May 2008
Volume 8, Issue 6
Vision Sciences Society Annual Meeting Abstract  |   May 2008
Testing filter-overlap models of contour integration
Author Affiliations
  • Keith May
    McGill Vision Research, McGill University, Montreal, Canada, and Vision Science Research Group, University of Bradford, UK
  • Robert Hess
    McGill Vision Research, McGill University, Montreal, Canada
Journal of Vision May 2008, Vol.8, 72. doi:
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      Keith May, Robert Hess; Testing filter-overlap models of contour integration. Journal of Vision 2008;8(6):72.

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      © ARVO (1962-2015); The Authors (2016-present)

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Most models of contour integration belong to one of two broad classes: those with explicit connections that link different regions of space (association field models, e.g. Field, Hayes & Hess, 1993, Vision Research, 33, 173–193), and those which depend on spatial overlap in the filter responses to adjacent elements (filter-overlap models). In some filter-overlap models, processing occurs separately within each orientation channel. These models do not adequately account for human foveal contour detection performance because (1) their performance decreases too rapidly with increasing curvature (Hess & Dakin, 1997, Nature, 390, 602–604), and (2) their performance decreases as the contour becomes smoother (Lovell, 2005, Journal of Vision, 5(8), 469a), while human observers generally show the opposite effect (Pettet, 1999, Vision Research, 39, 551–557; Lovell, 2005). The filter-overlap model's ability to detect smooth or highly curved contours can be improved by allowing it to link spatially-overlapping filter responses from adjacent orientation channels. We set up two types of orientation-linking filter-overlap model. One used 1st-order filters to detect snakes (i.e. contours composed of Gabor elements parallel to the path of the contour); the other used 2nd-order filters to detect ladders (in which the elements are perpendicular to the path). Both models were good at detecting smooth, highly curved contours, but showed little effect of contour smoothness or curvature. In contrast, human performance on snakes increased substantially with increasing smoothness and, for the most jagged contours, decreased substantially with increasing curvature. Human performance on ladders showed little effect of smoothness (unlike separate-channels filter-overlap models), but was strongly disrupted by an increase in curvature (unlike orientation-linking filter-overlap models). Thus, neither type of filter-overlap model could account for the pattern of results for snakes or ladders. We conclude that, despite their successful detection performance, filter-overlap models are not realistic models of contour integration in human vision.

May, K. Hess, R. (2008). Testing filter-overlap models of contour integration [Abstract]. Journal of Vision, 8(6):72, 72a,, doi:10.1167/8.6.72. [CrossRef]
 This research was funded by the Natural Sciences and Engineering Research Council of Canada (RGPIN 46528-06 awarded to Robert F. Hess).

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