Applying Slow Feature Analysis to image sequences yields a rich repertoire of complex cell properties

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2415 LNCS, Issue , 2002, Pages 81-86

  Berkes, Pietro ^a   Wiskott, Laurenz ^a  

^a HUMBOLDT UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX NETWORKS; NEURAL NETWORKS; COMPUTER VISION;

COMPLEX CELLS; IMAGE SEQUENCE; NATURAL IMAGES; PRIMARY VISUAL CORTEX; RECEPTIVE FIELDS; SLOW FEATURE ANALYSIS; SLOW FEATURE ANALYSIS(SFA); SPATIAL TRANSFORMATION;

IMAGE ANALYSIS;

EID: 84902137011     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-46084-5_14     Document Type: Conference Paper

References (13)

1. Földiák, P.: Learning invariance from transformation sequences. Neural Computation 3 (1991) 194-200
2. Stone, J.V.: Learning perceptually salient visual parameters using spatiotemporal smoothness constraints. Neural Computation 8 (1996) 1463-1492
3. Kayser, C., Einhäuser, W., Dümmer, O., König, P., Körding, K.: Extracting slow subspaces from natura1 Videos leads to complex cells. In: Artificial Neural Networks - ICANN 2001 Proceedings, Springer (2001) 1075-1080
4. Wiskott, L., Sejnowski, T.: Slow feature analysis: Unsupervised learning of invariarnces. Neural Computation 14 (2002) 715-770
5. Wiskott, L.: Learning invariance manifolds. In: Proc. Computational Neuro-science Meeting, CNS'98, Santa Barbara. (1999) Special issue of Neurocomputing, 26/27:925-932.
6. van Hateren J.H., van der Schaaf A.: Independent component filters of natural images compared with simple cells in primary visual cortex. Proc. R. Sec. Lond. B (1998) 359-366
7. Hubel, D., Wiesel, T.: Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology 160 (1962) 106-154
8. Mechler, F., Ringach, D.L.: On the classification of simple and complex cells. Vision Research (2002) 1017-1033
9. Hyvärinnen, A., Hoyer, P.: Emergence of Phase and shift invariant features by decomposition of natura1 images into independent features subspaces. Neural Computation 12 (2000) 1705-1720
10. De Valois, R., Yund, E., Hepler, N.: The orientation and direction selectivity of cells in macaque visual cortex. Vision Res. 22 (1982) 531-44
11. De Valois, R., Albrecht, D., Thorell, L.: Spatial frequency selectivity of cells in macaque visual cortex. Vision Res. 22 (1982) 545-559
12. Schiller, P., Finlay, B., Volman, S.: Quantitative studies of single-cell properties in monkey striate cortex. 1. Spatiotemporal organization of receptive fields. J. Neurophysiol. 39 (1976) 1288-1319
13. Krüger, N., Peters, G.: Object recognition with banana wavelets. In: Proceedings of the ESANN97. (1997)