Name
Abstract | ||
|---|---|---|
A new generic object recognition (GOR) method for robot perception is proposed in this paper, based on multi-feature fusion of two-dimensional (2D) and 3D scale invariant feature transform descriptors drawn from 2D images and 3D point clouds. The trained support vector machine is utilized to construct multi-category classifiers that recognize the objects. According to our results, this new GOR approach achieves higher recognition rates than classical methods tested, even when one has large intra-class variations, or high inter-class similarities of the objects. Simulation results demonstrate the effectiveness and efficiency of the proposed GOR approach. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.2316/Journal.206.2016.5.206-4706 | INTERNATIONAL JOURNAL OF ROBOTICS & AUTOMATION |
Keywords | Field | DocType |
Generic object recognition, Point cloud, SIFT, Feature fusion, SVM, belief functions | Computer vision,Feature fusion,3D single-object recognition,Robot perception,Artificial intelligence,Engineering,Cognitive neuroscience of visual object recognition | Journal |
Volume | Issue | ISSN |
31 | 5 | 0826-8185 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinde Li | 1 | 50 | 11.00 |
| Chaomin Luo | 2 | 49 | 6.28 |
| Jean Dezert | 3 | 777 | 61.59 |
| Yingzi Tan | 4 | 0 | 1.35 |