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In this book, an interdisciplinary study is presented. In this study, a method for three-dimensional (3D) shape characterization and classification is proposed for the six different aggregates. In the first phase, a new 3D laser based imaging system is designed to capture images of aggregates. The imaging system has been optimized to minimize the errors during image capturing. In the second phase, novel 3D shape characterization parameters of the aggregates are extracted. Geometrical parameters of the aggregates are calculated in 3D spatial domain. The last phase, the aggregates are classified by using different classifier models (ANN, FLDA and KNN) with the help of these parameters. Among the classifier types, multi-layer perceptron neural network model that has two hidden layers gives the best performance that is 99.20 percent. The performance of the proposed system is evaluated using manual measurement method and two-dimensional image processing method. Results are analyzed and compared with other studies given in the literature.
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Developing 3 dimensional image analysis methods for aggregates
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LAP LAMBERT Academic Publishing Sep 2015, 2015
ISBN 10: 3659774898
ISBN 13: 9783659774898
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, an interdisciplinary study is presented. In this study, a method for three-dimensional (3D) shape characterization and classification is proposed for the six different aggregates. In the first phase, a new 3D laser based imaging system is designed to capture images of aggregates. The imaging system has been optimized to minimize the errors during image capturing. In the second phase, novel 3D shape characterization parameters of the aggregates are extracted. Geometrical parameters of the aggregates are calculated in 3D spatial domain. The last phase, the aggregates are classified by using different classifier models (ANN, FLDA and KNN) with the help of these parameters. Among the classifier types, multi-layer perceptron neural network model that has two hidden layers gives the best performance that is 99.20 percent. The performance of the proposed system is evaluated using manual measurement method and two-dimensional image processing method. Results are analyzed and compared with other studies given in the literature. 108 pp. Englisch. Codice articolo 9783659774898
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Developing 3 dimensional image analysis methods for aggregates
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659774898
ISBN 13: 9783659774898
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Sinecen MahmutHe has received his BS from Firat Uni., MS from Pamukkale Uni., and the PhD from Dokuz Eylul Uni. His current research interests image processing, software development, fuzzy logic, artificial neural networks, and patt. Codice articolo 158876502
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Developing 3 dimensional image analysis methods for aggregates
Editore:
LAP LAMBERT Academic Publishing Sep 2015, 2015
ISBN 10: 3659774898
ISBN 13: 9783659774898
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book, an interdisciplinary study is presented. In this study, a method for three-dimensional (3D) shape characterization and classification is proposed for the six different aggregates. In the first phase, a new 3D laser based imaging system is designed to capture images of aggregates. The imaging system has been optimized to minimize the errors during image capturing. In the second phase, novel 3D shape characterization parameters of the aggregates are extracted. Geometrical parameters of the aggregates are calculated in 3D spatial domain. The last phase, the aggregates are classified by using different classifier models (ANN, FLDA and KNN) with the help of these parameters. Among the classifier types, multi-layer perceptron neural network model that has two hidden layers gives the best performance that is 99.20 percent. The performance of the proposed system is evaluated using manual measurement method and two-dimensional image processing method. Results are analyzed and compared with other studies given in the literature.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch. Codice articolo 9783659774898
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Developing 3 dimensional image analysis methods for aggregates
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659774898
ISBN 13: 9783659774898
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, an interdisciplinary study is presented. In this study, a method for three-dimensional (3D) shape characterization and classification is proposed for the six different aggregates. In the first phase, a new 3D laser based imaging system is designed to capture images of aggregates. The imaging system has been optimized to minimize the errors during image capturing. In the second phase, novel 3D shape characterization parameters of the aggregates are extracted. Geometrical parameters of the aggregates are calculated in 3D spatial domain. The last phase, the aggregates are classified by using different classifier models (ANN, FLDA and KNN) with the help of these parameters. Among the classifier types, multi-layer perceptron neural network model that has two hidden layers gives the best performance that is 99.20 percent. The performance of the proposed system is evaluated using manual measurement method and two-dimensional image processing method. Results are analyzed and compared with other studies given in the literature. Codice articolo 9783659774898
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Developing 3 dimensional image analysis methods for aggregates
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LAP LAMBERT Academic Publishing, 2015
ISBN 10: 3659774898
ISBN 13: 9783659774898
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Developing 3 dimensional image analysis methods for aggregates | Mahmut Sinecen | Taschenbuch | 108 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659774898 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 104199995
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