21 March 1989 Integrating Top-Down Control With Intermediate-Level Vision: A Case Study
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The AI approach to vision has been heralded as reducing the computational burden of traditional bottom-up systems by ap-plying knowledge-based control. AI-style systems use knowledge to focus attention and processing resources on the most promising hypotheses and combine information from multiple knowledge sources and/or sensors. Our case study compares the complexity of an intermediate-level grouping task with and without top-down control. The results provide clear empirical support for the claim that knowledge-directed control reduces the computation required for object identification. The Rectilinear Line Grouping System (RLGS) is a bottom-up line grouping system designed to extract manmade structures from static images. The Schema System is a knowledge-based system shell for controlling computer vision tasks. In this paper we consider the task of finding instances of two objects (telephone poles and road signs) in complex natural scenes. First we apply the RLGS in the original bottom-up manner in which it was designed, noting the number of line relations that must be computed, as well as the complexity of the graph matching task that must be performed. Then we place the RLGS primitives under the direction of the Schema System, noting the reduction in required computation.
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Bruce A. Draper, Bruce A. Draper, J. Ross Beveridge, J. Ross Beveridge, Edward M. Riseman, Edward M. Riseman, } "Integrating Top-Down Control With Intermediate-Level Vision: A Case Study", Proc. SPIE 1095, Applications of Artificial Intelligence VII, (21 March 1989); doi: 10.1117/12.969317; https://doi.org/10.1117/12.969317

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