Two dimensional object recognition using generalized Hough Transform and genetic algorithm
This thesis focuses on model-based matching which is one of the fundamental components of a general scene interpretation scheme. The model based matching problem is framed within the hypothesis and verification paradigm. Its function is to recognize and to locate instances of a model in a scene base...
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sg-ntu-dr.10356-205082020-09-27T20:14:08Z Two dimensional object recognition using generalized Hough Transform and genetic algorithm Sim, Hak Chuah. Wong, Kok Cheong School of Applied Science DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision This thesis focuses on model-based matching which is one of the fundamental components of a general scene interpretation scheme. The model based matching problem is framed within the hypothesis and verification paradigm. Its function is to recognize and to locate instances of a model in a scene based on matching of extracted scene features to stored model features. The obvious and simple approach to obtain the best match is to evaluate all the possibilities, but the incredibly large problem space will take intolerably long computation time to attain a satisfactory result. Current literature reports a number of solutions based on a variety of assumptions and approaches to solve these problems. Master of Applied Science 2009-12-15T03:09:22Z 2009-12-15T03:09:22Z 1995 1995 Thesis http://hdl.handle.net/10356/20508 en NANYANG TECHNOLOGICAL UNIVERSITY 150 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision Sim, Hak Chuah. Two dimensional object recognition using generalized Hough Transform and genetic algorithm |
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This thesis focuses on model-based matching which is one of the fundamental components of a general scene interpretation scheme. The model based matching problem is framed within the hypothesis and verification paradigm. Its function is to recognize and to locate instances of a model in a scene based on matching of extracted scene features to stored model features. The obvious and simple approach to obtain the best match is to evaluate all the possibilities, but the incredibly large problem space will take intolerably long computation time to attain a satisfactory result. Current literature reports a number of solutions based on a variety of assumptions and approaches to solve these problems. |
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Wong, Kok Cheong |
author_facet |
Wong, Kok Cheong Sim, Hak Chuah. |
format |
Theses and Dissertations |
author |
Sim, Hak Chuah. |
author_sort |
Sim, Hak Chuah. |
title |
Two dimensional object recognition using generalized Hough Transform and genetic algorithm |
title_short |
Two dimensional object recognition using generalized Hough Transform and genetic algorithm |
title_full |
Two dimensional object recognition using generalized Hough Transform and genetic algorithm |
title_fullStr |
Two dimensional object recognition using generalized Hough Transform and genetic algorithm |
title_full_unstemmed |
Two dimensional object recognition using generalized Hough Transform and genetic algorithm |
title_sort |
two dimensional object recognition using generalized hough transform and genetic algorithm |
publishDate |
2009 |
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http://hdl.handle.net/10356/20508 |
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1681056348384526336 |