Multiscale segmentation by combining motion and intensity cues

We present a multiscale method for motion segmentation. Our method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion. In addition, as the aggregation process proceeds and larger aggregates ar...

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Bibliographic Details
Published in2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) Vol. 1; pp. 256 - 263 vol. 1
Main Authors Meirav Galun, Alexander Apartsin, Ronen Basri
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
Aggregates
Clustering algorithms
Computer science
Computer vision
Fluid flow measurement
Image motion analysis
Image segmentation
Mathematics
Motion estimation
Motion segmentation
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Summary:We present a multiscale method for motion segmentation. Our method begins with local, ambiguous optical flow measurements. It uses a process of aggregation to resolve the ambiguities and reach reliable estimates of the motion. In addition, as the aggregation process proceeds and larger aggregates are identified it employs a progressively more complex model to describe the motion. In particular, we proceed by recovering translational motion at fine levels, through affine transformation at intermediate levels, to 3D motion (described by a fundamental matrix) at the coarsest levels. Finally, the method is integrated with a segmentation method that uses intensity cues. We further demonstrate the utility of the method on both random dot and real motion sequences.
ISBN:0769523722
9780769523729
ISSN:1063-6919
1063-6919
DOI:10.1109/CVPR.2005.244