Image feature enhancement based on the time-controlled total variation flow formulation
Pattern Recognition Letters
Unsupervised multiphase segmentation: A recursive approach
Computer Vision and Image Understanding
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images
International Journal of Computer Vision
Scale selection for compact scale-space representation of vector-valued images
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Quasi-random nonlinear scale space
Pattern Recognition Letters
Generalized probabilistic scale space for image restoration
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
A four-pixel scheme for singular differential equations
Scale-Space'05 Proceedings of the 5th international conference on Scale Space and PDE Methods in Computer Vision
Multiscale edge detection based on Gaussian smoothing and edge tracking
Knowledge-Based Systems
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In this paper, we introduce a framework that merges classical ideas borrowed from scale-space and multiresolution segmentation with nonlinear partial differential equations. A nonlinear scale-space stack is constructed by means of an appropriate diffusion equation. This stack is analyzed and a tree of coherent segments is constructed based on relationships between different scale layers. Pruning this tree proves to be a very efficient tool for unsupervised segmentation of different classes of images (e.g., natural, medical, etc.). This technique is light on the computational point of view and can be extended to nonscalar data in a straightforward manner.