A low complexity approximation of probabilistic appearance models

Pattern Recognition

Volumn 36, Issue 5, 2003, Pages 1107-1118

  Hamdan, Raouf ^a   Heitz, Fabrice ^a   Thoraval, Laurent ^a  

^a UNIVERSITÉ DE STRASBOURG   (France)

Author keywords

Appearance models; Fast algorithms; Maximum likehood decision; Object detection; Probabilistic models; Statistical image representation

Indexed keywords

APPROXIMATION THEORY; COMPUTER SIMULATION; PROBABILISTIC LOGICS;

PROBABILISTIC APPEARANCE MODELS;

FEATURE EXTRACTION;

PATTERN RECOGNITION;

EID: 0037405959     PISSN: 00313203     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0031-3203(02)00228-5     Document Type: Article

References (20)

1. B. Moghaddam, A. Pentland, Probabilistic visual learning for object detection, in: Proceedings of the International Conference on Computer Vision, Cambridge, MA, June 1995, pp. 687-694.
2. B. Moghaddam, A. Pentland, Probabilistic visual learning for object representation, IEEE Trans. Pattern Anal. Mach. Intell. 19 (7) (1997) 696-710.
3. M.E. Tipping, C.M. Bishop, Probabilistic principal component analysis, Technical Report NCRG/97/010, Neural Computing Research Group, Dept. of Computer Science & Applied Mathematics, Aston University, Birmingham, UK, 1997.
4. M.E. Tipping, C.M. Bishop, Mixtures of principal component analyzers, Technical Report NCRG/97/003, Neural Computing Research Group, Dept. of Computer Science & Applied Mathematics, Aston University, Birmingham, UK, 1997.
5. D. Lowe, Fitting parameterized 3D models to images, IEEE Trans. Pattern Anal. Mach. Intell. 13 (5) (1991) 441-450.
6. H. Murase, S.K. Nayar, Visual learning and recognition of 3-D objects from appearance, Int. J. Comput. Vision 14 (1) (1995) 5-24.
7. M. Turk, A. Pentland, Eigenfaces for recognition, J. Cognitive Neurosci. 3 (1) (1991) 71-86.
8. A.K. Jain, R. Duin, J. Mao, Statistical pattern recognition: a review, IEEE Trans. Pattern Anal. Mach. Intell. 22 (1) (2000) 4-37.
9. H. Schneiderman, T. Kanade, Statistical method for 3D object detection applied to faces and cars, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, SC, June 2000.
10. B. Moghaddam, Principal manifolds and probabilistic subspaces for visual recognition, IEEE Trans. Pattern Anal. Mach. Intell. 24 (6) (2002) 780-788.
11. B. Moghaddam, T. Jebara, A. Pentland, Bayesian modeling of facial similarity, in: Advances in Neural Information Processing Systems, Vol. 11, MIT Press, Cambridge, MA, 1999.
12. K. Fukunaga, Introduction to Statistical Pattern Recognition, Academic Press, New York, 1990.
13. G.H. Golub, C.F. Van Loan, Matrix Computations, John Hopkins Series in the Mathematical Sciences, John Hopkins University Press, Baltimore, MD, 1989.
14. W. Murase, M. Lindenbaum, Partial eigenvalue decomposition of large images using spatial temporal adaptive method, IEEE Trans. Image Process. 4 (5) (1995) 620-629.
15. A. Van-Der-Veen, E.F. Deprettere, A.L. Swindlehurst, Subspace-based signal analysis using singular value decomposition, Proc. IEEE 81 (9) (1993) 1277-1308.
16. H.C. Andrews, B.R. Hunt, Digital Image Restoration, Prentice-Hall, Englewood CliIs, NJ, 1977.
17. W. Pratt, Digital Image Processing, Wiley, New York, 1991.
18. R. Hamdan, F. Heitz, L. Thoraval, Gesture localization and recognition using probabilistic visual learning, in: IEEE International Conference on Computer Vision Pattern Recognition, Fort Collins, CO, June 1999.
19. B. Moghaddam, T. Jebara, A. Pentland, Bayesian face recognition, Pattern Recognition 33 (11) (2000) 1771-1782.
20. T. Minka, Automatic choice of dimensionalityfor PCA, Technical Report TR No 514, MIT, Medialab, Vision and Modeling Group, December 1999.