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.
| Original language | English |
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| Title of host publication | 18th. International Conference on Pattern Recognition 2006. ICPR 2006 |
| Number of pages | 4 |
| Volume | 2 |
| Publication date | 2006 |
| Pages | 581-584 |
| Publication status | Published - 2006 |
| Event | 18th International Conference on Pattern Recognition, 2006. ICPR 2006 - Hong Kong, China Duration: 20 Aug 2006 → 24 Aug 2006 Conference number: 18 |
Conference
| Conference | 18th International Conference on Pattern Recognition, 2006. ICPR 2006 |
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| Number | 18 |
| Country/Territory | China |
| City | Hong Kong |
| Period | 20/08/2006 → 24/08/2006 |