UCL logo

UCL Discovery

UCL home » Library Services » Electronic resources » UCL Discovery

Active exploration and keypoint clustering for object recognition

Kootstra, G.; Ypma, J.; De Boer, B.; (2008) Active exploration and keypoint clustering for object recognition. In: Hutchinson, S., (ed.) IEEE International Conference on Robotics and Automation (ICRA) 2008. (pp. pp. 1005-1010). Institute of Electrical and Electronics Engineers (IEEE): Piscataway, US. Green open access

[img]
Preview
PDF
18741.pdf

Download (908kB)

Abstract

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.

Type: Proceedings paper
Title: Active exploration and keypoint clustering for object recognition
ISBN-13: 9781424416462
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/ROBOT.2008.4543336
Publisher version: http://ieeexplore.ieee.org/xpls/abs_all.jsp?isnumb...
Language: English
Additional information: ©2008 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
UCL classification: UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of SandHS > Dept of Economics
URI: http://discovery.ucl.ac.uk/id/eprint/18741
Downloads since deposit
410Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item