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
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
Geometric methods for analysis of ridges in n-dimensional images
Geometric methods for analysis of ridges in n-dimensional images
Computation of object cores from grey-level images
Computation of object cores from grey-level images
International Journal of Computer Vision
Minimal Surfaces Based Object Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Zoom-invariant vision of figural shape: the mathematics of cores
Computer Vision and Image Understanding
Linear Scale-Space has First been Proposed in Japan
Journal of Mathematical Imaging and Vision
Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations (Applied Mathematical Sciences)
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 2
A general framework for low level vision
IEEE Transactions on Image Processing
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
International Journal of Computer Vision
Scale Selection for Compact Scale-Space Representation of Vector-Valued Images
International Journal of Computer Vision
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In this paper, we propose an evolution equation for the active contours in scale spaces. This evolution equation is based on the Polyakov functional that has been first introduced in physics and has been then used in image processing in [17] for image denoising. Our active contours are hypersurfaces implicitly and intrinsically represented by a level set function embedded in a scale space. The scale spaces used in our approach are defined by a family of metric tensors given by the general heat diffusion equation. The well-known scale spaces such as the linear scale space, i.e. the Gaussian scale space, the Perona-Malik scale space, the mean curvature scale space and the total variation scale space can be used in this framework. A possible application of this technique is in shape analysis. For example, our multiscale segmentation technique can be coupled with the shape recognition and the shape registration algorithms to improve their robustness and their performance.