Edge Relaxation and Boundary Continuity.
MASSACHUSETTS UNIV AMHERST DEPT OF COMPUTER AND INFORMATION SCIENCE
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Many image analysis tasks require the construction of a boundary representation as a means of partitioning an image. This paper develops a parallel relaxation algorithm for updating initial heuristic estimates of the likelihood of edges so that continuous boundaries are formed. Bayesian probability theory is used to analyze the probability updating of a single edge based upon the joint probabilities of the edges in its local surrounding context. The relationships between edges, sometimes referred to as compatibility coefficients in relaxation algorithms, are embodied as conditional probabilities between the central edge and the context of edges. The set of conditional probabilities are theoretically derived from a model of desired line drawings that satisfy basic notions of boundary continuity. The local updating function attempts to drive the likelihood of each central edge into consistency with the surrounding context. Experiments involving the iterative parallel application of this non-linear Bayesian updating function to all edge probabilities demonstrates serious problems in the formulation. A variety of heuristic modifications, guided by theoretical considerations, are examined empirically. The final formulation is an algorithm that performs in an effective manner on several very complex images. Author
- Human Factors Engineering and Man Machine Systems