| 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)
皆本 晃弥 九州大学, 大学院システム情報科学研究科, 助手 (00294900)
高橋 規一 九州大学, 大学院システム情報科学研究科, 助教授 (60284551)
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| Project Period (FY) |
1999 – 2002
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| Project Status |
Completed (Fiscal Year 2002)
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| Budget Amount *help |
¥12,900,000 (Direct Cost: ¥12,900,000)
Fiscal Year 2002: ¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2001: ¥2,000,000 (Direct Cost: ¥2,000,000)
Fiscal Year 2000: ¥3,100,000 (Direct Cost: ¥3,100,000)
Fiscal Year 1999: ¥5,600,000 (Direct Cost: ¥5,600,000)
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| Keywords | lifting wavelet filter / free parameter / learning method / learning ability / extraction / noise reduction / multiresolution analysis / 3D surface generation / 学習方式 / 特定信号 / ダイアディックウェーブレット / フィルタ / 抽出 / メッシュ単純化アルゴリズム / メッシュ細分化アルゴリズム / 学習理論 / 地磁気水平成分 / 磁気嵐急始部 / 顔画像 / 異常信号検出装置 / 鮮鋭化 / リフティグウェーブレット変換 / 抽出理論 / 磁気嵐開始部 / ピーク性雑音 / 合成積型ウェーブレット / 信号圧縮 / 自由パラメータの学習 / 部分画像の検出 / 画像の特徴抽出 / 磁気嵐の自動検出 |
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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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