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In this paper, we focus on techniques for vector-valued image regularization, based on variational methods and PDEs. Starting from the study of PDE-based formalisms previously proposed in the literature for the regularization of scalar and vector-valued data, we propose a unifying expression that gathers the majority of these previous frameworks into a(More)
We propose an integral concept for tractography to describe crossing and splitting fibre bundles based on the fibre orientation distribution function (ODF) estimated from high angular resolution diffusion imaging (HARDI). We show that in order to perform accurate probabilistic tractography, one needs to use a fibre ODF estimation and not the diffusion ODF.(More)
Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90's, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all(More)
In this paper, we propose a novel and efficient approach for active unsurpervised texture segmentation. First, we show how we can extract a small set of good features for texture segmentation based on the structure tensor and nonlinear diffusion. Then, we propose a variational framework that incorporates these features in a level set based unsuper-vised(More)
Corners and vertices are strong and useful features in Computer Vision for scene analysis, stereo matching and motion analysis. This paper deals with the development of a computational approach to these important features. We consider rst a corner model and study analytically its behavior once it has been smoothed using the well-known Gaussian lter. This(More)
Deened as the apparent motion in a sequence of images, the optical ow is very important in the Computer Vision community where its accurate estimation is strongly needed for many applications. It is one of the most studied problem in Computer Vision. In spite of this, not much theoretical analysis has been done. In this article, we rst present a review of(More)
This paper presents a novel variational framework for dealing with frame partition problems in Computer Vision by the propagation of curves. This framework integrates boundary and region-based frame partition modules under a curve-based energy framework, which aims at nding a set of minimal length curves that preserve three main properties: (i) they are(More)