April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
A Spatial Vision Modeling Approach for Predicting Photostress Recovery Times
Author Affiliations & Notes
  • Leon N McLin
    711th Human Performance Wing/RHDO, Air Force Research Laboratory, JBSA Fort Sam Houston, TX
  • Peter Smith
    TASC, Inc., JBSA Fort Sam Houston, TX
  • Harith M Ahmed
    TASC, Inc., JBSA Fort Sam Houston, TX
  • Thomas J Baker
    TASC, Inc., JBSA Fort Sam Houston, TX
  • Paul V Garcia
    TASC, Inc., JBSA Fort Sam Houston, TX
  • Brenda J Novar
    711th Human Performance Wing/RHDO, Air Force Research Laboratory, JBSA Fort Sam Houston, TX
  • Michelle T Aaron
    711th Human Performance Wing/RHDO, Air Force Research Laboratory, JBSA Fort Sam Houston, TX
  • Footnotes
    Commercial Relationships Leon McLin, None; Peter Smith, None; Harith Ahmed, None; Thomas Baker, None; Paul Garcia, None; Brenda Novar, None; Michelle Aaron, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 787. doi:https://doi.org/
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    • Get Citation

      Leon N McLin, Peter Smith, Harith M Ahmed, Thomas J Baker, Paul V Garcia, Brenda J Novar, Michelle T Aaron; A Spatial Vision Modeling Approach for Predicting Photostress Recovery Times. Invest. Ophthalmol. Vis. Sci. 2014;55(13):787. doi: https://doi.org/.

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

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Abstract

Purpose: To develop a spatial vision modeling approach to predict photostress recovery times for images in complex scenes. The modeling was based on measured spatial frequency thresholds and the spatial frequencies comprising a complex scene. Adaptation after the flash exposure was modeled as a veiling glare field that decreased in luminance with time.

Methods: Visual recovery times for detecting the orientation of Gabor patch stimuli and complex stimuli were measured after exposure to intense broadband flashes. The flash exposures subtended 16), were 100 ms in duration, and were 6.5 or 7.5 log troland-seconds. Gabor stimuli (4) were used with 5 spatial frequencies (1, 3, 6, 12 and 18 cycles per degree), at three contrast levels (80, 40, and 20%) and three levels of average luminance (300, 100 and 10 cd/m2 ). The complex scenes were images of military vehicles taken from TNO’s Search_2 training image data set. Visual recovery was simulated by superimposing an equivalent background on the image of the stimulus, and using this image as input to a pyramidal bandpass-filtered spatial vision model employing a contrast based edge detection approach. Model predictions of recovery times for both the Gabor patch stimuli, and the complex images were compared to the experimentally determined recovery times.

Results: An analysis of variance (ANOVA) was performed on the flash recovery time data, with recovery time as the dependent variable and flash intensity, spatial frequency, Gabor contrast and average luminance as independent variables. There were main effects of flash intensity (F(2, 495) = 31.32, p < 0.001), spatial frequency (F(4, 495) = 10.33, p < 0.001), target contrast (F(2, 495) = 5.13, p < 0.05) and average luminance (F(2, 495) = 16.08, p < 0.001). The data were used to determine a decay function for the veiling glare, expressed in terms of an equivalent background luminance versus time. Similar dependencies of recovery time on flash intensity and average luminance were found for the complex images. Comparisons of model estimates of recovery times with laboratory data revealed that the model predicted the main effects of the independent variables (r 2 = 0.75).

Conclusions: A novel spatial vision-based approach to estimating recovery times for identification of objects in a complex image following exposure to a bright flash was developed. The model accurately predicted recovery times for both simple and complex targets.

Keywords: 496 detection • 501 discrimination • 719 scene perception  
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