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Object Recognition Based on Radial Basis Function Neural Networks: experiments with RGB-D camera embedded on mobile robots
University of Angers, Angers, France.ORCID iD: 0000-0003-3498-0783
University of Angers, Angers, France.
2012 (English)Conference paper, Oral presentation only (Refereed)
Abstract [en]

An object recognition strategy based on artificial radial basis functions neural networks is presented in this paper. The general context of this work is to recognize object from captures made by a mobile robot. Unlike classical approaches which always select the closest object, our method outputs a set of potential candidates if the input information is not enough discriminant. There are three main steps in our approach: objects segmentation, signature extraction and classification. Segmentation is inspired from previous works and is shortly described. Signature extraction based on global geometric and color features is detailed. Classification based on artificial neural networks is also explained and architecture of the network is justified. Finally a real experiment made with a RGB-D camera mounted on a mobile robot is presented and classification results is criticized.

Place, publisher, year, edition, pages
2012.
National Category
Robotics
Identifiers
URN: urn:nbn:se:hh:diva-20920OAI: oai:DiVA.org:hh-20920DiVA: diva2:587245
Conference
1st International Conference on Systems and Computer Science (ICSCS 2012), Lille, France, August 29-31
Projects
Cart-O-matic Project
Available from: 2013-01-14 Created: 2013-01-14 Last updated: 2015-08-25Bibliographically approved

Open Access in DiVA

S_Gholami_icscs2012_submission(1117 kB)415 downloads
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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf