Abstract
Three different machine learning algorithms applied to 3D object modeling are compared.The methods considered, (Support Vector Machine, Growing Grid and Kohonen Feature Map) were compared in their capacity to model the surface of several synthetic and experimental 3D objects.The preliminary experimental results show that with slight modifications these learning algorithms can be very well adapted to the task of object modeling.In particular the Support Vector Machine Kernel method seems to be a very promising tool.
This research was supported by the Fondo Nacional de Ciencia, Tecnología e Innovación (FONACIT) under project G-97000651.
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References
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Of Computer Vision 2 (1998) 321–331
Gibson, S.F.F, Mirtich, B.: A Survey of Deformable Modeling in Computer Graphics. Mitsubishi Electric Research Laboratory Technical Report (1997)
Delingette, H.: Simplex Meshes: A General Representation for 3D Shape Reconstruction. Technical Report 2214, INRIA, France (1994)
Delingette, H.: General Object Reconstruction based on Simplex Meshes. Technical Report 3111, INRIA, France (1997)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press (2000)
Kohonen, T.: Self-Organization Associative Memory.3rd edn.Springer-Verlag, Berlin (1989)
Fritzke, B.: Some Competitive Learning Methods. Institute for Neural Computation, Ruhr-Universität Bochum, Draft Report (1997)
Bro-Nielsen, B.: Active Nets and Cubes. Technical Report, Institute of Mathematical Modeling Technical University of Denmark (1994)
Metaxas, D.: Physics-based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging.Kluwer Academic, Boston (1997)
Yoshino, K., Kawashima, T., Aoki, Y.: Dynamic Reconfiguration of Active Net Structure. Proc.Asian Conf.Computer Vision (1993) 159–162
Ben-Hur, A., Horn, D., Siegelmann, H. T., Vapnik, V.: A Support Vector Method for Clustering.In ternational Conference on Pattern Recognition (2000)
Platt, J.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. http://www.research.microsoft.com/ jplatt
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García, C., Alí Moreno, J. (2002). Application of Learning Machine Methods to 3D Object Modeling. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_55
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DOI: https://doi.org/10.1007/3-540-36131-6_55
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