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Active Exploration and Keypoint Clustering for Object Recognition
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
2008 (English)In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2008), IEEE , 2008, 1005-1010 p.Conference paper, Published paper (Refereed)
Abstract [en]

Object recognition is a challenging problem for artificial systems. This is especially true for objects that are placed in cluttered and uncontrolled environments. To challenge this problem, we discuss an active approach to object recognition. Instead of passively observing objects, we use a robot to actively explore the objects. This enables the system to learn objects from different viewpoints and to actively select viewpoints for optimal recognition. Active vision furthermore simplifies the segmentation of the object from its background. As the basis for object recognition we use the Scale Invariant Feature Transform (SIFT). SIFT has been a successful method for image representation. However, a known drawback of SIFT is that the computational complexity of the algorithm increases with the number of keypoints. We discuss a growing-when-required (GWR) network for efficient clustering of the key- points. The results show successful learning of 3D objects in real-world environments. The active approach is successful in separating the object from its cluttered background, and the active selection of viewpoint further increases the performance. Moreover, the GWR-network strongly reduces the number of keypoints.

Place, publisher, year, edition, pages
IEEE , 2008. 1005-1010 p.
Keyword [en]
Active Vision, Object Recognition, Interest Point Clustering
National Category
Robotics
Identifiers
URN: urn:nbn:se:kth:diva-47177DOI: 10.1109/ROBOT.2008.4543336ISBN: 978-1-4244-1646-2 (print)OAI: oai:DiVA.org:kth-47177DiVA: diva2:454599
Conference
IEEE International Conference on Robotics and Automation (ICRA 2008)
Note
© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. QC 20111115Available from: 2011-11-15 Created: 2011-11-07 Last updated: 2011-11-15Bibliographically approved

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Citation style
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