Abstract
Matching street-level images to a database of airborne images is hard because of extreme viewpoint and illumination differences. Color/gradient distributions or local descriptors fail to match forcing us to rely on the structure of self-similarity of patterns on facades. We propose to capture this structure with a novel “scale-selective self-similarity” (S 4) descriptor which is computed at each point on the facade at its inherent scale. To achieve this, we introduce a new method for scale selection which enables the extraction and segmentation of facades as well. Matching is done with a Bayesian classification of the street-view query S 4 descriptors given all labeled descriptors in the bird’s-eye-view database. We show experimental results on retrieval accuracy on a challenging set of publicly available imagery and compare with standard SIFT-based techniques.
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Bansal, M., Daniilidis, K., Sawhney, H. (2012). Ultra-wide Baseline Facade Matching for Geo-localization. In: Fusiello, A., Murino, V., Cucchiara, R. (eds) Computer Vision – ECCV 2012. Workshops and Demonstrations. ECCV 2012. Lecture Notes in Computer Science, vol 7583. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33863-2_18
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DOI: https://doi.org/10.1007/978-3-642-33863-2_18
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