A Neural Network for Recognizing Rotation, Translation and Scale-Change Transformation of Patterns
Project/Area Number |
04650332
|
Research Category |
Grant-in-Aid for General Scientific Research (C)
|
Allocation Type | Single-year Grants |
Research Field |
情報工学
|
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
|
Project Status |
Completed (Fiscal Year 1993)
|
Budget Amount *help |
¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 1993: ¥600,000 (Direct Cost: ¥600,000)
|
Keywords | neural Network / back-propagation method / rotation-invariant / translation-invariant / scale-change-invariant / copy-learning / three-layr / recognition / 誤差〓伝播法 / 回転,位置ずれ,拡大縮小 / 学習・並びに認識 / パターン認識 / 複写学習モデル / パターン回転モデル / 回転マトリックス / パターン作成 |
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.
|
Report
(3 results)
Research Products
(24 results)