Multiresolution wavelet framework models brightness induction effects

Abstract

A new multiresolution wavelet model is presented here, which accounts for brightness assimilation and contrast effects in a unified framework, and includes known psychophysical and physiological attributes of the primate visual system (such as spatial frequency channels, oriented receptive fields, contrast sensitivity function, contrast non-linearities, and a unified set of parameters). Like other low-level models, such as the ODOG model [Blakeslee, B., & McCourt, M. E. (1999). A multiscale spatial filtering account of the white effect, simultaneous brightness contrast and grating induction. Vision Research, 39, 4361-4377], this formulation reproduces visual effects such as simultaneous contrast, the White effect, grating induction, the Todorović effect, Mach bands, the Chevreul effect and the Adelson-Logvinenko tile effects, but it also reproduces other previously unexplained effects such as the dungeon illusion, all using a single set of parameters.

Dynamic brightness induction in V1: Analyzing simulated and empirically acquired fMRI data in a “common brain space” framework

“…In addition, our surface representation at the earliest cortical stages of visual processing is also not in agreement with multiscale spatial filtering theories assuming that brightness perception does not require an explicit spreading mechanism (Blakeslee et al, 2005;Dakin and Bex, 2003;McCourt, 1982;Purves et al, 1999;Stromeyer et al, 1984). Based on multiscale spatial filtering theory, some models were developed to simulate brightness assimilation (Barkan et al, 2008;Blakeslee and McCourt, 1999Otazu et al, 2008), such as in the classic White's effect (White, 1979). The most well-established model in this category is the oriented difference-of-Gaussians (ODOG) model of Blakeslee and McCourt (1999) that performs an oriented multiscale spatial filtering of input and a subsequent global contrast normalization to equalize responses at each orientation across the visual field.…”

Bioplausible Multiscale Filtering in Retinal to Cortical Processing as a Model of Computer Vision

“…Jameson 1985;Westheimer, 2007) connecting 'brightness induction' illusions and 'geometric illusions', related to our study. For instance, some explanations for 'SBC' (Simultaneous Brightness Contrast) (Figure6-left) illusion, where a gray test patch looks darker on a white background compared to an identical patch on a black background, suggested the involvement of some neurons with small excitatory centre and elongated surround (nCRFs) either implemented with "wavelet based modelling" (Otazu et al, 2008) or "DoG based models" (Blakeslee & McCourt, 1999, 2004.…”

Mach bands explained by response normalization

“…This class of model combines multi-scale filtering with response (often termed contrast) normalization (Blakeslee and McCourt, 1999; Dakin and Bex, 2003; Blakeslee et al, 2005; Robinson et al, 2007; Otazu et al, 2008). However only one of these models, the contextual interaction model of Otazu et al (2008), has been applied to Mach bands.…”

“…This class of model combines multi-scale filtering with response (often termed contrast) normalization (Blakeslee and McCourt, 1999; Dakin and Bex, 2003; Blakeslee et al, 2005; Robinson et al, 2007; Otazu et al, 2008). However only one of these models, the contextual interaction model of Otazu et al (2008), has been applied to Mach bands. While the model successfully predicts Mach bands in a trapezoidal edge, it also predicts Mach bands at a step edge (Otazu, personal communication), and it remains to be determined whether it can account for the observed differences in the magnitude and width of Mach bands across the variety of stimuli that are considered here.…”