Open Access Paper
11 March 2005 Surface color perception as an inverse problem in biological vision
Laurence T. Maloney, Huseyin Boyaci, Katja Doerschner
Author Affiliations +
Proceedings Volume 5674, Computational Imaging III; (2005) https://doi.org/10.1117/12.598463
Event: Electronic Imaging 2005, 2005, San Jose, California, United States
Abstract
The spectral power distribution (SPD) of the light reflected from a matte surface patch in a three-dimensional complex scene depends not only on the surface reflectance of the patch but also on the SPD of the light incident on the patch. When there are multiple light sources in the scene that differ in location, SPD, and spatial extent, the SPD of the incident light depends on the location and the orientation of the patch. Recently, we have examined how well observers can recover surface color in rendered, binocularly-viewed scenes with more than one light source. To recover intrinsic surface color, observers must solve an inverse problem, effectively estimating the light sources present in the scene and the light from each that reaches the surface patch. We will formulate the forward and inverse problems for surface color perception in three-dimensional scenes and present experimental evidence that human observers can solve such problems [1-3]. We will also discuss how human observers estimate the spatial distribution of light sources and their chromaticities from the scene itself. [1] Boyaci, Doerschner, Maloney (2004), Journal of Vision, 4, 664-679. [2] Doerschner, Boyaci, Maloney (2004), Journal of Vision, 4, 92-105. [3] Boyaci, Doerschner, Maloney (2004), AIC’05, submitted.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Laurence T. Maloney, Huseyin Boyaci, and Katja Doerschner "Surface color perception as an inverse problem in biological vision", Proc. SPIE 5674, Computational Imaging III, (11 March 2005); https://doi.org/10.1117/12.598463
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Cited by 12 scholarly publications.
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KEYWORDS
Light sources

Inverse problems

Light sources and illumination

Visual system

Light

Color vision

Visual process modeling

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