Name
Abstract | ||
|---|---|---|
We propose a novel statistical measure for robust event detection, called 'Radial Reach filter' (RRF). The capability of detecting new objects (events) from a time-series image is an important problem of vision systems. The usual method of detecting new objects is simple background subtraction, that is to subtract current image from a background image. However, simple background subtraction is susceptible to illumination change such as shadows. Moreover, when the brightness difference between events and a background is small, it cannot detect the difference. In order to solve such problems, we propose the RRF which evaluates a local texture and realizes robust event detection. Experiments using real images show the effectiveness of the proposed methods. Furthermore, an experiment using an all-directional image from a stereo omni-directional system (SOS) shows the possibility of application to an environment-monitoring system. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1109/ICPR.2002.1048379 | Pattern Recognition, 2002. Proceedings. 16th International Conference |
Keywords | Field | DocType |
filtering theory,image sequences,image texture,object detection,stereo image processing,time series,all-directional image,background subtraction,brightness difference,environment-monitoring system,illumination change,local texture,object detection,radial reach filter,robust event detection,shadows,statistical measure,stereo omni-directional system,time-series image,vision systems | Background subtraction,Object detection,Computer vision,Machine vision,Pattern recognition,Image texture,Computer science,Optical filter,Artificial intelligence,Real image,Filtering theory,Brightness | Conference |
Volume | ISSN | Citations |
2 | 1051-4651 | 10 |
PageRank | References | Authors |
0.83 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Satoh, Y. | 1 | 10 | 0.83 |
| Tanahashi, H. | 2 | 10 | 1.16 |
| Wang, C. | 3 | 78 | 12.21 |
| Shun'ichi Kaneko | 4 | 230 | 35.34 |