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Abstract:
Observer translation relative to the world creates image flow
that expands from the observer's direction of translation
(heading) from which the observer can recover heading direction.
Yet, the image flow is often more complex, depending on rotation
of the eye, scene layout and translation velocity. A number of
models [1-4] have been proposed on how the human visual system
extracts heading from flow in a neurophysiologically plausible
way. These models represent heading by a set of neurons that
respond to large image flow patterns and receive input from
motion sensed at different image locations. We analysed these
models to determine the exact receptive field of these heading
detectors. We find most models predict that, contrary to
widespread believe, the contributing motion sensors have a
preferred motion directed circularly rather than radially around
the detector's preferred heading. Moreover, the results suggest
to look for more refined structure within the circular flow, such
as bi-circularity or local motion-opponency.
References
[1] Beintema, J. A., and A. V. van den Berg (1998). Heading
detection using motion templates and eye velocity gain fields.
Vision Research
, 38(14):2155-2179.
[2] Lappe, M., and J. P. Rauschecker (1993). A neural network
for the processing of optic flow from ego-motion in man and
higher mammals.
Neural Computation
, 5:374-391.
[3] Perrone, J. A., and L. S. Stone (1994). A model for the
self-motion estimation within primate extrastriate visual cortex.
Vision Research
, 34:2917-2938.
[4] Royden, C. S. (1997). Mathematical analysis of
motion-opponent mechanisms used in the determination of heading
and depth.
Journal of the Optical Society of America A
, 14(9):2128-2143.
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