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Coupled Geodesic Active Regions for Image Segmentation

Nikolaos Paragios 1 Rachid Deriche
1 ROBOTVIS - Computer Vision and Robotics
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : This paper presents a novel variational method for image segmentation which is obtained by unifying boundary and region-based information sources under the Geodesic Active Region framework. A statistical analysis over the observed density function (image histogram) using a mixture of Gaussian elements, indicates the number of the different regions and their intensity properties. Then, the boundary information is determined using a probabilistic edge detector, while the region information is given directly from the observed image using the conditional probability density functions of the mixture model. The defined objective function is minimized using a gradient-descent method where a level set approach is used to implement the resulting PDE system. According to the motion equations [PDE], the set of initial curves is propagated towards the segmentation result under the influence of boundary and region-based segmentation forces, and being constrained by a regularity force. The changes of topology are naturally handled thanks to the level set implementation, while a coupled multi-phase propagation is adopted that increases the robustness and the convergence rate by introducing a coupled system of equations for the different level set functions. Besides, to reduce the required computational cost and to decrease the risk of convergence to a local minimum, a multi-scale approach is also considered. The performance of our method is demonstrated on a variety of synthetic and real images.
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Submitted on : Wednesday, May 24, 2006 - 11:09:53 AM
Last modification on : Friday, February 4, 2022 - 3:16:12 AM
Long-term archiving on: : Sunday, April 4, 2010 - 11:26:08 PM


  • HAL Id : inria-00072878, version 1



Nikolaos Paragios, Rachid Deriche. Coupled Geodesic Active Regions for Image Segmentation. RR-3783, INRIA. 1999. ⟨inria-00072878⟩



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