Ultrafuzziness Optimization Based on Type II Fuzzy Sets for Image Thresholding

Abstract

Abstract. Image thresholding is one of the processing techniques to provide high quality preprocessed image. Image vagueness and bad illumination are common obstacles yielding in a poor image thresholding output. By assuming image as fuzzy sets, several different fuzzy thresholding techniques have been proposed to remove these obstacles during threshold selection. In this paper, we proposed an algorithm for thresholding image using ultrafuzziness optimization to decrease uncertainty in fuzzy system by common fuzzy sets like type II fuzzy sets. Optimization was conducted by involving ultrafuzziness measurement for background and object fuzzy sets separately. Experimental results demonstrated that the proposed image thresholding method had good performances for images with high vagueness, low level contrast, and grayscale ambiguity.

Spatial Condition in Intuitionistic Fuzzy C-Means Clustering for Segmentation of Teeth in Dental Panoramic Radiographs

“…where is the area of the ground truth image, and is the area of the results of segmentation. as a comparison of our method, we are doing by comparing comparatively with the previous method, we have inferred Otsu thresholding -method [12], Hierarchical Cluster Analysis (HCA) [13], Fuzzy Sets Type II [14], and Multi adaptive thresholding (MAT) [15]. the smaller value of ME and RAE gives the better performance is shown in Table 2 and Table 3 for comparing methods.…”