Vision Through Rain and Snow

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Bad weather can severely limit the usefulness of computer vision inthe outdoors. The algorithms (segmentation, tracking, recognition,etc.) used in today's vision systems are not equipped to handle theadverse effects of weather. For outdoor vision systems to perform wellin all conditions, they must explicitly account for the diverseeffects of weather. The visual effects produced by different weatherconditions can be broadly classified into two categories: steady (fog,mist and haze) and dynamic (rain, hail and snow). This researchproject is devoted to the modeling and analysis of dynamic weathereffects. The first stage of the study is to develop compact and yetdescriptive photometric and geometric models of raindrops andsnowflakes. These models will be used to develop advanced models forthe dynamical and statistical properties of rain and snow. Usingthese models, efficient and robust algorithms will be developed fordetecting and removing rain and snow from videos. The proposed modelswill also be used to develop algorithms for fast and realisticrendering of rain and snow. In addition, the developed models will beused to design and implement real-time rain and snow gauges based onvideo cameras. Such gauges will be very inexpensive compared to onescurrently used in meteorological sciences, and yet will provide highaccuracy in the estimation of weather parameters.The results of the proposed research are anticipated to have a broadand tangible impact on both research and education. From theperspective of research, the results are expected to impact therobustness of outdoor vision systems and the realism that computergraphics can achieve. The models, algorithms and databases developedduring the project will be broadly disseminated in a timelyfashion. With regards to education, the PI is currently developing acourse titled ``Computer Vision through the Atmosphere,'' and theresults of this investigation will contribute significantly to thecontent of the course.