Name
Abstract | ||
|---|---|---|
Shape features are widely used in image target recognition due to their useful property of translation, rotation and scaling invariance. In this paper, low order Hu moments and other four shape features are combined together to classify different kinds of knives and guns with the support vector machine in the application of millimeter wave security imaging. Experimental results show that the combined features have better invariance and differences than other invariant moments. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICInfA.2016.7832097 | 2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA) |
Keywords | Field | DocType |
Feature extraction, Combined features, Invariants, Target recognition | Computer vision,Extremely high frequency,Invariant (physics),Pattern recognition,Computer science,Support vector machine,Feature extraction,Invariant (mathematics),Artificial intelligence,Scaling | Conference |
ISBN | Citations | PageRank |
9781509041022 | 0 | 0.34 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ling Dai | 1 | 15 | 2.74 |
| Hu Hong | 2 | 2 | 5.31 |
| Yifan Chen | 3 | 58 | 19.82 |
| Zhou Min | 4 | 0 | 0.34 |