Bayesian Approach to Image Interpretation / Edition 1

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Bayesian Approach to Image Interpretation will interest anyone working in image interpretation. It is complete in itself and includes background material. This makes it useful for a novice as well as for an expert. It reviews some of the existing probabilistic methods for image interpretation and presents some new results. Additionally, there is extensive bibliography covering references in varied areas.
For a researcher in this field, the material on synergistic integration of segmentation and interpretation modules and the Bayesian approach to image interpretation will be beneficial.
For a practicing engineer, the procedure for generating knowledge base, selecting initial temperature for the simulated annealing algorithm, and some implementation issues will be valuable.
New ideas introduced in the book include:

• New approach to image interpretation using synergism between the segmentation and the interpretation modules.
• A new segmentation algorithm based on multiresolution analysis.
• Novel use of the Bayesian networks (causal networks) for image interpretation.
• Emphasis on making the interpretation approach less dependent on the knowledge base and hence more reliable by modeling the knowledge base in a probabilistic framework.
Useful in both the academic and industrial research worlds, Bayesian Approach to Image Interpretation may also be used as a textbook for a semester course in computer vision or pattern recognition.

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Editorial Reviews

From The Critics
Writing for students and researchers in the field, Kopparapu (research and development for a private company in Bangalore, India) and Desai (electrical engineering, Indian Institute of Technology, Bombay) present a description and up-to-date treatment of image interpretation. The initial chapters describe the state of research, Markov random fields, their application to computer vision, the concept of cliques, and Bayesian network image interpretation. The authors then propose a new approach that applies synergism between the process of segmentation and interpretation in a multi-resolution framework and presents a joint segmentation and image interpretation algorithm. Annotation c. Book News, Inc., Portland, OR (
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Product Details

Table of Contents

List of Figures
List of Tables
1 Overview 1
2 Background 11
Markov Random Field Models 13
Multiresolution 24
3 MRF Framework for Image Interpretation 35
MRF on a Graph 35
4 Bayesian Net Approach to Interpretation 43
MRF model leading to Bayesian Network Formulation 44
Bayesian Networks and Probabilistic Inference 45
Probability Updating in Bayesian Networks 49
Bayesian Networks for Gibbsian Image Interpretation 51
Experimental Results 54
5 Joint Segmentation and Image Interpretation 59
Image Interpretation using Integration 60
The Joint Segmentation and Image Interpretation Scheme 64
Experimental Results 69
6 Conclusions 79
App. A Bayesian Reconstruction 81
App. B Proof of Hammersley-Clifford Theorem 83
App. C Simulated Annealing Algorithms - Selecting T[subscript 0] in practise 92
App. D Custom Made Pyramids 95
App. E Proof of Theorem 4.6 97
App. F k-means clustering 99
App. G Features used in Image Interpretation 101
App. H Knowledge Acquisition 107
App. I HMM for Clique Functions 113
References 115
Index 123
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