Global effects in Figure/Ground segregation by a model with only local interactions

with N. Rubin


Figure/Ground segregation is a fundamental problem in visual processing. Edges often arise because of occlusion, along the bounding contour of the occluding object. Having detected an edge, the visual system therefore has to decide whether it is there due to occlusion, and if so, which side of it belongs to the front surface (``the Figure''). This is known as the border ownership problem. Determining the border ownership of an edge cannot be done locally: it requires integrating information from an image region which contains the entire Figure or large portions of it. Thus, a neuron whose receptive field is small compared to the Figure cannot compute border ownership of an edge of that Figure in isolation. Recently it was reported that the responses of V2 cells to an edge can be strongly modulated by the polarity of border ownership of the edge (Zhou et al. 2000). These effects must therefore be the product of computations done by a network of neurons. One possibility is that such networks rely on direct long-range connections (of the scale of the Figure) and/or feedback from areas with cells of large receptive fields. The model presented here offers an alternative. It is shown that network of small receptive field cells which interact only locally can nevertheless compute Figure/Ground segregation. The long-range effects emerge as a result of iterative propagation of information through a cascade of short-range connections. The model produces results similar to those observed perceptually: it prefers enclosed, convex and/or smaller regions as the Figure. Our results suggest that considerable portions of Figure/Ground segregation can be accomplished by early visual cortex, without a need for feedback from higher areas.

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Acknowledgments: We thank Davi Geiger, Jean-Michel Hupe, J. Anthony Movshon, Ken Nakayama, Robert Shapley, Eitan Sharon, Shimon Ullman and Yair Weiss for helpful discussions and comments on the manuscript. NR is supported by NSF grant IBN-9720305 and an Alfred P. Sloan Fellowship . MP is supported by NSF grants DMS97-21430 and DMS99-71392 and an Alfred P. Sloan Fellowship.