Silhouette Extraction from Street View Images
Guzel, Mehmet Serdar
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This study addresses the issue of silhouette extraction of a street, and proposes two novel approaches to overcome this problem. The first, namely hybrid stitching considers the silhouette extraction as an image stitching problem and aims to use 2D street view images. The algorithm used in this method integrates a new composition technique into a conventional image stitching pipeline. The developed software using the proposed hybrid approach results in better stitching performances when compared with the popular stitching tools in the literature. Despite the results of the proposed method are better than the state-of-the-art image stitching techniques in many cases, they are not reliable enough to handle all of the street view image sets. Accordingly, a second solution has been proposed, including 3D location information, namely, 3D Silhouette Extraction Pipeline. The pipeline involves several techniques and post-processing steps to handle both the transformation and projection of the obtained point cloud, and the elimination of misleading location information. The results reveal that compared with the 2D solutions, the proposed algorithm is very effective and more reliable in silhouette extraction of a street, which is critical in urban transformation and environmental protection.