Combination of texture and color cues in visual segmentation.

Abstract

The visual system can use various cues to segment the visual scene into figure and background. We studied how human observers combine two of these cues, texture and color, in visual segmentation. In our task, the observers identified the orientation of an edge that was defined by a texture difference, a color difference, or both (cue combination). In a fourth condition, both texture and color information were available, but the texture and color edges were not spatially aligned (cue conflict). Performance markedly improved when the edges were defined by two cues, compared to the single-cue conditions. Observers only benefited from the two cues, however, when they were spatially aligned. A simple signal-detection model that incorporates interactions between texture and color processing accounts for the performance in all conditions. In a second experiment, we studied whether the observers are able to ignore a task-irrelevant cue in the segmentation task or whether it interferes with performance. Observers identified the orientation of an edge defined by one cue and were instructed to ignore the other cue. Three types of trial were intermixed: neutral trials, in which the second cue was absent; congruent trials, in which the second cue signaled the same edge as the target cue; and conflict trials, in which the second cue signaled an edge orthogonal to the target cue. Performance improved when the second cue was congruent with the target cue. Performance was impaired when the second cue was in conflict with the target cue, indicating that observers could not discount the second cue. We conclude that texture and color are not processed independently in visual segmentation.

Full text

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Combination of texture and color cues in visual segmentation
Toni P. Saarela⇑, Michael S. Landy
Department of Psychology and Center for Neural Science, New York University, New York, NY, USA
a r t i c l ei n f o
Article history:
Received 8 December 2011
Received in revised form 16 January 2012
Available online 24 February 2012
Keywords:
Visual segmentation
Texture
Color
Cue combination
Signal detection theory
a b s t r a c t
The visual system can use various cues to segment the visual scene into figure and background. We stud-
ied how human observers combine two of these cues, texture and color, in visual segmentation. In our
task, the observers identified the orientation of an edge that was defined by a texture difference, a color
difference, or both (cue combination). In a fourth condition, both texture and color information were
available, but the texture and color edges were not spatially aligned (cue conflict). Performance markedly
improved when the edges were defined by two cues, compared to the single-cue conditions. Observers
only benefited from the two cues, however, when they were spatially aligned. A simple signal-detection
model that incorporates interactions between texture and color processing accounts for the performance
in all conditions. In a second experiment, we studied whether the observers are able to ignore a task-
irrelevant cue in the segmentation task or whether it interferes with performance. Observers identified
the orientation of an edge defined by one cue and were instructed to ignore the other cue. Three types
of trial were intermixed: neutral trials, in which the second cue was absent; congruent trials, in which
the second cue signaled the same edge as the target cue; and conflict trials, in which the second cue sig-
naled an edge orthogonal to the target cue. Performance improved when the second cue was congruent
with the target cue. Performance was impaired when the second cue was in conflict with the target cue,
indicating that observers could not discount the second cue. We conclude that texture and color are not
processed independently in visual segmentation.
? 2012 Elsevier Ltd. All rights reserved.
1. Introduction
Humans often combine multiple sensory cues to improve per-
ceptual performance. In many cases, human cue integration is opti-
mal in the sense of achieving maximal reliability (e.g., Ernst &
Banks, 2002; Landy et al., 1995). Near-optimal cue integration
has been demonstrated for several tasks including estimation of
size, slant, shape and location, for multiple visual cues (Hillis
et al., 2004; Knill & Saunders, 2003; Landy & Kojima, 2001) and
for combinations of cues from more than one sensory modality
(Alais & Burr, 2004; Ernst & Banks, 2002; Gepshtein & Banks,
2003; Hillis et al., 2002).
