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On some extensions of the level sets and the graph cuts towards their application to image and video segmentation

Olivier Juan
Abstract : Image processing techniques are now widely spread out over a large quantity of domains: like medical imaging, movies post-production, games... Automatic detection and extraction of regions of interest inside an image, a volume or a video is challenging problem since it is a starting point for many applications in image processing. However many techniques were developed during the last years and the state of the art methods suffer from some drawbacks: The Level Sets method only provides a local minimum while the Graph Cuts method comes from Combinatorial Community and could take advantage of the specificity of image processing problems. In this thesis, we propose two extensions of the previously cited methods in order to soften or remove these drawbacks. We first discuss the existing methods and show how they are related to the segmentation problem through an energy formulation. Then we introduce stochastic perturbations to the Level Sets method and we build a more generic framework: the Stochastic Level Sets (SLS). Later we provide a direct application of the SLS to image segmentation that provides a better minimization of energies. Basically, it allows the contours to escape from local minimum. Then we propose a new formulation of an existing algorithm of Graph Cuts in order to introduce some interesting concept for image processing community: like initialization of the algorithm for speed improvement. We also provide a new approach for layer extraction from video sequence that retrieves both visible and hidden layers in it.
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Contributor : Ecole Des Ponts Paristech <>
Submitted on : Tuesday, July 18, 2006 - 8:00:00 AM
Last modification on : Tuesday, July 18, 2006 - 8:00:00 AM
Long-term archiving on: : Thursday, September 30, 2010 - 7:26:56 PM


  • HAL Id : pastel-00001855, version 1



Olivier Juan. On some extensions of the level sets and the graph cuts towards their application to image and video segmentation. Mathematics [math]. Ecole des Ponts ParisTech, 2006. English. ⟨pastel-00001855⟩



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