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Open AccessArticle

An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies

1
School of Civil Engineering, Chongqing University, Chongqing 400045, China
2
Chongqing Survey Institute, Chongqing 401121, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 362; https://doi.org/10.3390/ijgi9060362
Received: 25 April 2020 / Revised: 22 May 2020 / Accepted: 31 May 2020 / Published: 1 June 2020
(This article belongs to the Special Issue GIS Software and Engineering for Big Data)
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. View Full-Text
Keywords: image feature matching; feature descriptor; CS-LBP; illumination robustness; street view images; augmented reality (AR) image feature matching; feature descriptor; CS-LBP; illumination robustness; street view images; augmented reality (AR)
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Xiang, Z.; Yang, R.; Deng, C.; Teng, M.; She, M.; Teng, D. An Illumination Insensitive Descriptor Combining the CSLBP Features for Street View Images in Augmented Reality: Experimental Studies. ISPRS Int. J. Geo-Inf. 2020, 9, 362.

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