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Active multi-view object recognition A unifying view on online feature selection and view planning.pdf
Robotics and Autonomous Systems 84 (2016) 31–47
Contents lists available at ScienceDirect
Robotics and Autonomous Systems
journal homepage: /locate/robot
Active multi-view object recognition: A unifying view on online feature selection and view planning
Christian Potthast ?, Andreas Breitenmoser, Fei Sha, Gaurav S. Sukhatme
University of Southern California, Department of Computer Science, Los Angeles, CA 90089, USA
highlights
? A unified approach to feature and viewpoint selection for multi-view object recognition is proposed. ? Online feature selection reduces the dimensionality and with that the computation time. ? View planning offers performance advantages whenever multiple views are required due to ambiguous situations or occlusions. ? Increased recognition accuracy and reduced computation cost are realized by an information-theoretic action selection framework.
article info
Article history: Received 26 May 2015 Received in revised form 14 January 2016 Accepted 27 June 2016 Available online 4 July 2016
Keywords: Viewpoint selection Feature selection Multi-view object recognition Information-theory Active perception Object change detection
abstract
Many robots are limited in their operating capabilities, both computational and energy-wise. A strong desire exists to keep computation cost and energy consumption to a minimum when executing tasks like object recognition with a mobile robot. Adaptive action selection is a paradigm, offering great flexibility in trading off the cost of acquiring information against making robust and reliable inference under uncertainty. In this paper, we study active multi-view object recognition and describe an informationtheoretic framework that combines and unifies two common techniques: online feature selection for reducing computational costs and view planning for resolving ambiguities and occlusions. Our algorithm adaptively chooses between the two strategies of either selecting only the features that are most informative to th
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