August 2023
Volume 23, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2023
A quantitative method for localizing RMS contrast in egocentric images

Author Affiliations & Notes
  • Evelina E Dineva
    Indiana University
  • Eric S Seemiller
    Air Force Research Laboratory
  • T Rowan Candy
    Indiana University
  • Linda B Smith
    Indiana University
  • Footnotes
    Acknowledgements  This work was supported by NIH-NEI grant 1R01EY032897.
Journal of Vision August 2023, Vol.23, 4779. doi:
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      Evelina E Dineva, Eric S Seemiller, T Rowan Candy, Linda B Smith; A quantitative method for localizing RMS contrast in egocentric images
. Journal of Vision 2023;23(9):4779.

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

  • Supplements

Visual systems must interpret the spatial arrangement of low-level properties of light in order to perceive the object created from those patterns. Extended experimental literature indicates that the spatial structure of images—and the centering of important visual information in a scene—supports feature extraction, discrimination, attention, and learning. Experiments on active egocentric vision suggest that perceivers purposely move their bodies, heads, eyes, and objects to place important information in the center of the head-centered visual field of view. However, there has been no direct quantitative assessment of the spatially-informed visual statistics of egocentric images at the scale of everyday life. Here, we introduce a new method that illustrates the perceivers' tendency to optimize contrast in the middle of head-centered images. For a grayscale image, we first quantify the local root mean square (RMS) contrast at each pixel location. Then, for this contrast image, we calculate the total contrast proportion within all possible aperture sizes around the center. The relationship between the proportion of total contrast and aperture size tends to be sigmoidal. It is well-fit with a Gaussian cumulative distribution function (CDF), thus, uniquely determined by two parameters. These parameters characterize the spatial structure of contrast in each image. We applied the algorithm to (i) a set of reference images with well-defined patterns of contrast, (ii) 50,000 egocentric images collected by toddlers and adults wearing head-mounted cameras, and (iii) a set of scrambled egocentric images. This approach indicates that two parameters characterize contrast information. For the egocentric scenes, the method reveals perceivers' bias to centering high-contrast information. Furthermore, the procedure lends itself to classifying other low-level features beyond achromatic contrast. It is, thus, suitable for characterizing the spatial patterns within images—with just two parameters—from large datasets, permitting determining how and when perceivers control the spatial layout of visual information.


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