2002 Fiscal Year Final Research Report Summary
Discrimination theory of wavelet filters with learning ability
| Project/Area Number |
11558039
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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 | 展開研究 |
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| Research Field |
Intelligent informatics
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| Research Institution | Kyushu University |
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Principal Investigator |
NIIJIMA Koichi Graduate School 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) |
TAKANO Shigeru Graduate School of Information Science and Electrical Engineering, Assistant Professor, 大学院・システム情報科学研究院, 助手 (70336064)
KUZUME Koichi Yuge National College of Technology, Professor, 教授 (80225151)
OKADA Yoshihiro Graduate School 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)
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| Project Period (FY) |
1999 – 2002
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| Keywords | lifting wavelet filter / free parameter / learning method / learning ability / extraction / noise reduction / multiresolution analysis / 3D surface generation |
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| Research Abstract |
Wavelet filters with learning ability indicate lifting wavelet filters proposed by Sweldens who is a researcher at Lucent Technology in USA. The lifting wavelet filters are consist of biorthogonal analysis and synthesis filters. A signal is decomposed into lowpass and highpass components using the analysis filter, and the original signal can be reconstructed from the lowpass and highpass components using the synthesis filter. This means that the original signal is equivalent to the decomposed lowpass and highpass components. The lifting wavelet filters are constructed by adding lifting filters to biorthogonal wavelet filters. The lifting filter contains free parameters which can be determined adaptive to signals and images.
In our research, we proposed several learning methods of the free parameters adaptive to specific parts of signals and images, and established a discrimination theory for extracting pieces similar to the specific parts. We also presented an impulse noise reduction method based on our learning method. Furthermore, we proposed a fast simplification algorithm for generating 3D surfaces by using an idea of multiresolution analysis of wavelets.
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