RGB-D object classification using covariance descriptors

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

Research output: Contribution to journalConference articlepeer-review

9 Scopus citations


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)
Article number6907663
Pages (from-to)5467-5472
Number of pages6
JournalProceedings - IEEE International Conference on Robotics and Automation
StatePublished - Sep 22 2014
Event2014 IEEE International Conference on Robotics and Automation, ICRA 2014 - Hong Kong, China
Duration: May 31 2014Jun 7 2014

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