|Department of Engineering|
|University of Cambridge > Engineering Department > Machine Intelligence Lab|
MOTION SEGMENTATION BY TRACKING EDGE INFORMATION OVER MULTIPLE FRAMES
Paul Smith, Tom Drummond, and Roberto Cipolla
This paper presents a new Bayesian framework for layered motion segmentation, dividing the frames of an image sequence into foreground and background layers by tracking edges. The first frame in the sequence is segmented into regions using image edges, which are tracked to estimate two affine motions. The probability of the edges fitting each motion is calculated using 1st order statistics along the edge. The most likely region labelling is then resolved using these probabilities, together with a Markov Random Field prior. As part of this process one of the motions is also identified as the foreground motion.
Good results are obtained using only two frames for segmentation. However, it is also demonstrated that over multiple frames the probabilities may be accumulated to provide an even more accurate and robust segmentation. The final region labelling can be used, together with the two motion models, to produce a good segmentation of an extended sequence.
If you have difficulty viewing files that end
which are gzip compressed, then you may be able to find
tools to uncompress them at the gzip
If you have difficulty viewing files that are in PostScript, (ending
'.ps.gz'), then you may be able to
find tools to view them at
We have attempted to provide automatically generated PDF copies of documents for which only PostScript versions have previously been available. These are clearly marked in the database - due to the nature of the automatic conversion process, they are likely to be badly aliased when viewed at default resolution on screen by acroread.
|| Search | CUED | Cambridge University ||
2005 Cambridge University Engineering Dept
Information provided by milab-maintainer