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B. Delabarre. Contribution to dense visual tracking and visual servoing using robust similarity criteria. PhD Thesis Université de Rennes 1, December 2014.

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In this document, we address the visual tracking and visual servoing problems. They are crucial thematics in the domain of computer and robot vision. Most of these techniques use geometrical primitives extracted from the images in order to estimate a motion from an image sequences. But using geometrical features means having to extract and match them at each new image before performing the tracking or servoing process. In order to get rid of this algorithmic step, recent approaches have proposed to use directly the information provided by the whole image instead of extracting geometrical primitives. Most of these algorithms, referred to as direct techniques, are based on the luminance values of every pixel in the image. But this strategy limits their use, since the criteria is very sensitive to scene perturbations such as luminosity shifts or occlusions. To overcome this problem, we propose in this document to use robust similarity mea- sures, the sum of conditional variance and the mutual information, in order to perform robust direct visual tracking and visual servoing processes. Several algorithms are then proposed that are based on these criteria in order to be robust to scene perturbations. These different methods are tested and analyzed in several setups where perturbations occur which allows to demonstrate their efficiency

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