Decision theory and artificial intelligence: I. A semantics-based region analyzer |
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Authors: | Jerome A. Feldman Yoram Yakimovsky |
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Affiliation: | Computer Science Department, Artificial Intelligence Laboratory, Stanford University, Stanford, Calif. 94305, U.S.A.;Jet Propulsion Laboratory, 4800 Oakgrove Drive, Pasadena, Calif. 91103, U.S.A. |
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Abstract: | Mathematical decision theory can be combined with heuristic techniques to attack Artificial Intelligence problems. As a first example, the problem of breaking an image into meaningful regions is considered. Bayesian decision theory is seen to provide a mechanism for including problem dependent (semantic) information in a general system. Some results are presented which make the computation feasible. A programming system based on these ideas and its application to road scenes is described. |
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