Name
Abstract | ||
|---|---|---|
Humans perform iconic gestures to refer to entities through embodying their shapes. For instance, people often gesture the outline of an object (e.g. a circle for a ball) when referring to it during communication. In this paper, we present a gesture-based object recognition algorithm that enables natural human-computer interaction involving iconic gestures. Based on our analysis of multiple gesture performances, we propose a new 3D motion description of iconic gestures, called Histograms of Guiding Strokes (HoGS), which successfully summarizes hand dynamic during gestures. Our gesture-based object recognition algorithm compares favorably to human judgment performance and outperforms most conventional gesture recognition approaches. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.5244/C.26.44 | PROCEEDINGS OF THE BRITISH MACHINE VISION CONFERENCE 2012 |
Field | DocType | Citations |
Histogram,Computer vision,Computer science,Gesture,Gesture recognition,Human judgment,Speech recognition,Artificial intelligence,Cognitive neuroscience of visual object recognition | Conference | 9 |
PageRank | References | Authors |
0.47 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amir Sadeghipour | 1 | 30 | 2.99 |
| Louis-Philippe Morency | 2 | 3220 | 200.79 |
| Stefan Kopp | 3 | 701 | 58.13 |