Pattern Recognition Letters
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
International Journal of Computer Vision
Improved Illumination Assessment for Vision-Based Traffic Monitoring
VS '98 Proceedings of the 1998 IEEE Workshop on Visual Surveillance
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Automatic fog detection and estimation of visibility distance through use of an onboard camera
Machine Vision and Applications
Photorealistic rendering of rain streaks
ACM SIGGRAPH 2006 Papers
International Journal of Computer Vision
Scene modeling and change detection in dynamic scenes: A subspace approach
Computer Vision and Image Understanding
Using the Shape Characteristics of Rain to Identify and Remove Rain from Video
SSPR & SPR '08 Proceedings of the 2008 Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
The global network of outdoor webcams: properties and applications
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Non-parametric statistical background modeling for efficient foreground region detection
Machine Vision and Applications
Analysis of Rain and Snow in Frequency Space
International Journal of Computer Vision
Falling snow motion estimation based on a semi-transparent and particle trajectory model
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
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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 a goodness-of-fit test, the Gaussian distribution is temporally smoothed and its amplitude allows deciding the presence of rain or snow. When the presence of rain or of snow is detected, the HOS makes it possible to detect the pixels of rain or of snow in the foreground images, and to estimate the intensity of the precipitation of rain or of snow. The applications of the method are numerous and include the detection of critical weather conditions, the observation of weather, the reliability improvement of video-surveillance systems and rain rendering.