@inproceedings {pub2579pub2692,
	title = {Detection of Camera Artifacts from Camera Images},
	author = { Harsh  Gandhi AND J{\"o}rg Deigm{\"o}ller AND Nils Einecke},
	year = {2014},
	month = {October},
	abstract = {Cameras are frequently used in state-of-the-art systems in order to get detailed
information of the environment. However, when cameras are used outdoors they
easily get dirty or scratches on the lens which leads to image artifacts that
can deteriorate a system{\textquoteright}s performance. Little work has yet been done on how to
detect and cope with such artifacts. Most of the previous work has concentrated
on detecting a specific artifact like rain drops on the windshield of a car. In
this paper, we show that on moving systems most artifacts can be detected by
analyzing the frames in a stream of images from a camera for static image
parts. Based on the observation that most artifacts are temporally stable in
their position in the image we compute pixel-wise correlations between
images. Since the system is moving the static artifacts will lead to a high
correlation value while the pixels showing only scene elemets will have a low
correlation value. For testing this novel algorithm, we recorded some outdoor
data with the three different artifacts: raindrops, dirt and scratches. The
results of our new algorithm on this data show, that it reliably detects all
three artifacts. Moreover, we show that our algorithm can be implemented
efficiently by means of box-filters which allows it to be used as a
self-checking routine running in background on low-power systems such as
autonomous field robots or advanced driver assistant systems on a vehicle.},
	publisher = {IEEE},
	booktitle = {International IEEE Conference on Intelligent Transportation Systems},
	pages = {603--610}
}