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Proceedings Paper

Fast computation of Gaussian mixture parameters and optimal segmentation
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Paper 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.

Paper Details

Date Published: 30 May 2000
PDF: 12 pages
Proc. SPIE 4067, Visual Communications and Image Processing 2000, (30 May 2000); doi: 10.1117/12.386600
Show Author Affiliations
Do-Jong Kim, Korea Advanced Institute of Science and Technology (South Korea)
Jae-Soo Cho, Korea Advanced Institute of Science and Technology (South Korea)
Dong-Jo Park, Korea Advanced Institute of Science and Technology (South Korea)


Published in SPIE Proceedings Vol. 4067:
Visual Communications and Image Processing 2000
King N. Ngan; Thomas Sikora; Ming-Ting Sun, Editor(s)

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