Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Segmentation Using Eigenvectors: A Unifying View
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Motion Segmentation and Tracking Using Normalized Cuts
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Control Theory and Fast Marching Techniques for Brain Connectivity Mapping
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
International Journal of Computer Vision
VLSM'05 Proceedings of the Third international conference on Variational, Geometric, and Level Set Methods in Computer Vision
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Owing to the inhomogeneity and ill defined edges present in images, the incorporation of prior knowledge into level set models, in image segmentation, is a field of active researches. In this paper, a new way of incorporating prior information to constrain the evolution of the level set model during the segmentation is presented. This technique allows resolving the problem of applying the same curvature coefficient in all image regions. We construct, based on Kruskal algorithm, the minimal weights covered tree of the initial density graph due to boundary curvature. The simulation results using different kind of images show that we get better results with respect to propagation, precision and homogeneity between the final propagating contour and local regions, compared to the classical level set method.