A Study on the Influence of Wavelet Number Change in the Wavelet Neural Network Architecture for 3D Mesh Deformation Using Trust Region Spherical Parameterization
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-01421-6_52
Reference14 articles.
1. Kshirsagar, S., Garchery, S., Magnenat Thalmann, N.: Feature point based mesh deformation applied to MPEG-4 facial animation. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) The International Federation for Information Processing, vol. 68. Springer, Boston (2001). https://doi.org/10.1007/978-0-306-47002-8_3
2. Dhibi, N., Elkefi, A., Bellil, W., Amar, C.B.: 3D High resolution mesh deformation based on multi library wavelet neural network architecture. 3D Res. 7, 31 (2016). https://doi.org/10.1007/s13319-016-0107-6
3. Othmani, M., Bellil, W., Amar, C.B., Alimi, M.A.: A novel approach for high dimension 3D object representation using multi-mother wavelet network. Int. J. Multimedia Tools Appl. MTAP 59(1), 7–24 (2012). https://doi.org/10.1007/s11042-010-0697-6
4. Dhibi, N., Bellil, W., Amar, C.B.: Study implementation of a new training algorithm for wavelet network based on genetic algorithm and multiresolution analysis for 3D objects modeling. In: IEEE Mediterranean Electrotechnical Conference (2012)
5. Lecture Notes in Computer Science;N Dhibi,2015
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