Superquadric-based geons recognition utilizing support vector machines

International Conference on Signal Processing Proceedings, ICSP

Volumn 2, Issue , 2004, Pages 1264-1267

  Xing, Weiwei ^a   Liu, Weibin ^a,b   Yuan, Baozong ^a  

^a BEIJING JIAOTONG UNIVERSITY   (China)

^b FUJITSU LABORATORIES LTD   (Japan)

Author keywords

Geons; Multi class classification; Superquadrics; SVMs

Indexed keywords

COMPUTATIONAL METHODS; FEEDBACK CONTROL; INFORMATION ANALYSIS; KNOWLEDGE BASED SYSTEMS; PARAMETER ESTIMATION; SURFACES; THREE DIMENSIONAL; VECTORS;

GEONS; MULTI-CLASS CLASSIFICATION; SUPERQUADRICS; SVM;

OBJECT RECOGNITION;

EID: 7744235733     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. I. Biederman, Recognition-by-Components: A theory of human image understanding, Psychological Review, 94, 115-147, 1987.
2. A. Barr, Superquadrics and angle-preserving transformations, IEEE CGA, 1(1), 11-23, 1981.
3. F. Solina, and R. Bajcsy, Recovery of parametric models from range images: the case for superquadrics with global deformation, PAMI, 12(2), 131-147, 1990.
4. A. Pentland. Perceptual organization and the representation of natural form, AI, 28(3), 293-331, 1986.
5. N. Raja and A. Jain. Recognizing geons from superquadrics fitted to range data, IVC, 10(3), 179-190, 1992.
6. K. Wu and M. Levine, 3D shape approximation using parametric geons, IVC, 15(2), 143-158, 1997.
7. L. Zhou and C. Kambhamettu, Representing and Recognizing Complete Set of Geons Using Extended Superquadrics, ICPR'2002, 713-718, 2002.
8. J. Platt, etc, Large margin DAGs for multi-class classification, Advances in Neural Information Processing Systems, 547-553, MIT Press, MA, 2000.
9. J. Platt, Fast training of support vector machines using sequential minimal optimization, Advances in kernel methods: support vector learning, 185-208, 1999.
10. V. Vapnik, Statistical Learning Theory, New York: Wiley-Interscience; 1998.
11. C. Cortes, V. Vapnik, Support vector networks, Machine Learn, 20, 273-297, 1995.
12. M. Pontil, A. Verri, Support vector machines for 3-d object recognition, PAMI, 20 (6), 637-646, 1998.
13. C. Hsu, C. Lin, A comparison of methods for multi-class support vector machines, IEEE Trans. on. Neural Networks, 13(2), 415-425, 2002.