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Rain or Snow Detection in Image Sequences through use of a Histogram of Orientation of Streaks

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Rain or Snow Detection in Image Sequences through use of a Histogram of Orientation of Streaks
The detection of bad weather conditions is crucial for meteorological centers, specially with demand for air, sea and ground traffic management. In this article, a system based on computer vision is presented which detects the presence of rain or snow. To separate the foreground from the background in image sequences, a classical Gaussian Mixture Model is used. The foreground model serves to detect rain and snow, since these are dynamic weather phenomena. Selection rules based on photometry and size are proposed in order to select the potential rain streaks. Then a Histogram of Orientations of rain or snow Streaks (HOS), estimated with the method of geometric moments, is computed, which is assumed to follow a model of Gaussian-uniform mixture. The Gaussian distribution represents the orientation of the rain or the snow whereas the uniform distribution represents the orientation of the noise. An algorithm of expectation maximization is used to separate these two distributions. Following...
Jérémie Bossu, Nicolas Hautière and Jean-Philip
Added 12 Apr 2012
Updated 12 Apr 2012
Type Journal
Year 2011
Where International Journal of Computer Vision
Authors Jérémie Bossu, Nicolas Hautière and Jean-Philippe Tarel
The bibtex reference is available at:
http://perso.lcpc.fr/tarel.jean-philippe/


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