Name
Abstract | ||
|---|---|---|
Vanishing point detection algorithms based on 2D histogramming techniques have been employed in a variety of computer vision systems. Previous algorithms achieved some good results but still failed to maintain a balanced performance in both accuracy and time. Recent research (Li et al., 2010) shows that, vanishing point detection could be converted to a 1D histogram search problem, which largely accelerates the procedure. In this paper, we further improve this idea and propose a complete scheme for vanishing point detection from images of the so called ''Manhattan world''. We test our algorithm and some commonly used vanishing point detection methods on public database YorkUrbanDB and our own implemented database PKUCampusDB. Our algorithm shows significant performance improvements. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.patrec.2011.09.027 | Pattern Recognition Letters |
Keywords | Field | DocType |
point detection method,balanced performance,point detection,manhattan world,vanishing point detection,public database,significant performance improvement,database pkucampusdb,complete scheme,previous algorithm,single image,hough transform,gaussian sphere | Histogram,Computer vision,Machine vision,Hough transform,Gaussian surface,Artificial intelligence,Search problem,Mathematics,Vanishing point | Journal |
Volume | Issue | ISSN |
33 | 1 | 0167-8655 |
Citations | PageRank | References |
20 | 0.86 | 17 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Li | 1 | 172 | 9.79 |
| Kun Peng | 2 | 27 | 1.39 |
| Xianghua Ying | 3 | 221 | 23.55 |
| Hongbin Zha | 4 | 2206 | 183.36 |