TY - GEN
T1 - Icon scanning
T2 - 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
AU - Friedman, Itamar
AU - Zelnik-Manor, Lihi
PY - 2012
Y1 - 2012
N2 - Undoubtedly, a key feature in the popularity of smartmobile devices is the numerous applications one can install. Frequently, we learn about an application we desire by seeing it on a review site, someone else's device, or a magazine. A user-friendly way to obtain this particular application could be by taking a snapshot of its corresponding icon and being directed automatically to its download link. Such a solution exists today for QR codes, which can be thought of as icons with a binary pattern. In this paper we extend this to App-icons and propose a complete system for automatic icon-scanning: it first detects the icon in a snapshot and then recognizes it. Icon scanning is a highly challenging problem due to the large variety of icons (500K in App-Store) and background wallpapers. In addition, our system should further deal with the challenges introduced by taking pictures of a screen. Nevertheless, the novel solution proposed in this paper provides high detection and recognition rates. We test our complete icon-scanning system on icon snapshots taken by independent users, and search them within the entire set of icons in App-Store. Our success rates are high and improve significantly on other methods.
AB - Undoubtedly, a key feature in the popularity of smartmobile devices is the numerous applications one can install. Frequently, we learn about an application we desire by seeing it on a review site, someone else's device, or a magazine. A user-friendly way to obtain this particular application could be by taking a snapshot of its corresponding icon and being directed automatically to its download link. Such a solution exists today for QR codes, which can be thought of as icons with a binary pattern. In this paper we extend this to App-icons and propose a complete system for automatic icon-scanning: it first detects the icon in a snapshot and then recognizes it. Icon scanning is a highly challenging problem due to the large variety of icons (500K in App-Store) and background wallpapers. In addition, our system should further deal with the challenges introduced by taking pictures of a screen. Nevertheless, the novel solution proposed in this paper provides high detection and recognition rates. We test our complete icon-scanning system on icon snapshots taken by independent users, and search them within the entire set of icons in App-Store. Our success rates are high and improve significantly on other methods.
UR - http://www.scopus.com/inward/record.url?scp=84866674670&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2012.6247793
DO - 10.1109/CVPR.2012.6247793
M3 - منشور من مؤتمر
SN - 9781467312264
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 1130
EP - 1137
BT - 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Y2 - 16 June 2012 through 21 June 2012
ER -