Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image selective smoothing and edge detection by nonlinear diffusion. II
SIAM Journal on Numerical Analysis
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Reliable and Efficient Computation of Optical Flow
International Journal of Computer Vision
Scale Space Tracking and Deformable Sheet Models for Computational Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review of Nonlinear Diffusion Filtering
SCALE-SPACE '97 Proceedings of the First International Conference on Scale-Space Theory in Computer Vision
Area and Length Minimizing Flows for Shape Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
A Common Framework for Curve Evolution, Segmentation and Anisotropic Diffusion
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Image segmentation by reaction-diffusion bubbles
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Deformable templates using large deformation kinematics
IEEE Transactions on Image Processing
A unified approach to noise removal, image enhancement, and shape recovery
IEEE Transactions on Image Processing
Coupled anisotropic diffusion for image selective smoothing
Signal Processing
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Image denoising and segmentation are fundamental problems in the field of image processing and computer vision with numerous applications. We propose a partial differential equation (PDE) based smoothing and segmentation framework wherein the image data are smoothed via an evolution equation that is controlled by a vector field describing a viscous fluid flow. Image segmentation in this framework is defined by locations in the image where the fluid velocity is a local maximum. The nonlinear image smoothing is selectively achieved to preserve edges in the image. The novelty of this approach lies in the fact that the selective term is derived from a nonlinearly regularized image gradient field unlike most earlier techniques which either used a constant (with respect to time) selective term or a time varying nonlinearly smoothed scalar valued term. Implementation results on synthetic and real images are presented to depict the performance of the technique in comparison to methods recently reported in literature.