Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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Journal of Computational Physics
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Journal of Computational Physics
Region-based strategies for active contour models
International Journal of Computer Vision
International Journal of Computer Vision
Learning in graphical models
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Applied Numerical Methods for Engineers and Scientists
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Multimodal Estimation of Discontinuous Optical Flow using Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
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CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Multiple View Geometry in Computer Vision
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An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Convergent Tree-Reweighted Message Passing for Energy Minimization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
On the Spatial Statistics of Optical Flow
International Journal of Computer Vision
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Global Minimization of the Active Contour/Snake Model
Journal of Mathematical Imaging and Vision
Markov random field modeled level sets method for object tracking with moving cameras
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
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IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
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This paper presents a new general framework for contour tracking based on the synergy of two powerful segmentation tools, namely, spatial temporal conditional random fields (CRFs) and geodesic active contours (GACs). The contours of targets are modeled using a level set representation. The evolution of the level sets toward the target contours is formulated as one of the joint region-based (CRF) and boundary-based (GAC) segmentations under a unified Bayesian framework. A variational inference technique is used to solve this otherwise intractable inference problem, leading to approximate MAP solutions of both the new 3D spatial temporal CRF and the GAC model. The tracking result of the previous frame is used to initialize the curve in the current frame. Typical contour tracking problems are considered and experimental results are given to illustrate the robustness of the method against noise and its accurate performance in moving objects boundary localization.