Name
Abstract | ||
|---|---|---|
Concerning the problem of lane detection in the Lane Departure Warning (LDW) system, this paper presents one method to detect the region of lane marking based on the CIELab color features clustering. Color space can provide us more precious information than gray scale. This algorithm proves that it is feasible to recognize lane marking by using color clustering. According to the geometry feature of road, quadratic curve is adopted to match the lane. And also, least square method is proposed to depict the parameters of quadratic curve. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/WKDD.2010.118 | WKDD |
Keywords | Field | DocType |
lane departure warning,color clustering,precious information,traffic engineering computing,lane detection,cielab color features clustering,square method,lane departure warning system,geometry feature,curve fitting,least square,color space,gray scale,least squares approximations,feature extraction,quadratic curve,road geometry feature,road traffic,least square method,cielab color,image colour analysis,clustering algorithms,pixel,histograms,image segmentation | Computer vision,Color space,Lane departure warning system,Curve fitting,Computer science,Feature extraction,Image segmentation,Quadratic function,Artificial intelligence,Cluster analysis,Grayscale | Conference |
ISBN | Citations | PageRank |
978-1-4244-5398-6 | 3 | 0.45 |
References | Authors | |
3 | 2 | |