Polygonal active contours (snakes) have been used with success for target segmentation and tracking. We propose to adapt a technique based on the minimum description length principle to estimate the complexity (proportional to the number of nodes) of the polygon used for the segmentation. We demonstrate that, provided that an up-and-down multiresolution strategy is implemented, it is possible to estimate efficiently this number of nodes without a priori knowledge and with a fast algorithm, leading to a segmentation criterion without free parameters. We also show that, for polygonal-shaped objects, this new technique leads to better results than using a simple regularization strategy based on the smoothness of the contour.

It has been shown many times that using different versions of a scene perturbed with different blurs improved the quality of a restored image compared with using a single blurred image. We focus on large defocus blurs, and we first consider a case in which two different blurring kernels are used. We analyze with numerical simulations the influence of the relative diameter of both kernels on the quality of restoration. We then quantitatively evaluate how the two-kernel approach improves the robustness of restoration to a difference between the kernels used in designing the algorithm and the actual kernels that have perturbed the image. We finally show that using three different kernels may not improve the restoration performance compared with the two-kernel approach but still improves the robustness to kernel estimation.

One of the main difficulties in visual tracking is to take into account appearance changes (not only of the target but also of or due to the scene, illumination for example). The use of a Bayesian framework is very flexible and has proven to be very efficient in visual tracking. Moreover, color or greylevel histograms allow to track an objet with a low computational cost. The recently proposed color-based trackers integrated in a probabilistic framework [1,3] are efficient for a given application (face tracking for example) but can not be generalized easily, due to the initialization and the adjustment of the different tracker parameters that are dependent on the input sequence. This paper presents a method based on color integrated in a particle filter that allows to cope with some of the usual problems of visual tracking (occlusions, target appearance changes, changes in resolution or in illumination) and to adapt easily to different applications (tracking of structures in aerial imagery as well as football players). The novelty of the tracker is its ability to automatically regulate all the parameters needed for tracking, which makes it flexible and easily usable for different applications.

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