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A supervised learning approach for fast object recognition from RGB-D data

Authors:

Vangelis Metsis,

Christopher McMurrough,

Fillia MakedonAuthors Info & Claims

PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

Article No.: 5, Pages 1 - 8

https://doi.org/10.1145/2674396.2674432

Published: 27 May 2014 Publication History

Abstract

Object recognition serves obvious purposes in assisted living environments, where robotic devices can be used as companions to assist humans in need. The recent introduction of vision based sensors, which are able to extract depth sensing information about the environment, in addition to the traditional RGB video, presents new opportunities and challenges for more accurate object recognition.

The current work, presents an object recognition approach that uses RGB-D point cloud data and a novel feature extraction methodology, in combination with well-known supervised learning algorithms, to achieve accurate, real-time recognition of a large number of objects. In our experiments, we use a dataset of household objects organized into 51 categories, and evaluate the recognition accuracy and time efficiency of a set of different supervised learning methods.

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Cited By

Yadav NSaraswat M(2024)A Review of RGB-D Image Classification MethodsAdvances in Data-Driven Computing and Intelligent Systems10.1007/978-981-99-9531-8_2(9-22)Online publication date: 11-Apr-2024
https://doi.org/10.1007/978-981-99-9531-8_2
Gao MJiang JZou GJohn VLiu Z(2019)RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A SurveyIEEE Access10.1109/ACCESS.2019.29070717(43110-43136)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2907071

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Information & Contributors

Information

Published In

cover image ACM Other conferences

PETRA '14: Proceedings of the 7th International Conference on PErvasive Technologies Related to Assistive Environments

May 2014

408 pages

ISBN:9781450327466

DOI:10.1145/2674396

Conference Chair:
Fillia Makedon
University of Texas at Arlington
,
Program Chairs:
Mark Clements
Georgia Institute of Technology
,
Catherine Pelachaud
TELECOM ParisTech, France
,
Vana Kalogeraki
Athens University of Economics and Bus
,
Ilias Maglogiannis
University of Piraeus, Greece

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

iPerform Center: iPerform Center for Assistive Technologies to Enhance Human Performance
CSE@UTA: Department of Computer Science and Engineering, The University of Texas at Arlington
HERACLEIA: HERACLEIA Human-Centered Computing Laboratory at UTA
U of Tex at Arlington: U of Tex at Arlington
NCRS: Demokritos National Center for Scientific Research
Fulbrigh, Greece: Fulbright Foundation, Greece

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 May 2014

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National Science Foundation

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PETRA '14

Sponsor:

iPerform Center
CSE@UTA
HERACLEIA
U of Tex at Arlington
NCRS
Fulbrigh, Greece

PETRA '14: The 7th International Conference on PErvasive Technologies Related to Assistive Environments

May 27 - 30, 2014

Rhodes, Greece

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Cited By

Yadav NSaraswat M(2024)A Review of RGB-D Image Classification MethodsAdvances in Data-Driven Computing and Intelligent Systems10.1007/978-981-99-9531-8_2(9-22)Online publication date: 11-Apr-2024
https://doi.org/10.1007/978-981-99-9531-8_2
Gao MJiang JZou GJohn VLiu Z(2019)RGB-D-Based Object Recognition Using Multimodal Convolutional Neural Networks: A SurveyIEEE Access10.1109/ACCESS.2019.29070717(43110-43136)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2907071

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