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Object detection and segmentation can be facilitated by the availability of a reference object. However, accounting for possible transformations between the different object views, as part of the segmentation process, remains a challenge. Recent works address this problem by using comprehensive training data. Other approaches are applicable only to limited object classes or can only accommodate similarity transformations. We suggest a novel variational approach to prior-based segmentation,doi:10.1109/iccv.2005.192 dblp:conf/iccv/Riklin-RavivKS05 fatcat:avpw2lvfkzfejp7o2ep5ncpqje