Abstract
A parametric and unsupervised method of automatic threshold selection for image segmentation is presented. An optimal threshold is selected by the fuzzy risk criterion, namely, so as to separate a given picture into meaningful gray level classes under the assumption of object and pixel gray level values being normally distributed. The effectiveness of the proposed approach is illustrated using some experimental images with 256 gray-levels, and the results are compared with minimum error thresholding method using the criterion functions such as uniformity, shape, and edge measures.
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