Bayesian Approach to Image Interpretation

Front Cover
Springer Science & Business Media, Jul 31, 2001 - Computers - 127 pages
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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Contents

1
2
BACKGROUND
11
2
16
2
29
MRF FRAMEWORK FOR IMAGE INTERPRETATION 35
34
BAYESIAN NET APPROACH TO INTERPRETATION
43
1
44
JOINT SEGMENTATION AND IMAGE INTERPRETATION
59
CONCLUSIONS
79
Simulated Annealing AlgorithmSelecting T0 in
91
Appendix F kmeans clustering
99
Description ofAreaand Convex Area
103
Appendix H Knowledge Acquisition 107
106
References
115
32
117
Index
123

2
62
1node Basis function
75

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