Abstract
Changes of the flight attitude of unmanned aircraft cause nonlinear distortion in the aerial images. Stitching these images without geometric rectification may cause the problem of mismatching. However, the geometric rectification model produces invalid regions at the edge of images. A direct cutting for these invalid regions leads to removing lots of useful information, whereas a noisy seam is visible in the stitched image if the edge cutting is omitted. We propose a solution of edge detection for detecting invalid and noisy regions. More precisely, our method includes two passes of edge detection for images with geometric rectification. The first pass of image edge detection is to find the boundary between valid and invalid scenes. The second pass is to detect the noisy regions. The invalid and noisy regions are finally removed for image stitching, and the effective areas of the image are kept to the maximum extent. Experimental results demonstrate the efficacy of the proposed method.
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Liu, ZX., Lin, QH., Hao, YG. (2018). Image Edge Detection for Stitching Aerial Images with Geometrical Rectification. In: Huang, T., Lv, J., Sun, C., Tuzikov, A. (eds) Advances in Neural Networks – ISNN 2018. ISNN 2018. Lecture Notes in Computer Science(), vol 10878. Springer, Cham. https://doi.org/10.1007/978-3-319-92537-0_61
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DOI: https://doi.org/10.1007/978-3-319-92537-0_61
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