Histogram Adaptive Fuzzy Segmentation Algorithm

The histogram adaptive fuzzy segmentation algorithm presented in this paper is a fuzzy segmentation approach based on statistical analysis of image histograms. While maintaining algorithmic simplicity, this method achieves exceptionally fast processing speeds. Through statistical examination of image histograms, the algorithm automatically adapts to image characteristics and performs fuzzy segmentation. The implementation typically involves calculating histogram distributions, identifying significant peaks and valleys, and applying fuzzy logic rules to determine optimal threshold values. The algorithm's simplicity, combined with its computational efficiency using basic histogram operations and minimal memory requirements, makes it particularly valuable in image processing applications requiring rapid processing of large image datasets. Key functions often include histogram computation, statistical feature extraction, and fuzzy membership function application for boundary determination.