Abstract
A method is proposed seeking thresholds for segmentation of grayscale images. The normalized image histogram is modeled as a Gaussian mixture, and the parameters associated with the Gaussian components are estimated iteratively with a set of learning automata. To reduce the parameter search space, the number of major components in the image and their associated parameter ranges are first specified using some desired properties of the Gaussian distribution. Thresholds are chosen based on the Gaussian parameter estimates after convergence. Illustrative examples are provided to demonstrate the learning process and the effectiveness of the proposed segmentation scheme.
Published Version
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