Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Edge detection in multispectral images
CVGIP: Graphical Models and Image Processing
International Journal of Computer Vision
Color Image Gradients for Morphological Segmentation
SIBGRAPI '00 Proceedings of the 13th Brazilian Symposium on Computer Graphics and Image Processing
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Level Set Evolution without Re-Initialization: A New Variational Formulation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A geometric active contour model without re-initialization for color images
Image and Vision Computing
Color Image Segmentation Based on a New Geometric Active Contour Model
MVHI '10 Proceedings of the 2010 International Conference on Machine Vision and Human-machine Interface
Distance regularized level set evolution and its application to image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Localizing Region-Based Active Contours
IEEE Transactions on Image Processing
Object segmentation based on location information for level set method
Proceedings of the 7th International Conference on Ubiquitous Information Management and Communication
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In this paper, we propose a novel segmentation algorithm for color images. This method is a combination of edge information with region information and a geometric active contour without re-initialization, called distance regularized level set evolution. The information given by a new edge detector using morphological gradient is more accurate than normal gradient computing methods for color images. And the information of the region containing objects is relied on Chan-Vese minimal variance criterion. With both of these information, the model can have its initial contour that is more flexible to construct anywhere, fast to evolve and quite exact to stop at the boundary of objects. The suggested algorithm has been applied on natural color images with good performance. Some experimental results have shown to compare our model with others with respect to accuracy and computational efficiency.