Active and adaptive vision: Neural network models

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

Volumn 1811, Issue , 2000, Pages 623-634

  Fukushima, Kunihiko ^a  

^a UNIVERSITY OF ELECTRO COMMUNICATIONS   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BINDING ENERGY; INFORMATION SCIENCE;

ADAPTIVE VISION; NEURAL NETWORK MODEL; RECEPTIVE FIELDS; SELECTIVE ATTENTION; SELF ORGANIZATIONS; SHIFT INVARIANT; VISUAL INFORMATION; VISUAL INFORMATION PROCESSING;

COMPUTER VISION;

EID: 84923529626     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45482-9_63     Document Type: Conference Paper

References (8)

1. K. Fukushima: “Neocognitron: a hierarchical neural network capable of visual pattern recognition”, Neural Networks, 1[2], pp. 119–130 (1988).
2. 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).
3. H. Hashimoto, K. Fukushima: “Recognition and segmentation of components of a face with selective attention” (in Japanese), Trans.IEICE D-II, J80-D-II[8], pp. 2194–2202 (Aug. 1997).
4. K. Fukushima: “Active vision: neural network models”, Brain-Like Computing and Intelligent Information Systems, eds.: N. Kasabov, S. Amari, pp. 3–24, Singapore: Springer-Verlag (1998).
5. K. Fukushima: “Neural network model for selective attention in visual pattern recognition and associative recall”, Applied Optics, 26[23], pp. 4985–4992 (Dec. 1987).
6. M. Kikuchi, K. Fukushima: “Neural network model of the visual system: Binding form and motion”, Neural Networks, 9[8], pp. 1417–1427 (Nov. 1996).
7. K. Fukushima: “Self-organization of shift-invariant receptive fields”, Neural Networks, 12[6], pp. 791–801 (July 1999).
8. P. Földiák: “Learning invariance from transformation sequences”, Neural Computation, 3, 194–200 (1991).