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Sparsity, redundancy and optimal image support towards knowledge-based segmentation

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Sparsity, redundancy and optimal image support towards knowledge-based segmentation
In this paper, we propose a novel approach to model shape variations. It encodes sparsity, exploits geometric redundancy, and accounts for the different degrees of local variation and image support. In this context we consider a control-point based shape representation. Their sparse distribution is derived based on a shape model metric learned from the training data, and the ambiguity of local appearance with regard to segmentation changes. The resulting sparse model of the object improves reconstruction and search behavior, in particular for data that exhibit a heterogeneous distribution of image information and shape complexity. Furthermore, it goes beyond conventional imagebased segmentation approaches since it is able to identify reliable image structures which are then encoded within the model and used to determine the optimal segmentation map. We report promising experimental results comparing our approach with standard models on MRI data of calf muscles - an application where t...
Salma Essafi, Georg Langs, Nikos Paragios
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2008
Where CVPR
Authors Salma Essafi, Georg Langs, Nikos Paragios
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