ABSTRACT
We investigate the ability of humans to perceive changes in the appearance of images of surface texture caused by the variation of their higher order statistics. We incrementally randomize their phase spectra while holding their first and second order statistics constant in order to ensure that the change in the appearance is due solely to changes in third and other higher order statistics. Stimuli comprise both natural and synthetically generated naturalistic images, with the latter being used to prevent observers from making pixel-wise comparisons. A difference scaling method is used to derive the perceptual scales for each observer, which show a sigmoidal relationship with the degree of randomization. Observers were maximally sensitive to changes within the 20%-60% randomization range. In order to account for this behavior we propose a biologically plausible model that computes the variance of local measurements of phase congruency.
Subject(s)
Visual Perception , Analysis of Variance , Humans , Models, Biological , Probability , Spectrum Analysis , Surface PropertiesABSTRACT
We present synthetic surface textures as a novel class of stimuli for use in visual search experiments. Surface textures have certain advantages over both the arrays of abstract discrete items commonly used in search studies and photographs of natural scenes. In this study we investigate how changing the properties of the surface and target influence the difficulty of a search task. We present a comparison with Itti and Koch's saliency model and find that it fails to model human behaviour on these surfaces. In particular it does not respond to changes in orientation in the same manner as human observers.
Subject(s)
Field Dependence-Independence , Pattern Recognition, Visual/physiology , Adult , Attention/physiology , Contrast Sensitivity/physiology , Depth Perception/physiology , Humans , Models, Psychological , Orientation , Photic Stimulation/methods , Psychophysics , Reaction Time/physiologyABSTRACT
The paper assesses the validity of a model, proposed by Kube and Pentland (1988), that relates a rough surface to its image texture. Simulation was used to assess whether a linear approximation is appropriate, and whether the optimal linear filter agrees with the predictions of Kube and Pentland's model. The predictions of the model about the image directionality were also assessed on real images. It was found that a linear model is capable of modeling the imaging process for surfaces of moderate roughness and Lambertian reflectance, and that, subject to a small modification, Kube and Pentland's model accurately predicts the relationship between surface and image spectra.