In this paper, we examine the task of visual segmentation, i.e.,
detection and identification of edges. Many cues may help the
viewer to segment the visual scene into figure and ground, includ-
ing differences in luminance, contrast, texture, color, motion, and
depth (e.g., Braddick, 1993; Landy & Graham, 2004; Li & Lennie,
2001; Nakayama, Shimojo, & Silverman, 1989). The larger the dif-
ference between two stimulus regions along any of these dimen-
sions, the easier they are to segment. For example, when two
regions differ in dominant pattern orientation, segmentation is
easier when the difference in orientation is increased (Landy &
Bergen, 1991; Nothdurft, 1985; Wolfson & Landy, 1995). But how
are regions segmented when multiple cues are available to signal
the region boundary, e.g., texture and color? A simple, but ineffi-
cient, solution is to use only one of the cues and ignore the other,
perhaps concentrating on the more reliable cue (choosing one of
the ‘‘streams’’ in Fig. 1A). Alternatively, one could process the cues
independently, and signal the presence of an edge if any cue sig-
naled that an edge was detected or base the decision on a combi-
nation of the outputs (Fig. 1B). Or, the effects of the different
cues could summate, so that a texture cue would somehow add
to a color cue in signaling the boundary. Such a model would
require a segmentation mechanism capable of using information
from multiple cues (Fig. 1C), as has been demonstrated for combi-
nations of visual (printed words) and auditory (spoken words) for
text recognition (Dubois, Poeppel, & Pelli, in preparation). For a
2-alternative multi-cue detection or discrimination task (as in
the experiment we discuss below), if the two alternatives share a
common Gaussian noise covariance, cue summation is the most
effective mechanism for combining multiple cues (Duda, Hart, &
Stork, 2001).
0042-6989/$ - see front matter ? 2012 Elsevier Ltd. All rights reserved.
doi:10.1016/j.visres.2012.01.019
⇑Corresponding author. Present Address: Department of Psychology, University
of Pennsylvania, Philadelphia, PA, USA.
E-mail address: saarela@cns.nyu.edu (T.P. Saarela).
Vision Research 58 (2012) 59–67
Contents lists available at SciVerse ScienceDirect
Vision Research
journal homepage: www.elsevier.com/locate/visres

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We concentrate here on the combination of color and texture
cues. Both color and texture can provide information for visual
segmentation. Both are also robust segmentation cues when lumi-
nance information is varying: Segmentation based on color is little
affected by variations in luminance (Hansen & Gegenfurtner,
2006; Li & Lennie, 2001), and the texture information in natural
images is only partially correlated with pure luminance informa-
tion, so that it likely provides additional segmentation information
(Schofield, 2000). We test the independence of texture and color
processing in segmentation using a simple task where observers
identify the orientation of an edge. In our stimuli, both the tex-
ture- and color-defined edges are second-order: The average lumi-
nance and chromaticity on either side of each edge is identical,
and only the spatial pattern of luminances or chromaticities varies
across these second-order boundaries. We first measure perfor-
mance in the segmentation task with either cue alone. We then
measure performance when both cues are present, with the tex-
ture and color edges aligned (cue combination) or orthogonal
(cue conflict). This design with cue-combination and cue-conflict
conditions is similar to those used in testing the independence
of spatial frequency and orientation channels in human vision
(e.g., Olzak & Thomas, 1991). Independent processing of texture
and color would predict equal performance in the cue-combina-
tion and cue-conflict conditions. We find substantial improvement
in the cue-combination condition relative to the single-cue condi-
tions but no improvement in the cue-conflict condition. This ar-
gues against independent processing. Further, observers perform
better in the cue-combination condition than the ‘‘optimal’’ inde-
pendent-processing prediction based on single-cue performance. A
signal-detection model incorporating interactions in the process-
ing of texture and color edges can account for all observations.
In the second experiment we report, observers perform the seg-
mentation task using a single cue (texture or color). On a given
trial, the second cue can be absent, aligned with, or orthogonal
to the target cue. We find that observers perform better on trials
where the second cue is aligned and worse on trials when the sec-
ond cue is orthogonal to the target cue. The observers thus cannot
ignore the other cue even when instructed to do so.
