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3D object recognition using 2D moments and HMLP network

Proceedings of The International Conference on Computer Graphics, Imaging and Visualization (CGIV 2004), 26th-29th July 2004 at Penang, Malaysia.

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Bibliographic Details
Main Authors: Mohd Yusoff, Mashor, Prof. Dr., Muhammad Khusairi, Osman, Mohd Rizal, Arshad
Other Authors: yusoff@unimap.edu.my
Format: Working Paper
Language:English
Published: IEEE Conference Publications 2014
Subjects:
Hybrid multi-layered perceptrons (HMLP)
Recognition rate
Object recognition
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/35438
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spelling my.unimap-354382014-06-12T08:33:08Z 3D object recognition using 2D moments and HMLP network Mohd Yusoff, Mashor, Prof. Dr. Muhammad Khusairi, Osman Mohd Rizal, Arshad yusoff@unimap.edu.my khusairi@eng.usm.my rizal@eng.usm.my Hybrid multi-layered perceptrons (HMLP) Recognition rate Object recognition Proceedings of The International Conference on Computer Graphics, Imaging and Visualization (CGIV 2004), 26th-29th July 2004 at Penang, Malaysia. This paper proposes a method for recognition and classification of 3D objects using 2D moments and HMLP network. 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. However, the current study proves that with some adaptation to multiple views technique, 2D moments are sufficient to model 3D objects. In addition, the simplicity of 2D moment's calculation reduces the processing time for feature extraction, thus decreases the recognition time. The 2D moments were then fed into a neural network for classification of the 3D objects. In the current study, hybrid multi-layered perceptron (HMLP) network is proposed to perform the classification. Two distinct groups of objects that are polyhedral and free-form objects were used to access the performance of the proposed method. The recognition results show that the proposed method has successfully classified the 3D object with the accuracy of up to 100%. 2014-06-12T08:33:08Z 2014-06-12T08:33:08Z 2004-07 Working Paper p. 126-130 0-7695-2178-9 http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1323972&tag=1 http://dspace.unimap.edu.my:80/dspace/handle/123456789/35438 http://dx.doi.org/10.1109/CGIV.2004.1323972 en Proceedings of The International Conference on Computer Graphics, Imaging and Visualization (CGIV 2004); IEEE Conference Publications
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Hybrid multi-layered perceptrons (HMLP)
Recognition rate
Object recognition
spellingShingle Hybrid multi-layered perceptrons (HMLP)
Recognition rate
Object recognition
Mohd Yusoff, Mashor, Prof. Dr.
Muhammad Khusairi, Osman
Mohd Rizal, Arshad
3D object recognition using 2D moments and HMLP network
description Proceedings of The International Conference on Computer Graphics, Imaging and Visualization (CGIV 2004), 26th-29th July 2004 at Penang, Malaysia.
author2 yusoff@unimap.edu.my
author_facet yusoff@unimap.edu.my
Mohd Yusoff, Mashor, Prof. Dr.
Muhammad Khusairi, Osman
Mohd Rizal, Arshad
format Working Paper
author Mohd Yusoff, Mashor, Prof. Dr.
Muhammad Khusairi, Osman
Mohd Rizal, Arshad
author_sort Mohd Yusoff, Mashor, Prof. Dr.
title 3D object recognition using 2D moments and HMLP network
title_short 3D object recognition using 2D moments and HMLP network
title_full 3D object recognition using 2D moments and HMLP network
title_fullStr 3D object recognition using 2D moments and HMLP network
title_full_unstemmed 3D object recognition using 2D moments and HMLP network
title_sort 3d object recognition using 2d moments and hmlp network
publisher IEEE Conference Publications
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/35438
_version_ 1643797312115310592
score 13.214268

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