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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution - Softcover
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Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven’t kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images is a tedious task because due to lack of spatial information and pixel intensities are non-homogenous. In this regard region based segmentation is impossible. So this is the reason we consider clustering algorithms in pre-processing to improve the cluster efficiency & overcome the obstacles present in traditional methods. In the proposed method we were having two stages, the first stage, in order to pre-process the image we were utilizing the fuzzy logic and k-means clustering known as Fuzzy-k-Means clustering. Here the clustered segmentation results suffering from boundaries and edge leak.
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution
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LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Softcover
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering
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LAP LAMBERT Academic Publishing Apr 2019, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven't kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images is a tedious task because due to lack of spatial information and pixel intensities are non-homogenous. In this regard region based segmentation is impossible. So this is the reason we consider clustering algorithms in pre-processing to improve the cluster efficiency & overcome the obstacles present in traditional methods. In the proposed method we were having two stages, the first stage, in order to pre-process the image we were utilizing the fuzzy logic and k-means clustering known as Fuzzy-k-Means clustering. Here the clustered segmentation results suffering from boundaries and edge leak. 56 pp. Englisch. Seller Inventory # 9786200007124
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering : Via Level Set Evolution
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven't kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images is a tedious task because due to lack of spatial information and pixel intensities are non-homogenous. In this regard region based segmentation is impossible. So this is the reason we consider clustering algorithms in pre-processing to improve the cluster efficiency & overcome the obstacles present in traditional methods. In the proposed method we were having two stages, the first stage, in order to pre-process the image we were utilizing the fuzzy logic and k-means clustering known as Fuzzy-k-Means clustering. Here the clustered segmentation results suffering from boundaries and edge leak. Seller Inventory # 9786200007124
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering: Via Level Set Evolution
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Paperback. Condition: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock. Seller Inventory # zk6200007128
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering
Published by
LAP LAMBERT Academic Publishing, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Softcover
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kama RamuduMr. Ramudu Kama is Assistant Professor at Kakatiya Institute of Technology and Science Warangal. Smt. Kalyani Chenigaram is Assistant Professor at Kakatiya Institute of Technology and Science Warangal. Dr. Raghotham Reddy . Seller Inventory # 289577119
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Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering
Published by
LAP LAMBERT Academic Publishing Apr 2019, 2019
ISBN 10: 6200007128
ISBN 13: 9786200007124
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Taschenbuch
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Taschenbuch. Condition: Neu. Neuware -Due to the difficulties occurred in remote sensing image information, an analysis algorithms growth of a large scale image segmentation haven¿t kept a place with the requirement for the methods which to develop the final accuracy of object detection as well as the recognition. Traditional Level set segmentation methods which are Chan-Vese (CV), IVC 2010, ACM with SBGFRLS, Online Region Based ACM (ORACM) were suffered from more amount of time complexity, as well as low segmentation accuracy due to the large intensity homogeneities and the noise. The robust segmentation of remote sensing images is a tedious task because due to lack of spatial information and pixel intensities are non-homogenous. In this regard region based segmentation is impossible. So this is the reason we consider clustering algorithms in pre-processing to improve the cluster efficiency & overcome the obstacles present in traditional methods. In the proposed method we were having two stages, the first stage, in order to pre-process the image we were utilizing the fuzzy logic and k-means clustering known as Fuzzy-k-Means clustering. Here the clustered segmentation results suffering from boundaries and edge leak.Books on Demand GmbH, Überseering 33, 22297 Hamburg 56 pp. Englisch. Seller Inventory # 9786200007124
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