Image sequence segmentation using 3-D structure tensor and curve evolution
J Zhang, J Gao, W Liu - … on Circuits and Systems for Video …, 2001 - ieeexplore.ieee.org
J Zhang, J Gao, W Liu
IEEE Transactions on Circuits and Systems for Video Technology, 2001•ieeexplore.ieee.orgWe describe a novel approach for image sequence segmentation. It contains three parts:
global motion compensation, robust frame differencing, and curve evolution. It is
computationally efficient, does not require dense-field motion estimation, and is insensitive
to noise and global/background motion. It works for black-and-white and color image
sequences. The efficacy of this approach is demonstrated on both TV and surveillance
image sequences.
global motion compensation, robust frame differencing, and curve evolution. It is
computationally efficient, does not require dense-field motion estimation, and is insensitive
to noise and global/background motion. It works for black-and-white and color image
sequences. The efficacy of this approach is demonstrated on both TV and surveillance
image sequences.
We describe a novel approach for image sequence segmentation. It contains three parts: global motion compensation, robust frame differencing, and curve evolution. It is computationally efficient, does not require dense-field motion estimation, and is insensitive to noise and global/background motion. It works for black-and-white and color image sequences. The efficacy of this approach is demonstrated on both TV and surveillance image sequences.
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