Synopsis
Skin segmentation deals with identification of skin regions from an image for effective analysis. Skin segmentation is an important activity in many real time systems, dealing with human computer interactions. Skin colour segmentation is a complex task involving several compound activities. Several authors developed various segmentation techniques with the assumption that the feature vector associated with the skin region follows a Gaussian or mixture of Gaussian distribution. The skin colour segmentation methods serve well only when the feature vector consists of hue and saturation values of the pixels in the skin image region which are symmetric and meso-kurtic. However, in many images the feature vector may not be symmetric and meso-kurtic. To have an accurate skin colour segmentation it is needed to have skin colour segmentation methods based on non Gaussian bivariate mixture distributions. Hence, this thesis deals with development and analysis of skin colour segmentation methods based on bivariate Pearsonian mixture distributions for different races of human skin namely, African, Asian and European separately.
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
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ISBN 10: 6138941322
ISBN 13: 9786138941323
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Skin segmentation deals with identification of skin regions from an image for effective analysis. Skin segmentation is an important activity in many real time systems, dealing with human computer interactions. Skin colour segmentation is a complex task involving several compound activities. Several authors developed various segmentation techniques with the assumption that the feature vector associated with the skin region follows a Gaussian or mixture of Gaussian distribution. The skin colour segmentation methods serve well only when the feature vector consists of hue and saturation values of the pixels in the skin image region which are symmetric and meso-kurtic. However, in many images the feature vector may not be symmetric and meso-kurtic. To have an accurate skin colour segmentation it is needed to have skin colour segmentation methods based on non Gaussian bivariate mixture distributions. Hence, this thesis deals with development and analysis of skin colour segmentation methods based on bivariate Pearsonian mixture distributions for different races of human skin namely, African, Asian and European separately. 176 pp. Englisch. Seller Inventory # 9786138941323
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
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ISBN 10: 6138941322
ISBN 13: 9786138941323
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Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Jagadesh Dr. B. N.Dr. B. N. Jagadesh is working as Professor of CSE and Dean-Academics, Srinivasa Institute of Engineering & Technology, Amalapuram.Skin segmentation deals with identification of skin regions from an image for eff. Seller Inventory # 408394476
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
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Taschenbuch. Condition: Neu. Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models | B. N. Jagadesh | Taschenbuch | Englisch | 2020 | Scholars' Press | EAN 9786138941323 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 119103455
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
Published by
Scholars' Press Sep 2020, 2020
ISBN 10: 6138941322
ISBN 13: 9786138941323
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Skin segmentation deals with identification of skin regions from an image for effective analysis. Skin segmentation is an important activity in many real time systems, dealing with human computer interactions. Skin colour segmentation is a complex task involving several compound activities. Several authors developed various segmentation techniques with the assumption that the feature vector associated with the skin region follows a Gaussian or mixture of Gaussian distribution. The skin colour segmentation methods serve well only when the feature vector consists of hue and saturation values of the pixels in the skin image region which are symmetric and meso-kurtic. However, in many images the feature vector may not be symmetric and meso-kurtic. To have an accurate skin colour segmentation it is needed to have skin colour segmentation methods based on non Gaussian bivariate mixture distributions. Hence, this thesis deals with development and analysis of skin colour segmentation methods based on bivariate Pearsonian mixture distributions for different races of human skin namely, African, Asian and European separately.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 176 pp. Englisch. Seller Inventory # 9786138941323
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Skin Colour Segmentation Using Bivariate Pearsonian Mixture Models
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Skin segmentation deals with identification of skin regions from an image for effective analysis. Skin segmentation is an important activity in many real time systems, dealing with human computer interactions. Skin colour segmentation is a complex task involving several compound activities. Several authors developed various segmentation techniques with the assumption that the feature vector associated with the skin region follows a Gaussian or mixture of Gaussian distribution. The skin colour segmentation methods serve well only when the feature vector consists of hue and saturation values of the pixels in the skin image region which are symmetric and meso-kurtic. However, in many images the feature vector may not be symmetric and meso-kurtic. To have an accurate skin colour segmentation it is needed to have skin colour segmentation methods based on non Gaussian bivariate mixture distributions. Hence, this thesis deals with development and analysis of skin colour segmentation methods based on bivariate Pearsonian mixture distributions for different races of human skin namely, African, Asian and European separately. Seller Inventory # 9786138941323
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