Implicit Active-Contouring with MRF

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Implicit Active-Contouring with MRF
In this paper, we present a new image segmentation method based on energy minimization for iteratively evolving an implicit active contour. Methods for active contour evolution is important in many applications ranging from video post-processing to medical imaging, where a single object must be chosen from a multi-object collection containing objects sharing similar characteristics. Level set methods has played a fundamental role in many of these applications. These methods typically involve minimizing functionals over the infinite-dimensional space of curves and can be quite cumbersome to implement. Developments of markov random field (MRF) based algorithms, ICM and graph-cuts, over the last decade has led to fast, robust and simple implementations. Nevertheless, the main drawback of current MRF methods is that it is intended for global segmentation of objects. We propose a new MRF formulation that combines the computational advantages of MRF methods and enforces active contour evol...
Pierre-Marc Jodoin, Venkatesh Saligrama, Janusz Ko
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Authors Pierre-Marc Jodoin, Venkatesh Saligrama, Janusz Konrad
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