Journal article
A novel feature representation for automatic 3D object recognition in cluttered scenes
Neurocomputing, Vol.205, pp.1-15
2016
Abstract
We present a novel local surface description technique for automatic three dimensional (3D) object recognition. In the proposed approach, highly repeatable keypoints are first detected by computing the divergence of the vector field at each point of the surface. Being a differential invariant of curves and surfaces, the divergence captures significant information about the surface variations at each point. The detected keypoints are pruned to only retain the keypoints which are associated with high divergence values. A keypoint saliency measure is proposed to rank these keypoints and select the best ones. A novel integral invariant local surface descriptor, called 3D-Vor, is built around each keypoint by exploiting the vorticity of the vector field at each point of the local surface. The proposed descriptor combines the strengths of signature-based methods and integral invariants to provide robust local surface description. The performance of the proposed fully automatic 3D object recognition technique was rigorously tested on three publicly available datasets. Our proposed technique is shown to exhibit superior performance compared to state-of-the-art techniques. Our keypoint detector and descriptor based algorithm achieves recognition rates of 100%, 99.35% and 96.2% respectively, when tested on the Bologna, UWA and Ca׳ Foscari Venezia datasets.
Details
- Title
- A novel feature representation for automatic 3D object recognition in cluttered scenes
- Authors/Creators
- S.A.A. Shah (Author/Creator)M. Bennamoun (Author/Creator)F. Boussaid (Author/Creator)
- Publication Details
- Neurocomputing, Vol.205, pp.1-15
- Publisher
- Elsevier BV
- Identifiers
- 991005541604507891
- Copyright
- © 2015 Elsevier B.V.
- Murdoch Affiliation
- Murdoch University
- Language
- English
- Resource Type
- Journal article
UN Sustainable Development Goals (SDGs)
This output has contributed to the advancement of the following goals:
Source: InCites
Metrics
29 Record Views
InCites Highlights
These are selected metrics from InCites Benchmarking & Analytics tool, related to this output
- Citation topics
- 4 Electrical Engineering, Electronics & Computer Science
- 4.116 Robotics
- 4.116.133 Simultaneous Localization and Mapping
- Web Of Science research areas
- Computer Science, Artificial Intelligence
- ESI research areas
- Computer Science