Constraints on deformable models: recovering 3D shape and nongrid motion
Artificial Intelligence
Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Region-based strategies for active contour models
International Journal of Computer Vision
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision
Global Minimum for Active Contour Models: A Minimal Path Approach
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Some Remarks on the Equivalence between 2D and 3D Classical Snakes and Geodesic Active Contours
International Journal of Computer Vision
An Adaptive Level Set Method for Medical Image Segmentation
IPMI '01 Proceedings of the 17th International Conference on Information Processing in Medical Imaging
Knowledge-based Registration & Segmentation of the Left Ventricle: A Level Set Approach
WACV '02 Proceedings of the Sixth IEEE Workshop on Applications of Computer Vision
A Real-Time Algorithm for Medical Shape Recovery
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
B-spline active contour with handling of topology changes for fast video segmentation
EURASIP Journal on Applied Signal Processing
An Accessible Viewer for Digital Comic Books
ICCHP '08 Proceedings of the 11th international conference on Computers Helping People with Special Needs
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Deformable contours are now widely used in image segmentation, using different models, criteria and numerical schemes. Some theoretical comparisons between some deformable model methods have already been published [1]. Yet, very few experimental comparative studies on real data have been reported. In this paper, we compare a levelset with a B-spline based deformable model approach in order to understand the mechanisms involved in these widely used methods and to compare both evolution and results on various kinds of image segmentation problems. In general, both methods yield similar results. However, specific differences appear when considering particular problems.