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Issue No. 09 - September (1994 vol. 16)
ISSN: 0162-8828
pp: 901-905
<p>Extracting geometric primitives from geometric sensor data is an important problem in model-based vision. A minimal subset is the smallest number of points necessary to define a unique instance of a geometric primitive. A genetic algorithm based on a minimal subset representation is used to perform primitive extraction. It is shown that the genetic approach is an improvement over random search and is capable of extracting more complex primitives than the Hough transform.</p>
feature extraction; computer vision; genetic algorithms; optimisation; geometry; geometric primitive extraction; genetic algorithm; geometric sensor data; model-based vision; minimal subset; random search; Hough transform

G. Roth and M. Levine, "Geometric Primitive Extraction Using a Genetic Algorithm," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 16, no. , pp. 901-905, 1994.
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