S
Sigeru Omatu
Researcher at Hiroshima University
Publications - 129
Citations - 1459
Sigeru Omatu is an academic researcher from Hiroshima University. The author has contributed to research in topics: Artificial neural network & Adaptive control. The author has an hindex of 18, co-authored 129 publications receiving 1394 citations. Previous affiliations of Sigeru Omatu include Universiti Teknologi Malaysia & California Institute of Technology.
Papers
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Journal ArticleDOI
Rotation-invariant neural pattern recognition system with application to coin recognition
TL;DR: A neural pattern recognition system which is insensitive to rotation of input pattern by various degrees is proposed and was used in a rotation-invariant coin recognition problem to distinguish between a 500 yen coin and a 500 won coin.
Book
Distributed parameter systems: theory and applications
Sigeru Omatu,John H. Seinfeld +1 more
TL;DR: In this paper, a formal approach to optimal filtering and control of distributed parameter systems is presented. But it does not address the problem of finding the optimal filter for a distributed parameter system.
Proceedings Article
Neural network approach to land cover mapping
T. Yoshida,Sigeru Omatu +1 more
TL;DR: In this paper, a pattern classification method is proposed for remote sensing data using neural networks, where the training data set is selected based on geographical information and Kohonen's self-organizing feature map.
Journal ArticleDOI
Neural network approach to land cover mapping
T. Yoshida,Sigeru Omatu +1 more
TL;DR: In this article, a pattern classification method is proposed for remote sensing data using neural networks, where the training data set is selected based on geographical information and Kohonen's self-organizing feature map.
Patent
Pattern recognition apparatus and method of optimizing mask for pattern recognition according to genetic algorithm
TL;DR: In this article, column masks are used to mask a large number of strip-shaped segments, and some of these segments are masked with column areas of masks, which can be used to reduce the scale of the neural network and control system.