Using Dynamic Programming for Solving Variational Problems in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
On active contour models and balloons
CVGIP: Image Understanding
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
Combinatorial optimization
Nonlinear Shape Statistics in Mumford-Shah Based Segmentation
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Geodesic Active Regions for Supervised Texture Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Energy Minimization via Graph Cuts: Settling What is Possible
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Level Set Based Shape Prior Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
What Metrics Can Be Approximated by Geo-Cuts, Or Global Optimization of Length/Area and Flux
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
An integral solution to surface evolution PDEs via geo-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
A Continuous Labeling for Multiphase Graph Cut Image Partitioning
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Efficient Global Minimization for the Multiphase Chan-Vese Model of Image Segmentation
EMMCVPR '09 Proceedings of the 7th International Conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
An active surface model for volumetric image segmentation
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
The piecewise smooth Mumford-Shah functional on an arbitrary graph
IEEE Transactions on Image Processing
Discrete optimization of the multiphase piecewise constant mumford-shah functional
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Completely Convex Formulation of the Chan-Vese Image Segmentation Model
International Journal of Computer Vision
Smooth Chan-Vese segmentation via graph cuts
Pattern Recognition Letters
Variational nonlocal image segmentation using split-Bregman
Proceedings of the 4th International Conference on Internet Multimedia Computing and Service
Computer Vision and Image Understanding
Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior
Journal of Mathematical Imaging and Vision
Hi-index | 0.00 |
In this paper, we introduce a Graph Cut Based Level Set (GCBLS) formulation that incorporates graph cuts to optimize the curve evolution energy function presented earlier by Chan and Vese. We present a discrete form of the level set energy function, prove that it is graph-representable, and minimize it using graph cuts. The major advantages of this formulation include the existence of global minimum and its insensitivity to initialization. Numerical implementations show that minimizing the energy function in this non-iterative manner improves the speed of the algorithm dramatically. This makes it more appealing to real time applications such as object tracking and image guided surgery. Yet, all the advantages of using level sets methods will still be preserved.