Object localization based on Markov random fields and symmetry interest points

Authors:
René Donner;Branislav Micusik;Georg Langs;Lech Szumilas;Philipp Peloschek;Klaus Friedrich;Horst Bischof
Affiliations:
Institute for Computer Graphics and Vision, Graz University of Technology, Austria and Pattern Recognition and Image Processing Group, Vienna University of Technology, Austria;Pattern Recognition and Image Processing Group, Vienna University of Technology, Austria;Institute for Computer Graphics and Vision, Graz University of Technology, Austria and GALEN Group, Laboratoire de Mathématiques Appliquées aux Systèmes, Ecole Centrale de Paris, Fr ...;Pattern Recognition and Image Processing Group, Vienna University of Technology, Austria;Department of Radiology, Medical University of Vienna, Austria;Department of Radiology, Medical University of Vienna, Austria;Pattern Recognition and Image Processing Group, Vienna University of Technology, Austria
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

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Abstract

We present an approach to detect anatomical structures by configurations of interest points, from a single example image. The representation of the configuration is based on Markov Random Fields, and the detection is performed in a single iteration by the MAX-SUM algorithm. Instead of sequentially matching pairs of interest points, the method takes the entire set of points, their local descriptors and the spatial configuration into account to find an optimal mapping of modeled object to target image. The image information is captured by symmetry-based interest points and local descriptors derived from Gradient Vector Flow. Experimental results are reported for two data-sets showing the applicability to complex medical data.