title: Increasing robustness against background noise: visual pattern recognition by a neocognitron.
abstract: The neocognitron is a hierarchical multi-layered neural network capable of robust visual pattern recognition. It has been demonstrated that recent versions of the neocognitron exhibit excellent performance for recognizing handwritten digits. When characters are written on a noisy background, however, recognition rate was not always satisfactory. To find out the causes of vulnerability to noise, this paper analyzes the behavior of feature-extracting S-cells. It then proposes the use of subtractive inhibition to S-cells from V-cells, which calculate the average of input signals to the S-cells with a root-mean-square. Together with this, several modifications have also been applied to the neocognitron. Computer simulation shows that the new neocognitron is much more robust against background noise than the conventional ones.
authors.affiliation: Fuzzy Logic Systems Institute, Iizuka, Fukuoka, Japan. fukushima@m.ieice.org
authors.forename: Kunihiko
authors.initials: K
authors.lastname: Fukushima
mesh_terms: D001185:Artificial Intelligence; D003071:Cognition; D003198:Computer Simulation; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010364:Pattern Recognition, Visual