Name
Abstract | ||
|---|---|---|
A foreground segmentation method, including image enhancement, trajectory classification and object segmentation, is proposed for moving cameras under low illumination conditions. Gradient-field-based image enhancement is designed to enhance low-contrast images. On the basis of the dense point trajectories obtained in long frames sequences, a simple and effective clustering algorithm is designed to classify foreground and background trajectories. By combining trajectory points and a marker-controlled watershed algorithm, a new type of foreground labeling algorithm is proposed to effectively reduce computing costs and improve edge-preserving performance. Experimental results demonstrate the promising performance of the proposed approach compared with other competing methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005695100650071 | ICPRAM |
Field | DocType | Citations |
Computer vision,Scale-space segmentation,Pattern recognition,Segmentation,Computer science,Segmentation-based object categorization,Watershed,Artificial intelligence,Cluster analysis,Trajectory | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Wang | 1 | 3 | 3.44 |
| Weili Li | 2 | 4 | 2.41 |
| Xiaoqing Yin | 3 | 0 | 2.03 |
| Yu Liu | 4 | 7 | 4.48 |
| Maojun Zhang | 5 | 314 | 48.74 |