Name
Abstract | ||
|---|---|---|
Background segmentation methods are exposed to the effects of different kinds of noise due to the limitations of image acquisition devices. This type of distortion can worsen the performance of segmentation methods because the input pixel values are altered. In this paper we study how several well-known background segmentation methods perform when the input is corrupted with several levels of uniform and Gaussian noise. Furthermore, few situations are reported where instead of an inconvenience, adding noise to the input may be desirable to attenuate some limitations of a method. In this work, the performance of nine well known methods is studied under both kinds of noise. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s10462-016-9525-3 | Artif. Intell. Rev. |
Keywords | Field | DocType |
Foreground detection,Background modeling,Uniform noise,Gaussian noise,Covariance matrix regularization | Value noise,Computer vision,Median filter,Noise measurement,Noise (signal processing),Computer science,Salt-and-pepper noise,Image noise,Artificial intelligence,Gaussian noise,Gradient noise | Journal |
Volume | Issue | ISSN |
49 | 3 | 0269-2821 |
Citations | PageRank | References |
1 | 0.35 | 24 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Francisco Javier López-Rubio | 1 | 30 | 3.18 |
| Ezequiel López-Rubio | 2 | 323 | 39.73 |
| Miguel A. Molina-Cabello | 3 | 14 | 5.56 |
| Rafael Marcos Luque-Baena | 4 | 96 | 13.24 |
| Esteban J. Palomo | 5 | 95 | 14.79 |
| Enrique Domínguez | 6 | 133 | 21.24 |