| Improving reflectance estimation by BRDF-consistent region clustering |
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| 作者姓名： | ZHANG Hongxin WU Xiangyang LIN Steve BAO Hujun |
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| 作者单位： | 1. State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou 310027, China; 2. Visual Computing Group, Microsoft Research Asia, Beijing 100080, China |
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| 基金项目： | 国家自然科学基金;国家自然科学基金;国家重点基础研究发展计划(973计划) |
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| 摘 要： | Previous studies in reflectance estimation generally require prior segmentation of an image into regions of uniform reflectance. Due to the measurement noise and limited sampling of the BRDF (bi-directional reflectance function) directions, such estimated results of reflectance are not accurate. In this paper, we propose a novel method for reducing uncertainty in reflectance estimates by merging image regions which have consistent reflectance observations. Each image region acts as a reflectance subspace, so merging of the image regions can result in subspace reduction. We propose a Bayesian segmentation framework to decrease the reflectance uncertainty by using novel merging criteria. Finally, a maximum likelihood reflectance estimation is made for each resulting image region. Experimental results verify the feasibility and superiority of this reflectance-oriented region merging method.
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| 关 键 词： | texture shading grouping and segmentation reflectance estimation BRDF uncertainty. |
Improving reflectance estimation by BRDF-consistent region clustering |
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| ZHANG Hongxin,WU Xiangyang,LIN Steve,BAO Hujun.Improving reflectance estimation by BRDF-consistent region clustering[J].Progress in Natural Science,2006,16(3):313-320. |
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| Authors: | ZHANG Hongxin WU Xiangyang LIN Steve BAO Hujun |
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| Institution: | 1. State Key Laboratory of CAD&CG,Zhejiang University,Hangzhou 310027,China
2. Visual Computing Group,Microsoft Re-search Asia,Beijing 100080,China |
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| Abstract: | Previous studies in reflectance estimation generally require prior segmentation of an image into regions of uniform reflectance. Due to the measurement noise and limited sampling of the BRDF (bi-directional reflectance function) directions, such estimated results of reflectance are not accurate. In this paper, we propose a novel method for reducing uncertainty in reflectance estimates by merging image regions which have consistent reflectance observations. Each image region acts as a reflectance subspace, so merging of the image regions can result in subspace reduction. We propose a Bayesian segmentation framework to decrease the reflectance uncertainty by using novel merging criteria. Finally, a maximum likelihood reflectance estimation is made for each resulting image region. Experimental results verify the feasibility and superiority of this reflectance-oriented region merging method. |
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| Keywords: | texture shading grouping and segmentation reflectance estimation BRDF uncertainty |
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