| dc.contributor.author | Smedt, Quentin De | en_US |
| dc.contributor.author | Wannous, Hazem | en_US |
| dc.contributor.author | Vandeborre, Jean-Philippe | en_US |
| dc.contributor.author | Guerry, J. | en_US |
| dc.contributor.author | Saux, B. Le | en_US |
| dc.contributor.author | Filliat, D. | en_US |
| dc.contributor.editor | Ioannis Pratikakis and Florent Dupont and Maks Ovsjanikov | en_US |
| dc.date.accessioned | 2017-04-22T17:17:41Z | |
| dc.date.available | 2017-04-22T17:17:41Z | |
| dc.date.issued | 2017 | |
| dc.identifier.isbn | 978-3-03868-030-7 | |
| dc.identifier.issn | 1997-0471 | |
| dc.identifier.uri | http://dx.doi.org/10.2312/3dor.20171049 | |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/3dor20171049 | |
| dc.description.abstract | Hand gesture recognition is recently becoming one of the most attractive field of research in pattern recognition. The objective of this track is to evaluate the performance of recent recognition approaches using a challenging hand gesture dataset containing 14 gestures, performed by 28 participants executing the same gesture with two different numbers of fingers. Two research groups have participated to this track, the accuracy of their recognition algorithms have been evaluated and compared to three other state-of-the-art approaches. | en_US |
| dc.publisher | The Eurographics Association | en_US |
| dc.subject | I.3.5 [Computer Graphics] | |
| dc.subject | Computational Geometry and Object Modeling | |
| dc.subject | I.2.10 [Artificial Intelligence] | |
| dc.subject | Vision and Scene Understanding | |
| dc.subject | Shape | |
| dc.title | 3D Hand Gesture Recognition Using a Depth and Skeletal Dataset | en_US |
| dc.description.seriesinformation | Eurographics Workshop on 3D Object Retrieval | |
| dc.description.sectionheaders | SHREC Session I | |
| dc.identifier.doi | 10.2312/3dor.20171049 | |
| dc.identifier.pages | 33-38 | |
