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3D Object Recognition Using Multiple Views And Neural Networks.

This paper proposes a method for recognition and classification of 3D objects. The method is based on 2D moments and neural networks. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged using multiple views technique. 2D moments are commonly...

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Bibliographic Details
Main Authors: Mashor, M Y, Osman, M K, Arshad, M R
Format: Conference or Workshop Item
Language:English
Published: 2006
Subjects:
TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:http://eprints.usm.my/14448/1/paper2.pdf
http://eprints.usm.my/14448/
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id my.usm.eprints.14448
record_format eprints
spelling my.usm.eprints.14448 http://eprints.usm.my/14448/ 3D Object Recognition Using Multiple Views And Neural Networks. Mashor, M Y Osman, M K Arshad, M R TK1-9971 Electrical engineering. Electronics. Nuclear engineering This paper proposes a method for recognition and classification of 3D objects. The method is based on 2D moments and neural networks. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged using multiple views technique. 2D moments are commonly used for 2D pattern recognition. 2006 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.usm.my/14448/1/paper2.pdf Mashor, M Y and Osman, M K and Arshad, M R (2006) 3D Object Recognition Using Multiple Views And Neural Networks. In: International Conference on Man-Machine Systems (ICoMMS 2006), 15-16 September 2006, City Bayview Hotel, Langkawi, Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Mashor, M Y
Osman, M K
Arshad, M R
3D Object Recognition Using Multiple Views And Neural Networks.
description This paper proposes a method for recognition and classification of 3D objects. The method is based on 2D moments and neural networks. The 2D moments are calculated based on 2D intensity images taken from multiple cameras that have been arranged using multiple views technique. 2D moments are commonly used for 2D pattern recognition.
format Conference or Workshop Item
author Mashor, M Y
Osman, M K
Arshad, M R
author_facet Mashor, M Y
Osman, M K
Arshad, M R
author_sort Mashor, M Y
title 3D Object Recognition Using Multiple Views And Neural Networks.
title_short 3D Object Recognition Using Multiple Views And Neural Networks.
title_full 3D Object Recognition Using Multiple Views And Neural Networks.
title_fullStr 3D Object Recognition Using Multiple Views And Neural Networks.
title_full_unstemmed 3D Object Recognition Using Multiple Views And Neural Networks.
title_sort 3d object recognition using multiple views and neural networks.
publishDate 2006
url http://eprints.usm.my/14448/1/paper2.pdf
http://eprints.usm.my/14448/
_version_ 1643702667709513728
score 13.251813

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