Computer Vision: Models, Learning, and Inference (Record no. 120779)

MARC details
000 -LEADER
fixed length control field nam a22 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200625b ||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781107011793
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.37
Item number PRI
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Prince, Simon J. D.
9 (RLIN) 57861
245 ## - TITLE STATEMENT
Title Computer Vision: Models, Learning, and Inference
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Name of publisher, distributor, etc Cambridge University Press
Place of publication, distribution, etc USA
Date of publication, distribution, etc 2012
300 ## - PHYSICAL DESCRIPTION
Extent 580p
500 ## - GENERAL NOTE
General note Part 1. Probability:<br/>1. Introduction to probability<br/>2. Common probability distributions<br/>3. Fitting probability models<br/>4. The normal distribution<br/>Part 2. Machine Learning for Machine Vision:<br/>5. Learning and inference in vision<br/>6. Modeling complex data densities<br/>7. Regression models<br/>8. Classification models<br/>Part 3. Connecting Local Models:<br/>9. Graphical models<br/>10. Models for chains and trees<br/>11. Models for grids<br/>Part 4. Preprocessing:<br/>12. Image preprocessing and feature extraction<br/>Part 5. Models for Geometry:<br/>13. The pinhole camera<br/>14. Models for transformations<br/>15. Multiple cameras<br/>Part 6. Models for Vision:<br/>16. Models for style and identity<br/>17. Temporal models<br/>18. Models for visual words<br/>Part 7. Appendices: A. Optimization<br/>B. Linear algebra<br/>C. Algorithms.
600 ## - SUBJECT ADDED ENTRY--PERSONAL NAME
Personal name Electronics and Communication
9 (RLIN) 48726
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Item type Book

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