TY - GEN
T1 - Classification of images in fog and fog-free scenes for use in vehicles
AU - Pavlic, Mario
AU - Rigoll, Gerhard
AU - Ilic, Slobodan
PY - 2013
Y1 - 2013
N2 - Today modern vehicles are often equipped with a camera, which captures the scene in front of the vehicle. The recognition of weather conditions with this camera can help to improve many applications as well as establish new ones. In this article we will show how it is possible to distinguish between scenes with clear and foggy weather situations. The proposed method uses only gray-scale images as input signal and is running in real time. Using spectral features and a simple linear classifier, we can achieve high detection rates in both daytime and night-time scenes. Furthermore, we will show that in our application area these features outperform others.
AB - Today modern vehicles are often equipped with a camera, which captures the scene in front of the vehicle. The recognition of weather conditions with this camera can help to improve many applications as well as establish new ones. In this article we will show how it is possible to distinguish between scenes with clear and foggy weather situations. The proposed method uses only gray-scale images as input signal and is running in real time. Using spectral features and a simple linear classifier, we can achieve high detection rates in both daytime and night-time scenes. Furthermore, we will show that in our application area these features outperform others.
UR - http://www.scopus.com/inward/record.url?scp=84892402260&partnerID=8YFLogxK
U2 - 10.1109/IVS.2013.6629514
DO - 10.1109/IVS.2013.6629514
M3 - Conference contribution
AN - SCOPUS:84892402260
SN - 9781467327558
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 481
EP - 486
BT - 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
T2 - 2013 IEEE Intelligent Vehicles Symposium, IEEE IV 2013
Y2 - 23 June 2013 through 26 June 2013
ER -