Abstract

Current vision systems are designed to perform in clear weather. Needless to say, in any outdoor application, there is no escape from "bad" weather. Ultimately, computer vision systems must include mechanisms that enable them to function (even if somewhat less reliably) in the presence of haze, fog, rain, hail and snow. We begin by studying the visual manifestations of different weather conditions. For this, we draw on what is already known about atmospheric optics. Next, we identify effects caused by bad weather that can be turned to our advantage. Since the atmosphere modulates the information carried from a scene point to the observer it can be viewed as a mechanism of visual information coding. Based on this observation, we develop models and methods for recovering pertinent scene properties, such as three-dimensional structure, from images taken under poor weather conditions.

Keywords

HazeSnowComputer scienceRain and snow mixedObserver (physics)MeteorologyWeather predictionWeather forecastingAtmosphere (unit)Extreme weatherAtmospheric modelEnvironmental scienceArtificial intelligenceClimate changeGeographyGeology

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Publication Info

Year
1999
Type
article
Pages
820-827 vol.2
Citations
854
Access
Closed

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Shree K. Nayar, Srinivasa G. Narasimhan (1999). Vision in bad weather. Proceedings of the Seventh IEEE International Conference on Computer Vision , 820-827 vol.2. https://doi.org/10.1109/iccv.1999.790306

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DOI
10.1109/iccv.1999.790306

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Data completeness: 77%