3D Object Model Reconstruction Based on Polywogs Wavelet Network Parametrization
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3D Object Model Reconstruction Based on Polywogs Wavelet Network Parametrization

Authors: Mohamed Othmani, Yassine Khlifi


This paper presents a technique for compact three dimensional (3D) object model reconstruction using wavelet networks. It consists to transform an input surface vertices into signals,and uses wavelet network parameters for signal approximations. To prove this, we use a wavelet network architecture founded on several mother wavelet families. POLYnomials WindOwed with Gaussians (POLYWOG) wavelet families are used to maximize the probability to select the best wavelets which ensure the good generalization of the network. To achieve a better reconstruction, the network is trained several iterations to optimize the wavelet network parameters until the error criterion is small enough. Experimental results will shown that our proposed technique can effectively reconstruct an irregular 3D object models when using the optimized wavelet network parameters. We will prove that an accurateness reconstruction depends on the best choice of the mother wavelets.

Keywords: 3D object, optimization, parametrization, Polywog wavelets, reconstruction, wavelet networks.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1125325

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[1] Amir Abolfazl Suratgar, Mohammad Bagher Tavakoli, and Abbas Hoseinabadi. Modified Levenberg-Marquardt Method for Neural Networks Training. World Academy of Science, Engineering and Technology 6 2005.
[2] A. Waleed Mahmud, A. Mayce Ibrahim, M. Talib Jawad, Image Reconstruction Using Multi-activation Wavelet Network Australian Journal of Basic and Applied Sciences, 6(6): 410-417, 2012
[3] Becerikli Y, Oysal Y, Konar AF (2003) On a dynamic wavelet network and its modeling application. Lect Notes Comput Sci (LNCS) 2714:710718
[4] E. Franchini, S. Morigi, and F. Sgallari, Implicit shape reconstruction of unorganized points using PDE-based deformable 3D manifolds, Numerical Mathematics: Theory, Methods and Applications, 2010.
[5] Golub, G. H., Van Loan, C.F. : Matrix Computations, Baltimore, MD: John Hopkins University Press, 2nd ed, 1989.
[6] J. Jin, M. Dai, H. Bao, and Q. Peng. Watermarking on 3d mesh based on spherical wavelet transform. Journal of Zhejiang University Science, 5(3), pp. 251258, 2004.
[7] K. Zhou, H. Bao, and J. Shi. 3d surface filtering using spherical harmonics. ComputerAided Design, 36(4), pp. 363375, 2004.
[8] M. Othmani, W. Bellil, C. Ben Amar and Adel M. Alimi. A new structure and training procedure of multi–mother wavelet network. International Journal of Wavelets, Multiresolution and Information Processing:World Scientific Publishing Company, 8, 2010.
[9] M. Othmani, W. Bellil, C. Ben Amar and Adel M. Alimi. 3D object modeling using multi-mother wavelet network. 2010 IEEE/ACS International Conference on Computer Systems and Applications (AICCSA).
[10] Q.Zhang. Using wavelet network in nonparametric estimation. IEEE Transactions on Neural Networks, 8, 1997.
[11] R. Paulsen, J. Baerentzen, and R. Larsen, Markov random field surface reconstruction, IEEE Transactions on Visualization and Computer Graphics, pp. 636646, 2009.
[12] Ruqin Zhang, Eliot Winer and James H. Oliver. Subdivision-Based 3D Remeshing With a Fast Spherical Parameterization Method. Volume 3: 30th Computers and Information in Engineering Conference, pp. 1039-1048, 2010.
[13] Titsias, M.K.,Likas, A.C. Shared kernel model for class conditional density estimation. IEEE transaction on Neural Network, 12, 2001.
[14] Vera, S., Miguel A. Gonzalez Ballester, Debora Gil, Anatomical Parameterization for Volumetric Meshing of the Liver, Medical Imaging 2014: Image-Guided Procedures, Robotic Interventions, and Modeling, 2014.
[15] W. Sweldens and P. Schrder. Using wavelet network in nonparametric estimation. Digital geometric signal processing, course notes 50. In SIGGRAPH 2001 Conference Proceedings, 2001.
[16] Yihua Yan, Bo Peng, Xizhen Zhang, ”Noise Suppression with Wavelets in Image Reconstruction for Aperture Synthesis”, Beijing Astronomical Observatory, Chinese Academy of Sciences, 2007.