A Generalized Fuzzy Entropy Thresholding Segmentation Method Based on the Sugeno Complement Operator
基于Sugeno补的广义模糊熵阈值分割方法

For images with bad illumination, the traditional fuzzy entropy thresholding segmentation method can not achieve satisfactory results. In this paper a generalized fuzzy entropy thresholding method based on the Sugeno complement function is presented. Firstly, nine thresholds are obtained for an image based on the variations of the fixed point in the Sugeno complement function. Secondly, the nine thresholds are evaluated by an image segmentation quality evaluation principle. Finally, the threshold with the maximum quality evaluation value among the nine thresholds is chosen as the optimal threshold. Compared with the traditional fuzzy entropy method, new method increases the opportunity of choosing an optimal threshold and obtains better segmentation result for images with bad illumination.