Abstract
A spatiotemporal (ST) image cube, created by stacking a temporally
dense sequence of images together, is a temporally coherent data
representation. Using ST surface flow, i.e., the extension of optical
flow to ST surfaces, it is shown how ST flow curves can be recovered and
then used to detect groups of flow curves such that each group
represents a single object or surface in the scene undergoing motion.
The algorithm forms clusters of similar flow curves and is based on
constraints called the temporal uniqueness constraints. First, a point
in an image can only move to at most one point in the next image.
Second, a point in an image can come from at most one point in the
previous image. When these constraints are violated, or it appears that
they are violated, occlusion or disocclusion has occurred and therefore
can also be detected. Successful grouping of coherent regions of the ST
cube for two gray-level image sequences is shown
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