A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual reconstruction
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Variational methods in image segmentation
Variational methods in image segmentation
Local Scale Control for Edge Detection and Blur Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
Computing optical flow via variational techniques
SIAM Journal on Applied Mathematics
A Variational Model for Image Classification and Restoration
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Influence of the gamma-Parameter on Feature Detection with Automatic Scale Selection
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Mumford and Shah Functional: VLSI Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithmic Differentiation: Application to Variational Problems in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Approach to Reconstructing Images Corrupted by Poisson Noise
Journal of Mathematical Imaging and Vision
Classification of silhouettes using contour fragments
Computer Vision and Image Understanding
Superresolution reconstruction using nonlinear gradient-based regularization
Multidimensional Systems and Signal Processing
A vision system for recognizing objects in complex real images
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
ICIAR'05 Proceedings of the Second international conference on Image Analysis and Recognition
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Abstract--Often an image g(x,y) is regularized and even restored by minimizing the Mumford-Shah functional. Properties of the regularized image u(x,y) depends critically on the numerical value of the two parameters \alpha and \gamma controlling smoothness and fidelity. When \alpha and \gamma are constant over the image, small details are lost when an extensive filtering is used in order to remove noise. In this paper, it is shown how the two parameters \alpha and \gamma can be made self-adaptive. In fact, \alpha and \gamma are not constant but automatically adapt to the local scale and contrast of features in the image. In this way, edges at all scales are detected and boundaries are well-localized and preserved. In order to preserve trihedral junctions \alpha and \gamma become locally small and the regularized image u(x,y) maintains sharp and well-defined trihedral junctions. Images regularized by the proposed procedure are well-suited for further processing, such as image segmentation and object recognition.