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
Region Competition: Unifying Snakes, Region Growing, and Bayes/MDL for Multiband Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Matching Distance Functions: A Shape-to-Area Variational Approach for Global-to-Local Registration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part II
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
Multiscale conditional random fields for image labeling
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A graphical model framework for coupling MRFs and deformable models
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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We present a geometric deformable model driven by dynamically updated probability fields. The shape is defined with the signed distance function, and the internal (smoothness) energy consists of a C1continuity constraint, a shape prior, and a term that forces the zero-level of the shape distance function towards a connected form. The image probability fields are estimated by our collaborative Conditional Random Field (CoCRF), which is updated during the evolution in an active learning manner: it infers class posteriors in pixels or regions with feature ambiguities by assessing the joint appearance of neighboring sites and using the classification confidence. We apply our method to Optical Coherence Tomography fundus images for the segmentation of geographic atrophies in dry age-related macular degeneration of the human eye.