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
An Active Contour Model without Edges
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Motion Competition: A Variational Approach to Piecewise Parametric Motion Segmentation
International Journal of Computer Vision
Maintaining the point correspondence in the level set framework
Journal of Computational Physics
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
SIMUL '09 Proceedings of the 2009 First International Conference on Advances in System Simulation
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
IEEE Transactions on Image Processing
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We introduce a non-linear stochastic filtering technique to track the state of a free curve from image data. 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 partial differential equation with a Brownian motion of low dimension. Specific noise models lead to the traditional level set evolution law 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 photometric information, some velocity induced by the curve displacement and an uncertainty modeling of the dynamics. The associated filter capabilities are demonstrated on various sequences with highly deformable objects.