Kama ramudu (9 risultati)

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Da: Books Puddle, Woodside, NY, U.S.A.Books Puddle
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 55,39
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Paperback. Condizione: Brand New. 56 pages. 8.66x5.91x0.13 inches. In Stock.

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Da: preigu, Osnabrück, Germaniapreigu
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EUR 29,45
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Taschenbuch. Condizione: Neu. Segmentation of Remote Sensing Images Using Fuzzy-K-Means Clustering | Via Level Set Evolution | Ramudu Kama (u. a.) | Taschenbuch | 56 S. | Englisch | 2019 | LAP LAMBERT Academic Publishing | EAN 9786200007124 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condizione: 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.…

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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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 .…

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Da: moluna, Greven, Germaniamoluna
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 38,74
EUR 48,99 spedizioneSpedito da Germania a U.S.A.Quantità: Più di 20 disponibili
Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Kalyani ChenigaramThis is Ch. Kalyani Asst. Prof from Kakatiya Institute of Technology & Sciences, Warangal. I do have seven years of teaching experience and very much interested in field of image processing.I was very pleased and it.…

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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 32,90
EUR 60,00 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibile
Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 56 pp. Englisch.…