Pattern Recognition
Fuzzy thresholding: mathematical framework, bound functions and weighted moving average technique
Pattern Recognition Letters
Digital Image Processing: PIKS Inside
Digital Image Processing: PIKS Inside
Digital Image Processing
Unsupervised Image Segmentation Using Automated Fuzzy c-Means
CIT '07 Proceedings of the 7th IEEE International Conference on Computer and Information Technology
Image thresholding using type II fuzzy sets
Pattern Recognition
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
Image segmentation based on fuzzy connectedness using dynamic weights
IEEE Transactions on Image Processing
Hierarchical fuzzy logic based approach for object tracking
Knowledge-Based Systems
Entropic image thresholding based on GLGM histogram
Pattern Recognition Letters
Skin cancer extraction with optimum fuzzy thresholding technique
Applied Intelligence
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In this paper, an automatic histogram threshold approach based on a fuzziness measure is presented. This work is an improvement of an existing method. Using fuzzy logic concepts, the problems involved in finding the minimum of a criterion function are avoided. Similarity between gray levels is the key to find an optimal threshold. Two initial regions of gray levels, located at the boundaries of the histogram, are defined. Then, using an index of fuzziness, a similarity process is started to find the threshold point. A significant contrast between objects and background is assumed. Previous histogram equalization is used in small contrast images. No prior knowledge of the image is required.