A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model | Zendy

B. N. Jagadesh | Zendy; K. Srinivasa Rao | Zendy; Ch. Satyanarayana | Zendy

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A Robust Skin Colour Segmentation Using Bivariate Pearson Type IIαα (Bivariate Beta) Mixture Model

Author(s) -

B. N. Jagadesh,

K. Srinivasa Rao,

Ch. Satyanarayana

Publication year - 2012

Publication title -

international journal of image graphics and signal processing

Language(s) - English

Resource type - Journals

eISSN - 2074-9082

pISSN - 2074-9074

DOI - 10.5815/ijigsp.2012.11.01

Subject(s) - bivariate analysis , mathematics , statistics , pearson product moment correlation coefficient , segmentation , type (biology) , beta (programming language) , pattern recognition (psychology) , artificial intelligence , computer science , biology , ecology , programming language

Probability distributions formulate the basic framework for developing several segmentation algorithms. Among the various segmentation algorithms, skin colour segmentation is one of the most important algorithms for human computer interaction. Due to various random factors influencing the colour space, there does not exist a unique algorithm which serve the purpose of all images. In this paper a novel and new skin colour segmentation algorithms is proposed based on bivariate Pearson type II a

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