Name
Abstract | ||
|---|---|---|
Estimating image vanishing points has many applications in the computer vision field, such as robotic navigation, visual measurement, camera calibration, 3D reconstruction and augmented reality, which requires a balance between accuracy and rate. In this paper, we present an algorithm to accurately and efficiently detect vanishing points and classify lines through the clustering method and binary particle swarm optimization (BPSO). First, lines are clustered according to their slope angles based on an iterative BPSO process, since parallel lines, in a medium-to-long range scene, present similar slopes. The solutions are continuously evaluated using multiple factors, such as the number and length of the line segments and their distance to the related vanishing points. The coefficient of variation is applied to weigh these factors. As a result, all possible non-orthogonal vanishing points in the image are directly detected, irrespective of the camera calibration parameters to avoid mapping segments on the Gaussian sphere. Compared with other algorithms on the York Urban Database, the proposed algorithm exhibits significant performance improvements. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/s11760-016-0883-8 | Signal, Image and Video Processing |
Keywords | Field | DocType |
Vanishing point estimation, Visual measurement, BPSO, Variance coefficient | Computer vision,Line segment,Pattern recognition,Augmented reality,Parallel,Camera resectioning,Artificial intelligence,Gaussian surface,Cluster analysis,Vanishing point,Mathematics,3D reconstruction | Journal |
Volume | Issue | ISSN |
11 | 1 | 1863-1711 |
Citations | PageRank | References |
1 | 0.35 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lei Han | 1 | 1 | 0.69 |
| Chenrong Huang | 2 | 3 | 1.41 |
| Shengnan Zheng | 3 | 1 | 0.35 |
| Zhen Zhang | 4 | 1 | 0.35 |
| Xu Lizhong | 5 | 155 | 24.51 |