Abstract
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 language | English (US) |
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Pages (from-to) | 948-955 |
Number of pages | 8 |
Journal | Proceedings of the IEEE International Conference on Computer Vision |
Volume | 2 |
State | Published - 1999 |
Event | Proceedings of the 1999 7th IEEE International Conference on Computer Vision (ICCV'99) - Kerkyra, Greece Duration: Sep 20 1999 → Sep 27 1999 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition