RGB-D object classification using covariance descriptors

Duc Fehr; William J. Beksi; Dimitris Zermas; Nikolaos Papanikolopoulos

doi:10.1109/ICRA.2014.6907663

RGB-D object classification using covariance descriptors

Duc Fehr, William J. Beksi, Dimitris Zermas, Nikolaos Papanikolopoulos

Computer Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

12 Scopus citations

Abstract

In This paper, we introduce a new covariance based feature descriptor To be used on 'colored' point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, There is not yet a clear consensus on how To best Tackle 'colored' point clouds. We present The notion of a covariance on RGB-D data. Covariances have not only been proven To be successful in image processing, but in other domains as well. Their main advantage is That They provide a compact and flexible description of point clouds. Our work is a first step Towards demonstrating The usability of The concept of covariances in conjunction with RGB-D data. Experiments performed on an RGB-D database and compared To previous results show The increased performance of our method.

Original language	English (US)
Title of host publication	Proceedings - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5467-5472
Number of pages	6
ISBN (Electronic)	9781479936854, 9781479936854
DOIs	https://doi.org/10.1109/ICRA.2014.6907663
State	Published - Sep 22 2014
Event	2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China Duration: May 31 2014 → Jun 7 2014

Publication series

Name	Proceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)	1050-4729

Other

Other	2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/Territory	China
City	Hong Kong
Period	5/31/14 → 6/7/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

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10.1109/ICRA.2014.6907663

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Fehr, D., Beksi, W. J., Zermas, D., & Papanikolopoulos, N. (2014). RGB-D object classification using covariance descriptors. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5467-5472). Article 6907663 (Proceedings - IEEE International Conference on Robotics and Automation). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2014.6907663

RGB-D object classification using covariance descriptors. / Fehr, Duc; Beksi, William J.; Zermas, Dimitris et al.
Proceedings - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers Inc., 2014. p. 5467-5472 6907663 (Proceedings - IEEE International Conference on Robotics and Automation).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Fehr, D, Beksi, WJ, Zermas, D & Papanikolopoulos, N 2014, RGB-D object classification using covariance descriptors. in Proceedings - IEEE International Conference on Robotics and Automation., 6907663, Proceedings - IEEE International Conference on Robotics and Automation, Institute of Electrical and Electronics Engineers Inc., pp. 5467-5472, 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, 5/31/14. https://doi.org/10.1109/ICRA.2014.6907663

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abstract = "In This paper, we introduce a new covariance based feature descriptor To be used on 'colored' point clouds gathered by a mobile robot equipped with an RGB-D camera. Although many recent descriptors provide adequate results, There is not yet a clear consensus on how To best Tackle 'colored' point clouds. We present The notion of a covariance on RGB-D data. Covariances have not only been proven To be successful in image processing, but in other domains as well. Their main advantage is That They provide a compact and flexible description of point clouds. Our work is a first step Towards demonstrating The usability of The concept of covariances in conjunction with RGB-D data. Experiments performed on an RGB-D database and compared To previous results show The increased performance of our method.",

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RGB-D object classification using covariance descriptors

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