Abstract
To capture and process visual information flexibly and efficiently from changing external world, the function of active and adaptive information processing is indispensable. Visual information processing in the brain can be interpreted as a process of eliminating irrelevant information from a flood of signals received by the retina. Selective attention is one of the essential mechanisms for this kind of active processing. Selforganization of the neural network is another important function for flexible information processing. This paper introduces some neural network models for these mechanisms from the works of the author: such as “recognition of partially occluded patterns”, “recognition and segmentation of face with selective attention”, “binding form and motion with selective attention” and “self-organization of shift-invariant receptive fields”.
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References
K. Fukushima: “Neocognitron: a hierarchical neural network capable of visual pattern recognition”, Neural Networks, 1[2], pp. 119–130 (1988).
K. Fukushima, K. Nagahara, H. Shouno: “Training neocognitron to recognize handwritten digits in the real world”, pAs’97 (The Second Aizu International Symposium on Parallel Algorithms/Architectures Synthesis, Aizu-Wakamatsu, Japan), pp. 292–298 (March, 1997).
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K. Fukushima: “Self-organization of shift-invariant receptive fields”, Neural Networks, 12[6], pp. 791–801 (July 1999).
P. Földiák: “Learning invariance from transformation sequences”, Neural Computation, 3, 194–200 (1991).
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Fukushima, K. (2000). Active and Adaptive Vision: Neural Network Models. In: Lee, SW., Bülthoff, H.H., Poggio, T. (eds) Biologically Motivated Computer Vision. BMCV 2000. Lecture Notes in Computer Science, vol 1811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45482-9_63
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DOI: https://doi.org/10.1007/3-540-45482-9_63
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