JSEA  Vol.4 No.5 , May 2011
3D Object Recognition by Classification Using Neural Networks
ABSTRACT
In this Paper, a classification method based on neural networks is presented for recognition of 3D objects. Indeed, the objective of this paper is to classify an object query against objects in a database, which leads to recognition of the former. 3D objects of this database are transformations of other objects by one element of the overall transformation. The set of transformations considered in this work is the general affine group.

Cite this paper
nullM. Elhachloufi, A. Oirrak, A. Driss and M. Mohamed, "3D Object Recognition by Classification Using Neural Networks," Journal of Software Engineering and Applications, Vol. 4 No. 5, 2011, pp. 306-310. doi: 10.4236/jsea.2011.45033.
References
[1]   T. Murao, “Descriptors of Polyhedral Data for 313-Shape Similarity Search,” Proposal P177, MPEG-7 Proposal Evaluation Meeting, Lancaster, February 1999.

[2]   M. Elad, A. Tal and S. Ar, “Directed Search in a 3D Objects Database Using SVM,” Hewlett-Packard Research Report HPL-2000-20R1, 2000.

[3]   C. Zhang and T. Chen, “Efficient Feature Extraction for 2D/3D Objects in Mesh Representation,” Proceeding of the International Conference on Image Processing (ICIP 2001), Thessaloniki, Greece, 2001.

[4]   T. Zaharia and F. Prêteux, “3D-Shape-Based Retrieval within the MPEG-7 Framework,” Proceeding SPIE Conference on Nonlinear Image Processing and Pattern Analysis XII, San Jose, Vol. 4304, 2001, pp. 133-145.

[5]   G. Taubin and D. B. Cooper, “Object Recognition Based on Moment (or Algebraic) Invariants,” In: J. L. Mundy and A. Zisserman, Eds., Geometric Invariants in Computer Vision, MIT Press, Cambridge, 1992.

[6]   S. J. Dickinson, D. Metaxas and A. Pentland, “The Role of Model-Based Segmentation in the Recovery of Volumetric Parts from Range Data,” IEEE Transactions on PAMI, Vol. 19, No. 3, 1997, pp. 259-267. doi:10.1109/34.584104

[7]   S. Dickinson, A. Pentland and S. Stevenson, “Viewpoint-?Invariant Indexing for Contentbased Image Retrieval,” Proceedings of the 1998 International Workshop on Content-Based Access of Image and Video Databases, Washington, 3 January 1998.

[8]   J. W. H. Tangelder and R. C. Veltkamp, “Polyhedral Model Retrieval Using Weighted Point Sets,” Rapport Technique No UU-CS-2002-019, Universitd de Utrecht, Pays-Bas, 2002.

[9]   P. V. C. Hough, “Method and Means for Recognizing Complex Patterns,” US Patent 3 069 654, 1962.

[10]   D. H. Ballard, “Generalizing the Hough Transform to Detect Arbitrary Shapes,” Pattern Recognition, Vol. 13, No. 2, pp. 111-122, 1981. doi:10.1016/0031-3203(81)90009-1

[11]   J. Illingworth and J. Kittler, “A Survey of the Hough Transform,” Computer Vision, Graphics and Image Processing, Vol. 44, No. 1, pp. 87-116, 1988. doi:10.1016/S0734-189X(88)80033-1

[12]   A. Benyettou, A. Mesbahi, H. Abdoune and A. Ait-ouali, “La Reconnaissance de Formes Spatio-Temporelles par les Réseaux de Neurones a Délais Temporels,” Conference Nationale sur L’Ingénierie de L’Electronique— CNIE’02, University USTOran, Algérie, 2002 .

[13]   B. Muller, J. Reinhardt and M. T. Strckland, “Neural Networks: An Introduction,” Springer-Verlag, Berlin, 1995.

[14]   R. Lepage and B. Solaiman, “Les Réseaux de Neurones Artificiels et Leurs Applications en Imagerie et en Vision par Ordinateur,” Ecole de Technologie Supérieure, 2003.

[15]   I. Khanfir, K. Taouil, M. S. Bouhlel and L. Kamoun, “Strategie de Traitement des Images de Lesions Derma- tologiques,” In: M. S. Bouhlel, B. Solaiman and L. Kamoun, Eds., Sciences Electronique, Technologies de L’Information et des Télécommunications, ISBN 9973- 41-685-6, 2003.

[16]   I. Maglogiannis, P. D. Koutsouris and D. Koutsouris, “An Integrated Computer Supported Acquisition, Handling, and Characterization System for Pigmented Skin Lesions in Dermatological Images,” IEEE Transactions on Information Technology in Biomedicine, Vol. 9, No. 1, March 2005, pp. 86-98. doi:10.1109/TITB.2004.837859

[17]   S. E. Fahlman, “An Empirical Study of Learning Speed in Backpropagation Networks,” Computer Science Department, Carhengie Mellon University, Pittsburgh, 1988.

[18]   F. Bloyo and M.Verleysen, “Les Réseaux de Neurones Artificiels,” Presse Universitaire de France, Paris, 1996.

 
 
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