| Project/Area Number |
15100003
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| Research Category |
Grant-in-Aid for Scientific Research (S)
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| Allocation Type | Single-year Grants |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Tokyo Institute of Technology |
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Principal Investigator |
KOSUGI Yukio Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and Engineering, Professor (30108237)
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| Co-Investigator(Kenkyū-buntansha) |
KAMEYAMA Keisuke University of Tsukuba, Graduate School of Systems and Information Engineering, Associate Professor (40242309)
UTO Kuniaki Tokyo Institute of Technology, Interdisciplinary Graduate School of Science and Enginnering, Faculty of Medicine, Assistant Professor (90345356)
KOSAKA Naoko Tokyo Institute of Technology, Graduate School of Enginnering, Associate Professor (50436713)
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| Project Period (FY) |
2003 – 2007
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| Project Status |
Completed (Fiscal Year 2007)
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| Budget Amount *help |
¥62,660,000 (Direct Cost: ¥48,200,000、Indirect Cost: ¥14,460,000)
Fiscal Year 2007: ¥8,970,000 (Direct Cost: ¥6,900,000、Indirect Cost: ¥2,070,000)
Fiscal Year 2006: ¥10,400,000 (Direct Cost: ¥8,000,000、Indirect Cost: ¥2,400,000)
Fiscal Year 2005: ¥12,870,000 (Direct Cost: ¥9,900,000、Indirect Cost: ¥2,970,000)
Fiscal Year 2004: ¥12,610,000 (Direct Cost: ¥9,700,000、Indirect Cost: ¥2,910,000)
Fiscal Year 2003: ¥17,810,000 (Direct Cost: ¥13,700,000、Indirect Cost: ¥4,110,000)
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| Keywords | hyperspectral images / insect pest / particle swarm optimization / sugar-content estimation / neural networks / medical images / satellite images / change detection / 病虫害推定 / ナラ枯れ / 手術支援画像 / 潮風害 / ヒト肌検出 / 高所観測 / 土壌水分推定 / 地震災害 / ハイパースペクトル / 葉焼病 / 相互領域拡張法 / セグメンテーション / 独立成分分析 / 植生 |
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| Research Abstract |
For the accurate land-use and land-cover analysis of satellite and aerial images, as well as for automatic diagnosis and surgical planning based on medical images, we are aiming at establishing image-interpretation frameworks using a priori knowledge inherent to the subject under consideration. In the study of high-resolution images as well as hyperspectral images, Models with excessive freedom sometimes mislead us to inconsistent results. Sufficient information can be obtained by introducing models with adequately limited degree of freedom, as shown in the following results :
Damaged area estimation : The satellite image photographed before disaster was handled as a priori information, and the image photographed after the disaster was compared in nonlinear map algorithm based on the principle of "coincidence enhancement", and change detection of damaged houses was carried out. This system was applied to satellite images of the Bam earthquake of December 26, 2003, and the damaged area w
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as extracted. The processing result was provided to a Japanese field investigation group, and the validity of the detection result was verified via the field study.
Sugar content estimation of crops : The flavor of crops is dependent on the fraction of contents such as sugar, amino acid and protein etc. In this study, we tried to estimate these quantities from the hyperspectral image of the crops before the harvest. In the estimation system, we optimized the mathematical operation among large number of wavelength of the hyperspectral images by combining the neural network with the PSO(particle swarm optimization) algorithm. In this estimation, the set of measured values obtained after the harvest was used as a tutorial data.
The derivation of human skin extraction index : We analyzed hyperspectral image of short wavelength infrared region, to make use of a priori information effectively in detecting the human victims in the water or lying on the gravel or ground after the disaster. As a result, NHI(normalized human index) was proposed to make the skin tangible by utilizing the spectral information inherent to the human skin.
Independent Component Analysis of Periodically Distributed Vegetation Images : As a basic study for the development of high-altitude observation of hyperspectral images of Japanese agricultural fields, we experimentally and theoretically consider the use of a priori knowledge in the effective use of independent component analysis for resolving mixture pixels found in images observed beyond the limitation of physical resolution. Less
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