Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
On active contour models and balloons
CVGIP: Image Understanding
Region-based strategies for active contour models
International Journal of Computer Vision
An Introduction to Variational Methods for Graphical Models
Machine Learning
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging
Physics-Based Deformable Models: Applications to Computer Vision, Graphics, and 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
Gradient Vector Flow: A New External Force for Snakes
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image Segmentation Based on the Integration of Pixel Affinity and Deformable Models
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Topologically adaptable snakes
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Belief Propagation and Revision in Networks with Loops
Belief Propagation and Revision in Networks with Loops
Variational learning in mixed-state dynamic graphical models
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Simultaneous Segmentation and Pose Estimation of Humans Using Dynamic Graph Cuts
International Journal of Computer Vision
MRI Bone Segmentation Using Deformable Models and Shape Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Geometric Deformable Model Driven by CoCRFs: Application to Optical Coherence Tomography
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Robust Higher Order Potentials for Enforcing Label Consistency
International Journal of Computer Vision
CoCRF deformable model: a geometric model driven by collaborative conditional random fields
IEEE Transactions on Image Processing
Generalized sparse MRF appearance models
Image and Vision Computing
A Markov random field model for extracting near-circular shapes
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Deformable probability maps: Probabilistic shape and appearance-based object segmentation
Computer Vision and Image Understanding
Dynamic background discrimination with a recurrent network
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Modeling micro-patterns for feature extraction
AMFG'05 Proceedings of the Second international conference on Analysis and Modelling of Faces and Gestures
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
POSECUT: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A generative model for simultaneous estimation of human body shape and pixel-level segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part V
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This paper proposes a new framework for image segmentation based on the integration of MRFs and deformable models using graphical models. We first construct a graphical model to represent the relationship of the observed image pixels, the true region labels and the underlying object contour. We then formulate the problem of image segmentation as the one of joint region-contour inference and learning in the graphical model. The graphical model representation allows us to use an approximate structured variational inference technique to solve this otherwise intractable joint inference problem. Using this technique, the MAP solution to the original model is obtained by finding the MAP solutions of two simpler models, an extended MRF model and a probabilistic deformable model, iteratively and incrementally. In the extended MRF model, the true region labels are estimated using the BP algorithm in a band area around the estimated contour from the probabilistic deformable model, and the result in turn guides the probabilistic deformable model to an improved estimation of the contour. Experimental results show that our new hybrid method outperforms both the MRF-based and the deformable model-based methods.