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Exploring objects for recognition in the real world

Kootstra, G.; Ypma, J.; De Boer, B.; (2007) Exploring objects for recognition in the real world. In: Proceedings of IEEE International Conference on Robotics and Biomimetics. (pp. pp. 429-434). Institute of Electrical and Electronics Engineers (IEEE): New York, USA. Green open access

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Perception in natural systems is a highly active process. In this paper, we adopt the strategy of natural systems to explore objects for 3D object recognition using robots. The exploration of objects enables the system to learn objects from different viewpoints, which is essential for 3D bject recognition. Exploration furthermore simplifies the segmentation of the object from its background, which is important for object learning in real-world environments, which are usually highly cluttered. We use the Scale Invariant Feature Transform (SIFT) as the basis for our object recognition system. We discuss our active vision approach to learn and recognize 3D objects in cluttered and uncontrolled environments. Furthermore, we propose a model to reduce the number of SIFT keypoints stored in the object database. It is a known drawback of SIFT that the computational complexity of the algorithm increases rapidly with the number of keypoints. We discuss the use of a growing-when-required (GWR) network, which is based on the Kohonen Self Organizing Feature Map, for efficient clustering of the keypoints. The results show successful learning of 3D objects in a cluttered and uncontrolled environment. Moreover, the GWR-network strongly reduces the number of keypoints.

Type: Proceedings paper
Title: Exploring objects for recognition in the real world
ISBN-13: 9781424417612
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ROBIO.2007.4522200
Publisher version: http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumb...
Language: English
Additional information: ©2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Paper presented at the IEEE International Conference on Robotics and Biomimetics, pages 429-434, December 15-18, 2007, Sanya, China
Keywords: Active vision, object exploration, object recognition, SIFT, clustering
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS > Dept of Economics
URI: https://discovery.ucl.ac.uk/id/eprint/18739
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