| Project/Area Number |
13450163
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
System engineering
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| Research Institution | University of Tsukuba |
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Principal Investigator |
YASUNAGA Moritoshi University of Tsukuba, Institute of Information Sciences and Electronics, Professor, 電子・情報工学系, 教授 (80272178)
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| Project Period (FY) |
2001 – 2003
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| Project Status |
Completed (Fiscal Year 2003)
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| Budget Amount *help |
¥6,700,000 (Direct Cost: ¥6,700,000)
Fiscal Year 2003: ¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 2002: ¥2,400,000 (Direct Cost: ¥2,400,000)
Fiscal Year 2001: ¥2,700,000 (Direct Cost: ¥2,700,000)
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| Keywords | Image-understanding / Motion Picture / Integrated Circuits / FPGA / Parzen Window / Neural Networks / Face Image / 動画像 / 回路 |
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| Research Abstract |
The goal of this project is to realize a high-speed image-understanding system that has not been realized under the conventional technologies. In order to achieve the system, we implement the circuits designed directly from the images onto the VLSIs. And we apply this design approach to the "Parzen Window Method" and the "Probabilistic Neural Network Method". In both methods, the functions constructed using a great number of sample images are used, and the functions are directly designed from the image sample data. Because of this data-direct-implementation, the functions are calculated much faster than those used in the conventional approaches
The prototype system was established using reconfigurable LSIs (FPGAs) and following were obtained
1)Window functions in the Parzen Window Method were optimized using the GA (genetic algorithm). High recognition accuracy was obtained with the window functions in the face recognition problem
2)The proposed idea of the data-direct-implementation was efficiently applied not only to the image recognition but to the sonar-spectrum recognition
3)The image-understanding system based on the "Probabilistic Neural Network Method" was developed and is connected with a PC. 'The system was effectively and precisely evaluated using the PC. The PC was also used to apply the principal component analysis to the outputs from the system in order to improve the recognition accuracy
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