TY - GEN
T1 - Image based fog detection in vehicles
AU - Pavlić, Mario
AU - Belzner, Heidrun
AU - Rigoll, Gerhard
AU - Ilić, Slobodan
PY - 2012
Y1 - 2012
N2 - Modern vehicles are equipped with many cameras and their use in many practical applications is extensive. Detecting the presence of fog from images of a camera mounted in vehicles is a very challenging task with the potential to be used in many practical applications. Approaches introduced until now analyze properties of local objects in the image like lane markings, traffic signs, back lights of vehicles in front or head lights of approaching vehicles. By contrast to all these related works we propose to use image descriptors and a classification procedure in order to distinguish images with fog present from those free of fog. These image descriptors are global and describe the entire image using Gabor filters at different frequencies, scales and orientations. Our experiments demonstrated hight potential of the proposed method for fog detection on daytime images.
AB - Modern vehicles are equipped with many cameras and their use in many practical applications is extensive. Detecting the presence of fog from images of a camera mounted in vehicles is a very challenging task with the potential to be used in many practical applications. Approaches introduced until now analyze properties of local objects in the image like lane markings, traffic signs, back lights of vehicles in front or head lights of approaching vehicles. By contrast to all these related works we propose to use image descriptors and a classification procedure in order to distinguish images with fog present from those free of fog. These image descriptors are global and describe the entire image using Gabor filters at different frequencies, scales and orientations. Our experiments demonstrated hight potential of the proposed method for fog detection on daytime images.
UR - http://www.scopus.com/inward/record.url?scp=84865023487&partnerID=8YFLogxK
U2 - 10.1109/IVS.2012.6232256
DO - 10.1109/IVS.2012.6232256
M3 - Conference contribution
AN - SCOPUS:84865023487
SN - 9781467321198
T3 - IEEE Intelligent Vehicles Symposium, Proceedings
SP - 1132
EP - 1137
BT - 2012 IEEE Intelligent Vehicles Symposium, IV 2012
T2 - 2012 IEEE Intelligent Vehicles Symposium, IV 2012
Y2 - 3 June 2012 through 7 June 2012
ER -