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A Parallel Voting Scheme for Aspect Recovery
ABSTRACT
Recently, a qualitative approach was proposed for 3-D shape recovery
based on a hybrid object representation. In this approach,
aspect recovery is the most important stage which binds regions in
the image into meaningful aspects to support 3-D primitive recovery.
There is no known polynomial time algorithm to solve this problem. The
previous approach dealt with this problem by using a heuristic method
based on the conditional probability. Unlike the previous method,
in this paper, we present a novel parallel voting scheme to conquer
the problem for efficiency. For this purpose, the previous global aspect
representation is replaced with a distributed representation of aspects.
Based on this representation, we propose a three-layer parallel voting
network to complete aspect recovery. For evaluating likelihood, a
continuous Hopfield net is employed so that we can enumerate all aspect
coverings in decreasing order of likelihood. We describe this method
in detail and demonstrate its usefulness with simulations.
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