Abstract
The entropy method for image thresholding suggested by Kapur et al. has been modified and a more pertinent information measure of the image is obtained. Essentially this consists of viewing the image as a compositum of two fuzzy sets corresponding to the two classes with membership coefficient associated with each gray level a function of its frequency of occurrence as well as its distance from the intermediate threshold selected. An extension of this technique to consider the semantic content of the image is also discussed. The superiority of the suggested method over artificial histograms modelled by Gaussian distributions is demonstrated. Experimental results on several images are also presented to support the validity of the concepts used.
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