Name
Title | ||
|---|---|---|
Multi-scale and Multi-orientation Local Feature Extraction for Lane Detection Using High-Level Information. |
Abstract | ||
|---|---|---|
Task-specifed computer vision systems usually need to detect certain targets at multiple scales of resolution and multiple orientations. For a vision-based lane detection system, it is essential to detect the lane-markings at different scales and orientations. In this paper, we illustrate an effcient local feature extraction algorithm for the lane detection system, which is tuned by the high-level information about the lane-markings. Firstly, we deduced the explicit expression of the scale and orientation for the local feature of the lane markings. Secondly, a flter bank for local feature extraction is designed using the SVD approach for certain orientation and scale. Thirdly, the flter bank is used to tune a special lane-marking detector to expected orientation and scale at different locations of the image. Then, non-maxima suppression is performed along the corresponding direction at that location. Lastly, a hysteresis thresholding is applied to identify the exact feature points. Unlike other works in which the authors try to remove the false local feature points with the help of high-level information, we prefer to introduce the high-level information to the local feature detection stage as early as possible. Experiment results show that the proposed algorithm is very effcient for lane detection especially in very complex road seniors. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICIG.2011.129 | ICIG |
Keywords | Field | DocType |
local feature detection stage,false local feature point,lane detection,high-level information,multi-orientation local feature extraction,exact feature point,lane detection system,flter bank,effcient local feature extraction,local feature,local feature extraction,feature extraction,singular value decomposition,computer vision,edge detection,filter bank | Object detection,Singular value decomposition,Computer vision,Feature detection (computer vision),Pattern recognition,Edge detection,Computer science,Feature (computer vision),Feature extraction,Artificial intelligence,Thresholding,Detector | Conference |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangjing An | 1 | 226 | 12.15 |
| Jian Li | 2 | 49 | 6.61 |
| Er-Ke Shang | 3 | 24 | 3.10 |
| Hangen He | 4 | 307 | 23.86 |