Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
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
Efficient Region Tracking With Parametric Models of Geometry and Illumination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region Tracking via Level Set PDEs without Motion Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour-Based Object Tracking with Occlusion Handling in Video Acquired Using Mobile Cameras
IEEE Transactions on Pattern Analysis and Machine Intelligence
Affine Object Tracking with Kernel-Based Spatial-Color Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Interactive Graph Cut Based Segmentation with Shape Priors
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Active contours for tracking distributions
IEEE Transactions on Image Processing
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A new approach for tracking a non-rigid target is presented. Tracking is formulated as a Maximum A Posteriori (MAP) segmentation problem where each pixel is assigned a binary label indicating whether it belongs to the target or not. The label field is modeled as a Markov Random Field whose Gibbs energy comprises three terms. The first term quantifies the error in matching the object model with the object's appearance as given by the current segmentation. Coping with the deformations of the target while avoiding optical flow computation is achieved by marginalizing this likelihood over all possible motions per pixel. The second term penalizes the lack of continuity in the labels of the neighbor pixels, thereby encouraging the formation of a smoothly shaped object mask, without holes. Finally, for the sake of increasing robustness, the third term constrains the object mask to assume an elliptic shape model with unknown parameters. MAP optimization is performed iteratively, alternating between estimating the shape parameters and recomputing the segmentation using updated parameters. The latter is accomplished by discriminating each pixel via a simple hypothesis test.We demonstrate the efficiency of our approach on synthetic and real video sequences.