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• W1972168313 abstract "In this paper, a new mixture model for image segmentation is presented. We propose a new way to incorporate spatial information between neighboring pixels into the Gaussian mixture model based on Markov random field (MRF). In comparison to other mixture models that are complex and computationally expensive, the proposed method is fast and easy to implement. In mixture models based on MRF, the M-step of the expectation-maximization (EM) algorithm cannot be directly applied to the prior distribution π <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>ij</sub> for maximization of the log-likelihood with respect to the corresponding parameters. Compared with these models, our proposed method directly applies the EM algorithm to optimize the parameters, which makes it much simpler. Experimental results obtained by employing the proposed method on many synthetic and real-world grayscale and colored images demonstrate its robustness, accuracy, and effectiveness, compared with other mixture models." @default.
• W1972168313 created "2016-06-24" @default.
• W1972168313 creator A5036382866 @default.
• W1972168313 creator A5064666806 @default.
• W1972168313 date "2013-04-01" @default.
• W1972168313 modified "2023-10-12" @default.
• W1972168313 title "Fast and Robust Spatially Constrained Gaussian Mixture Model for Image Segmentation" @default.
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• W1972168313 doi "https://doi.org/10.1109/tcsvt.2012.2211176" @default.
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