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Illumination invariant feature based on neighboring radiance ratio

机译:基于邻近辐射比的照度不变特征

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In many object recognition applications, especially in face recognition, varying illuminations can adversely affect the robustness of the object recognition system. In this paper, we propose a novel illumination invariant feature called Neighboring Radiance Ratio (NRR) which is insensitive to both intensity and direction of light. NRR is derived and analyzed based on a physical image formation model. The computation of NRR does not need any prior information or any training data and NRR is far less sensitive to the border of shadows than most existing methods. The analysis of the illumination invariance of NRR is also presented. The proposed NRR feature is tested on Extended Yale B and CMU-PIE databases and compared with several previous methods. The experimental results corroborate our analysis and demonstrate that NRR is highly robust image feature against illumination changes.
机译:在许多物体识别应用中,特别是在面部识别中,变化的照明会不利地影响物体识别系统的鲁棒性。在本文中,我们提出了一种新颖的照度不变特征,称为邻域辐射比(NRR),该特征对光的强度和方向均不敏感。基于物理图像形成模型导出和分析NRR。 NRR的计算不需要任何先验信息或任何训练数据,并且NRR对阴影边界的敏感性远不如大多数现有方法。还介绍了NRR的照度不变性。提议的NRR功能已在Extended Yale B和CMU-PIE数据库上进行了测试,并与几种先前的方法进行了比较。实验结果证实了我们的分析,并证明了NRR是针对光照变化的高度鲁棒的图像功能。

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