Fuzzy Image Threshold Segmentation Based on Maximum Entropy
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This code implements fuzzy image threshold segmentation based on the maximum fuzzy entropy principle. The algorithm processes blurred images to extract regions of interest by determining the optimal threshold through fuzzy maximum entropy calculation. The implementation involves computing the entropy measure across different threshold values and selecting the threshold that maximizes the fuzzy entropy criterion. Key functions include image fuzzification, entropy calculation, and optimal threshold determination. Developed based on recent research advancements, this solution demonstrates high efficiency and accuracy in handling ambiguous image boundaries. Using this code enables improved processing of blurred images and yields superior segmentation results through mathematically robust threshold selection.
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