  We present a method for segmenting an arbitrary number of moving objects in
image sequences using the geometry of 6 points in 2D to infer motion
consistency. The method has been evaluated on the Hopkins 155 database and
surpasses current state-of-the-art methods such as SSC, both in terms of
overall performance on two and three motions but also in terms of maximum
errors. The method works by finding initial clusters in the spatial domain, and
then classifying each remaining point as belonging to the cluster that
minimizes a motion consistency score. In contrast to most other motion
segmentation methods that are based on an affine camera model, the proposed
method is fully projective.
