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Computer Vision Models, Learning, and Inference
출판사 : Cambridge University Press
저 자 : Prince,
ISBN : 9781107011793
발행일 : 2012-6
도서종류 : 외국도서
발행언어 : 영어
페이지수 : 598
판매가격 : 79,000원
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   Computer Vision Models, Learning, and Inference 목차

Description Contents Resources Courses About the Authors
Table of Contents

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.
   도서 상세설명   


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

Self contained book that includes all of the background mathematics
Presents a detailed treatment of modern computer vision topics including graph cuts, machine learning and geometry
Contains descriptions of 80 algorithms in sufficient detail to implement

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