2. Methods
2.1. Observers
Three observers (ages 21–33 years, 2 female) participated in the
experiments. All observers had normal, uncorrected visual acuity
and color vision.
2.2. Equipment
The stimuli were presented on a Mitsubishi Diamond Pro 900u
CRT monitor that was driven by a 10-bit, NVIDIA GeForce 7300 GT
graphics card. The screen had a resolution of 1024 ? 768 pixels,
and an 85 Hz refresh rate. From the viewing distance of 57 cm used
in the experiment, the screen subtended 34 ? 25.5 deg. The moni-
tor phosphor spectra were measured with a Photo Research PR-650
SpectraScan spectroradiometer. The gamma function for each gun
was measured using a Minolta LS-100 photometer. Mean lumi-
nance of the screen was about 30 cd/m2.
2.3. Stimuli
Stimuli were 12 ? 12 arrays of Gabor patches. The patch in ar-
ray position (i,j) was defined as
Gijðx;yÞ ¼ Aexpð?ðx02þ y02Þ=2r2Þsinð2pfx0Þ;
x0¼ ðx ? xjÞcosðhijÞ ? ðy ? yiÞsinðhijÞ;
y0¼ ðx ? xjÞsinðhijÞ þ ðy ? yiÞcosðhijÞ;
where Gijdescribes the modulation in a given direction in cone-con-
trast space (see below) of a Gabor that is centered at array element
location (xj,yi), and has an orientation of hijand modulation contrast
controlled by A. The spatial frequency f of the Gabors was 1 cycle/
deg, the space constant r was 0.35?, and the center-to-center spac-
ing of the Gabors in the array was 2?.
In each stimulus, there were two types of Gabors (four in the
cue-conflict stimulus, see below), which differed from each other
in orientation, in color, or both. These Gabors were arranged in
alternating vertical or horizontal stripes of each Gabor type (each
stripe was three Gabors wide, giving a spatial frequency of 2 cy-
cle/image, and stripe phase was chosen randomly from among
the six possible phases). When there was no orientation difference
between the Gabors in adjacent stripes, all Gabor orientations were
45?. When there was an orientation difference, the two Gabor ori-
entations were 45 ± Dh deg. When there was no color difference
between the Gabors in adjacent stripes, all Gabors were achromatic
with a carrier luminance contrast of 0.25 (corresponding to a peak
luminance contrast of 0.17). The color difference was a red–green
modulation added to the luminance modulation. The color modu-
lation was in opposite phase between adjacent stripes, resulting in
dark-red/bright-green and bright-red/dark-green Gabors. Thus, the
average luminance of each sine-phase Gabor was equal to the
background luminance, and the average chromaticity of each Ga-
bor was achromatic. In this sense, the stripes were a second-order
stimulus, differing only in the pattern of luminance and chromatic-
ity, but not in their average luminance or chromaticity (see below
how the stimulus colors were defined in cone-contrast space).
There were four stimulus conditions. In the single-cue condi-
tions, the stripes were defined by a single cue, texture or color
(Fig. 2A and B). In the cue-combination condition (Fig. 2C), the
ð1Þ
Fig. 1. Three schematic models of the processing of texture and color in visual
segmentation. (A) An image (top) contains an edge signaled by changes in both
texture and color (second row). Texture and color are processed by separate and
independent segmentation mechanisms that extract the edge (third row), with
independent decision stages (bottom row). (B) Texture and color are processed by
separate and independent segmentation mechanisms, but the outputs of the two
mechanisms are subsequently combined. (C) Texture and color are processed by a
single segmentation mechanism that sums responses to color and texture
differences.
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stripes were defined by both texture and color cues (i.e., the two
cues were spatially aligned).
