Icon Scanning
Abstract Results Software
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.
This code learns K tone-mapping deformation models. Given as training a set of original images and a set of deformed versions of them, the code learns K tone-mapping deformations that fit the training data. The modeling of the tone-mapping and the algorithms used for training are described in the paper:
  1. I. Friedman, L. Zelnik-Manor "Icon Scanning: Towards Next Generation QR Codes" Computer Vision and Pattern Recognition, Providence, Rhode-Island, USA, Jun. 2012."
Please cite this work if you use our software.

Please read the ReadMe file.

See our data-set section to obtain more relevant data-sets. (The required data-set for this code is already included in the zip package.)

An ios version of the application cen be downloaded from the Appstore

The code in this website is for demo purposes only. Individuals or academic institutes are free to use outcome generated using this version, or the code itself, as long as they acknowledge its use. Commercial licensing is managed by the Technion Industry Liaison Office. Please contact Hovav Gazit for details.

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