Increasing robustness against background noise: Visual pattern recognition by a neocognitron

Neural Networks

Volumn 24, Issue 7, 2011, Pages 767-778

  Fukushima, Kunihiko ^a  

^a FUZZY LOGIC SYSTEMS INSTITUTE   (Japan)

Author keywords

Background noise; Feature extraction; Neocognitron; Root mean square; Subtractive inhibition; Visual pattern recognition

Indexed keywords

BACKGROUND NOISE; NEOCOGNITRON; ROOT MEAN SQUARES; SUBTRACTIVE INHIBITION; VISUAL PATTERN RECOGNITION;

CHARACTER RECOGNITION; COMPUTER SIMULATION; FEATURE EXTRACTION; NETWORK LAYERS;

FEEDFORWARD NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER SIMULATION; CONTROLLED STUDY; DATA EXTRACTION; INTERMETHOD COMPARISON; NEOCOGNITRON; NOISE; PRIORITY JOURNAL;

ARTIFICIAL INTELLIGENCE; COGNITION; COMPUTER SIMULATION; MODELS, NEUROLOGICAL; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PATTERN RECOGNITION, VISUAL;

EID: 79960890980     PISSN: 08936080     EISSN: 18792782     Source Type: Journal    
DOI: 10.1016/j.neunet.2011.03.017     Document Type: Article

References (23)

1. Carandini M., Demb J.B., Mante V., Tolhurst D.J., Dan Y., Olshausen B.A., Gallant J.L., Rust N.C. Do we know what the early visual system does?. Journal of Neuroscience 2005, 25(46):10577-10597.
2. Cardoso Â, Wichert A. Neocognitron and the map transformation cascade. Neural Networks 2010, 23:74-88.
3. Eccles J.C. The physiology of synapses 1964, Springer-Verlag, Berlin, Heidelberg, New York.
4. Elliffe M.C.M., Rolls E.T., Stringer S.M. Invariant recognition of feature combinations in the visual system. Biological Cybernetics 2002, 86:59-71.
5. Fukushima K. Cognitron: a self-organizing multilayered neural network. Biological Cybernetics 1975, 20(3/4):121-136.
6. Fukushima K. Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 1980, 36(4):193-202.
7. Fukushima K. Neocognitron: a hierarchical neural network capable of visual pattern recognition. Neural Networks 1988, 1(2):119-130.
8. Fukushima K. Neocognitron for handwritten digit recognition. Neurocomputing 2003, 51:161-180.
9. Fukushima K. Neocognitron trained with winner-kill-loser rule. Neural Networks 2010, 23(7):926-938.
10. Fukushima K. Increased robustness against background noise: pattern recognition by a neocognitron. LNCS 2010, Vol. 6444:574-581. Springer-Verlag, Berlin, Heidelberg.
11. Fukushima K., Miyake S. Neocognitron: a new algorithm for pattern recognition tolerant of deformations and shifts in position. Pattern Recognition 1982, 15(6):455-469.
12. Fukushima K., Tanigawa M. Use of different thresholds in learning and recognition. Neurocomputing 1996, 11(1):1-17.
13. Hebb D.O. Organization of behavior 1949, John Wiley & Sons, New York, London, Sydney.
14. Heeger D.J. Normalization of cell responses in cat striate cortex. Visual Neuroscience 1992, 9:181-197.
15. Hildebrandt T.H. Optimal training of thresholded linear correlation classifiers. IEEE Transactions on Neural Networks 1991, 2(6):577-588.
16. Hubel D.H., Wiesel T.N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of physiology (London) 1962, 106(1):106-154.
17. Hubel D.H., Wiesel T.N. Receptive fields and functional architecture in two nonstriate visual areas (18 and 19) of the cat. Journal of Neurophysiology 1965, 28(2):229-289.
18. LeCun Y., Boser B., Denker J.S., Henderson D., Howard R.E., Hubbard W., Jackel L.J. Backpropagation applied to handwritten zip code recognition. Neural Computation 1989, 1:541-551.
19. LeCun Y., Bottou L., Bengio Y., Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE 1998, 86(11):2278-2324.
20. Lo S.B., Chan H., Lin J., Li H., Freedman M.T., Mun S.K. Artificial convolution neural network for medical image pattern recognition. Neural Networks 1995, 8(7/8):1201-1214.
21. Ohzawa I., DeAngelis G.C., Freeman R.D. Stereoscopic depth discrimination in the visual cortex: neurons ideally suited as disparity detectors. Science 1990, 249(4972):1037-1041.
22. Riesenhuber M., Poggio T. Hierarchical models of object recognition in cortex. Nature Neuroscience 1999, 2(11):1019-1025.
23. Satoh S., Kuroiwa J., Aso H., Miyake S. Recognition of rotated patterns using a neocognitron. Knowledge based intelligent techniques in character recognition 1999, 49-64. CRC Press. L.C. Jain, B. Lazzerini (Eds.).