Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Tracking level sets by level sets: a method for solving the shape from shading problem
Computer Vision and Image Understanding
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
An Active Contour Model without Edges
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Variational Space-Time Motion Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours
International Journal of Computer Vision
Dynamical Statistical Shape Priors for Level Set-Based Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Tracking Deforming Objects Using Particle Filtering for Geometric Active Contours
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Variational Technique for Time Consistent Tracking of Curves and Motion
Journal of Mathematical Imaging and Vision
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
IEEE Transactions on Image Processing
A variant of particle filtering using historic datasets for tracking complex geospatial phenomena
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
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The joint analysis of motions and deformations is crucial in a number of computer vision applications. In this paper, we introduce a non-linear stochastic filtering technique to track the state of a free curve. The approach we propose is implemented through a particle filter which includes color measurements characterizing the target and the background respectively. We design a continuous-time dynamics that allows us to infer inter-frame deformations. The curve is defined by an implicit level-set representation and the stochastic dynamics is expressed on the level-set function. It takes the form of a stochastic differential equation with Brownian motion of low dimension. Specific noise models lead to traditional evolution laws based on mean curvature motions, while other forms lead to new evolution laws with different smoothing behaviors. In these evolution models, we propose to combine local motion information extracted from the images and an incertitude modeling of the dynamics. The associated filter we propose for curve tracking thus belongs to the family of conditional particle filters. Its capabilities are demonstrated on various sequences with highly deformable objects.