A belief maintenance scheme for hierarchical knowledge‐based image analysis systems

A variety of belief maintenance schemes for image analysis have been suggested and used to date. In the recent past, several researchers have suggested the use of the Dempster‐Shafer theory of evidence for representation of belief. This approach appears to be particularly suited for knowledge‐based image analysis systems because of its intuitively convincing ways of representing beliefs, support, plausibility, ignorance, dubiety, and a host of other measures that can be used for the purpose of decision making. It also provides a very attractive technique to combine these measures obtained from disparate knowledge sources. In this article, we show how the Dempster‐Shafer theoretic concepts of refinement and coarsening can be used to aggregate and propagate evidence in a multi‐resolution image analysis system based on a hierarchical knowledge base.

[1]  Ze-Nian Li,et al.  Uncertainty management in a pyramid vision system , 1989, Int. J. Approx. Reason..

[2]  Olivier D. Faugeras,et al.  Improving Consistency and Reducing Ambiguity in Stochastic Labeling: An Optimization Approach , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Khaled Mellouli,et al.  Propagation of belief functions: a distributed approach , 1986, UAI.

[4]  Leonard P. Wesley.  Evidential knowledge-based computer vision , 1986 .

[5]  Victor R. Lesser,et al.  A Multi-Level Organization For Problem Solving Using Many, Diverse, Cooperating Sources Of Knowledge , 1975, IJCAI.

[6]  Henry E. Kyburg,et al.  Bayesian and Non-Bayesian Evidential Updating , 1987, Artificial Intelligence.

[7]  Jeffrey A. Barnett,et al.  Computational Methods for a Mathematical Theory of Evidence , 1981, IJCAI.

[8]  Judea Pearl,et al.  On Evidential Reasoning in a Hierarchy of Hypotheses , 1990, Artif. Intell..

[9]  Lotfi A. Zadeh,et al.  Syllogistic reasoning in fuzzy logic and its application to usuality and reasoning with dispositions , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[10]  Raj Reddy,et al.  The HEARSAY Speech Understanding System , 1974 .

[11]  John D. Lowrance,et al.  An Inference Technique for Integrating Knowledge from Disparate Sources , 1981, IJCAI.

[12]  Rodney A. Brooks,et al.  Symbolic Reasoning Among 3-D Models and 2-D Images , 1981, Artif. Intell..

[13]  Glenn Shafer,et al.  Implementing Dempster's Rule for Hierarchical Evidence , 1987, Artif. Intell..

[14]  Samir I. Shaheen,et al.  A Modular Computer Vision System for Picture Segmentation and Interpretation , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Thomas O. Binford,et al.  Bayesian inference in model-based machine vision , 1987, Int. J. Approx. Reason..

[16]  Judea Pearl,et al.  Fusion, Propagation, and Structuring in Belief Networks , 1986, Artif. Intell..

[17]  John Freeman,et al.  The modelling of spatial relations , 1975 .

[18]  Edward H. Shortliffe,et al.  A Method for Managing Evidential Reasoning in a Hierarchical Hypothesis Space , 1985, Artif. Intell..

[19]  Avinash C. Kak,et al.  Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System , 1988, AI Mag..