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Please use this identifier to cite or link to this item: http://eprint.iitd.ac.in/handle/2074/384

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DC FieldValueLanguage
dc.contributor.authorMontek Singh-
dc.contributor.authorChatterjee, Amitabha-
dc.contributor.authorChaudhury, Santanu-
dc.date.accessioned2005-06-25T12:36:24Z-
dc.date.available2005-06-25T12:36:24Z-
dc.date.issued1997-
dc.identifier.citationPattern Recognition, 30(9), 1451-1462en
dc.identifier.urihttp://eprint.iitd.ac.in/dspace/handle/2074/384-
dc.description.abstractThis paper presents a genetic algorithm for solving the problem of structural shape matching. Both sequential and parallel versions of the algorithm have been presented. The genetic operators-- reproduction,crossover and mutation--have been constructed for this specific problem. A new variation of the crossover operator, called the color crossover, is presented. This operator has resulted in significant improvement in runtime and algorithm efficiency. Parallelization has been achieved using an "island" model, with several subpopulations and occasional migration. A complete framework for an object recognition system using this genetic algorithm has been presented. Encouraging experimental results have been obtained.en
dc.format.extent452076 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoenen
dc.subjectStructural descriptionsen
dc.subjectGraph isomorphismen
dc.subjectGenetic algorithmsen
dc.subjectParallelizationen
dc.titleMatching structural shape descriptions using genetic algorithmsen
dc.typeArticleen
Appears in Collections:Electrical Engineering

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