Application of learning machine methods to 3D object modeling

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 2527, Issue , 2002, Pages 536-545

  García, Cristina ^a   Moreno, José Alí ^a  

^a UNIVERSIDAD CENTRAL DE VENEZUELA   (Venezuela)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; LEARNING SYSTEMS; SUPPORT VECTOR MACHINES; THREE DIMENSIONAL COMPUTER GRAPHICS; LEARNING ALGORITHMS;

3D OBJECT; 3D OBJECT MODELING; FEATURE MAP; KERNEL METHODS; KOHONEN; LEARNING MACHINES; OBJECT MODEL; MACHINE LEARNING ALGORITHMS;

LEARNING ALGORITHMS; THREE DIMENSIONAL;

EID: 17144373118     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/3-540-36131-6_55     Document Type: Conference Paper

References (12)

1. Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int. J. Of Computer Vision 2 (1998) 321-331
2. Gibson, S.F.F, Mirtich, B.: A Survey of Deformable Modeling in Computer Graphics. Mitsubishi Electric Research Laboratory Technical Report (1997)
3. Delingette, H.: Simplex Meshes: A General Representation for 3D Shape Reconstruction. Technical Report 2214, INRIA, France (1994)
4. Delingette, H.: General Object Reconstruction based on Simplex Meshes. Technical Report 3111, INRIA, France (1997)
5. Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines. Cambridge University Press (2000)
6. Kohonen, T.: Self-Organization Associative Memory.3rd edn.Springer-Verlag, Berlin (1989)
7. F ritzke, B.: Some Competitive Learning Methods. Institute for Neural Computation, Ruhr-Universität Bochum, Draft Report (1997)
8. Bro-Nielsen, B.: Active Nets and Cubes. Technical Report, Institute of Mathematical Modeling Technical University of Denmark (1994)
9. Metaxas, D.: Physics-based Deformable Models: Applications to Computer Vision, Graphics, and Medical Imaging. Kluwer Academic, Boston (1997)
10. Yoshino, K., Kawashima, T., Aoki, Y.: Dynamic Reconfiguration of Active Net Structure. Proc. Asian Conf. Computer Vision (1993) 159-162
11. Ben-Hur, A., Horn, D., Siegelmann, H. T., Vapnik, V.: A Support Vector Method for Clustering. International Conference on Pattern Recognition (2000)
12. Platt, J.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. http://www.research.microsoft.com/jplatt