Scaling Theorems for Zero Crossings
IEEE Transactions on Pattern Analysis and Machine Intelligence
A color clustering technique for image segmentation
Computer Vision, Graphics, and Image Processing
Iterative methods for total variation denoising
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color Images Based on k -means Clustering in the Chromaticity Plane
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
Edge Flow: A Framework of Boundary Detection and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
Automatic watershed segmentation of randomly textured color images
IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
Hi-index | 0.00 |
A new approach to color image segmentation is demonstrated here. The color image, which is usually in the RGB space, is translated into the CIE(Lab) color space. The three components are smoothed using a variation-based approach. By minimizing an energy functional with a nonconvex regular function, we can get a smoothed image. During the iteration, the edges of the image are preserved. A soft C-means clustering algorithm, which is an improvement on the hard C-means algorithm, is employed to segment them after smoothing. This algorithm overcomes the problem of dependence on the initializations. Finally, an experiment is given to show the effectiveness and robustness of the method.