Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color image segmentation: Rough-set theoretic approach
Pattern Recognition Letters
Rough Image Colour Quantisation
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Rough sets and near sets in medical imaging: a review
IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Vector order statistics operators as color edge detectors
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Aiming at the problems of histogram-based thresholding, rough set theory is applied to construct the roughness measure for segmenting color image. However, the extant roughness measure is a qualitative description of neighborhood similarity and tends to over focus on the trivial homogeneity. An improved roughness measure is proposed in this paper. The novel roughness is computed from smoothed local differences and quantified homogeneity, thus can form the accurate representation of homogeneous regions. The experimental results indicate that the segmentation based on improved roughness has good performances on most testing images.