December 2001
Volume 1, Issue 3
Vision Sciences Society Annual Meeting Abstract  |   December 2001
Cue combination for texture-defined edge localization
Author Affiliations
  • M. S. Landy
    Department of Psychology and Center for Neural Science, New York University, New York, NY, USA
Journal of Vision December 2001, Vol.1, 460. doi:
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      M. S. Landy; Cue combination for texture-defined edge localization. Journal of Vision 2001;1(3):460.

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

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Many visual tasks can be carried out using several sources of information simultaneously. The most accurate estimates of scene properties require the observer to utilize all available information and to combine the information sources in an optimal manner. We carried out two experiments that required observers to judge the relative location of two texture-defined edges (a Vernier task). The edges were signaled by a change across the edge of two texture properties [either frequency and orientation (Exp. 1) or contrast and orientation (Expt. 2)]. The reliability of each cue was controlled by varying the distance over which the change (in frequency, orientation or contrast) occurred — a kind of “texture blur.” In some conditions, the position of the edge signaled by one cue was shifted relative to the other (“perturbation analysis”). An ideal-observer model previously used in studies of depth perception and color constancy, Modified Weak Fusion (MWF; Landy et al., Vision Research 35, 389–412, 1995), was fit to the data. Although the fit can be rejected relative to some more elaborate models (i.e. with additional free parameters), especially given the large quantity of data, the MWF model does account for most trends in the data. A second, sub-optimal model that switches between the available cues from trial to trial does a poor job of accounting for the data.

Landy, M.S.(2001). Cue combination for texture-defined edge localization [Abstract]. Journal of Vision, 1( 3): 460, 460a,, doi:10.1167/1.3.460. [CrossRef]
 Support: NIH EY08266; AFOSR 93NL366; NRC-NASA Ames Research Center Associateship.

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