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Asphalt Pavement Crack Classification : A Comparative Study of Three AI Approaches: Multilayer Perceptron, Genetic Algorithms and Self-Organizing Maps

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dc.contributor.author Rababaah, Haroun
dc.date.accessioned 2013-09-20T20:07:18Z
dc.date.available 2013-09-20T20:07:18Z
dc.date.issued 2005-05
dc.identifier.uri https://www.iusb.edu/math-compsci/graduate-thesis.php en_US
dc.identifier.uri http://hdl.handle.net/2022/16813
dc.description Thesis ( M.S.) Indiana University South Bend, 2008. en_US
dc.description.abstract This study presents a comparison of three Artificial Intelligence (AI) approaches: multilayer perception (MLP), genetic algorithms (GA) and self-organizing maps (SOM) to improve automated asphalt pavement crack classification using computer vision en_US
dc.language.iso en_US en_US
dc.publisher Indiana University South Bend en_US
dc.subject Computer vision en_US
dc.subject Pavements, Asphalt -- Cracking en_US
dc.title Asphalt Pavement Crack Classification : A Comparative Study of Three AI Approaches: Multilayer Perceptron, Genetic Algorithms and Self-Organizing Maps en_US
dc.type Thesis en_US


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