  We present an algorithm (SOFAS) to estimate the optical flow of events
generated by a dynamic vision sensor (DVS). Where traditional cameras produce
frames at a fixed rate, DVSs produce asynchronous events in response to
intensity changes with a high temporal resolution. Our algorithm uses the fact
that events are generated by edges in the scene to not only estimate the
optical flow but also to simultaneously segment the image into objects which
are travelling at the same velocity. This way it is able to avoid the aperture
problem which affects other implementations such as Lucas-Kanade. Finally, we
show that SOFAS produces more accurate results than traditional optic flow
algorithms.
