A neural-network appearance-based 3-D object recognition using independent component analysis

IEEE Transactions on Neural Networks

Volumn 14, Issue 1, 2003, Pages 138-149

  Sahambi, Harkirat S ^a,b   Khorasani, K ^a  

^a Gina Cody School of Engineering and Computer Science   (Canada)

^b MCGILL UNIVERSITY   (Canada)

Author keywords

Appearance based object recognition; Blind source separation; Independent component analysis (ICA); Principle component analysis (PCA)

Indexed keywords

BLIND SOURCE SEPARATION; CHARGE COUPLED DEVICES; COMPUTER VISION; DATABASE SYSTEMS; INDEPENDENT COMPONENT ANALYSIS; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

APPEARANCE-BASED OBJECT RECOGNITION;

OBJECT RECOGNITION;

EID: 0037274255     PISSN: 10459227     EISSN: None     Source Type: Journal    
DOI: 10.1109/TNN.2002.806949     Document Type: Article

References (43)

1. S. Amari, "Natural gradient works efficiently in learning," Neural Comput., vol. 10, pp. 251-276, 1998.
2. S. Amari, A. Cichocki, and H. H. Yang, "A new learning algorithm for blind signal separation," in Advances in Neural Information Processing Systems 8, D. Touretzky, M. Mozer, and M. Hasselmo, Eds. Cambridge, MA: MIT Press, 1996, pp. 752-763.
3. S. Amari, S. C. Douglas, A. Cichocki, and H. H. Yang, "Multichannel blind deconvolution and equalization using the natural gradient," in Proc. IEEE Int. Workshop Wireless Commum., Apr. 1997, pp. 101-104.
4. A. H. Barr, "Superquadratics and angle preserving transformations," IEEE Comput. Graphics Applicat., vol. 1, no. 1, pp. 11-23, Jan. 1981.
5. M. S. Bartlett, H. M. Lades, and T. J. Sejnowski, "Independent component representations for face recognition," in Proc. SPIE Symp. Electron. Imaging: Science Technology; Conf. Human Vision Electronic Imaging III, San Jose, CA, 1998, pp. 528-539.
6. M. S. Bartlett and T. J. Sejnowski, "Viewpoint invariant face recognition using independent component analysis and attractor networks," in Advances in Neural Information Processing Systems 9, M. Mozer, M. Jordan, and T. Petsche, Eds. Cambridge, MA: MIT Press, 1997, pp. 817-823.
7. A. Bell and T. Sejnowski, "An information maximization approach to blind separation and blind deconvolution," Neural Comput., vol. 7, pp. 1129-1159, 1995.
8. _, "The independent components of natural scenes are edge filters," Vision Res., vol. 37, no. 23, pp. 3327-3338, 1997.
9. P. J. Besl and R. C. Jain, "Three-dimensional object recognition," Comput. Surveys, vol. 1, no. 1, pp. 11-23, 1985.
10. I. Biederman and P. Kalocsai, "Neurocomputational bases of object and face recognition," Philosophical Trans. Royal Soc.: Biol. Sci., vol. 352, pp. 1203-1219, 1997.
11. C. J. C. Burges, "A tutorial on support vector machines for pattern recognition," Data Mining Knowledge Discovery, vol. 2, no. 2, pp. 121-167, 1998.
12. R. T. Chin and C. R. Dyer, "Model-based recognition in robot vision," ACM Computi. Surveys, vol. 18, no. 1, 1986.
13. S. Choi and O. Lee, "Factorial code representation of faces for recognition," in Lecture Notes in Computer Science, 1811, Biologically Motivated Computer Vision, S. W. Lee, H. H. Bülthoff, and T. Poggio, Eds. New York: Springer-Verlag, 2000, pp. 42-51.
14. P. Comon, "Independent component analysis-a new concept?," Signal Processing, vol. 36, no. 3, pp. 287-314, 1994.
15. J. T. Feddema, C. S. G. Lee, and O. R. Mitchell, "Weighted selection of image features for resolved rate visual feedback control," IEEE Trans. Robot. Automat., vol. 7, pp. 31-47, Feb. 1991.
16. K. Fukunaga, Introduction to Statistical Pattern Recognition. London, U.K.: Academic, 1990.
17. D. P. Huttenlocher, R. H. Lilien, and C. F. Olson, "View-based recognition using an eigenspace approximation to the Hansdorff measure," IEEE Trans. Pattern Anal. Machine Intell., vol. 21, p. 951, Sept. 1999.
18. A. Hyvärinen, J. Karhunen, and E. Oja, Independent Component Analysis: Wiley, 2001.
