Skin Colour Segmentation Using Finite Bivariate Pearsonian Type-Iib Mixture Model and K-Means

Studies on Colour Image Segmentation Method Based on Finite Left Truncated Bivariate Gaussian Mixture Model with K-Means

Colour Image segmentation is one of the prime requisites for computer vision and analysis. Much work has been reported in literature regarding colour image segmentation under HSI colour space and Gaussian mixture model (GMM). Since the Hue and Saturation values of the pixel in the image are non-negative. And may not be meso-kurtic, it is needed left truncate the Gaussian variate and is used to ...

Image Segmentation Based on a Finite Generalized New Symmetric Mixture Model with K – Means

In this paper a novel image segmentation and retrieval method based on finite new symmetric mixture model with K-means clustering is developed. Here it is considered that pixel intensities in each image region follow a new symmetric distribution. The new symmetric distribution includes platykurtic and meso-kurtic distributions. This also includes Gaussian mixture model as a particular case. The...

IMAGE SEGMENTATION USING GAUSSIAN MIXTURE MODEL

  Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we have learned Gaussian mixture model to the pixels of an image. The parameters of the model have estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image is made by Bayes rule. In fact, ...

­­Image Segmentation using Gaussian Mixture Model

Abstract: Stochastic models such as mixture models, graphical models, Markov random fields and hidden Markov models have key role in probabilistic data analysis. In this paper, we used Gaussian mixture model to the pixels of an image. The parameters of the model were estimated by EM-algorithm.   In addition pixel labeling corresponded to each pixel of true image was made by Bayes rule. In fact,...

Segmentation of Photospheric Solar Images by Using c-Means,k-Means, and FCM Algorithms