Proceedings PaperRole Of Adaptive Operators In Image Understanding
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A system that hopes to accomplish robust automatic Image Understanding (IU) clearly needs techniques which can reason about what it is seeing and what it is trying to see. One need for reasoning is found in the ubiquitous operator parameter problem: the problem of setting parameters for low-level image computations. This paper explores this problem in the context of a complete IU system and presents an approach by which parameters are tuned with respect to high level features. Adaptive Operators accomplish this tuning by using 1) evaluations derived from specific object level features that rate the "goodness" of a segmented region; and 2) search methods which adaptively search for segmentation parameters producing the best region according to the evaluation. Adaptive Operators represent reasoning which ties together both the high-level, generally a-priori knowledge of an IU system and the lowest, purely computational level. Examples of Adaptive Operators and their performance are given in the context of a system to recognize buildings in aerial photographs.