Fast computation of Gaussian mixture parameters and optimal segmentation
Do-Jong Kim, Jae-Soo Cho, Dong-Jo Park · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
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.