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Metaheuristics for unsupervised classification: Application to remote sensing image segmentation - Softcover
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In this work, we have approached the image segmentation problem by means of metaheuristics, which have proven to be very efficient in providing good approximate solutions to various optimization problems. The first step was to reformulate the segmentation problem into a single-objective optimization problem, in a first step, and a multi-objective one, in a second step. The second step consisted in the extraction of the spectral and textural information of the image. The extraction of the textural information was done using co-occurrence matrices. An equiprobable requantization has been done beforehand in order to reduce the computation time. For the spectral information, we considered the mean and the variance of the gray levels on the image.
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Metaheuristics for unsupervised classification
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Metaheuristics for unsupervised classification
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Metaheuristics for unsupervised classification
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. In this work, we have approached the image segmentation problem by means of metaheuristics, which have proven to be very efficient in providing good approximate solutions to various optimization problems. The first step was to reformulate the segmentation p. Seller Inventory # 524338335
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Metaheuristics for unsupervised classification
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Metaheuristics for unsupervised classification
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this work, we have approached the image segmentation problem by means of metaheuristics, which have proven to be very efficient in providing good approximate solutions to various optimization problems. The first step was to reformulate the segmentation problem into a single-objective optimization problem, in a first step, and a multi-objective one, in a second step. The second step consisted in the extraction of the spectral and textural information of the image. The extraction of the textural information was done using co-occurrence matrices. An equiprobable requantization has been done beforehand in order to reduce the computation time. For the spectral information, we considered the mean and the variance of the gray levels on the image.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 92 pp. Englisch. Seller Inventory # 9786203594188
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Metaheuristics for unsupervised classification : Application to remote sensing image segmentation
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ISBN 10: 6203594180
ISBN 13: 9786203594188
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this work, we have approached the image segmentation problem by means of metaheuristics, which have proven to be very efficient in providing good approximate solutions to various optimization problems. The first step was to reformulate the segmentation problem into a single-objective optimization problem, in a first step, and a multi-objective one, in a second step. The second step consisted in the extraction of the spectral and textural information of the image. The extraction of the textural information was done using co-occurrence matrices. An equiprobable requantization has been done beforehand in order to reduce the computation time. For the spectral information, we considered the mean and the variance of the gray levels on the image. Seller Inventory # 9786203594188
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Metaheuristics for unsupervised classification | Application to remote sensing image segmentation
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ISBN 10: 6203594180
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Taschenbuch. Condition: Neu. Metaheuristics for unsupervised classification | Application to remote sensing image segmentation | Lotfi Hocini | Taschenbuch | Englisch | 2021 | Our Knowledge Publishing | EAN 9786203594188 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 120774871