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
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph cut optimization for the Mumford-Shah model
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
A note on the discrete binary Mumford-Shah model
MIRAGE'07 Proceedings of the 3rd international conference on Computer vision/computer graphics collaboration techniques
IEEE Transactions on Image Processing
Deconvolving Active Contours for Fluorescence Microscopy Images
ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part I
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In this paper, we introduce a graph cut based active surface model that incorporates graph cuts optimization tools with implicit surface representation to solve the segmentation problem. We will introduce a discrete formulation of the surface evolution problem, prove that the discrete energy function is graph representable and propose how to optimize it using graph cuts. The advantage of this model is two fold: First, Graph cuts are mostly global optimization tools which makes the model very robust and not sensitive to initialization, moreover, the dynamic labeling associated with graph cuts optimization tools makes the model very fast. Second, the implicit representation of the surface makes it robust to topology changes.