Name
Title | ||
|---|---|---|
Vehicle Verification Using Features From Curvelet Transform and Generalized Gaussian Distribution Modeling |
Abstract | ||
|---|---|---|
This paper presents a new feature descriptor for vehicle verification. The object detection scheme generates the vehicle hypothesis (candidate) that requires subsequent confirmation in the vehicle verification stage with specific feature descriptors. In the procedure of vehicle verification, an image descriptor is generated from the statistical parameter of the curvelet-transformed (CT) subbands. The marginal distribution of CT output is a heavy-tailed bell-shaped function, which can be approximated as Gaussian, Laplace, and generalized Gaussian distribution (GGD) with high accuracy. The maximum likelihood estimation (MLE) produces the distribution parameters of each CT subband for the generation of the image feature descriptor. The classifier then assigns a class label for the vehicle hypothesis based on this descriptor information. As documented in the experimental results, this feature descriptor is effective and outperforms the existing methods in the vehicle verification tasks. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TITS.2014.2386535 | Intelligent Transportation Systems, IEEE Transactions |
Keywords | Field | DocType |
curvelet transform (ct),maximum likelihood estimation (mle),supervised classification,vehicle verification,feature extraction,gaussian distribution,maximum likelihood estimation,computed tomography | Statistical parameter,Computer vision,Object detection,Laplace transform,Pattern recognition,Feature extraction,Gaussian,Artificial intelligence,Classifier (linguistics),Mathematics,Marginal distribution,Generalized normal distribution | Journal |
Volume | Issue | ISSN |
PP | 99 | 1524-9050 |
Citations | PageRank | References |
6 | 0.43 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jing-Ming Guo | 1 | 830 | 77.60 |
| Heri Prasetyo | 2 | 127 | 9.82 |
| Mahmoud E. Farfoura | 3 | 6 | 0.43 |
| Hua Lee | 4 | 109 | 11.38 |