| Project/Area Number |
20500155
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Perception information processing/Intelligent robotics
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| Research Institution | Kyushu University |
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Principal Investigator |
HARA Kenji Kyushu University, 芸術工学研究院, 准教授 (50380712)
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| Co-Investigator(Kenkyū-buntansha) |
INOUE Kohei 九州大学, 大学院・芸術工学研究院, 助教 (70325570)
浦浜 喜一 九州大学, 大学院・芸術工学研究院, 教授 (10150492)
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| Co-Investigator(Renkei-kenkyūsha) |
URAHAMA Kiichi 九州大学, 大学院・芸術工学研究院, 教授 (10150492)
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| Project Period (FY) |
2008 – 2010
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| Project Status |
Completed (Fiscal Year 2010)
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| Budget Amount *help |
¥3,250,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥750,000)
Fiscal Year 2010: ¥650,000 (Direct Cost: ¥500,000、Indirect Cost: ¥150,000)
Fiscal Year 2009: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2008: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 画像情報処理 / 反射特性 / 光源状況 / RBFネットワーク / EMアルゴリズム / 方向統計BRDF / MRFモデル / 擬似RBF / I-divergence / 制約付き最適化 |
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| Research Abstract |
Estimating the illumination and the reflectance properties of an object surface from a sparse set of images is an important but inherently ill-posed problem. The problem becomes even harder if we wish to account for the spatial variation of material properties on the surface. In this research, we derived a novel method for estimating the spatially varying specular reflectance properties, of a surface of known geometry, as well as the illumination distribution from a specular-only image, for instance, captured using polarization to separate reflection components. Unlike previous work, we did not assume the illumination to be a single point light source. We modeled specular reflection with a spherical statistical distribution and encoded its spatial variation with radial basis function network of their parameters values. This allowed us to formulate the simultaneous estimation of spatially varying specular reflectance and illumination as a constrained optimization based on the I-divergence measure. To solve it, we derived an expectation maximization algorithm. At the same time, we accomplished the optimum encoding of the specular reflectance properties by learning the number, centers and widths of the RBFs.
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