9786139832781 - color image segmentation using markov random field models di panda, sucheta; nanda, dr.pradipta kumar (9 risultati)
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Condizione: New.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Paperback. Condizione: Brand New. 212 pages. 8.66x5.91x0.48 inches. In Stock.
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Da: preigu, Osnabrück, Germaniapreigu
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Taschenbuch. Condizione: Neu. Color Image Segmentation Using Markov Random Field Models | Sucheta Panda (u. a.) | Taschenbuch | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139832781 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbiete…r: 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 -This book focuses on a prime research area in Image Processing i.e. Color Image Segmentation. We have used Stochastic Models more particularly Markov Random Field (MRF) models for the problem of Color Image Segmentation. In order t…o improve the efficiency of the color model, the notion of controlled correlation among different color planes has been introduced and hence a new MRF model called Compound MRF (COMRF) model has been proposed. The controlled correlation feature has been achieved by controlling the associated MRF model parameter . This notion proved to be effective in modeling color images. In order to model both color texture and scene images, a unifying MRF model called Constrained MRF (CMRF) model has been proposed. The constrained condition has been used to develop Constrained Compound MRF (CCOMRF) model and Double Constrained Compound MRF (DCCOMRF) model. The efficacy of these models have been tested with color image segmentation and it has been found that DCCOMRF model proved to be best for modeling color texture and scene images. The segmentation problem is cast as a pixel labeling problem and the pixel label estimation problem has been formulated using MAP. 212 pp. Englisch.
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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EUR 88,41
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Condizione: New. Print on Demand.
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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: Panda SuchetaReceived her PhD degree from National Institute of Technology(NIT), Rourkela, India. Currently she is working as an Associate Professor at the Department of Computer Application, VSSUT, Bu…rla, Sambalpur, Odisha, India. H.
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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EUR 91,58
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Condizione: New. PRINT ON DEMAND.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 55,90
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book focuses on a prime research area in Image Processing i.e. Color Image Segmentation. We have used Stochastic Models more particularly Markov Random Field (MRF) models for the problem of Color Image Segmentation. In order to im…prove the efficiency of the color model, the notion of controlled correlation among different color planes has been introduced and hence a new MRF model called Compound MRF (COMRF) model has been proposed. The controlled correlation feature has been achieved by controlling the associated MRF model parameter . This notion proved to be effective in modeling color images. In order to model both color texture and scene images, a unifying MRF model called Constrained MRF (CMRF) model has been proposed. The constrained condition has been used to develop Constrained Compound MRF (CCOMRF) model and Double Constrained Compound MRF (DCCOMRF) model. The efficacy of these models have been tested with color image segmentation and it has been found that DCCOMRF model proved to be best for modeling color texture and scene images. The segmentation problem is cast as a pixel labeling problem and the pixel label estimation problem has been formulated using MAP.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 212 pp. Englisch.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 56,57
EUR 61,67 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book focuses on a prime research area in Image Processing i.e. Color Image Segmentation. We have used Stochastic Models more particularly Markov Random Field (MRF) models for the problem of Color Image Segmentation. In order to imp…rove the efficiency of the color model, the notion of controlled correlation among different color planes has been introduced and hence a new MRF model called Compound MRF (COMRF) model has been proposed. The controlled correlation feature has been achieved by controlling the associated MRF model parameter . This notion proved to be effective in modeling color images. In order to model both color texture and scene images, a unifying MRF model called Constrained MRF (CMRF) model has been proposed. The constrained condition has been used to develop Constrained Compound MRF (CCOMRF) model and Double Constrained Compound MRF (DCCOMRF) model. The efficacy of these models have been tested with color image segmentation and it has been found that DCCOMRF model proved to be best for modeling color texture and scene images. The segmentation problem is cast as a pixel labeling problem and the pixel label estimation problem has been formulated using MAP.
