The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Video matting of complex scenes
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
ACM SIGGRAPH 2004 Papers
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH 2005 Papers
Consistent Segmentation for Optical Flow Estimation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Bilayer Segmentation of Live Video
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
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
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Bilateral filtering-based optical flow estimation with occlusion detection
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Rain or Snow Detection in Image Sequences Through Use of a Histogram of Orientation of Streaks
International Journal of Computer Vision
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This paper presents a motion estimation method for semitransparent objects with a long-range displacement between frames, i.e., falling snow in video. Previous optical flow based methods have been treated with non-transparent, rigid, and fluid-like moving objects in a short-range displacement. However, they fail to match between frames when moving objects are transparent/homogenoeous color in a long-range displacement. To meet with such objects' properties, a two-step algorithm is proposed from rough to refined motion estimation via an energy minimization. First, rough motion of every snow particles is extracted from video using a novel "time filter" method in order to obtain/update a quasi-stationary background in every 30 fps. Second, using such rough optical flow from the first step, the long-range snowflakes' trajectories are estimated and refined by propagation, linking, pruning, and optimization. Experimental results using real falling snow videos show that the proposed method is more effective than a previous optical flow method. Our proposed method is useful for the analysis of natural environment changes.