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Specular Highlights. Full Surface Specularity. Uniform Background. Cue Promotion. Illuminant Estimate. Scene. Dynamic Re-Weighting. #1255 S URFACE C OLOR AND S PECULARITY : T ESTING THE D’Z MURA -L ENNIE -L EE M ODEL

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Chf

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 ...

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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

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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

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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.

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D65 A

A D65

Multi-Matte Scene

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2. EXPERIMENTAL DESIGN

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A D65

Single-Matte

Multi-Matte

Apparatus: Observers viewed stimuli in a

computer-controlled Wheatstone stereoscope.

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REFERENCES

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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

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