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.
A real-time adaptive segmentation method based on new distance features is proposed for the centroid tracker. These novel features are distances from the center point of a predicted target to each pixel by a tracking filter in extraction of a moving target. The proposed method restricts clutters with target-like intensity from entering the tracking window with low computational complexity for real- time applications compared with other complex feature-based methods. Comparative experiments show that the proposed method is superior to the other segmentation methods based on the intensity feature only in target detection and tracking.
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