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Mark J. Brady, Gordon Legge, Daniel Kersten; Effects of natural backgrounds on spatial filter responses near object contours. Journal of Vision 2004;4(8):535. doi: 10.1167/4.8.535.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose: The description of an object's contour is often thought to be solely a property of the object's image. However, objects typically appear in complex backgrounds. Thus for any 2D sample region (SR), both the preferred orientation and the magnitude of spatial contrast filters can be significantly perturbed by the background. The degree of perturbation is a function of filter size and the position of the filter relative to the contour. Further, the response of larger filters may be less sensitive to positional uncertainty. Our goal was to measure this perturbation by natural backgrounds. Methods: Images of natural and artificial objects were photographed and hand-segmented from their natural backgrounds. Composite images were made by digitally superimposing and translating the image of an object across various natural image backgrounds. 2D histograms of orientation and magnitude responses were measured from a set of steerable multi-scale filters for SRs across contours and on the background. In order to quantify discriminability, we measured the performance of an ideal observer. The ideal was required to discriminate between specific contour and background SRs, given prior knowledge of the conditional distributions estimated from the histograms. Results: Histograms of SRs on contours varied systematically as a function of displacement from the contour and were distinctly different than those from the background. The performance of the ideal observer was surprisingly good, with an overall correct response rate of .78. When centered on the contour, correct response rate was .82. Larger filter kernels were less sensitive to centering on the contour. Conclusions: Background has a significant effect on contour filter responses. However, these perturbations form predictable patterns, and allow discrimination with reasonable accuracy. Filter size helps to minimize spatial uncertainty.
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