Color vision and image intensities: When are changes material?

Abstract

-Marr has emphasized the difficulty in understanding a biological system or its components without some idea of its goals. In this paper, a preliminary goal for color ision is proposed and analyzed. That goal is to determine where changes of material occur in a scene (using only spectral information). This goal is challenging for two reasons. First, the effects of many processes (shadowing, shading from surface orientation changes, highlights, variations in pigment density) are confounded with the effects of material changes in the available image itensities. Second. material changes are essentially arbitrary. We are consequently led to a strategy of rejecting the presence of such confounding processes. We show there is a unique condition, the spectral crosspoint, that allows rejection of the hypothesis that measured image intensities arise from one of the confounding processes. (If plots are made of image intensity versus wavelength from two image regions, and the plots intersect, we say that there is a spectral crosspoint.) We restrict our attention to image intensities measured from regions on opposite sides of an edge because material changes almost always cause edges. Also, by restricting our attention to luminance discontinuities, we can avoid peculiar conspirliies of confounding processes that might mimic a material chage. Our crosspoint conjecture is that biological visual systems interpret spectral crosspoints across edges as material changes. A circularly symmetric operator is designed to detect crosspoints; it turns out to resemble the double-opponent cell which is commonplace in biological color vision systems.

Photoreceptor sectral sensitivities in terrestrial animals: adaptations for luminance and colour vision

“…Spectral information is represented by chromatic signals, which are based on differences between receptor quantum catches rather than absolute values. Chromaticity is probably relatively stable (constant) in natural illumination, so that it gives information about surface reflectance, pigmentation and other material properties (Rubin & Richards 1982;Gegenfurtner & Kiper 2003). Colour vision is therefore likely to be important for object detection or classification.…”

A scaling analysis of the snake lightness illusion

“…Moreover, illumination borders are usually fuzzier than reflectance borders, so, by analyzing the spatial frequency spectrum of a luminance border, the visual system may decide if it is produced by an illumination or by a material change (Land & McCann, 1971). Also, illumination borders produce mainly luminance contrast at the border, whereas material borders typically involve chromatic as well as luminance contrast (e.g., Párraga, Troscianko, & Tolhurst, 2000;Rubin & Richards, 1982).…”

Lookup-Table-Based Gradient Field Reconstruction

“…Of course intensity also changes at material boundaries, and so it is of interest to find a method for disentangling material from shape changes [29]. Below we begin by briefly recapitulating one method, that set forth in [3].…”