Figure 9.

Demonstration that non-linear features could capture discontinuity of textures without using a statistical model that explicitly measures the local statistics. Left: the input image, adapted from (Bruce & Tsotsos, 2006). Middle: the response of a linear DoG filter. Right: the response of a non-linear feature. The non-linear feature is constructed by applying a DoG filter, then non-linearly transforming the output before another DoG is applied (see Shan et al., 2007, for details on the non-linear transformation). Whereas the linear feature has zero response to the white hole in the image, the non-linear feature responds strongly in this region, consistent with the white region’s perceptual salience.