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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Fornaciari, M. Prati, A. Grana, C. Cucchiara, R. |
| Copyright Year | 2013 |
| Description | Author affiliation: DIEF, Univ. of Modena & Reggio Emilia, Modena, Italy (Fornaciari, M.; Grana, C.; Cucchiara, R.) || DPPAC, Univ. IUAV of Venice, Venice, Italy (Prati, A.) |
| Abstract | The diffusion of powerful mobile devices has posed the basis for new applications implementing on the devices (which are embedded devices) sophisticated computer vision and pattern recognition algorithms. This paper describes the implementation of a complete system for automatic recognition of places localized on a map through the recognition of significant signs by means of the camera of a mobile device (smartphone, tablet, etc.). The paper proposes a novel classification algorithm based on the innovative use of bag-of-words on ORB features. The recognition is achieved using a simple yet effective search scheme which exploits GPS localization to limit the possible matches. This simple solution brings several advantages, such as the speed also on limited-resource devices, the usability also with limited training samples and the easiness of adapting to new training samples and classes. The overall architecture of the system is based on a REST-JSON client-server architecture. The experimental results have been conducted in a real scenario and evaluating the different parameters which influence the performance. |
| Starting Page | 1 |
| Ending Page | 6 |
| File Size | 2290409 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479921645 |
| DOI | 10.1109/ICDSC.2013.6778220 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-10-29 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Histograms Hamming distance Computer architecture Vectors Mobile handsets Servers |
| Content Type | Text |
| Resource Type | Article |
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