Abstract
This paper is dedicated to the problem of automatic skyline extraction in digital images. The study is motivated by the needs, expressed by urbanists, to describe in terms of geometrical features, the global shape created by man-made buildings in urban areas. Skyline extraction has been widely studied for navigation of Unmanned Aerial Vehicles (drones) or for geolocalization, both in natural and urban contexts. In most of these studies, the skyline is defined by the limit between sky and ground objects, and can thus be resumed to the sky segmentation problem in images. In our context, we need a more generic definition of skyline, which makes its extraction more complex and even variable. The skyline can be extracted for different depths, depending on the interest of the user (far horizon, intermediate buildings, near constructions, ...), and thus requires a human interaction. The main steps of our method are as follows: we use a Canny filter to extract edges and allow the user to interact with filter’s parameters. With a high sensitivity, all the edges will be detected, whereas with lower values, only most contrasted contours will be kept by the filter. From the obtained edge map, an upper envelope is extracted, which is a disconnected approximation of the skyline. A graph is then constructed and a shortest path algorithm is used to link discontinuities. Our approach has been tested on several public domain urban and natural databases, and have proven to give better results that previously published methods.
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Notes
- 1.
See Wikipedia: https://en.wikipedia.org/wiki/Skyline.
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Acknowledgments
This work was part of the “ANR-12-VBDU-0008 - Skyline” project, funded by the “Agence Nationale de la Recherche (ANR)” and the Labex (Laboratoire d’Excellence) “Intelligence des mondes Urbains (IMU)”.
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Ayadi, M., Suta, L., Scuturici, M., Miguet, S., Ben Amar, C. (2016). A Parametric Algorithm for Skyline Extraction. In: Blanc-Talon, J., Distante, C., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2016. Lecture Notes in Computer Science(), vol 10016. Springer, Cham. https://doi.org/10.1007/978-3-319-48680-2_53
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