Edge, Junction, and Corner Detection Using Color Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Color Image Segmentation for Multimedia Applications
Journal of Intelligent and Robotic Systems
Characterization of empirical discrepancy evaluation measures
Pattern Recognition Letters
A novel edge detection method based on the maximizing objective function
Pattern Recognition
Automatic generation of consensus ground truth for the comparison of edge detection techniques
Image and Vision Computing
Unsupervised colour image segmentation applied to printing quality assessment
Image and Vision Computing
Boolean derivatives with application to edge detection for imaging systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Review article: Edge and line oriented contour detection: State of the art
Image and Vision Computing
Roughness approach to color image segmentation through smoothing local difference
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Automatic edge detection using vector distance and partial normalization
WSEAS Transactions on Computers
Edge detection using a complex-valued directional vector representation
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
An efficient color quantization based on generic roughness measure
Pattern Recognition
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Color edge detection is approached in this paper using vector order statistics. Based on the R-ordering method, a class of color edge detectors is defined. These detectors function as vector operators as opposed to component-wise operators. Specific edge detectors can be obtained as special cases of this class. Various such detectors are defined and analyzed. Experimental results show the noise robustness of the vector order statistics operators. A quantitative evaluation and comparison to other color edge detectors favors our approach. Edge detection results obtained from real color images demonstrate the effectiveness of the proposed approach in real applications