Computer simulation using particles
Computer simulation using particles
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The fast construction of extension velocities in level set methods
Journal of Computational Physics
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Gradient Vector Flow Fast Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
CPM: A Deformable Model for Shape Recovery and Segmentation Based on Charged Particles
IEEE Transactions on Pattern Analysis and Machine Intelligence
RAGS: region-aided geometric snake
IEEE Transactions on Image Processing
Analysis of Relevant Maxima in Distance Transform. An Application to Fast Coarse Image Segmentation
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
Shape recovery by a generalized topology preserving SOM
Neurocomputing
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Distance maps from unthresholded magnitudes
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Efficient numerical schemes for gradient vector flow
Pattern Recognition
Distance maps from unthresholded magnitudes
Pattern Recognition
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We propose a novel active contour model by incorporating particle based electrostatic interactions into the geometric active contour framework. The proposed active contour, embedded in level sets, propagates under the joint influence of a boundary attraction force and a boundary competition force. Unlike other contour models, the proposed vector field dynamically adapts by updating itself when a contour reaches a boundary. The model is then more invariant to initialisation and possesses better convergence abilities. Analytical and comparative results are presented on synthetic and real images.