Dr. Michael E. Rudd Profile

Dr. Michael E. Rudd

Adjunct Faculty at Univ of Washington

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SPIE Journal Paper | 27 June 2017

Lightness computation by the human visual system

Michael Rudd

JEI, Vol. 26, Issue 03, 031209, (June 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.3.031209

KEYWORDS: Visual process modeling, Visual system, Visualization, Data modeling, Neurophysiology, Visual cortex

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A model of achromatic color computation by the human visual system is presented, which is shown to account in an exact quantitative way for a large body of appearance matching data collected with simple visual displays. The model equations are closely related to those of the original Retinex model of Land and McCann. However, the present model differs in important ways from Land and McCann’s theory in that it invokes additional biological and perceptual mechanisms, including contrast gain control, different inherent neural gains for incremental, and decremental luminance steps, and two types of top-down influence on the perceptual weights applied to local luminance steps in the display: edge classification and spatial integration attentional windowing. Arguments are presented to support the claim that these various visual processes must be instantiated by a particular underlying neural architecture. By pointing to correspondences between the architecture of the model and findings from visual neurophysiology, this paper suggests that edge classification involves a top-down gating of neural edge responses in early visual cortex (cortical areas V1 and/or V2) while spatial integration windowing occurs in cortical area V4 or beyond.

Proceedings Article | 17 June 2003 Paper

Progress on a computational model of achromatic color processing

Michael Rudd

Proceedings Volume 5007, (2003) https://doi.org/10.1117/12.477767

KEYWORDS: Data modeling, Signal generators, Signal processing, Modulation, Visual process modeling, Visualization, Diffusion, Mathematical modeling, Color vision, Electronic imaging

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This paper reports further progress on a computational model of human achromatic color perception first presented at Human Vision and Electronic Imaging VI. The model predicts the achromatic colors of regions within 2D images comprising arbitrary geometric arrangements of luminance patches separated by sharp borders (i.e., Land Mondrian patterns). The achromatic colors of regions of homogeneous luminance are computed from the log luminance ratios at borders. Separate lightness and darkness induction signals are generated at the locations of borders in a cortical representation of the image and spread directionally over several degrees of visual angle. The color assigned to each point in the image is a weighted sum of all of the lightness and darkness signals converging on that point. The spatial convergence of induction signals can be modeled as a diffusive color filling-in process and realized in a neural network. The model has previously been used to predict lightness matches in psychophysical experiments conducted with stimuli consisting of disks and surrounding rings. Here a formal connection is made between the model equations used to predict lightness matches in these experiments and Stevens' power law model of the relationship between brightness and physical intensity. A neural mechanism involving lateral interactions between neurons that detect borders and generate spreading achromatic color induction signals proposed to account for observed changes in the parameters of the model, including the brightness law exponent, with changes in surround size.

Proceedings Article | 8 June 2001 Paper

Lightness computation by a neural filling-in mechanism

Michael Rudd

Proceedings Volume 4299, (2001) https://doi.org/10.1117/12.429510

KEYWORDS: Data modeling, Mathematical modeling, Reflectivity, Visualization, Visual process modeling, Signal generators, Performance modeling, Statistical analysis, Eye, Visual system

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A growing body of evidence suggests that the brain computes lightness in a two-stage process that involves (1) an early neural encoding of contrast at the locations of luminance borders in the visual image, and (2) a subsequent filling-in of the lightnesses of the regions lying between the borders. I will review evidence that supports this theory and present a computational model of lightness based on filling-in by a spatially-spreading cortical diffusion mechanism. The behavior of the model will be illustrate by showing how it quantitatively accounts for the lightness matching data of Rudd and Arrington. The model's performance will be compared that of other theories of lightness, including retinex theory, a modified version of retinex theory that assumes edge integration with a falloff in spatial weighting of edge information with distance, lightness anchoring based on the highest luminance rule, and the BCS/FCS filling-in model developed by Grossberg and his colleagues.

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