Gaussian-Mixture-Model-Based Spatial Neighborhood Relationships for Pixel Labeling Problem - 2012 PROJECT TITLE :Gaussian-Mixture-Model-Based Spatial Neighborhood Relationships for Pixel Labeling Problem - 2012ABSTRACT: In this paper, we present a new algorithm for pixel labeling and image segmentation based on the standard Gaussian mixture model (GMM). Unlike the standard GMM where pixels themselves are considered independent of each other and the spatial relationship between neighboring pixels is not taken into account, the proposed method incorporates this spatial relationship into the standard GMM. Moreover, the proposed model requires fewer parameters compared with the models based on Markov random fields. In order to estimate model parameters from observations, instead of utilizing an expectation-maximization algorithm, we employ gradient method to minimize a higher bound on the data negative log-likelihood. The performance of the proposed model is compared with methods based on both standard GMM and Markov random fields, demonstrating the robustness, accuracy, and effectiveness of our method. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Contrast-Independent Curvilinear Structure Detection in Biomedical Images - 2012 Image Prediction Based on Neighbor Embedding Methods - 2012