(Fig. 2D), there were four types of Gabors (both orientations, each
with both color phases) arranged so that the orientation cue cre-
ated one stripe orientation (e.g., horizontal) and the color cue
was arranged in orthogonal stripes (in this case, vertical). Note that
the cue-combination and cue-conflict stimuli nonetheless have ex-
actly the same texture and color differences, only the alignment of
the cues differs. As we have two stimulus dimensions of interest
(texture and color), the four stimulus conditions can be repre-
sented in a two-dimensional stimulus space (Fig. 3A). In this stim-
ulus space, color contrast of an edge is represented on the x-axis
In thecue-conflict condition
and orientation contrast of an edge is represented on the y-axis.
Positive values correspond to vertical edges and negative values
correspond to horizontal edges. The single-cue conditions lie on
the two axes (as the value of the other cue is zero), and the cue-
combination and cue-conflict conditions lie in the four quadrants.
The stimulus colors were defined in cone-contrast space, where
each of the three axes corresponds to the relative activation of each
of the three cone classes with respect to the background (Cole,
Hine, & McIlhagga, 1993; Sankeralli & Mullen, 1996, 1997). Cone
excitations were computed using the Stockman and Sharpe
(2000) 10-degree cone fundamentals. Cone contrasts CL, CM, and
CS (for long-, middle-, and short-wavelength-sensitive cones,
respectively) were computed for cone excitations L, M, and S as
CL= (L ? L0)/L0, CM= (M ? M0)/M0, and CS= (S ? S0)/S0; where L0,
M0, and S0are the cone excitations in response to the gray back-
ground. Achromatic Gabors were defined along the direction
(CL+ CM+ CS), which isolates the L + M (‘‘luminance’’) mechanism.
For the chromatic Gabors, a chromatic modulation was added to
the achromatic one. The direction of this chromatic modulation
was the L ? M, or ‘‘red–green’’ isolating direction, which was deter-
mined individually for each subject (see below). Choosing the chro-
matic modulation as the direction that isolates the L ? M
mechanism assured that the chromatic and achromatic Gabors
had the same luminance contrast (the L ? M isolating direction is
by definition a ‘‘null’’ direction for the luminance mechanism).
2.4. Determining L ? M isolating stimuli
The L ? M isolating direction in cone-contrast space was deter-
mined using the minimum-motion paradigm (Anstis & Cavanagh,
1983). The stimulus was a 12 ? 12 array of Gabor patches with
the same spatial parameters as in the main experiment. The Gabor
envelopes were static, but their sine wave carriers drifted at 1 Hz.
The observer adjusted the relative weights of CLand CMto mini-
mize perceived motion. The value of CSwas always chosen so that
the modulation was orthogonal to the S ? 0.5 ? (L + M), or ‘‘blue–
yellow’’, mechanism (that is, the colors were confined to a plane
orthogonal to the direction CS? 0.5 ? (CL+ CM)). The pooled cone
contrast
C2
S
was kept constant during the adjust-
Lþ C2
Mþ C2
??0:5
??
ment, so changing the CLand CMweights rotated a vector on that
plane. Three cone-contrast levels were used, and the observer
made 10 adjustments at each level. The agreement between these
30 adjustments was very good when plotted in cone-contrast
space. The L ? M isolating direction was determined by fitting a
straight line to the data in cone-contrast space, which gave a good
fit (r2= 0.79–0.91).
Fig. 2. Example stimuli. (A) Texture-cue only. The edges are second-order edges
defined by an orientation difference. (B) Color-cue only. All the Gabor patches have
the same orientation, and the edges are defined by color differences (dark-red/
bright-green vs. bright-red/dark-green Gabors). (C) Cue combination. The edges are
defined by both texture and color cues. (D) Cue conflict. Both texture and color cues
are present, but the edges they define are orthogonal to each other. Note that the
cue-combination and cue-conflict stimuli contain the same texture and color
differences, only the alignment of the edges differs. All panels show cropped
versions of the actual stimuli used, which were 12 ? 12 arrays of Gabors. The
‘‘stripes’’ were three Gabors wide, so there were four stripes—or two cycles—per
stimulus. The phases of the texture/color edges were randomized during the
experiments.
