Conference paper
A novel local surface description for automatic 3D object recognition in low resolution cluttered scenes
2013 IEEE International Conference on Computer Vision Workshops
IEEE International Conference on Computer Vision Workshops 2013 (Sydney, NSW, 02/12/2013–08/12/2013)
2013
Abstract
Local surface description is a critical stage for feature matching and recognition tasks. This paper presents a rotation invariant local surface descriptor, called 3D-Div. The proposed descriptor is based on the concept of 3D vector field's divergence, extensively used in electromagnetic theory. To generate a 3D-Div descriptor of a 3D surface, a local surface patch is parameterized around a randomly selected 3D point at a fixed scale. A unique Local Reference Frame (LRF) is then constructed at that 3D point using all the neighboring points forming the patch. A normalized 3D vector field is then computed at each point in the patch and referenced with LRF vectors. The 3D-Div descriptor is finally generated as the divergence of the reoriented 3D vector field. We tested our proposed descriptor on the challenging low resolution Washington RGB-D (Kinect) object dataset, for the task of automatic 3D object recognition. Reported experimental results show that 3D-Div based recognition achieves 93% accuracy as compared to 85% for existing state-of-the-art depth kernel descriptors [2].
Details
- Title
- A novel local surface description for automatic 3D object recognition in low resolution cluttered scenes
- Authors/Creators
- S.A.A. Shah (Author/Creator)M. Bennamoun (Author/Creator)F. Boussaid (Author/Creator)A.A. El-Sallam (Author/Creator)
- Publication Details
- 2013 IEEE International Conference on Computer Vision Workshops
- Conference
- IEEE International Conference on Computer Vision Workshops 2013 (Sydney, NSW, 02/12/2013–08/12/2013)
- Identifiers
- 991005540899207891
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Conference paper
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