A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Histogram Thresholding using Beam Theory and Ambiguity Measures
Fundamenta Informaticae - New Frontiers in Scientific Discovery - Commemorating the Life and Work of Zdzislaw Pawlak
Automatic gradient threshold determination for edge detection
IEEE Transactions on Image Processing
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
Image segmentation by automatic histogram thresholding
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
IEEE Transactions on Intelligent Transportation Systems
Rough entropy hierarchical agglomerative clustering in image segmentation
Transactions on rough sets XIII
Segmentation of color images using a linguistic 2-tuples model
Information Sciences: an International Journal
Maximum similarity thresholding
Digital Signal Processing
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This paper presents a novel histogram thresholding methodology using fuzzy and rough set theories. The strength of the proposed methodology lies in the fact that it does not make any prior assumptions about the histogram unlike many existing techniques. For bilevel thresholding, every element of the histogram is associated with one of the two regions by comparing the corresponding errors of association. The regions are considered ambiguous in nature, and, hence, the error measures are based on the fuzziness or roughness of the regions. Multilevel thresholding is carried out using the proposed bilevel thresholding method in a tree structured algorithm. Segmentation, object/background separation, and edge extraction are performed using the proposed methodology. A quantitative index to evaluate image segmentation performance is also proposed using the median of absolute deviation from median measure, which is a robust estimator of scale. Extensive experimental results are given to demonstrate the effectiveness of the proposed methods in terms of both qualitative and quantitative measures.