On active contour models and balloons
CVGIP: Image Understanding
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dynamic Programming for Detecting, Tracking, and Matching Deformable Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Journal of Mathematical Imaging and Vision - Special issue on topology and geometry in computer vision
Inferring global perceptual contours from local features
International Journal of Computer Vision - Special issue on computer vision research at the University of Southern California
International Journal of Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Multiple Contour Finding and Perceptual Grouping using Minimal Paths
Journal of Mathematical Imaging and Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimal Paths in 3D Images and Application to Virtual Endoscopy
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Local Parallel Computation of Stochastic Completion Fields
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Stochastic completion fields: a neural model of illusory contour shape and salience
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geodesic Methods in Computer Vision and Graphics
Foundations and Trends® in Computer Graphics and Vision
Geodesic voting for the automatic extraction of tree structures. Methods and applications
Computer Vision and Image Understanding
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We address the problem of finding a set of contour curves in an image. We consider the problem of perceptual grouping and contour completion, where the data is a set of points in the image. A new method to find complete curves from a set of contours or edge points is presented. Our approach is an extension of previous work on finding a set of contours as minimal paths between end points using the fast marching algorithm. Given a set of key points, we find the pairs of points that have to be linked and the paths that join them. We use the saddle points of the minimal action map. The paths are obtained by backpropagation from the saddle points to both points of each pair. We also propose an extension of this method for contour completion where the data is a set of connected components. We find the minimal paths between each of these components, until the complete set of these "regions" is connected. The paths are obtained using the same backpropagation from the saddle points to both components.