David Gustavsson

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In this paper we propose a method for variational segmentation and contour matching of non-rigid objects in image sequences which can deal with the occlusions. The method is based on a region-based active contour model of the Chan-Vese, augmented with a frame-to-frame interaction term which uses the segmentation result from the previous frame as a shape(More)
Images are composed of geometric structure and texture. Large scale structures are considered to be the geometric structure, while small scale details are considered to be the texture. In this dissertation, we will argue that the most important difference between geometric structure and texture is not the scale instead, it is the requirement on(More)
We address the problem of nonrigid object segmentation in image sequences in the presence of occlusions. The proposed variational segmentation method is based on a region-based active contour of the Chan-Vese model augmented with a frame-to-frame interaction term as a shape prior. The interaction term is constructed to be pose-invariant by minimizing over a(More)
We discuss a method suitable for inpainting both large scale geometric structures and stochastic texture components. We use the wellknown FRAME model for inpainting. We introduce a temperature term in the learnt FRAME Gibbs distribution. By using a fast cooling scheme a MAP-like solution is found that can reconstruct the geometric structure. In a second(More)
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