Particle Systems—a Technique for Modeling a Class of Fuzzy Objects
ACM Transactions on Graphics (TOG)
International Journal of Computer Vision
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
International Journal of Computer Vision
A Robust Subspace Approach to Layer Extraction
MOTION '02 Proceedings of the Workshop on Motion and Video Computing
An Integrated Bayesian Approach to Layer Extraction from Image Sequences
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Multi-body Factorization with Uncertainty: Revisiting Motion Consistency
International Journal of Computer Vision
Photorealistic rendering of rain streaks
ACM SIGGRAPH 2006 Papers
Particle Video: Long-Range Motion Estimation using Point Trajectories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Artist-directable real-time rain rendering in city environments
NPH'06 Proceedings of the Second Eurographics conference on Natural Phenomena
Detecting and extracting natural snow from videos
Information Processing Letters
Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks
International Journal of Computer Vision
Hi-index | 0.00 |
Dynamic weather such as rain and snow causes complex spatio-temporal intensity fluctuations in videos. Such fluctuations can adversely impact vision systems that rely on small image features for tracking, object detection and recognition. While these effects appear to be chaotic in space and time, we show that dynamic weather has a predictable global effect in frequency space. For this, we first develop a model of the shape and appearance of a single rain or snow streak in image space. Detecting individual streaks is difficult even with an accurate appearance model, so we combine the streak model with the statistical characteristics of rain and snow to create a model of the overall effect of dynamic weather in frequency space. Our model is then fit to a video and is used to detect rain or snow streaks first in frequency space, and the detection result is then transferred to image space. Once detected, the amount of rain or snow can be reduced or increased. We demonstrate that our frequency analysis allows for greater accuracy in the removal of dynamic weather and in the performance of feature extraction than previous pixel-based or patch-based methods. We also show that unlike previous techniques, our approach is effective for videos with both scene and camera motions.