Abstract
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.
Highlights
Augmented reality (AR) is an emerging form of experience in which the real world (RW) is enhanced by computer-generated content, which is tied to specific locations and/or activities
We design a series of experiments to compare the matching performance between the combined descriptors, the center-symmetric local binary pattern (CS-local binary pattern (LBP)) descriptors and the original descriptors
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Summary
Augmented reality (AR) is an emerging form of experience in which the real world (RW) is enhanced by computer-generated content, which is tied to specific locations and/or activities. Marker-based AR is to use visual features or an object to be a trigger, while markerless-based AR is to use some technology to detect the relative position (feature matching) between virtual objects and the real world [2]. The tracking based on image processing uses natural features that, color, shape, texture and interest point, in images to calculate a camera’s pose [3]. It uses the homography matrix between adjacent frame images obtained by image matching to solve the position and pose of the camera for the registration [4]. GIS (Geographic Information System) technological researches are carried out to design and build a geographic database that can store and query these and can add the features that are generated by the augmented reality system [5]
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