Abstract
Flow patterns are two-dimensional orientation structures that arise from the projection of certain kinds of surface coverings (such as fur) onto images. Detecting orientation changes within them is an important task, since the changes often correspond to significant events such as corners, occluding edges, or surface creases. We model such patterns as random-dot Moiré patterns, and examine sensitivity to change in orientation within them. We show that the amount of structure available from which orientation and curvature can be estimated is critical, and introduce a path-length parameter to model it. For short path lengths many discontinuities are smoothed over, which has further implications for computational modeling of orientation selectivity.
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