CHF

1. Specular Highlights Full Surface Specularity Uniform Background Cue Promotion Illuminant Estimate Scene Dynamic Re-Weighting #1255 SURFACE COLORAND SPECULARITY: TESTINGTHE D’ZMURA-LENNIE-LEE MODEL J. N. YANG & L. T. MALONEY, Department of Psychology and Center for Neural Science, New York University 3. PERTURBATION METHOD • Many computational models of surface color perception share a common structure: • 1. estimate the chromaticity of the illuminant, • 2. correct surface colors for the estimated illuminant chromaticity. • The algorithms differ mainly in the physical cues to the illuminant they employ. • There are many possible cues to the illuminant (Maloney, 1999), not all of which are • present in every scene. We treat illuminant estimation as a cue combination problem • and seek to determine which cues to the illuminant are used in particular scenes. • Last year (Yang, Maloney & Landy, 1999) we reported that information about the • illuminant conveyed by surface specularity influenced judgments of color appearance. 4. EXPERIMENTAL CONDITIONS JNY Our rendered scenes contain many potential illuminant cues,all signaling exactly the same information about the illuminant. In order to determine the influence of cues based on specularity, we need to perturb the specularity cues so that they signal slightly discrepant information concerning the illuminant. Target A Base D65 Illuminant A Specularity cues perturbed ... v’ Single-Matte [I]n our observations with the sense of vision, we always start out by forming a judgment about the colors of bodies, eliminating the differences of illumination by which a body is revealed to us. -- von Helmholtz Perturbed Illuminant D65 (matte) Illuminant A (specular) Illuminant D65 ILLUMINANTCUECOMBINATION target A baseD65perturbed u’ Single-Matte The first and third scene above are a single-matte scene illuminated under two different illuminants. The middle scene is perturbed: all illuminant cues except specularity signal D65 (the base illuminant) while all specularity cues signal A. We measure the observer’s achromatic setting in all three scenes. If the observer’s achromatic setting for the perturbed scene is identical to that for the base D65 scene, then the perturbation had no effect. The observer is not influenced by the specular information. If the observer’s achromatic setting for the perturbed scene is identical to that for the target A scene, then only specularity influences the observer’s judgment. Perturbing specularity is equivalent to changing the illuminant on the scene. We expect that the achromatic setting for the perturbed scene will fall somewhere between the achromatic settings for the base scene D65 and the achromatic setting for the target scene A, and we use this to quantify the influence of the cue. The roles of the two illuminants can be reversed with A as base, D65 as target. 1. SPECULAR CUES Illuminant A The influence of the specularity cues can be quantified as the ratio of the length of the solid vector (the effect of perturbation) to the length of the dotted line connecting the base and target conditions (the effect of changing the illuminant): I = There are currently two kinds of specularity-based algorithms for estimating illuminant chromaticity. In the first method, we use the chromaticity of isolated specular highlights as an estimate of illuminant chromaticity. This specular highlight cue is available if a visual system can identify neutral specular highlights in scenes. The illuminant chromaticity estimate based on this specular highlight cue can be contaminated by the color of the matte(non-specular) component of a surface. SPECULAR HIGHLIGHT SPECULAR HIGHLIGHT CUE Multi-Matte Illuminant D65 || || || - || Multi-Matte Single-Matte Scene D’ZMURA-LENNIE-LEE CUE Lee (1986) and D’Zmura & Lennie (1986) independently proposed methods for removing the ‘matte’ contamination. Both methods require that there be two or more surfaces with distinct matte components with some specularity in the scene. The scene to the right satisfies this condition. The scene above it does not. The apples all share the same matte component. We compare surface color perception in scenes where specular objects have a single common matte component (Single-Matte Scenes) and where they have multiple distinct matte components (Multi-Matte Scenes). 5. RESULTS 6. CONCLUSIONS BRM EC 0.52 v’ 0.45 GT CHF Surface color appearance is affected by the chromaticity of the specular component of surfaces in some scenes, under some illuminants (Yang, Maloney & Landy, 1999). We measured achromatic matching performance in two classes of scenes containing evident specular cues to the illuminant: Single-Matte and Multi-Matte. The D’Zmura-Lennie-Lee specularity cue is available in the Multi-Matte scenes but is weak or absent in the Single-Matte scenes. Specularity had no influence on achromatic performance in the Multi-Matte scenes. We conclude that the visual system is not making use of the D’Zmura-Lennie-Lee specular cue in these scenes. 0.52 v’ 0.45 D65 A A D65 Multi-Matte Scene GT CHF 0.52 v’ 0.45 BRM EC 0.52 v’ 0.45 0.16 u’ 0.24 0.16 u’ 0.24 2. EXPERIMENTAL DESIGN 0.16 u’ 0.24 0.16 u’ 0.24 A D65 Single-Matte Multi-Matte Apparatus: Observers viewed stimuli in a computer-controlled Wheatstone stereoscope. JA 0.52 v’ 0.45 REFERENCES EC GT CHF 0.52 v’ 0.45 Stimulus Characteristics: Observers viewed simulated (rendered) binocular scenes comprising a flat background and 11 spheres. All surfaces were Matte-Specular (Shafer, 1985) with matte component matched to specific chips taken from the Nickerson-Munsell collection. In the Single-Matte Scenes, all sphere surfaces shared a single matte component, in Multi-Matte Scenes they had 11 distinct matte components. Scenes were rendered under either of two reference illuminants, A and D65 ( Wyszecki & Stiles, 1982). Task: achromatic matching. D’Zmura, M. & Lennie, P. (1986), Mechanisms of color constancy. JOSA A, 3, 1662-1672. Landy, M. S., Maloney, L. T., Johnston, E. J. & Young, M. (1995), Measurement and modeling of depth cue combination: In defense of weak fusion. Vision Research, 35, 389-412. Lee, H.-C. (1986), Method for computing the scene illuminant chromaticity from specular highlights. JOSA A, 3, 1694-1699. Maloney, L. T. (1999), Physics-based models of surface color perception. In Gegenfurtner, K. R. & Sharpe, L. T. [Eds] (1999), Color Vision: From Genes to Perception. Cambridge, UK: Cambridge University Press, 387-418. Maloney, L. T. & Yang, J. N. (in press), The illumination estimation hypothesis. In Mausfeld, R. & Heyer, D. [Eds] (in press) Colour Vision: From Light to Object. Oxford: Oxford University Press. Yang, J. N., Maloney, L. T. & Landy, M. S. (1999), Analysis of illuminant cues in simulated scenes viewed binocularly. IOVS, 40. D65 A A D65 GT CHF 0.52 v’ 0.45 JA 0.52 v’ 0.45 EC 0.16 u’ 0.24 0.16 u’ 0.24 0.16 u’ 0.24 0.16 u’ 0.24