August 2014
Volume 14, Issue 10
Free
Vision Sciences Society Annual Meeting Abstract  |   August 2014
Fixation search in natural scenes: a new role for contrast normalization
Author Affiliations
  • Jared Abrams
    Center for Perceptual Systems, University of Texas, Austin, USA
    Speaker
  • Chris Bradley
    Center for Perceptual Systems, University of Texas, Austin
    Author
  • Wilson S. Geisler
    Center for Perceptual Systems, University of Texas, Austin
    Author
Journal of Vision August 2014, Vol.14, 1445. doi:10.1167/14.10.1445
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    • Get Citation

      Jared Abrams, Chris Bradley, Wilson S. Geisler; Fixation search in natural scenes: a new role for contrast normalization. Journal of Vision 2014;14(10):1445. doi: 10.1167/14.10.1445.

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

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Abstract

Visual search is a fundamental behavior, yet little is known about search in natural scenes. Previously, we introduced the ELM (entropy limit minimization) fixation selection rule, which selects fixations that maximally reduce uncertainty about the location of the target. This rule closely approximates the Bayesian optimal decision rule, but is simpler computationally, making the ELM rule a useful benchmark for characterizing human performance. Previously, we found that the ELM rule predicts several aspects of fixation selection in naturalistic (1/f) noise, including the distributions of fixation location, saccade magnitude, and saccade direction. However, the ELM rule is only optimal when the detectability of the target (the visibility map) falls off from the point of fixation in the same way for all potential fixation locations, which holds for backgrounds with relatively constant spatial structure, like statistically stationary 1/f noise. Most natural scenes do not satisfy this assumption; they are highly non-stationary. By combining empirical measurements of target detectability in natural backgrounds with a straight-forward mathematical analysis, we arrive at a generalized ELM rule (nELM rule) that is optimal for non-stationary backgrounds. The nELM searcher divides (normalizes) the current target probability map (posterior-probability map) by the estimated local contrast at each location in the map. It then blurs (convolves) this normalized map with the visibility map for a uniform background. The peak of the blurred map is the optimal location for the next fixation. We will describe the predictions and performance of the nELM searcher.

Meeting abstract presented at VSS 2014

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