RGB-D object classification using covariance descriptors

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 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 languageEnglish (US)
Title of host publicationProceedings - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5467-5472
Number of pages6
ISBN (Electronic)9781479936854, 9781479936854
DOIs
StatePublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

Publication series

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

Other

Other2014 IEEE International Conference on Robotics and Automation, ICRA 2014
Country/TerritoryChina
CityHong Kong
Period5/31/146/7/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

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