International Journal of Computer Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Modeling the Space of Camera Response Functions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic fog detection and estimation of visibility distance through use of an onboard camera
Machine Vision and Applications
On-Road Vehicle Detection: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM SIGGRAPH 2008 papers
Color-based road detection in urban traffic scenes
IEEE Transactions on Intelligent Transportation Systems
Real-time dense stereo for intelligent vehicles
IEEE Transactions on Intelligent Transportation Systems
Real-time disparity contrast combination for onboard estimation of the visibility distance
IEEE Transactions on Intelligent Transportation Systems
Real-Time Speed Sign Detection Using the Radial Symmetry Detector
IEEE Transactions on Intelligent Transportation Systems
Contrast restoration of weather degraded images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced fog detection and free-space segmentation for car navigation
Machine Vision and Applications
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In adverse weather conditions, in particular, in daylight fog, the contrast of images grabbed by in-vehicle cameras in the visible light range is drastically degraded, which makes current driver assistance that relies on cameras very sensitive to weather conditions. An onboard vision system should take weather effects into account. The effects of daylight fog vary across the scene and are exponential with respect to the depth of scene points. Because it is not possible in this context to compute the road scene structure beforehand, contrary to fixed camera surveillance, a new scheme is proposed. Fog density is first estimated and then used to restore the contrast using a flat-world assumption on the segmented free space in front of a moving vehicle. A scene structure is estimated and used to refine the restoration process. Results are presented using sample road scenes under foggy weather and assessed by computing the visibility level enhancement that is gained by the method. Finally, we show applications to the enhancement in daylight fog of low-level algorithms that are used in advanced camera-based driver assistance.