 1 0 | ▼a Computer vision : ▼b models, learning, and inference / ▼c Simon J.D. Prince. |
| 260 | ▼a New York : ▼b Cambridge University Press, ▼c 2012. | |
| 300 | ▼a xi, 580 p. : ▼b ill. (some col.) ; ▼c 26 cm. | |
| 504 | ▼a Includes bibliographical references (p. 533-566) and index. | |
| 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"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Computer vision. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.37 P957c | 등록번호 121229290 (16회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
A modern treatment focusing on learning and inference, with minimal prerequisites, real-world examples and implementable algorithms.
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. - Covers cutting-edge techniques, including graph cuts, machine learning and multiple view geometry - A unified approach shows the common basis for solutions of important computer vision problems, such as camera calibration, face recognition and object tracking - More than 70 algorithms are described in sufficient detail to implement - More than 350 full-color illustrations amplify the text - The treatment is self-contained, including all of the background mathematics - Additional resources at www.computervisionmodels.com
정보제공 :
목차
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.
정보제공 :