Computer Vision: Models, Learning, and Inference
Cambridge University Press, 6/18/2012
EAN 9781107011793, ISBN10: 1107011795
Hardcover, 598 pages, 25.3 x 17.7 x 2.8 cm
Language: English
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
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.
'Computer vision and machine learning have married and this book is their child. It gives the machine learning fundamentals you need to participate in current computer vision research. It's really a beautiful book, showing everything clearly and intuitively. I had lots of 'aha!' moments as I read through the book. This is an important book for computer vision researchers and students, and I look forward to teaching from it.' William T. Freeman, Massachusetts Institute of Technology