Markov Random Field Modeling in Image Analysis (Advances in Computer Vision and Pattern Recognition)
by Stan Z. Li
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Fulfillment optionsMarkov Random Field Modeling in Image Analysis (Advances in Computer Vision and Pattern Recognition)
Markov random field (MRF) theory provides a basis for modeling contextual constraints in visual processing and interpretation. It enables us to develop optimal vision algorithms systematically when used with optimization principles. This book presents a comprehensive study on the use of MRFs for solving computer vision problems. Various vision models are presented in a unified framework, including image restoration and reconstruction, edge and region segmentation, texture, stereo and motion, object matching and recognition, and pose estimation. This third edition includes the most recent advances and has new and expanded sections on topics such as: Bayesian Network; Discriminative Random Fields; Strong Random Fields; Spatial-Temporal Models; Learning MRF for Classification. This book is an excellent reference for researchers working in computer vision, image processing, statistical pattern recognition and applications of MRFs. It is also suitable as a text for advanced courses in these areas.
Details
- Publisher
- Springer London
- Author
- Stan Z. Li
- Format
- Paperback
- Published
- Oct 21, 2010
- ISBN-13
- 9781849967679
- Pages
- 362
- Printing
- Printed on demand
Markov Random Field Modeling in Image Analysis (Advances in Computer Vision and Pattern Recognition) for groups: pricing
Volume pricing applies automatically in your cart. No codes, no account needed.
| Copies | Price per copy | You save |
|---|---|---|
| 25 – 49 | $149.59 | 12% off list |
| 50 – 99 | $147.89 | 13% off list |
| 100+ | $146.19 | 14% off list |
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