Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Variational methods in image segmentation
Variational methods in image segmentation
Journal of Mathematical Imaging and Vision
International Journal of Computer Vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Geometry and Color in Natural Images
Journal of Mathematical Imaging and Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images
International Journal of Computer Vision
Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
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We propose a segmentation scheme for digital color images using vectorial multiscale anisotropic diffusion. By integrating the edge information, diffusion based schemes can remove noise effectively and create fine to coarse set of images known as scale-space. Segmentation is performed by effectively tracking edges in an inter-scale manner across this scale space family of images. The regions are connected according to color coherency, and scale relation along time axis of the family is taken into account for the final segmentation result. Fast total variation diffusion and anisotropic diffusion facilitate denoising and create homogenous regions separated by strong edges. They provide a roadmap for further segmentation with persistent edges and flat regions. The scheme is general in the sense that other anisotropic diffusion schemes can be incorporated depending upon the requirement. Numerical simulations show the advantage of the proposed scheme on noisy color images.