A DOG filter model of the occurrence of Mach bands on spatial contrast discontinuities

A discrete magno-parvo additive model in early vision for explaining brightness perception in varying contrastive contexts

A varying contrastive context filter (VCCF)-based model of brightness perception has been proposed. It is motivated first by a recently proposed difference of difference-of-Gaussian (DDOG) filter. Alongside, it is also inspired from the fact that the nature evolves various discrete systems and mechanisms to carry out many of its complex tasks. A weight factor, used for the linear combination of two filters representing the magnocellular and parvocellular channels in the central visual pathway, has been defined and termed as the factor of contrastive context (FOCC) in the present model. This is a binary variable that lends a property of discretization to the DDOG filter. By analyzing important brightness contrast as well as brightness assimilation illusions, we arrive at the minimal set of values (only two) for FOCC, using which one is able to successfully predict the direction of brightness shift in both situations of brightness contrast, claimed and categorized here as low contrastive context, and those of brightness assimilation, claimed and categorized here as high contrastive context perception, depending upon whether the initial M-channel-filtered stimulus is above or below a threshold of the contrastive context. As distinct from Michelson/Weber/RMS contrast, high or low, the contrastive context claimed is dependent on the edge information in the stimulus determined by the Laplacian operator, also used in the DDOG model. We compared the proposed model against the already well-established oriented difference-of-Gaussian (ODOG) model of brightness perception. Extensive simulations suggest that though for most illusions both ODOG and VCCF produce correct output, for certain intricate cases in which the ODOG filter fails to correctly predict the illusory effect, our proposed VCCF model continues to remain effective.

An adaptive scale Gaussian filter to explain White's illusion from the viewpoint of lightness assimilation for a large range of variation in spatial frequency of the grating and aspect ratio of the targets

The variation between the actual and perceived lightness of a stimulus has strong dependency on its background, a phenomena commonly known as lightness induction in the literature of visual neuroscience and psychology. For instance, a gray patch may perceptually appear to be darker in a background while it looks brighter when the background is reversed. In the literature it is further reported that such variation can take place in two possible ways. In case of stimulus like the Simultaneous Brightness Contrast (SBC), the apparent lightness changes in the direction opposite to that of the background lightness, a phenomenon often referred to as lightness contrast, while in the others like neon colour spreading or checkerboard illusion it occurs opposite to that, and known as lightness assimilation. The White's illusion is a typical one which according to many, does not completely conform to any of these two processes. This paper presents the result of quantification of the perceptual strength of the White's illusion as a function of the width of the background square grating as well as the length of the gray patch. A linear filter model is further proposed to simulate the possible neurophysiological phenomena responsible for this particular visual experience. The model assumes that for the White's illusion, where the edges are strong and quite a few, i.e., the spectrum is rich in high frequency components, the inhibitory surround in the classical Difference-of-Gaussians (DoG) filter gets suppressed, and the filter essentially reduces to an adaptive scale Gaussian kernel that brings about lightness assimilation. The linear filter model with a Gaussian kernel is used to simulate the White's illusion phenomena with wide variation of spatial frequency of the background grating as well as the length of the gray patch. The appropriateness of the model is presented through simulation results, which are highly tuned to the present as well as earlier psychometric results.