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In this paper, we propose a new algorithm for an integrated image segmentation based on the combination of both Markov Random Fields (MRF) and Graph Cuts (GC). In the well-known GrabCut method, the T-link weights do not take into account the spatial relationship between the neighboring pixels. The proposed algorithm, unlike GrabCut method, incorporates this spatial relationship right into the T-link...
Standard Gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for spatial dependences between image pixels. However,...
In this paper, we will present a new algorithm which is extended from the standard Gaussian mixture model to segment the noisy image based on the correlation among neighboring pixels. Firstly, we use the correlation between each centre pixel and its neighboring pixels in 3 times 3 window in building the prior probability, and this centre pixel is used to construct the conditional density function...
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