Abstract
With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.
Highlights
With the development of unmanned aerial vehicle (UAV) techniques, aerial images are becoming cheaper, accessed, and of higher resolution
According to our experiments, compared with ORB, Speeded up Robust Features (SURF) has a higher quality of feature points, which means a higher accuracy of matching
Blackpoint points in I1 are discarded, and the feature point that has the largest response other than this point will try to be the anchor points selected according to the method of Section 4.2, and the right endpoints of the lines matched as thepoints anchorthat point, andthe so on
Summary
With the development of unmanned aerial vehicle (UAV) techniques, aerial images are becoming cheaper, accessed, and of higher resolution. Sensors 2020, 20, 2007 sufficient accuracy for stitching To address this problem, this paper proposes a stitching approach that uses some optimization methods to simplify the stitching computation with position and pose information. This paper proposes a stitching approach that uses some optimization methods to simplify the stitching computation with position and pose information This approach stitches images by finding several anchor-matches instead of a large number of feature-matches, and reducing the range where features need to be extracted and the number of feature points that need to be matched, which is why it is faster.
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