Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Fast reaction, slow diffusion, and curve shortening
SIAM Journal on Applied Mathematics
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Γ-Convergence approximation to piecewise constant mumford-shah segmentation
ACIVS'05 Proceedings of the 7th international conference on Advanced Concepts for Intelligent Vision Systems
Unsupervised texture segmentation with nonparametric neighborhood statistics
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
IEEE Transactions on Image Processing
A multiresolution flow-based multiphase image segmentation
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Segmentation of Stochastic Images using Level Set Propagation with Uncertain Speed
Journal of Mathematical Imaging and Vision
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Recently, the Phase Field Method has shown to be a powerful tool for variational image segmentation. In this paper, we present a novel multi-phase model for probability based image segmentation. By interpreting the phase fields as probabilities of pixels belonging to a certain phase, we obtain the model formulation by maximizing the mutual information between image features and the phase fields. For optimizing the model, we derive the Euler Lagrange equations and present their efficient implementation by using a narrow band scheme. We present experimental results on segmenting synthetic, medical and natural images.