13 April 2018 Aerial images visual localization on a vector map using color-texture segmentation
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Proceedings Volume 10696, Tenth International Conference on Machine Vision (ICMV 2017); 106961T (2018) https://doi.org/10.1117/12.2310138
Event: Tenth International Conference on Machine Vision, 2017, Vienna, Austria
Abstract
In this paper we study the problem of combining UAV obtained optical data and a coastal vector map in absence of satellite navigation data. The method is based on presenting the territory as a set of segments produced by color-texture image segmentation. We then find such geometric transform which gives the best match between these segments and land and water areas of the georeferenced vector map. We calculate transform consisting of an arbitrary shift relatively to the vector map and bound rotation and scaling. These parameters are estimated using the RANSAC algorithm which matches the segments contours and the contours of land and water areas of the vector map. To implement this matching we suggest computing shape descriptors robust to rotation and scaling. We performed numerical experiments demonstrating the practical applicability of the proposed method.
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I. A. Kunina, L. M. Teplyakov, A. P. Gladkov, T. M. Khanipov, D. P. Nikolaev, "Aerial images visual localization on a vector map using color-texture segmentation", Proc. SPIE 10696, Tenth International Conference on Machine Vision (ICMV 2017), 106961T (13 April 2018); doi: 10.1117/12.2310138; https://doi.org/10.1117/12.2310138
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