2006 Fiscal Year Final Research Report Summary
Semantic feature extraction from signal and image using lifting wavelet filters.
| Project/Area Number |
15300048
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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 |
Intelligent informatics
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| Research Institution | KYUSHU UNIVERCITY |
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Principal Investigator |
NIIJIMA Koichi Kyushu University, Faculty of Information Science and Electrical Engineering, Professor, 大学院システム情報科学研究院, 教授 (30047881)
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| Co-Investigator(Kenkyū-buntansha) |
OKADA Yoshihiro Kyushu University, Faculty of Information Science and Electrical Engineering, Associate Professor, 大学院システム情報科学研究院, 助教授 (70250488)
KUZUME Koichi Yuge National College of Maritime Technology, Information Science and Technology Department, Professor, 情報工学科, 教授 (80225151)
TAKANO Shigeru Kyushu University, Faculty of Information Science and Electrical Engineering, Research Associate, 大学院システム情報科学研究院, 助手 (70336064)
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| Project Period (FY) |
2003 – 2006
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| Keywords | biorthogonal wavelet / dyadic wavelet / lifting scheme / free parameters / learning / multi-resolution analysis / person identification |
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| Research Abstract |
Lifting wavelet is called the second generation wavelet, which is developed by Wim Sweldens of Lucent Technologies' Bell Labs. The lifting wavelet has a lifting term which incorporates controllable free parameters. Lifting scheme is a set of the down sampling filters constructed by adding the lifting term to initial biorthogonal wavelet filters. The constructed filters are also biorthogonal wavelet filters and keep the perfect reconstruction. These are very important properties for the lifting wavelet filters.
In this research, using such properties, we proposed a new learning method for determining the free parameters in the lifting term and developed a tracking system of moving objects based on the learned lifting scheme. And then, the various learning methods to determine the lifting parameters were produced by our lifting dyadic wavelet scheme which is extended version of the biorthogonal lifting scheme. Using the learned lifting scheme, we developed the image extraction algorithm and constructed person identification system via facial images captured by video frames. Furthermore, using the dyadic lifting scheme, we presented the technique for designing biorthogonal wavelets by dyadic wavelet. In addition, we studied to generate 3D objects by utilizing the wavelet multi-resolution analysis.
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Research Products
(42 results)
