Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Shape Modeling with Front Propagation: A Level Set Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
A fast level set method for propagating interfaces
Journal of Computational Physics
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Geodesic Active Contours and Level Sets for the Detection and Tracking of Moving Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Gradient flows and geometric active contour models
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
IEEE Transactions on Image Processing
Tracking Closed Curves with Non-linear Stochastic Filters
SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
Hi-index | 0.00 |
In many applications it is necessary to track a moving and deforming boundary on the plane from infrequent, sparse measurements. For instance, each of a set of mobile observers may be able to tell the position of a point on the boundary. Often boundary components split, merge, appear, and disappear over time. Data are typically sparse and noisy and the underlying dynamics is uncertain. To address these issues, we use a particle filter to represent a distribution in the large space of all plane curves and propose a full-fledged combination of level sets and particle filters. Our main contribution is in controlling the potentially high expense of multiplying the cost of a level set representation of boundaries by the number of particles needed. Experiments on tracking the boundary of a colon in tomographic imagery from sparse edge measurements show the promise of the approach.