30 May 2000 Fast computation of Gaussian mixture parameters and optimal segmentation
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Proceedings Volume 4067, Visual Communications and Image Processing 2000; (2000) https://doi.org/10.1117/12.386600
Event: Visual Communications and Image Processing 2000, 2000, Perth, Australia
Abstract
We present a fast parameter estimation method for image segmentation using the maximum likelihood function. The segmentation is based on a parametric model in which the probability density function of the gray levels in the image is assumed to be a mixture of two Gaussian density functions. For the more accurate parameter estimation and segmentation, the algorithm is formulated as a compact iterative scheme. In order to reduce computation time and make convergence fast, histogram information is combined into the algorithm. In the iterative computation, the performance of the algorithm greatly depends on the initial values and properly selected initial estimates make convergence fast. A reasonable approach about the computation of initial parameter is also proposed.
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Do-Jong Kim, Do-Jong Kim, Jae-Soo Cho, Jae-Soo Cho, Dong-Jo Park, Dong-Jo Park, } "Fast computation of Gaussian mixture parameters and optimal segmentation", Proc. SPIE 4067, Visual Communications and Image Processing 2000, (30 May 2000); doi: 10.1117/12.386600; https://doi.org/10.1117/12.386600
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