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
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
An Active Contour Model without Edges
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
A Multiphase Dynamic Labeling Model for Variational Recognition-driven Image Segmentation
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prior-based Segmentation and Shape Registration in the Presence of Perspective Distortion
International Journal of Computer Vision
Topological control of level set method depending on topology constraints
Pattern Recognition Letters
IEEE Transactions on Image Processing
A new level-set based algorithm for bimodal depth segmentation
ACIVS'12 Proceedings of the 14th international conference on Advanced Concepts for Intelligent Vision Systems
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In this paper, we introduce two simple shape priors into the Chan and Vese level-set method, namely, a prescribed area and a prescribed area-to-perimeter ratio of particular objects. It is remarkable that these priors may be easily incorporated into the Euler-Lagrange equation of the original method. As a side effect of our experimenting with the method, we also introduce a new probability-based level-set function, which removes the need for reinitialisation and usually, according to our experience, speeds up the computation. Finally, we also propose a method how to treat, in a simple way, the situation in which the particular objects differ in brightness. Although the mentioned changes make the segmentation more reliable, they almost do not complicate the computation. The results of experiments are also presented.