Internal response:
orientation contrast
Internal response: color contrast
B
Orientation contrast
Color contrast
A
Fig. 3. Signal-detection models for our tasks. (A) Stimulus space for the experiment. The abscissa indicates the color contrast (positive for vertical edges, negative for
horizontal edges) and the ordinate indicates the orientation difference. The observer’s task was to discriminate between pairs of stimuli located along the two axes (single-cue
conditions) or along the diagonals (two-cue conditions). (B) Two model decision spaces. The horizontal axes show the differential response to the vertical and horizontal
color-defined edges, the vertical axis for the texture-defined edges. Left panel: independent processing of the texture and color cues; right panel: non-independence of the
two cues. The non-independence is reflected in the non-zero covariance, which makes the iso-probability contours elliptical and ‘‘tilted’’ in this decision space.
T.P. Saarela, M.S. Landy/Vision Research 58 (2012) 59–67
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2.5. Procedure
2.5.1. General procedure
The observer was seated 57 cm from the monitor in a dark
room. The observer’s task was to identify the orientation—vertical
or horizontal—of the stripes defined by texture, color, or both. Per-
formance was measured using a single-interval design. On each
trial, a single stimulus was presented for 247 ms (21 refresh cycles)
in the center of the screen. The observer made a binary judgment
about which of the two possible stimuli had been presented. The
inter-trial interval was 1000 ms, during which a fixation dot was
visible in the middle of the screen. Auditory feedback was provided
after each trial.
There were equal numbers of vertical and horizontal stimuli in
each block of trials, and their order of presentation was random-
ized. The phase of the stripes was randomized across trials.
2.5.2. Determining orientation and color contrast
In a preliminary experiment, observers practiced extensively on
the single-cue conditions. The orientation and color differences
were varied using two interleaved staircases in each block (one
1-up–2-down and one 1-up–3-down staircase were used to adjust
orientation contrast Dh or chromatic contrast) during these preli-
minary sessions. Psychometric functions (Weibull) were fit to the
resulting data by maximum likelihood (Wichmann & Hill, 2001)
and the fits were used to estimate values for the orientation and
chromatic contrast that would lead to a performance level of
d0= 1 (which corresponds to 69% correct in our single-interval
task). These values were used in the main experiments.
2.5.3. Experiment 1
Each block of trials consisted of 10 practice trials, which were
not included in the final analysis, followed by 120 experimental
trials. The four stimulus conditions (texture-only, color-only, cue-
combination, and cue-conflict) were blocked, that is, the cue did
not vary from trial to trial within a block, and the observer always
knew what the relevant cue was. Each block was repeated 6–10
times, depending on observer’s availability.
In the cue-conflict condition it is not possible to give a simple
vertical/horizontal response because the stimulus has both a verti-
cal and a horizontal edge, one defined by texture, the other defined
by color. However, the conditions were blocked so that the obser-
ver always knew what the two possible stimuli were. The task used
in all conditions is essentially an identification task (two possible
stimuli and two possible response categories). For the cue-conflict
condition these categories were color-vertical/texture-horizontal
and color-horizontal/texture-vertical. The observers were trained
in the identification task before the actual experiments, and the re-
sponse time was not limited, giving observers adequate time to
determine their response. The assignment of response buttons in
the cue-conflict blocks was consistent with the texture-only task,
which might favor attending only to the texture cue in the conflict
task. However, none of the observers reported any confusion about
the task or the mapping of the response keys in the cue-conflict
blocks.
