1. Overview -- 2. Background -- Markov Random Field Models -- Multiresolution -- 3. MRF Framework for Image Interpretation -- MRF on a Graph -- 4. Bayesian Net Approach to Interpretation -- MRF model leading to Bayesian Network Formulation -- Bayesian Networks and Probabilistic Inference -- Probability Updating in Bayesian Networks -- Bayesian Networks for Gibbsian Image Interpretation -- Experimental Results -- 5. Joint Segmentation and Image Interpretation -- Image Interpretation using Integration -- The Joint Segmentation and Image Interpretation Scheme -- Experimental Results -- 6. Conclusions -- App. A. Bayesian Reconstruction -- App. B. Proof of Hammersley-Clifford Theorem -- App. C. Simulated Annealing Algorithms - Selecting T[subscript 0] in practise -- App. D. Custom Made Pyramids -- App. E. Proof of Theorem 4.6 -- App. F. k-means clustering -- App. G. Features used in Image Interpretation -- App. H. Knowledge Acquisition -- App. I. HMM for Clique Functions
Bibliography
Includes bibliographical references (pages [115]-122) and index