2002 Fiscal Year Final Research Report Summary
Precision Motion Detection Algorithm using Neural Networks
| Project/Area Number |
13650411
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
情報通信工学
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| Research Institution | EHIME UNIVERSITY |
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Principal Investigator |
YAMADA Yoshio Ehime University, Engineering (Dept.Electrical and Electronic Eng.,) Professor, 工学部, 教授 (00110833)
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| Co-Investigator(Kenkyū-buntansha) |
MINAMI Noriaki Hiroshima Kokusai Gakuin University, Faculty of Contemporary Sociology, Associate Professor, 現代社会学部, 助教授 (60320024)
TSUZUKI Shinji Ehime University, Engineering (Dept.Electrical and Electronic Eng.,) Associate Professor, 工学部, 助教授 (60236924)
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| Project Period (FY) |
2001 – 2002
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| Keywords | Template Matching / Motion Estimation / Neural Network / Cross Correlation / Peak Detection / White Gaussian Noise |
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| Research Abstract |
Template matching is one of the essential image processing techiniques used in the areas such as pattern recoginition and image analysis, etc. Continuous template matching method, which is referred to as the conventional method hereinafter, was already proposed by the investigators of this project in order to enable precision detection of small displacements between two digital images. In the proposed method, a steepest descent method based successive approximation algorithm is used to detect the peak position of bandlimited interpolated continuous function. And this leads to the problem in computaional complexity and fluctuation of computational time. In order to solve the problem, a novel neural networks based template matching method is proposed in this research project. Accuracy and computational complexity of the proposed method are evaluated by computer simulations and compared with those of the conventional method. As a noise model for input images, as the first order approximation, the additive white Gaussian noise is assumed. It is found that, by using the proposed method, the computational complexity of peak position detection is reduced to 1/13 as compared with that of the conventional method without loss of the accuracy of displacement detection.
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Research Products
(2 results)
