| Budget Amount *help |
¥43,290,000 (Direct Cost: ¥33,300,000、Indirect Cost: ¥9,990,000)
Fiscal Year 2017: ¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
Fiscal Year 2016: ¥8,710,000 (Direct Cost: ¥6,700,000、Indirect Cost: ¥2,010,000)
Fiscal Year 2015: ¥8,060,000 (Direct Cost: ¥6,200,000、Indirect Cost: ¥1,860,000)
Fiscal Year 2014: ¥8,580,000 (Direct Cost: ¥6,600,000、Indirect Cost: ¥1,980,000)
Fiscal Year 2013: ¥9,360,000 (Direct Cost: ¥7,200,000、Indirect Cost: ¥2,160,000)
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| Outline of Final Research Achievements |
By replacing observer's eyes to artificial intelligence, we have made it possible to identify anatomically closely related species that were impossible so far on the basis of microscopic observation. At the same time, it enabled interdisciplinary development of identification technology to other fields. In addition to combinations of feature extraction and discriminators designed by humans, deep learning that leaves all processes from feature extraction to intellectual judgment to multilayered neural networks was implemented for wood identification. On the application side, a discrimination model based on CT images that can be used for nondestructive diagnosis of cultural property wood products was constructed.
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