Attention can improve a simple model for object recognition

Image and Vision Computing

Volumn 26, Issue 6, 2008, Pages 776-787

  Bermudez Contreras, E ^a   Buxton, H ^a   Spier, E ^a  

^a UNIVERSITY OF SUSSEX   (United Kingdom)

Author keywords

Active vision; Attention; Foveation; HMAX; Object recognition; Scale invariance; Translation invariance; Visual cortex

Indexed keywords

COMPUTER VISION; HIERARCHICAL SYSTEMS; MATHEMATICAL MODELS; NEUROLOGY;

ACTIVE VISION; SCALE INVARIANCE; TRANSLATION INVARIANCE; VISUAL CORTEX;

OBJECT RECOGNITION;

EID: 40949107784     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2007.08.014     Document Type: Article

References (32)

1. In: Aloimonos Y. (Ed). Active Perception (1993), Erlbaum, Hillsdale, NJ
2. Ballard D.H. Animate vision. Artificial Intelligence (1991)
3. E. Bermudez, A biologically inspired solution for an evolved simulated agent, in: Accepted for the Genetic and Evolutionary Computation Conference (GECCO). ACM, July 2007.
4. A. Bernardino, J. Santos-Victor, A binocular stereo algorithm for log-polar foveated systems, in: 2nd Workshop on Biological Motivated Computer Vision, BMCV 2002, 2002, pp. 127-136.
5. Booth M.C., and Rolls E.T. View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. Cerebral Cortex 8 (1998) 510-523
6. Deco G., and Sing Lee T. The role of early visual cortex in visual integration: a neural model of recurrent interaction. European Journal of Neuroscience 20 (2004) 1089-1100
7. S. Duvdevani-Bar, Shimon Edelman, A.J. Howell, H. Buxton, A similarity-based method for the generalization of face recognition over pose and expression, in: Proc. 3rd IEEE International Conference on Automatic Face & Gesture Recognition (FG'98), Tara, Japan, 1998, pp. 118-123.
8. Edelman S., and Duvdevani-Bar S. A model of visual recognition and categorization. Phil. Trans. R Soc. Lond. B 352 1358 (1997) 1191-2002
9. Floreano D., Kato T., Marocco D., and Sauser E. Coevolution of active vision and feature selection. Biological Cybernetics 90 (2004) 218-228
10. J. Howell, H. Buxton, Receptive fields functions for face recognition, in: Proc. 2nd International Workshop on Parallel Modelling of Neural Operators for Pattern Recognition, November 1995, pp 221-226.
11. Hubel D.H., and Weisel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology 160 (1962) 106-154
12. Hung C.P., Kreiman G., Poggio T., and DiCarLo J.J. Fast readout of object identity from macaque inferior temporal cortex. Science 310 (2005) 863-866
13. Itti L., and Koch C. Computational modelling of visual attention. Nature 2 (2001) 194-203
14. Land M.F., and Nilsson D.E. Animal Eyes (2002), Oxford University Press
15. Laughlin S., and Sejnowski T. Communication in neuronal networks. Science 301 5641 (2003) 1870-1874
16. Sing Lee T. Analysis and synthesis of visual images in the brain: evidence for pattern theory. In: Olver P., and Tannenbaum A. (Eds). Image Analysis and Higher Level Vision. Lecture notes in Mathematics and its Application (2003), Springer-Verlag 87-106
17. T. Lindenberg, A framework for handling image structures at multiple scales, in: CERN School of Computing, 1996, pp. 8-21.
18. Logothetis N., Pauls J., Bulthoff H., and Poggio T. View dependent object recognition by monkeys. Curr. Biol. 4 (1994) 401-414
19. M. Orr, Introduction to radial basis functions networks, Technical report, Centre of Cognitive Science, University of Edinburgh, , 1996.
20. Paletta L., Rome E., and Buxton H. Attention architectures for machine vision and mobile robots. Neurobiology of Attention (2005) 642-648
21. Poggio T., and Edelman S. A network that learns to recognize 3-d objects. Nature 343 (1990) 263-266
22. Riesenhuber M., and Poggio T. Are cortical models really bound by the "binding problem"?. Neuron 24 (1999) 87-93
23. Riesenhuber M., and Poggio T. Hierarchical models of object recognition in cortex. Nature Neuroscience 2 11 (1999) 1019-1025
24. Riesenhuber M., and Poggio T. Models of object recognition. Nature Neuroscience 3 (2000) 1199-1204
25. M. Riesenhuber, T. Poggio, How visual cortex recognizes objects: the tale of the standard model, in: The Visual Neurosciences, MIT Press, 2003.
26. R. Schneider, M. Riesenhuber, On the difficulty of feature-based attentional modulations in visual object recognition: a modeling study, Technical Report Memo 2004-004, Massachusetts Institute of Technology, February 2004, ftp://publications.ai.mit.edu/ai-publications/2004/AIM-2004-004.pdf.
27. T. Serre, M. Kouh, C. Cadieu, U. Knoblich, G. Kreiman, T. Poggio, A theory for object recognition: computations and circuits in the feedforward path of the ventral visual stream in primate visual cortex, Cbcl paper #259/ai memo #2005-036, Massachusetts Institute of Technology, October 2005.
28. T. Serre, L. Wolf, T. Poggio, Object recognition with features inspired by visual cortex, in: Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Patter Recognition (CVPR), June 2005.
29. Spier E. Behavioural categorisation: behaviour makes up bad vision. Artificial Life IX: Proceedings of the Ninth International Conference on the Simulation and Synthesis of Living Systems (2004) 133-139
30. D. Walther, L. Itti, M. Riesenhuber, T. Poggio, C. Koch, Attentional selection for object recognition - a gentle way, in: S.-W. Lee, H.H. Buelthoff, T. Poggio (Eds.), Second IEEE International Workshop, Biologically Motivated Computer Vision, 2002, pp. 387-397.
31. A. Witkin, Scale-space filtering, in: International Joint Conf. on Artificial Intelligence, 1983, pp. 1019-1022.
32. Zhaoping L. A saliency map in primary visual cortex. Trends in Cognitive Science 6 1 (2002) 9-16