Inhaltsangabe
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 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.
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Über die Autorin bzw. den Autor
Received 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, Burla, Sambalpur, Odisha, India. Her research interests include Computer vision, Image segmentation, Image Registration.
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Color Image Segmentation Using Markov Random Field Models
Verlag:
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139832780
ISBN 13: 9786139832781
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Taschenbuch. Zustand: 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 to 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. Bestandsnummer des Verkäufers 9786139832781
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Zustand: 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, Burla, Sambalpur, Odisha, India. H. Bestandsnummer des Verkäufers 385873335
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Color Image Segmentation Using Markov Random Field Models
Verlag:
LAP LAMBERT Academic Publishing Mai 2018, 2018
ISBN 10: 6139832780
ISBN 13: 9786139832781
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Taschenbuch. Zustand: 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 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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 212 pp. Englisch. Bestandsnummer des Verkäufers 9786139832781
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Color Image Segmentation Using Markov Random Field Models
Verlag:
LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139832780
ISBN 13: 9786139832781
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Taschenbuch. Zustand: 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 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. Bestandsnummer des Verkäufers 9786139832781
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Color Image Segmentation Using Markov Random Field Models
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ISBN 10: 6139832780
ISBN 13: 9786139832781
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Taschenbuch. Zustand: 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 | Anbieter: preigu. Bestandsnummer des Verkäufers 118706896
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