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Reseach Article

Feature based 3D Object Recognition using Artificial Neural Networks

by Abdel Karim Baareh, Alaa F. Sheta, Mohammad S. Al-batah
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 44 - Number 5
Year of Publication: 2012
Authors: Abdel Karim Baareh, Alaa F. Sheta, Mohammad S. Al-batah
10.5120/6256-8402

Abdel Karim Baareh, Alaa F. Sheta, Mohammad S. Al-batah . Feature based 3D Object Recognition using Artificial Neural Networks. International Journal of Computer Applications. 44, 5 ( April 2012), 1-7. DOI=10.5120/6256-8402

@article{ 10.5120/6256-8402,
author = { Abdel Karim Baareh, Alaa F. Sheta, Mohammad S. Al-batah },
title = { Feature based 3D Object Recognition using Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { April 2012 },
volume = { 44 },
number = { 5 },
month = { April },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-7 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume44/number5/6256-8402/ },
doi = { 10.5120/6256-8402 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:34:44.124235+05:30
%A Abdel Karim Baareh
%A Alaa F. Sheta
%A Mohammad S. Al-batah
%T Feature based 3D Object Recognition using Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 44
%N 5
%P 1-7
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The recognition of objects is one of the main goals for computer vision research. This paper formulates and solves the problem of three-dimensional (3D) object recognition for Polyhedral objects. A multiple view of 2D intensity images are taken from multiple cameras and used to model the 3D objects. The proposed methodology is based on extracting set of features from the 2D images which include the Affine, Zernike and Hu moments invariants to be used as inputs to train artificial neural network (ANN). Various architectures of ANN were explored to recognize a shape of Polyhedral objects. The experiments results show that 3D objects can be sufficiently modeled and recognized by set of multiple 2D views. The best ANN architecture was twenty input and single output model.

References
  1. Szeliski, R. , 2010, Computer Vision: Algorithms and Applications, Springer, New York.
  2. Sharath, P. , Chitra, D. , and Anil, K. , R. , 1993, Feature Detection for 3D Object Recognition and Matching, In Proceedings, of SPIE Conference on Geometric Methods In Computer Vision II, pp. 366-377.
  3. Murase, H. , and S. K. Nayar, "Visual Learning and Recognition of 3-D Objects from Appearance", International Journal of Computer Vision 14, 1995, pp. 5–24.
  4. Selinger, A. and R. Nelson, "A Perceptual grouping Hierarchy for Appearance-Based 3D Object Recognition", Computer Vision and Image Understanding Vol. 76, No. 1, 1999, pp. 83–92.
  5. Bebis. G, Louis. S, and Fadali. S, 1998, Using Genetic Algorithms for 3-D Object Recognition, In Proceeding 11th Int. Conf. Computer Applications in Industry and Engineering, Las Vegas, NV, pp. 13-16.
  6. Sheta, A. , Baareh, A. and Al-Batah, M. , "3D Object Recognition Using Fuzzy Mathematical Modeling of 2D Images", Accepted for publication at the 3rd International Conference on Multimedia Computing and Systems, May 10-12, Tangier, Morocco, 2012.
  7. Braik, M. , Sheta, A. , "A New Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks", Journal of Intelligent Computing Vol. 2, No. 2, June 2011, pp. 54-71.
  8. Baareh, A. K. , Sheta, A. , AL Khnaifes K. , "Forecasting River Flow in the USA: A Comparison between Auto-Regression and Neural Network Non-Parametric Models", Journal of Computer Science, Vol. 2, No. 10, 2006, pp. 775-780.
  9. Al-Batah, M. S. , Mat Isa, N. A. , Zamli, K. Z. , and Azizli, K. A, Modified Recursive Least Squares algorithm to train the Hybrid Multilayered Perceptron (HMLP) network, Applied Soft Computing, Vol. 10, No. 1, 2010, pp. 236-244.
  10. Seethe, M. , Muralikrisha, I. V. , Deekshatulu. B. L. , Artificial Neural Networks and Other Methods of Image Classifications, Journal of theoretical and applied information technology, 2005, pp. 1039-1053.
  11. Honjun Lu. , Rudy S. , huan lui. , 1996, Effective data mining Using Neural Networks, IEEE Transactions On Knowledge and Data Engineering, Vol. 8, No. 6, , pp. 957-961.
  12. Radha, V. , Nallammal. , N, October 19-21, 2011, "Neural Network Based Face Recognition Using RBFN Classifier" In Proceedings of the World Congress on Engineering and Computer Science, Vol. I, San Francisco, USA.
  13. Darwis, Y. and, Sheta, A. , 2008, "Minutiae Extraction for Fingerprint Recognition" In Proceedings of the Fifth IEEE International Multi-Conference on System, Signal and Devices (SSD'08), Amman, Jordan.
  14. Baareh, A. , Sheta, A, AL Khnaifes K. 2006, "Forecasting river flow in the USA: a comparison between auto-regression and neural network non-parametric models", SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modeling and Optimization, pp. 7-12.
  15. Michael, N. , 2005 Artificial Intelligence: A Guide to Intelligent Systems, Pearson Education Limited.
  16. Mat Isa. , Zamli, K. Z. , and Al-Batah, M. S. , "Automated intelligent real-time system for aggregate classification", International Journal of Mineral Processing, Vol. 100, No. 1-2, pp. 41 – 50, 2011.
  17. Munoz-Rodriguez, J. A. , Asundi, A. , and Rodriguez-Vera, R, "Recognition of a Light Line Pattern by Hu Moments for 3-D Reconstruction of a Rotated Object", Journal of Optics and Laser Technology, Vol. 37, No. 2, 2005, pp. 131-138.
  18. Realpe, A. , and Velazquez, C, "Pattern Recognition for Characterization of Pharmaceutical Powders", Journal of Powder Technology, Vol. 169, No. 2, 2006, pp. 108-113.
  19. Rizon, M. , Yazid, H. , Saad, P. , Md-Shakaff, A. Y. , Saad, A. R. , Mamat, M. R. , Yaacob, S. , Desa, H. , and Karthigayan, M, "Object Detection Using Geometric Invariant Moment, American Journal of Applied Sciences", Vol. 2, No. 6, 2006, pp. 1876-1878.
  20. Mat Isa, N. A. , Al-Batah, M. S. , Zamli, K. Z. , Azizli, K. A. , Joret, A. , and Mat Noor, N. R. , "Suitable features selection for the HMLP and MLP networks to identify the shape of aggregate", Construction and Building Materials, Vol. 22, No. 3, 2008, pp. 402-410.
  21. Al-Batah, M. S. , Mat Isa, N. A. , Zamli, K. Z. , Sani, Z. M. , and Azizli, K. A, A novel aggregate classification technique using moment invariants and cascaded multilayered perceptron network, International Journal of Mineral Processing, Vol. 92, No. (1-2), 2009, pp. 92-102.
  22. Hu, M. K. Visual, 1962, Pattern Recognition by Moment Invariants, computer methods in image analysis, IEEE Transactions on Information Theory, Vol. 8, No. 2, pp. 179-187.
  23. Pejnovic, P. , Buturovic, L. , and Stojiljkovic, Z. , 1992 Object Recognition by Invariants, In Proceeding of the 11th IAPR International Conference on Pattern Recognition, Vol. 2, pp. 434-437.
  24. Kadyrov, A. , and Petrou, M. , 2001, Object Descriptors Invariant to Affine Distortions, In Proceedings of the British Machine Vision Conference, pp. 391-400.
Index Terms

Computer Science
Information Sciences

Keywords

3d Object Recognition Moments Features Extraction Classifications Back Propagation