Border-ownership computation with consistency detection of surface properties

When an image is segmented, humans often perceive convex regions as figures. However, in certain conditions, this convexity preference is reduced. For example, if a convex region is in the middle of another surface and has the same color/texture as the background, it is often perceived as a hole. The preference of convex regions in repetitive columnar configurations is also reduced if the concave regions have inconsistent colors (Peterson & Salvagio, 2008, Journal of Vision, 8(16), 4.1–13). These data suggest that consistency of surface properties plays a key role in figure-ground organization. Importantly, Zhou et al. (2000, Journal of Neuroscience, 20(17), 6594–6611) showed that about 70% of border-ownership (BOWN) sensitive neurons in V2/V4 were also sensitive to contrast polarity. Accordingly, Zhaoping (2005, Neuron, 47(1), 143–153) developed a model in which BOWN signals are enhanced when they are consistent in both the ownership and the contrast polarity. Inspired by her model, we first developed a simplified algorithm to compute BOWN to model illusory contour perception (DISC model, Kogo et al, 2010, Psychological Review., 117(2), 406–439). We recently tested the performance of the model which also reflects the consistency of the surface colors at the location of the signals as in Zhaoping’s model. We report that this approach gives extremely robust responses to various images with complexities both in shapes and in depth orders such as the examples mentioned above, suggesting the importance of this approach for BOWN computation as first proposed by Zhaoping (2005).status: publishe