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Computer vision : = models, learning...

Prince, Simon J. D. (1972-)

Computer vision : = models, learning, and inference /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Computer vision :/ Simon J.D. Prince.
Reminder of title:	models, learning, and inference /
Author:	Prince, Simon J. D.
Published:	Cambridge ;Cambridge University Press, : 2012.,
Description:	xviii, 580 p. :ill. (some col.) ; : 27 cm.;
Subject:	Computer vision. -
Online resource:	http://assets.cambridge.org/97811070/11793/cover/9781107011793.jpg
ISBN:	9781107011793 (cloth) :

Computer vision : = models, learning, and inference /
Prince, Simon J. D.1972-

Computer vision :models, learning, and inference /Simon J.D. Prince. - Cambridge ;Cambridge University Press,2012. - xviii, 580 p. :ill. (some col.) ;27 cm.

Includes bibliographical references (p. [533]-566) and index.

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.

"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"--

ISBN: 9781107011793 (cloth) :NT2702

LCCN: 2012008187Subjects--Topical Terms:

561800
Computer vision.

LC Class. No.: TA1634 / .P75 2012

Dewey Class. No.: 006.3/7

Computer vision : = models, learning, and inference /
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100 1 $a Prince, Simon J. D. $q (Simon Jeremy Damion), $d 1972- $3 886081
245 1 0 $a Computer vision : $b models, learning, and inference / $c Simon J.D. Prince.
260 # $a Cambridge ; $a New York : $c 2012. $b Cambridge University Press,
300 $a xviii, 580 p. : $b ill. (some col.) ; $c 27 cm.
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"-- $c Provided by publisher.
650 # 0 $a Computer vision. $3 561800
856 4 2 $3 Cover image $u http://assets.cambridge.org/97811070/11793/cover/9781107011793.jpg