19. A. Hyvärinen and E. Oja, "Simple neuron models for independent component analysis," Int. J. Neural Syst., 1997.
20. C. Jutten and J. Herault, "Blind separation of sources, part 1: an adaptive algorithm based on neuromimetic architecture," Signal Processing, vol. 24, no. 1, pp. 1-10, 1991.
21. P. Kalocsai and I. Biederman, "Differences of face and object recognition in utilizing early visual information," in Face Recognition: From Theory to Applications. sen NATO ASI Series F, H. Wechsler, P. J. Phillips, V. Bruce, F. Fogelman Soulie, and T. Huang, Eds. New York: Springer-Verlag, 1998.
22. D. Keren, D. Cooper, and J. Subrahmonia, "Describing complicated objects by implicit polynomials," IEEE Trans. Pattern Anal. Machine Intell., vol. 16, pp. 38-53, Jan. 1994.
23. S. Makeig, A. Bell, T. P. Jung, and T. J. Sejnowski et al., "Independent component analysis in electroencephalograghic data," in Advances in Neural Information Processing Systems, M. Mozer et al., Eds. Cambridge, MA: MIT Press, 1996, vol. 8, pp. 145-151.
24. H. Murase and S. K. Nayar, "Learning and recognition of 3d objects from appearance," in Proc. IEEE 2nd Qualitative Vision Workshop, New York, June 1993.
25. _, "Visual learning and recognition of 3-d objects from appearance," Int. J. Comput. Vision, vol. 14, no. 1, pp. 5-24, 1995.
26. V. S. Nalwa, A Guided Tour of Computer Vision. Reading, MA: Addison-Wesley, 1993.
27. S. K. Nayar, S. A. Nene, and H. Murase, "Real-time 100 object recognition system," in Proc. IEEE Int. Conf. Robot. Automat., 1996.
28. _, "Subspace methods for robot vision," IEEE Trans. Robot. Automat., vol. 12, p. 750, Oct. 1996.
29. S. A. Nene, S. K. Nayar, and H. Murase. (1996, Feb.) Columbia object image library (coil-20). Columbia University. [Online] http://www.cs.columbia.edu/CAVE/
30. E. Oja, Subspace Methods of Pattern Recognition, ser. Research Studies Press, U.K.: Letchworth, 1983.
31. A. Pentland, B. Moghaddam, and T. Starner, "View-based and modular eigenspaces," in Proc. IEEE Conf. Comput. Vision Pattern Recognition, 1994, pp. 84-91.
32. T. Poggio and S. Edelman, "A network that learns to recognize 3D objects," Nature, vol. 343, pp. 263-266, 1990.
33. M. Pontil and A. Verri, "Support vector machines for 3d object recognition," IEEE Trans. Pattern Anal. Machine Intell., vol. 20, pp. 637-646, June 1998.
34. A. R. Pope and D. G. Lowe, "Learning object recognition models from images," in Proc. 4th Int. Conf Comput. Vision, Berlin, Germany, May 1993, pp. 296-301.
35. E. M. Dos Santos and H. M. Gomes, "Appearance-based object recognition using support vector machines," in 2001 Proceedings of XIV Brazilian Symposium on Computer Graphics and Image Processing, D. L. Borges and W. Ting, Eds, Brazil, Oct., 2001, p. 399.
36. B. Schölkopf. (1997) Support Vector Learning [Online] http://www.kernel-machines.org/papers/book_ref.ps.gz
37. L. Sirovich and M. Kirby, "Low-dimensional procedure for the characterization of human faces," J. Opt. Soc. Amer., vol. 4, no. 3, pp. 519-524, Mar. 1987.
38. Y.-H. Tseng, J.-N. Hwang, and F. H. Sheeham, "Three-dimensional object representation and invariant recognition using continuous distance transform neural networks," IEEE Trans. Neural Networks, vol. 8, pp. 141-147, Jan. 1997.
39. M. Turk and A. Pentland, "Eigenfaces for recognition," J. Cognitive Neurosci., vol. 3, no. 1, pp. 71-86, 1991.
40. M. A. Turk and A. P. Pentland, "Face recognition using eigenfaces," in Proc. IEEE Computer Society Conf. Computer Vision Pattern Recognition, Maui, HI, 1991, pp. 586-591.
41. V. Vapnik, Statistical Learning Theory: Wiley, 1998.
42. L. E. Weiss, A. C. Sanderson, and C. P. Neuman, "Dynamic sensor-based control of robots with visual feedback," IEEE Trans. Robot. Automat., vol. RA-3, no. 5, pp. 404-417, Oct. 1987.
43. J. Weng, N. Ahuja, and T. S. Huang, "Learning recognition and segmentation of 3-D objects from 2-D images," in Proc. 4th Int. Conf. Computer Vision, Berlin, Germany, May 1993, pp. 121-128.