Image segmentation driven by elements of form

While luminance, contrast, orientation, and terminators are well-established features that are extracted in early visual processing and support the parsing of an image into its component regions, the role of more complex features, such as closure and convexity, is less clear. A main barrier in understanding the roles of such features is that manipulating their occurrence typically entails changes in the occurrence of more elementary features as well. To address this problem, we developed a set of synthetic visual textures, constructed by replacing the binary coloring of standard maximum-entropy textures with tokens (tiles) containing curved or angled elements. The tokens were designed so that there were no discontinuities at their edges, and so that changing the correlation structure of the underlying binary texture changed the shapes that were produced. The resulting textures were then used in psychophysical studies, demonstrating that the resulting feature differences sufficed to drive segmentation. However, in contrast to previous findings for lower-level features, sensitivities to increases and decreases of feature occurrence were unequal. Moreover, the texture-segregation response depended on the kind of token (curved vs. angular, filled-in vs. outlined), and not just on the correlation structure. Analysis of this dependence indicated that simple closed contours and convex elements suffice to drive image segmentation, in the absence of changes in lower-level cues.