Measuring convexity for figure/ground separation

Hsing Kuo Pao, Davi Geiger, Nava Rubin

Research output: Contribution to journalConference articlepeer-review


In human perception, convex surfaces have a strong tendency to be perceived as the `figure'. Convexity has a stronger influence on figural organization than other global shape properties, such as symmetry ([9]). And yet, there has been very little work on convexity properties in computer vision. We present a model for figure/ground segregatation which exhibits a preference for convex regions as the figure (i.e., the foreground). The model also shows a preference for smaller regions to be selected as figures, which is also known to hold for human visual perception (e.g., Koffka [11]). The model is based on the machinery of Markov random fields/random walks/diffusion processes, so that the global shape properties are obtained via local and stochastic computations. Experimental results demonstrate that our model performs well on ambiguous figure/ground displays which were not captured before. In particular, in ambiguous displays where neither region is strictly convex, the model shows preference to the `more convex' region, thus offering a continuous measure of convexity in agreement with human perception.

Original languageEnglish (US)
Pages (from-to)948-955
Number of pages8
JournalProceedings of the IEEE International Conference on Computer Vision
StatePublished - 1999
EventProceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99) - Kerkyra, Greece
Duration: Sep 20 1999Sep 27 1999

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


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