| Project/Area Number |
10555013
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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 |
Applied optics/Quantum optical engineering
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| Research Institution | Saitama University |
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Principal Investigator |
TOYOOKA Satoru Saitama University, Graduate School of Science and Engineering, Associate Professor, 大学院・理工学研究科, 教授 (90019753)
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| Co-Investigator(Kenkyū-buntansha) |
ZHANG Qingchuan Saitama University, Graduate School of Science and Engineering, Assistant Professor, 大学院・理工学研究科, 助手 (00292649)
KADONO Hirofumi Saitama University, Graduate School of Science and Engineering, Associate Professor, 大学院・理工学研究科, 助教授 (70204518)
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| Project Period (FY) |
1998 – 2000
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| Project Status |
Completed (Fiscal Year 2000)
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| Budget Amount *help |
¥13,300,000 (Direct Cost: ¥13,300,000)
Fiscal Year 2000: ¥1,400,000 (Direct Cost: ¥1,400,000)
Fiscal Year 1999: ¥2,500,000 (Direct Cost: ¥2,500,000)
Fiscal Year 1998: ¥9,400,000 (Direct Cost: ¥9,400,000)
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| Keywords | Neural Network / Spectroscopic image analysis / Broad band filtering system / Vegetation monitoring / 広帯域フィルク分光撮像装置 / 広帯域フィルタ分光撮影装置 / 植生モニタ / シラビソ |
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| Research Abstract |
A spectral image include huge volume of data. Multi-variable analysis is usually used to extract important information in the image. On the contrary in the proposed system, important spectroscopic information in the images which we want to investigate is taken in advance in a learning phase and installed in a system as optimized filters. Filter optimization by self-organized map is discussed. Three proto-types of broad-band filtering systems to implement optimize filter functions which include liquid-crystal devises and optical components were constructed. Experimental results of spectral reconstruction from four filterer images by proposed system were compared with measured result by conventional method. A new intelligent eye which is sensitive to spectral characteristics of an object will be expected.
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