1993 Fiscal Year Final Research Report Summary
A Neural Network for Recognizing Rotation, Translation and Scale-Change Transformation of Patterns
Project/Area Number |
04650332
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Research Category |
Grant-in-Aid for General Scientific Research (C)
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Allocation Type | Single-year Grants |
Research Field |
情報工学
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Research Institution | FUKUOKA INSTITUTE OF TECHNOLOGY |
Principal Investigator |
SUZAKI Kenichi Department of Communication and Computer Engineering, Fukuoka Institute of Technology, Assistant Professor, 工学部・情報工学科, 助教授 (40148903)
|
Project Period (FY) |
1992 – 1993
|
Keywords | neural Network / back-propagation method / rotation-invariant / translation-invariant / scale-change-invariant / copy-learning / three-layr / recognition |
Research Abstract |
In character recognition, certain basic patterns need to be recognized correctly as the same patterns even if the patterns undergo transformations such as (1) scale-change, (2) translation, and (3) rotation. We developed three type of neural network model capable of learning and recognizing scale-change, translation and rotation of patterns. They are (1) A Rotation Invariant Learning Model ; (2) A Pattern Rotation Model ; (3) A Copy-Learning Model. These are all three layr neural networks. Each of the three models features a simple network structure of a compact size, and a substantially reduced time required for learning and recognition.
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Research Products
(12 results)