Purchase this article with an account.
Albert J. Ahumada, Bettina L. Beard, Karen M. Jones; Modeling the detection of blurred visual targets in non-homogeneous backgrounds. Journal of Vision 2005;5(8):484. doi: 10.1167/5.8.484.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Detection models work well for targets on uniform or homogeneous backgrounds. How well do they work for the detection of airframe cracks in their natural setting by actual inspectors? How well do they predict the performance when the images are blurred? We measured crack detection performance for 7 experienced inspectors and 2 non-inspectors. Signal images were formed by subtracting a crack-removed image from the original image. An attenuated-crack image at a desired contrast level was generated by attenuating the difference image and adding it back to the crack-removed image. The visual resolution was 30 pixels per deg. The display background screen had a luminance of 40 cd/m2. The images were Gaussian blurred with spreads of 0, 1.1, 1.7, and 2.3 arc min. Contrast attenuation thresholds were obtained using a 2IFC staircase. The crack-removed image remained on for the duration of a block of trials. At least 3 replications were obtained. Attenuation thresholds for 75% correct were estimated by probit analysis. Two measures of contrast energy were computed, the visible contrast energy in the signal and that energy attenuated by local visible contrast energy (gain-control masking). Local contrast was computed from a local luminance image computed by blurring the luminance image with the surround of a DoG CSF. The same 8 min spread was used to compute an average local masking energy image. The visible energy of the un-blurred signals correlated well with the average contrast thresholds over the 15 cracks (r=−0.89), and including masking raised the correlation (r=−0.95). Blur raised the thresholds much more than the loss in visible contrast energy. For the images with the greatest loss in visible contrast energy (4.7 dB) at the 1.7 min blur, the average threshold loss was 10 dB. This 5.3 dB discrepancy may result from a lack of experience with blur, or the blur may affect higher order processes such as edge extraction.
This PDF is available to Subscribers Only