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Robust 2-D Model-Based Object Recognition

Techniques, suitable for parallel implementation, for robust 2D model-based object recognition in the presence of sensor error are studied. Models and scene data are represented as local geometric features and robust hypothesis of feature matchings and transformations is considered. Bounds on the error in the image feature geometry are assumed constraining possible matchings and transformations. Transformation sampling is introduced as a simple, robust, polynomial-time, and highly parallel method of searching the space of transformations to hypothesize feature matchings. Key to the approach is that error in image feature measurement is explicitly accounted for. A Connection Machine implementation and experiments on real images are presented.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6823
Date01 May 1988
CreatorsCass, Todd A.
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format106 p., 10585533 bytes, 7511134 bytes, application/postscript, application/pdf
RelationAITR-1132

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