2.5.4. Experiment 2
The procedure in Experiment 2 was similar to that in Experi-
ment 1 with the following exceptions. There were two kinds of trial
block. In half of the blocks observers performed the task based on
the texture cue alone. In the other half, they performed the task
using only the color cue. Within a block, there were three types
of trial. As an example, consider the blocks where the target cue
was texture. First, there were ‘‘neutral’’ trials, on which the color
cue was absent (single-cue, texture-only stimuli). Second, there
were ‘‘congruent’’ trials, where the color cue was present and
defined the same edges as the texture cue (cue-combination stim-
uli). Finally, there were ‘‘conflict’’ trials, where the color cue was
present and defined edges orthogonal to the texture edges (cue-
conflict stimuli). In the blocks where color was the target cue,
the three types of trial were analogous to the ones described above
for the texture blocks. There were an equal number of each trial
type within a block. The observers knew this and they were told
to respond only to the target cue and ignore the other cue. The
three types of trial were randomly intermixed within a block.
2.6. Data analysis
2.6.1. Experiment 1
The data from each condition were pooled across blocks. We
computed d0for each condition in the standard way:
d0¼ U?1ðP\V"jVÞ ? U?1ðP\V"jHÞ;
where ‘‘V’’jV indicates responding ‘‘vertical’’ given a vertical stimu-
lus (a ‘‘hit’’), ‘‘V’’jH indicates responding ‘‘vertical’’ given a horizon-
tal stimulus (a ‘‘false alarm’’), and U?1is the inverse cumulative
normal distribution. Note that with our identification task, the
assignment of hits and false alarms is arbitrary (and one would
get the same result using P‘‘H’’jHand P‘‘ H’’jV). We bootstrapped 95%
confidence intervals for each d0by resampling the data with
replacement 10,000 times and computing a distribution of d0-values
from the resampled hit and false alarm rates.
We compared the performance in the two-cue conditions
against two predictions computed from the single-cue conditions.
First, to test whether the data are consistent with independent
processing of texture and color, we computed a prediction for
thetwo-cue conditionsbased
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
based on an assumption of independent processing and optimal
integration of the two cues. It gives an upper bound for perfor-
mance if the processing of the cues is independent. Better perfor-
mance indicates non-independence of the two cues. The second
prediction assumes perfect summation of the cues, that is,
d0
We also fit a two-dimensional signal-detection model to the
observed response rates from all conditions (Fig. 3B). The two
dimensions of the model correspond to the internal responses to
color and texture edges. We assume that for both color and texture,
there are detectors responding to vertical and horizontal edges.
Sensitivity on each dimension is determined by the difference in
the responses of these two detectors, so that responses to vertical
edges in the model are positive and responses to horizontal edges
are negative. Sensory responses on a single trial, in which a single
stimulus is presented, correspond to a point in this space, and the
observer’s task is to decide which of the distributions gave rise to
that particular response. The internal response distributions
associated with stimulus presentations were modeled as bivariate
normal distributions. For example, for the single-cue color-only
condition there is one distribution corresponding to each possible
color-cue-only stimulus. The means of these distributions are
(lcolor,0) for the vertical and (?lcolor,0) for the horizontal stimulus.
Similarly, the means of the distributions corresponding to the
presentation of the texture-cue-only stimuli are (0,ltexture) and
(0,?ltexture). The means of the cue-combination distributions are
then (lcolor,ltexture) and (?lcolor,?ltexture), and those of the cue-
conflict distributions (lcolor,?ltexture) and (?lcolor,ltexture). In this
space, the distribution corresponding to an achromatic stimulus
with no texture edge would be centered at the origin. The marginal
variances were fixed at unity. We compared two models: (1) a
‘‘separable-dimensions’’ model, with zero covariance between the
two dimensions, that is, independent processing of the two cues
ð2Þ
onsingle-cueperformance:
d0
ind¼
d02
textureþ d02
color
q
(Green & Swets, 1988). This prediction is
sum¼ d0
textureþ d0
color.
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(Fig. 3B, left panel), and (2) an ‘‘integral-dimensions’’ model, in
which the covariance was allowed to take on nonzero values and
thus reflect non-independent processing (Fig. 3B, right panel).
