Modeling rainfall drop size distribution in southern England using a Gaussian Mixture Model

­­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,...

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, ...

Mean Drop Size and Drop Size Distribution in a Hanson Mixer-Settler Extraction Column

Gas Distribution Modeling using Sparse Gaussian Process Mixture Models

In this paper, we consider the problem of learning a two dimensional spatial model of a gas distribution with a mobile robot. Building maps that can be used to accurately predict the gas concentration at query locations is a challenging task due to the chaotic nature of gas dispersal. We present an approach that formulates this task as a regression problem. To deal with the specific properties ...

Using Gaussian mixture modeling in speech recognition

tive way to improve the performance of recognizers. This paper describe a speaker-independent isolated word recognition system which uses a well known technique, the combination of vector quantization with hidden Markov modeling. The conventional vector quantization algorithm is substituted by a statistical clustering algorithm, the ExpectationMaximization algorithm, in this system. Based on th...