Computer Vision: Models, Learning, and Inference
Prince, Simon J. D.
Computer Vision: Models, Learning, and Inference - USA Cambridge University Press 2012 - 580p
Part 1. Probability:
1. Introduction to probability
2. Common probability distributions
3. Fitting probability models
4. The normal distribution
Part 2. Machine Learning for Machine Vision:
5. Learning and inference in vision
6. Modeling complex data densities
7. Regression models
8. Classification models
Part 3. Connecting Local Models:
9. Graphical models
10. Models for chains and trees
11. Models for grids
Part 4. Preprocessing:
12. Image preprocessing and feature extraction
Part 5. Models for Geometry:
13. The pinhole camera
14. Models for transformations
15. Multiple cameras
Part 6. Models for Vision:
16. Models for style and identity
17. Temporal models
18. Models for visual words
Part 7. Appendices: A. Optimization
B. Linear algebra
C. Algorithms.
9781107011793
Electronics and Communication
006.37 / PRI
Computer Vision: Models, Learning, and Inference - USA Cambridge University Press 2012 - 580p
Part 1. Probability:
1. Introduction to probability
2. Common probability distributions
3. Fitting probability models
4. The normal distribution
Part 2. Machine Learning for Machine Vision:
5. Learning and inference in vision
6. Modeling complex data densities
7. Regression models
8. Classification models
Part 3. Connecting Local Models:
9. Graphical models
10. Models for chains and trees
11. Models for grids
Part 4. Preprocessing:
12. Image preprocessing and feature extraction
Part 5. Models for Geometry:
13. The pinhole camera
14. Models for transformations
15. Multiple cameras
Part 6. Models for Vision:
16. Models for style and identity
17. Temporal models
18. Models for visual words
Part 7. Appendices: A. Optimization
B. Linear algebra
C. Algorithms.
9781107011793
Electronics and Communication
006.37 / PRI