Abstract
In this work we address the general bin-picking problem where 3D data is available. We apply Harmonic Shape Contexts (HSC) features since these are invariant to translation, scale, and 3D rotation. Each object is divided into a number of sub-models each represented by a number of HSC features. These are compared with HSC features extracted in the current data using a graph-based scheme. Results show that the approach is somewhat sensitive to noise, but works in presence of occlusion.
| Originalsprog | Engelsk |
|---|---|
| Titel | 18th. International Conference on Pattern Recognition 2006. ICPR 2006 |
| Antal sider | 4 |
| Vol/bind | 2 |
| Publikationsdato | 2006 |
| Sider | 581-584 |
| Status | Udgivet - 2006 |
| Begivenhed | 18th International Conference on Pattern Recognition, 2006. ICPR 2006 - Hong Kong, Kina Varighed: 20 aug. 2006 → 24 aug. 2006 Konferencens nummer: 18 |
Konference
| Konference | 18th International Conference on Pattern Recognition, 2006. ICPR 2006 |
|---|---|
| Nummer | 18 |
| Land/Område | Kina |
| By | Hong Kong |
| Periode | 20/08/2006 → 24/08/2006 |