Françoise Dibos

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This paper introduces a discrete scheme for mean curvature motion using a morphological image processing approach. After brieey presenting an axiomatic approach of image processing and the mean curvature PDE, the properties of the proposed scheme are studied, in particular consistency and convergence are proved. The applications of mean curvature motion in(More)
In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image(More)
In this paper, we propose a global method for estimating the motion of a camera which films a static scene. Our approach is direct, fast and robust, and deals with adjacent frames of a sequence. It is based on a quadratic approximation of the deformation between two images, in the case of a scene with constant depth in the camera coordinate system. This(More)
For comparison of shapes under subgroups of the projective group, we can use a lot of invariants and especially differential invariants coming from multiscale analysis. But such invariants, as we have to compute curvature, are very sensitive to the noise induced by the dicretization grid. In order to resolve this problem we use size functions which can(More)
In a video sequence, computing the motion of an object requires the continuity of the apparent velocity field. This property does not hold when the object is hidden by an occlusion during its motion. The minimization of an energy functional leads to a simple algorithm which allows the recovery of the most likely trajectory of the occluded object from(More)
In this article we present a real time algorithm for detecting moving objects in a video sequence taken with a fixed camera. When a background estimation is known, the algorithm is able to detect moving objects and locates them approximately. The method is based on a comparison between each single image of the sequence and the background, this gives us a(More)