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Anchorage, AK, USA
June 23, 2008 to June 28, 2008
ISBN: 978-1-4244-2339-2
pp: 1-8
Matthias Fuchs , Infmath Imaging, University of Innsbruck, 6020, Austria
Samuel Gerber , SCI Institute, University of Utah, Salt Lake City, 84112, USA
This paper presents a novel variational formulation incorporating statistical knowledge to detect shapes in images. We propose to train an energy based on joint shape and feature statistics inferred from training data. Variational approaches to shape detection traditionally involve energies consisting of a feature term and a regularization term. The feature term forces the detected object to be optimal with respect to image properties such as contrast, pattern or edges whereas the regularization term stabilizes the shape of the object. Our trained energy does not rely on these two separate terms, hence avoids the non-trivial task of balancing them properly. This enables us to incorporate more complex image features while still relying on a moderate number of training samples. Cell detection in microscope images illustrates the capability of the proposed method to automatically adapt itself to different image features. We also introduce a nonlinear energy and exemplarily compare it to the linear approach.
Matthias Fuchs, Samuel Gerber, "Variational shape detection in microscope images based on joint shape and image feature statistics", CVPRW, 2008, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops 2008, pp. 1-8, doi:10.1109/CVPRW.2008.4563012
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