A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
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On active contour models and balloons
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Fast Segmentation Methods Based on Partial Differential Equations and the Watershed Transformation
Mustererkennung 1998, 20. DAGM-Symposium
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CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Gradient flows and geometric active contour models
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A PDE-Based Level-Set Approach for Detection and Tracking of Moving Objects
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Anisotropic diffusion of multivalued images with applications to color filtering
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A general framework for low level vision
IEEE Transactions on Image Processing
Variational principles, surface evolution, PDEs, level set methods, and the stereo problem
IEEE Transactions on Image Processing
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Fast Difference Schemes for Edge Enhancing Beltrami Flow
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
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We use an unconditionally stable numerical scheme to implement a fast version of the geodesic active contour model. The proposed scheme is useful for object segmentation in images, like tracking moving objects in a sequence of images. The method is based on the Weickert-Romeney-Viergever [33] AOS scheme. It is applied at small regions, motivated by Adalsteinsson-Sethian [1] level set narrow band approach, and uses Sethian's fast marching method [26] for re-initialization. Experimental results demonstrate the power of the new method for tracking in color movies.