Abstract
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.
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Avenel, C., Mémin, E., Pérez, P. (2009). Tracking Closed Curves with Non-linear Stochastic Filters. In: Tai, XC., Mørken, K., Lysaker, M., Lie, KA. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2009. Lecture Notes in Computer Science, vol 5567. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02256-2_48
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DOI: https://doi.org/10.1007/978-3-642-02256-2_48
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