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This paper proposes a method for learning viewpoint detection models for object categories that facilitate sequential object category recognition and viewpoint planning. We have examined such models for several state-of-the-art object detection methods. Our learning procedure has been evaluated using an exhaustive multiview category database recently collected for multiview category recognition research. Our approach has been evaluated on a simulator that is based on real images that havedoi:10.1109/robot.2010.5509703 dblp:conf/icra/MegerGL10 fatcat:xkeoqjzmbfdrjaby3urlzr34p4