Measuring convexity for figure/ground separation

1999

We have been developing a stochastic model for figure-ground separation. The model selects/constructs the foreground with preference for figures with “more convex” shapes. When these models are applied to illusory figures they yield perceptually accurate selection of figure and background. The approach is based on an “entropy” measure of a region diffusion Markov model from a set of local figure/ground hypothesis. The contour boundaries are implicitly represented, via the thresholding of the diffusion result. What optimal properties do the illusory contours satisfies? We show that the entropy criteria selects contours such as to minimize a Taylor series of the even derivatives with respect to the length of the contour. The coefficients are positive and they get exponentially smaller as the derivatives increase. The zeroth order term suggest that small length contours are preferred, the second order terms suggests that curvature-like term is minimized (with less strength compared to the zero order one), and higher order derivatives give additional contour smoothness constraints.

2020

Figure Ground Organization (FGO) -- inferring spatial depth ordering of objects in a visual scene -- involves determining which side of an occlusion boundary is figure (closer to the observer) and which is ground (further away from the observer). A combination of global cues, like convexity, and local cues, like T-junctions are involved in this process. We present a biologically motivated, feed forward computational model of FGO incorporating convexity, surroundedness, parallelism as global cues and Spectral Anisotropy (SA), T-junctions as local cues. While SA is computed in a biologically plausible manner, the inclusion of T-Junctions is biologically motivated. The model consists of three independent feature channels, Color, Intensity and Orientation, but SA and T-Junctions are introduced only in the Orientation channel as these properties are specific to that feature of objects. We study the effect of adding each local cue independently and both of them simultaneously to the model...

Convexity has long been recognised as a factor that affects figure^ground segmentation, even when pitted against other factors such as symmetry Art and Artefacts Ed.M Henle (New York: Springer) pp 25^32]. It is accepted in the literature that the difference between concave and convex contours is important for the visual system, and that there is a prior expectation favouring convexities as figure. We used bipartite stimuli and a simple task in which observers had to report whether the foreground was on the left or the right. We report objective evidence that supports the idea that convexity affects figure^ground assignment, even though our stimuli were not pictorial in that depth order was specified unambiguously by binocular disparity.

Perception, 2008

Convexity has long been recognised as a factor that affects figure^ground segmentation, even when pitted against other factors such as symmetry Art and Artefacts Ed.M Henle (New York: Springer) pp 25^32]. It is accepted in the literature that the difference between concave and convex contours is important for the visual system, and that there is a prior expectation favouring convexities as figure. We used bipartite stimuli and a simple task in which observers had to report whether the foreground was on the left or the right. We report objective evidence that supports the idea that convexity affects figure^ground assignment, even though our stimuli were not pictorial in that depth order was specified unambiguously by binocular disparity.

Journal of Neuroscience, 2010

Psychonomic Bulletin & Review, 2013

Interest in convexity has a long history in vision science. For smooth contours in an image, it is possible to code regions of positive (convex) and negative (concave) curvature, and this provides useful information about solid shape. We review a large body of evidence on the role of this information in perception of shape and in attention. This includes evidence from behavioral, neurophysiological, imaging, and developmental studies. A review is necessary to analyze the evidence on how convexity affects (1) separation between figure and ground, (2) part structure, and (3) attention allocation. Despite some broad agreement on the importance of convexity in these areas, there is a lack of consensus on the interpretation of specific claims-for example, on the contribution of convexity to metric depth and on the automatic directing of attention to convexities or to concavities. The focus is on convexity and concavity along a 2-D contour, not convexity and concavity in 3-D, but the important link between the two is discussed. We conclude that there is good evidence for the role of convexity information in figure-ground organization and in parsing, but other, more specific claims are not (yet) well supported.

ABSTRACT The shape of the contour separating two regions strongly influences judgments of which region is “figure” and which is “ground”. Convexity and other figure-ground cues are generally assumed to indicate only which region is nearer, but nothing about how much the regions are separated in depth.

2012

Abstract Interest in convexity has a long history in vision science. For smooth contours in an image, it is possible to code regions of positive (convex) and negative (concave) curvature, and this provides useful information about solid shape. We review a large body of evidence on the role of this information in perception of shape and in attention. This includes evidence from behavioral, neurophysiological, imaging, and developmental studies.

Journal of Vision, 2007

Advances in Neural Information …, 2010

Figure/ground assignment, in which the visual image is divided into nearer (figural) and farther (ground) surfaces, is an essential step in visual processing, but its underlying computational mechanisms are poorly understood. Figural assignment (often referred to ...