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1255 SURFACE COLOR AND SPECULARITY TESTING THE
DZMURA-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
In 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
ILLUMINANT CUE COMBINATION
target A
base D65 perturbed
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
observers achromatic setting in all three
scenes. If the observers 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 observers
achromatic setting for the perturbed scene is
identical to that for the target A scene, then
only specularity influences the observers
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
DZMURA-LENNIE-LEE CUE
Lee (1986) and DZmura 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-Mat
te 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 DZmura-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 DZmura-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.
DZmura, 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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