Julien Olivier

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In this paper we propose a new supervised active contour model evolving with Haralick texture features. This model is divided in two stages. First, we use a supervised step where the user defines an ideal segmentation on a learning image. A linear programming model, modeling the behavior of the active contour, is then used to determine the weights of the(More)
Although the notion of mechanical noise is expected to play a key role in the non-linear rheology of athermally sheared amorphous systems, its characterization has so far remained elusive. Here, we show using molecular dynamic simulations that in spite of the presence of strong spatio-temporal correlations in the system, the local stress exhibits normal(More)
In this paper, we propose to improve an unsupervised segmentation algorithm based on the graph diffusion and regularization model described by Ta in [1] by using Haralick texture features. With this framework, segmentation is performed by diffusing an indicator function over a graph representing an image. The benefit of our approach is to combine two(More)
In this paper, we aim at proving the effectiveness of dictionary learning techniques on the task of retinal blood vessel segmentation. We present three different methods based on dictionary learning and sparse coding that reach state-of-the-art results. Our methods are tested on two, well-known, publicly available datasets: DRIVE and STARE. The methods are(More)
Active contours and surfaces are deformable models used for 2D and 3D image segmentation. In this paper, we propose two methods developed in order to accelerate 3D image segmentation process. They are adaptations on active surfaces of two methods developed for 2D active contour. We use them on a discrete 3D surface model (mesh) evolving with the greedy(More)