Work place: Department of Computer Science and Engineering Srinivasa Institute of Engineering & Technology NH-216, Cheyyeru, Amalapuram, A.P., INDIA

E-mail: nagajagadesh@gmail.com


Research Interests: Data Mining, Speech Synthesis, Image Processing


Mr.B.N.Jagadesh is presently working as Assistant Professor in Computer science and Engineering Department at Srinivasa Institute of Engineering & Technology, Cheyyeru. He presented several research papers in national and International conferences and seminars. He published a good number of papers in national and International journals. He guided several students for getting their M.Tech degrees in Computer Science & Engineering. His current research interests are Image Processing, Speech Processing and Data Mining.

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

By B.N.Jagadesh K Srinivasa Rao Ch. Satyanarayana

DOI: https://doi.org/10.5815/ijigsp.2012.11.01, Pub. Date: 8 Oct. 2012

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 mixture model since the hue and saturation values always lies between 0 and 1. The bivariate feature vector of the human image is to be modeled with a Pearson type II mixture (bivariate Beta mixture) model. Using the EM Algorithm the model parameters are estimated. The segmentation algorithm is developed under Bayesian frame. Through experimentation the proposed skin colour segmentation algorithm performs better with respect to segmentation quality metrics such as PRI, VOI and GCE. The ROC curves plotted for the system also revealed that the proposed algorithm can segment the skin colour more effectively than the algorithm with Gaussian mixture model for some images.

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