The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Perspectives on the theory and practice of belief functions
International Journal of Approximate Reasoning
Boundary Finding with Parametrically Deformable Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Level Set Model for Image Classification
International Journal of Computer Vision
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
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
Automated 3D Segmentation Using Deformable Models and Fuzzy Affinity
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Image Segmentation Based on the Integration of Markov Random Fields and Deformable Models
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Bayesian image restoration and segmentation by constrained optimization
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
Combining Top-Down and Bottom-Up Segmentation
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 4 - Volume 04
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
A Dynamic Conditional Random Field Model for Object Segmentation in Image Sequences
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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
Metamorphs: Deformable Shape and Appearance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Learning to combine bottom-up and top-down segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Deformable-Model based textured object segmentation
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
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We present a hybrid framework for integrating deformable models with learning-based classification, for image segmentation with region ambiguities. We show how a region-based geometric model is coupled with conditional random fields (CRF) in a simple graphical model, such that the model evolution is driven by a dynamically updated probability field. We define the model shape with the signed distance function, while we formulate the internal energy with a C1 continuity constraint, a shape prior, and a term that forces the zero level of the shape function towards a connected form. The latter can be seen as a term that forces different closed curves on the image plane to merge, and, therefore, our model inherently carries the property of merging regions. We calculate the image likelihood that drives the evolution using a collaborative formulation of conditional random fields (CoCRF), which is updated during the evolution in an online learning manner. The CoCRF infers class posteriors to regions with feature ambiguities by assessing the joint appearance of neighboring sites, and using the classification confidence to regulate the inference. The novelties of our approach are (i) the tight coupling of deformable models with classification, combining the estimation of smooth region boundaries with the robustness of the probabilistic region classification, (ii) the handling of feature variations, by updating the region statistics in an online learning manner, and (iii) the improvement of the region classification using our CoCRF. We demonstrate the performance of our method in a variety of images with clutter, region inhomogeneities, boundary ambiguities, and complex textures, from the zebra and cheetah examples to medical images.