Computer Vision: Models, Learning, and Inference

By:

Prince, Simon J. D

Material type: Text

TextPublication details: Cambridge University Press USA 2012Description: 580pISBN:

9781107011793

Subject(s):

Electronics and Communication

DDC classification:

006.37 PRI

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Holdings
Item type	Current library	Item location	Collection	Call number	Status	Date due	Barcode	Item holds
Reference Book	NIMA Knowledge Centre	7th Floor Silence Zone	Reference	006.37 PRI (Browse shelf(Opens below))	Not For Loan		T0050583

Total holds: 0

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.

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