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A novel local surface description for automatic 3D object recognition in low resolution cluttered scenes

Shah, S.A.A., Bennamoun, M., Boussaid, F. and El-Sallam, A.A. (2013) A novel local surface description for automatic 3D object recognition in low resolution cluttered scenes. In: IEEE International Conference on Computer Vision Workshops 2013, 2 - 8 December 2013, Sydney, NSW

Link to Published Version: https://doi.org/10.1109/ICCVW.2013.88
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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].

Item Type: Conference Paper
URI: http://researchrepository.murdoch.edu.au/id/eprint/50112
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