Skyline localization for mountain images
Yao-Ling Hung, Chih-Wen Su, Yuan‐Hsiang Chang, Jyh-Chian Chang, Hsiao-Rong Tyan · 2013
In this paper, we propose a novel method for automatically locating the skyline that represents the shape of mountains. The appearances of mountain and sky are variable because of the weather, season or region. In order to extract the skyline of mountains under complicated and variable circumstances, support vector machine (SVM) is applied for the prediction of a part of the skyline between sky region and mountain region by using the color, statistics features and location information of edge. Then, the linking of incomplete fragments of skyline is formulated as a shortest path problem and solved by dynamic programming strategy. Our experimental results demonstrate that the proposed method is accurate and robust.