Computer Vision Models, Learning, and Inference > 컴퓨터 비전

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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.