A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
Image selective smoothing and edge detection by nonlinear diffusion
SIAM Journal on Numerical Analysis
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Variational methods in image segmentation
Variational methods in image segmentation
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
Matrix computations (3rd ed.)
International Journal of Computer Vision
Variational Restoration and Edge Detection for Color Images
Journal of Mathematical Imaging and Vision
Tracking Medical 3D Data with a Deformable Parametric Model
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
Gabor-Space Geodesic Active Contours
AFPAC '00 Proceedings of the Second International Workshop on Algebraic Frames for the Perception-Action Cycle
Geometry Motivated Variational Segmentation for Color Images
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
A geometric-functional-based image segmentation and inpainting
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
A regularization approach to joint blur identification and image restoration
IEEE Transactions on Image Processing
A general framework for low level vision
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
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The Mumford-Shah functional and related algorithms for image segmentation involve a tradeoff between a two-dimensional image structure and one-dimensional parametric curves (contours) that surround objects or distinct regions in the image.We propose an alternative functional that is independent of parameterization; it is a geometric functional given in terms of the surfaces representing the data and image in the feature space. The Γ-convergence technique is combined with the minimal surfaces theory to yield a global generalization of the Mumford-Shah segmentation function.