In each experimental condition, the observer indicated which of
two possible stimuli had been presented. We modeled the decision
as based on the likelihood ratio of the two possible stimuli, with no
response bias. Thus, there were two free parameters in the separa-
ble model (the means, lcolorand ltexture) and three free parameters
in the integral model (including the covariance, q). The model was
fit to the data by maximum likelihood. The two models are nested
(the separable model is a constrained version of the integral mod-
el), and we compared their ability to account for the data with a
likelihood ratio test (Mood & Graybill, 1963).
Although the model axes give the distances between the re-
sponses to the vertical and horizontal stimuli, the use of this deci-
sion space does not require the assumption of an explicit stage
where a difference between vertical and horizontal detector re-
sponses is computed. A model with four dimensions—correspond-
ing to responses of mechanisms tuned to vertical color edges,
horizontal color edges, vertical texture edges, and horizontal tex-
ture edges—would still predict, for example, the square-root
improvement in the two-cue conditions for d0
here is effectively a projection of that four-dimensional space onto
two dimensions that show the distances relevant to the task.
ind. The space used
2.6.2. Experiment 2
The data from each condition were pooled across blocks. We
computed d0and confidence intervals as in Experiment 1.
3. Results
3.1. Experiment 1
Performance in the two single-cue conditions was roughly
equal for each subject and near d0= 1, as intended (Fig. 4). Perfor-
mance in the cue-combination condition was better than in the
single-cue conditions. Performance in the cue-conflict condition
was not, however, improved compared to the single-cue condi-
tions. Based on the performance in the single-cue conditions, we
computed two predictions for the two-cue conditions. The predic-
tions are indicated by horizontal lines in Fig. 4. The dashed line
shows d0
integration of the two cues. The solid line shows d0
sumes perfect summation of the two cues (Green & Swets, 1988).
The bootstrapped 95% confidence intervals are shown by the error
bars (for data points) and shaded areas (for predictions).
We tested for significant differences from the predictions with a
Monte Carlo permutation test. We resampled the observed hit and
false-alarm data 10,000 times. On each iteration, we computed d0
ind, which assumes independent processing and optimal
sum, which as-
predictions for the cue-combination and cue-conflict conditions
based on the single-cue data, as well as new d0values from the
resampled cue-combination and cue-conflict data. We then con-
structed a distribution for the differences in d0between predictions
and data. From this distribution, we computed 95% confidence
intervals for the differences and tested whether this interval con-
tained zero. All three observers performed significantly better than
the two-cue prediction d0
nation condition, and significantly worse than the prediction (two-
tailed p < 0.05) in the cue-conflict condition, although these two
stimuli (combination and conflict) had exactly the same texture
and color differences. On the other hand, none of the observers
reached the perfect-summation prediction d0
nation condition (the difference was significant for one of the three
observers, two-tailed p < 0.05); observed performance was always
between d0
The fact that performance in the cue-conflict condition was
worse than in the cue-combination condition indicates that
observers could only integrate texture and color information when
the two were spatially aligned. The fact that performance in the
cue-combination condition was better than predicted assuming
independent processing suggests that texture and color are not
processed independently of each other. To further investigate this
possibility, we fit two nested versions of a two-dimensional signal-
detection model to the data by maximum likelihood (Fig. 3B). Fig. 5
shows how well each model accounted for the sensitivity data. The
fit of the integral model is extremely good for each observer: all
open symbols lie on or near the diagonal. The model accurately ac-
counts for the greater sensitivity in the cue-combination condition
and the lack of improvement in the cue-conflict condition. The sep-
arable model, on the other hand, consistently predicts too low a
sensitivity in the cue-combination condition and too high a sensi-
tivity in the cue-conflict condition. Fig. 6 shows the observed and
modeled probabilities for hits and false alarms. Here, the agree-
ment between the model and data is better for both models, and
all the points lie near the diagonal. The reason why these small
deviations from the diagonal lead to large differences in sensitivity
with the separable model is that it tends to under-estimate p(hit)
and simultaneously over-estimate p(FA) for cue-combination, and
vice versa for cue-conflict. The integral-model fits are significantly
better than the separable-model fits as compared using a likeli-
hood ratio test (p < 0.01 for each observer). We also fit both models
with an additional bias parameter to account for non-optimal cri-
terion placement. Adding this parameter did not significantly im-
prove the overall fits and did not change the main finding: the
integral model fits the data better than the separable model.
