The common feature matching algorithms for street view images are sensitive to the illumination changes in augmented reality (AR), this may cause low accuracy of matching between street view images. This paper proposes a novel illumination insensitive feature descriptor by integrating the center-symmetric local binary pattern (CS-LBP) into a common feature description framework. This proposed descriptor can be used to improve the performance of eight commonly used feature-matching algorithms, e.g., SIFT, SURF, DAISY, BRISK, ORB, FREAK, KAZE, and AKAZE. We perform the experiments on five street view image sequences with different illumination changes. By comparing with the performance of eight original algorithms, the evaluation results show that our improved algorithms can improve the matching accuracy of street view images with changing illumination. Further, the time consumption only increases a little. Therefore, our combined descriptors are much more robust against light changes to satisfy the high precision requirement of augmented reality (AR) system.

中文翻译：

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结合CSLBP功能的增强现实中街景图像的照明不敏感描述符：实验研究

街景图像的常见特征匹配算法对增强现实（AR）中的光照变化敏感，这可能会导致街景图像之间的匹配精度降低。通过将中心对称局部二进制模式（CS-LBP）集成到通用特征描述框架中，提出了一种新颖的光照不敏感特征描述符。该提出的描述符可用于改善八种常用特征匹配算法的性能，例如SIFT，SURF，DAISY，BRISK，ORB，FREAK，KAZE和AKAZE。我们对五个具有不同照度变化的街景图像序列进行了实验。通过与八种原始算法的性能进行比较，评估结果表明，改进后的算法可以在光照变化的情况下提高街景图像的匹配精度。此外，时间消耗仅增加一点。因此，我们的组合描述符对光线变化的鲁棒性更高，可以满足增强现实（AR）系统的高精度要求。