Skyline plays a pivotal role in mountainous visual geo-localization and localization/navigation of planetary rovers/UAVs and virtual/augmented realityapplications . We present a novel mountainous skyline detection approach wherewe adapt a shallow learning approach to learn a set of filters to discriminate between edges belonging to sky-mountain boundary and others coming from different regions . The proposedapproach is computationally faster than earlier methods while providing comparable performance and is more suitable for resource constrained platformse.g., mobile devices, planetary . rovers and UAVs. Our code is available at\url{https://://://}. We compare our proposedmethods against earlier skyline detection methods using four different datasets using four . datasets .

Author(s) : Touqeer Ahmad, Ebrahim Emami, Martin Čadík, George Bebis

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Keywords : skyline - detection - mountainous - learning - shallow -

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