@inproceedings{b627c95749694acdac43f87aef92aaaa,
title = "Best-Buddies Similarity for robust template matching",
abstract = "We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs) - pairs of points in source and target sets, where each point is the nearest neighbor of the other. BBS has several key features that make it robust against complex geometric deformations and high levels of outliers, such as those arising from background clutter and occlusions. We study these properties, provide a statistical analysis that justifies them, and demonstrate the consistent success of BBS on a challenging real-world dataset.",
author = "Tali Dekel and Shaul Oron and Michael Rubinstein and Shai Avidan and Freeman, \{William T.\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015 ; Conference date: 07-06-2015 Through 12-06-2015",
year = "2015",
month = oct,
day = "14",
doi = "10.1109/CVPR.2015.7298813",
language = "الإنجليزيّة",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "2021--2029",
booktitle = "IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015",
address = "الولايات المتّحدة",
}