AUTHORS: Fatima Belhabi, Mohamed Ettaouil

Download as PDF

ABSTRACT: In this paper, we present a new modeling for Mesh-size refinement with finite elements and artificial neural networks adopted by standards actual videos based on the SOM for image one domain, in the form of a structure. We developed in this study a mesh based on the object of interest by finite elements method and reduce the effort required to apply finite element analysis to image, this presentation that allows the identification of edges is a good representation of the movement of network nodes, and then we approach the follow-up of objects on sequences of Mesh-size refinement images. The algorithm of SOM the Kohonen is one of the important methods; it is a biologically inspired data clustering technique. It is a question of determining the Mesh adapte of an object nets, from one image to another. For that we used the algorithm allowing following a deformable plane object. On the one hand, we improve its performance, and then we study the optimization of the error function by error the Mesh-size refinement object simplification of our model, among the different meshes associated with images references. At the end of this work, we present simulation results.

KEYWORDS: Refinement mesh-size, Learning Kohonen SOM, Mesh-size by Finite Elements, deformation the Mesh-size refinement, interpolation.

REFERENCES:

[1] M. Ayache, M. Khalil and F. Tranquart. 'Artificial Neural Network for Transfer Function Placental Development: DCT and DWT Approach'. IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 5, No 3, September 2011.

[2] G. Dryfus, J.M.Martinez, M.Samuelides, M.B.Gordan, “Reseaux de neurons Mthodologie et applications“. EYRLLES, SC 924, 2000.

[3] M. Ettaouil, and Y.Ghanou, and K. Elmoutaouakil, M. Lazaar. “A New Architecture Optimization Model for the Kohonen Networks and Clustering“, Journal of Advanced Research in Computer Science (JARCS), Volume 3, Issue 1, 2011, pp. 14 - 32.

[4] M. Ettaouil, and Y. Ghanou, and K. El Moutaouakil, M. Lazaar, “Image Medical Compression by A new Architecture Optimization Model for the Kohonen Networks“. International Journal of Computer Theory and Engineering vol. 3, no. 2, 2011, pp. 204-210.

[5] M. Ettaouil, and K. Elmoutaouakil, and Y.Ghanou. “The continuous hopfield networks (CHN) for the placement of the electronic circuits problem “, WSEAS Transactions on Computer,Volume 8 Issue 12, December 2009.

[6] M. Ettaouil, and E.Abdlatif and F.Belhabib and K. Elmoutaouakil. “Learning Algorithm of Kohonen Network With Selection Phase “, WSEAS Transactions on Computer,Volume 11 Issue 11, December 2012.

[7] M. ETTAOUI and M. LAZAAR, 'Improved Self-Organizing Maps and Speech Compression', International Journal of Computer Science Issues (IJCSI), Volume 9, Issue 2, No 1, pp. 197-205, 2012.

[8] A. Ghosh, and S.K. Pal, Object Background classification using Hopfield type neural networks. International Journal of Patten Recognition and artificial Intelligence, 1992, pp. 989-1008.

[9] S. Ghorbel, M. Ben Jemaa and M. Chtourou. 'Object-based Video compression using neural networks'. IJCSI International Journal of Comput er Science Issues, Vol. 8, Issue 4, No 1, July 2011.

[10] J.J. Hopfield, Neurons with graded response have collective computational properties like those of two-states neurons. proceedings of the National academy of sciences of the USA 81 ,1984,pp. 3088-3092.

[11] C. C. Hsu, Generalizing Self-Organizing Map for Categorical Data.IEEE Transactions on neural networks, Vol. 17, No. 2, 2006.,pp. 294- 304.

[12] S .Marsland, S.U .Nehmzow,., and Shapiro, J., “A self-organizing network that grows when required”, in Neural Networks, Volume 15, 2002, Issue 8-9, pp.1041-1058.

[13] N. Nasrabadi and Y. Feng, ”Vector quantization of images based upon the Kohonen self-organizing feature maps”, in IEEE Int. Conf. Neural Networks, San Diego, CA, vol. 1, pp. 101-108, 1988.

[14] T. Kohonen. Self Organizing Maps. Springer, 3e edition, 2001.

[15] H. Shah-Hosseini, and R. Safabakhsh. TASOM: The Time Adaptive Self-Organizing Map. The International Conference on Information Technology: Coding and Computing (ITCC’00), 2000, 422.

[16] H. Yin. “ViSOMA Novel Method for Multivariate Data Projection and Structure Visualization“. IEEE Transactions on Neural Networks, Vol 13, 2002, 1,pp. 237-243.

[17] www.ics.uci.edu/mlearn/MLRepository.html.

[18] V. Selvi, R.Umarani ‘’ Comparative analysis of ant colony and Particle Swarm optimization techniques ‘’ International journal of Computer Application Volume 5-No.4, August 2010,pp.0975-8887

[19] D. Wang, ‘Fast Constructive-Coverting Algorithm for neural networks and its implement in classification’, Applied Soft Computing 8 ,2008, pp. 166-173.

[20] D. Wang, N.S. Chaudhari, ‘A constructive unsupervised learning algorithm for Boolean neural networks based on multi-level geometrical expansion’, Neuro computing 57C, 2004, pp.455-461.

[21] M.C. Rivara, Local modification of meshes for adaptive and/or Multigrid finite-element methods, J. Comput. T. Meinders, Developments in numerical simulations of the real life deep drawing process, Ph.D. Thesis, U Ponsen & Looijen Wageningen (publ.), ISBN 90-36514002, 2000.

[22] G. ROBERT, Reprèsentation et codage De sèquences vidèo Par hybridation de fractales et d èlèments finis. PhD thesis, Universitè Joseph Fourier-Grenoble 1 Sciences Géographie, 2000.

[23] C. Toklu. Object-based Digital Video Processing using 2-D Meshes. PhD Thesis University of Rochester, USA, 1998.

[24] Y. Wang and O. Lee. Active mesh : “A feature seeking and tracking image sequence representation scheme”. IEEE Transactions on Image Processing,3(5) :, 1994,pp 610–624.

[25] O.C. ZienKiewicz, ” La méthode des éléments finis ”, 1973, Edi-science, Paris.

[26] P.L George. “Génération Automatique de maillage- Applications aux méthodes d’éléments Finis“. Masson 1991.

[27] K.HO-Le, “Finite elemnt mesh generation methods: Areview and classification l comput. Aidet Deseign 20(1), January 1988, pp 27-38.

[28] ISO/IEC 14496-10 and ITU-T Rec. “H.264. Advanced Video Coding”. 2003.

[29] H. B. Jung and K. Kim, “A New Parameterisation Method for NURBS Surface Interpolation”, The International journal of Advanced Manufacturing Technology, Vol. 16, 2000, p. 784-790.

[30] F. Jurie and M. Dhome. “Hyperplane approximation for template matching”. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 24(7), 2002, pp996 1000,.

[31] Iain E. G. Richardson. H.264 and MPEG-4 “Video Compression: Video Coding for Nextgeneration Multimedia”. Ed. John Wiley & Sons, 2003.

[32] J.A. Robinson and M.A. Ren. Data-dependant sampling of twodimentional signals.Multidimensional Systems and Signal Processing, 6, 1995, 89–111.

[33] A. Perera, C-L. Tsai, R. Flatland et C. Stewart. Maintaining valid topology with active contours : theory and application. IEEE International Conference on Computer Vision .

[34] G. Turk. “Re-tiling polygonal surfaces”. Computer,Graphics SIGGRAPH ’92 Proceedings), 26(2):, July 1992,pp 55–64.