Name
Abstract | ||
|---|---|---|
This paper presents a new algorithm for background subtraction that can model the background image from a sequence of images, even if there are foreground objects in each image frame. In contrast with Gaussian mixture model algorithm, in our proposed method the problem of distinguishing between background and foreground kernels becomes trivial. The key idea of our method lies in the identification of the background based on QR-decomposition method in linear algebra. R-values taken from QR-decomposition can be applied to decompose a given system to indicate the degree of the significance of the decomposed parts. We split the image into small blocks and select the background blocks with the weakest contribution, according to the assigned R-values. Simulation results show the better background detection performance with respect to some others. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1109/ICASSP.2007.366102 | Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference |
Keywords | Field | DocType |
Gaussian processes,image sequences,linear algebra,Gaussian mixture model algorithm,QR decomposition-based algorithm,background image,background subtraction,image frame,images sequence,linear algebra,Image processing,Image segmentation,Linear algebra,Matrix decomposition,Object detection | Kernel (linear algebra),Background subtraction,Object detection,Pattern recognition,Computer science,Matrix decomposition,Image processing,Algorithm,Image segmentation,Artificial intelligence,Mixture model,QR decomposition | Conference |
Volume | ISSN | ISBN |
1 | 1520-6149 E-ISBN : 1-4244-0728-1 | 1-4244-0728-1 |
Citations | PageRank | References |
2 | 0.38 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amintoosi, M. | 1 | 2 | 0.38 |
| Farzam Farbiz | 2 | 475 | 52.46 |
| Mahmood Fathy | 3 | 482 | 63.71 |
| Analoui, M. | 4 | 2 | 0.38 |