Name
Abstract | ||
|---|---|---|
Background estimation and removal based on the joint use of range and color data produces superior results than can be achieved with either data source alone. This is in- creasingly relevant as inexpensive, real-time, passive range systems become more accessible through novel hardware and increased CPU processing speeds. Range is a power- ful signal for segmentation which is largely independent of color, and hence not effected by the classic color segmenta- tion problems of shadows and objects with color similar to the background. However, range alone is also not sufficient for the good segmentation: depth measurements are rarely available at all pixels in the scene, and foreground objects may be indistinguishable in depth when they are close to the background. Color segmentation is complementary in these cases. Surprisingly, little work has been done to date on joint range and color segmentation. We describe and demonstrate a background estimation method based on a multidimensional (range and color) clustering at each im- age pixel. Segmentation of the foreground in a given frame is performed via comparison with background statistics in range and normalized color. Important implementation is- sues such as treatment of shadows and low confidence mea- surements are discussed in detail. In this paper we present a passive method for background estimation and removal based on the joint use of range and color which produces superior results than can be achieved with either data source alone. This approach is now prac- tical for general applications as inexpensive real-time pas- sive range data is becoming more accessible through novel hardware(10) and increased CPU processing speeds. The joint use of color and range produces cleaner segmenta- tion of the foreground scene in comparison to the com- monly used color-based background subtraction or range- based segmentation. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1109/CVPR.1999.784721 | CVPR |
Keywords | Field | DocType |
hardware,real time systems,statistics,segmentation,real time,application software,computer vision,image segmentation,multidimensional systems,background subtraction,layout,gaussian processes | Computer vision,Scale-space segmentation,Pattern recognition,Color histogram,Range segmentation,Segmentation,Computer science,Image segmentation,Color balance,Pixel,Artificial intelligence,Cluster analysis | Conference |
Volume | Issue | ISSN |
2 | 1 | 1063-6919 |
Citations | PageRank | References |
74 | 6.53 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gaile G. Gordon | 1 | 148 | 12.63 |
| Trevor Darrell | 2 | 22413 | 1800.67 |
| Michael Harville | 3 | 369 | 35.55 |
| J. Woodfill | 4 | 1337 | 239.65 |