Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical approach to pattern recognition
Fuzzy mathematical techniques with applications
Fuzzy mathematical techniques with applications
A measure of edge ambiguity using fuzzy sets
Pattern Recognition Letters
Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Image enhancement and thresholding by optimization of fuzzy compactness
Pattern Recognition Letters
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Optimum Image Thresholding via Class Uncertainty and Region Homogeneity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition
Image thresholding using fuzzy entropies
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Thresholding technique with adaptive window selection for uneven lighting image
Pattern Recognition Letters
On minimum variance thresholding
Pattern Recognition Letters
Locally adaptive block thresholding method with continuity constraint
Pattern Recognition Letters
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
Image thresholding using type II fuzzy sets
Pattern Recognition
Application of a hybrid ant colony optimization for the multilevel thresholding in image processing
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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Image segmentation plays an important role in various image processing applications including robot vision and document image analysis and understanding. In contrast to classical set theory, fuzzy set theory, which takes into account the uncertainty intrinsic to various images, has found great success in the area of image thresholding. In this paper, an image thresholding approach based on the index of nonfuzziness maximization of the 2-D grayscale histogram is proposed. The threshold Vector (T, S), where T is a threshold for pixel intensity and S is another threshold for the local average of pixels, is obtained by an exhaustive searching algorithm. In this approach, the difference between these two components (T and S) is guaranteed to be within a relatively small range, which leads to reasonable results from the viewpoint of human vision perception. This cannot be achieved in certain entropy-based methods. Experimental results have shown that our proposed approach not only performs well and effectively but also is more robust when applied to noisy images.