A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image enhancement and thresholding by optimization of fuzzy compactness
Pattern Recognition Letters
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Index of area coverage of fuzzy image subsets and object extraction
Pattern Recognition Letters
Fuzzy geometry in image analysis
Fuzzy Sets and Systems
Histogram thresholding by minimizing graylevel fuzziness
Information Sciences: an International Journal
Image segmentation using fuzzy correlation
Information Sciences: an International Journal
Fuzzy sets and fuzzy logic: theory and applications
Fuzzy sets and fuzzy logic: theory and applications
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Granular computing, rough entropy and object extraction
Pattern Recognition Letters
IEEE Transactions on Fuzzy Systems
Automatic grey level thresholding through index of fuzziness and entropy
Pattern Recognition Letters
Image segmentation by histogram thresholding using fuzzy sets
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
A new denoising filter for brain MR images
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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This paper presents a novel histogram thresholding technique based on the beam theory of solid mechanics and the minimization of ambiguity in information. First, a beam theory based histogram modification process is carried out. This beam theory based process considers a distance measure in order to modify the shape of the histogram. The ambiguity in the overall information given by the modified histogram is then minimized to obtain the threshold value. The ambiguity minimization is carried out using the theories of fuzzy and rough sets, where a new definition of rough entropy is presented. The applications of the proposed scheme in performing object and edge extraction in images are reported and compared with those of a few existing classical and ambiguity minimization based schemes for thresholding. Experimental results are given to demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative measures.