14 February 2015 Object detection using categorised 3D edges
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Proceedings Volume 9445, Seventh International Conference on Machine Vision (ICMV 2014); 94450C (2015) https://doi.org/10.1117/12.2180551
Event: Seventh International Conference on Machine Vision (ICMV 2014), 2014, Milan, Italy
Abstract
In this paper we present an object detection method that uses edge categorisation in combination with a local multi-modal histogram descriptor, all based on RGB-D data. Our target application is robust detection and pose estimation of known objects. We propose to apply a recently introduced edge categorisation algorithm for describing objects in terms of its different edge types. Relying on edge information allow our system to deal with objects with little or no texture or surface variation. We show that edge categorisation improves matching performance due to the higher level of discrimination, which is made possible by the explicit use of edge categories in the feature descriptor. We quantitatively compare our approach with the state-of-the-art template based Linemod method, which also provides an effective way of dealing with texture-less objects, tests were performed on our own object dataset. Our results show that detection based on edge local multi-modal histogram descriptor outperforms Linemod with a significantly smaller amount of templates.
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Lilita Kiforenko, Lilita Kiforenko, Anders Glent Buch, Anders Glent Buch, Leon Bodenhagen, Leon Bodenhagen, Norbert Krüger, Norbert Krüger, } "Object detection using categorised 3D edges", Proc. SPIE 9445, Seventh International Conference on Machine Vision (ICMV 2014), 94450C (14 February 2015); doi: 10.1117/12.2180551; https://doi.org/10.1117/12.2180551
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