Fig. 7 shows the best-fitting decision space for each observer.
The correlation between the two dimensions is also shown for each
observer (the correlation is the same as the covariance because the
variance along each axis was fixed at 1). The correlation is non-zero
ind(two-tailed p < 0.05) in the cue-combi-
sumin the cue-combi-
indand d0
sum.
0
1
2
3
O1
d’
Texture ColorComb. Conflict
0
1
2
3
O2
n.s.
Texture ColorComb. Conflict
0
1
2
3
O3
n.s.
Texture ColorComb. Conflict
Fig. 4. Sensitivity (d0) in the segmentation task for the three observers in the four stimulus conditions. The dashed horizontal line shows the predicted sensitivity for the two-
cue conditions assuming independent processing and optimal integration of texture and color. The solid horizontal line shows the prediction based on linear summation of
sensitivities to the single cues. The predictions are the same for the cue-combination and cue-conflict stimuli. In all cases, sensitivity in the cue-combination condition was
significantly higher, and sensitivity in the cue-conflict condition was significantly lower than predicted based on independent processing of the two cues. Error bars and
shaded areas show 95% confidence intervals. Asterisks indicate significant differences (two-tailed p < 0.05).
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63

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and negative in each case, making the equal-probability contours
elliptical and ‘‘tilted’’ in the decision space. The negative correla-
tion between the dimensions effectively improves the signal-to-
noise ratio in the cue-combination condition: the distance of the
two cue-combination distributions (upper right and lower left)
relative to the amount of noise along the relevant direction is in-
creased relative to the single-cue conditions. The amount of noise
relative to the distance between the two cue-conflict distributions
(upper left and lower right), however, is not changed substantially
relative to single-cue conditions because the noise is increased but
the distance between the two stimuli (i.e., the signal) is increased
by a similar amount, reflecting the lack of improvement in this
condition.
3.2. Experiment 2
Experiment 2 was designed to look for interactions in the pro-
cessing of texture and color when observers were instructed to
use only one of the cues to do the task. This experiment, in other
words, tested whether color information interferes with texture
processing and vice versa.
Fig. 8 shows the performance in Experiment 2. Different panels
correspond to different observers. The d0-values for the texture
blocks are on the x-axis, and the d0-values for the color blocks are
on the y-axis. There are two shaded areas in each plot. First, the area
labeled ‘‘mutual facilitation’’ is the region where performance is
above the 95% confidence interval (of neutral–trial performance)
in both the texture task and the color task. If a data point falls in this
region, then in that condition the presence of the color cue tends to
improve performance with the texture cue and texture also im-
proves color performance. The second shaded area is labeled ‘‘mu-
tual masking’’. It shows the region where performance is below the
95% confidence interval (of neutral–trial performance) in both the
texture task and the color task. A data point falling in this area indi-
cates that in that condition, color tends to impair performance
when observers are using the texture cue and vice versa.
In the ‘‘congruent’’ condition, the second cue was present and in
agreement with the target cue. Each of the data points from the
012
0
1
2
O1
Observed d’
Model d’
012
0
1
2
O2
012
0
1
2
O3
Texture
Color
Combined
Conflict
Separable−
model
dimensions
Integral−
model
dimensions
Fig. 5. Measured vs. model sensitivity. The separable model, which assumes independent processing of texture and color, consistently predicts too-low sensitivity in the cue-
combination condition (filled diamonds) and too-high sensitivity in the cue-conflict condition (filled squares). The integral model, which allows for interdependence of color
and texture processing, fits the data well, with all points lying on or near the diagonal.
0 0.51
0
0.5
1
O1
Observed p(HIT) or p(FA)
Model p(HIT) or p(FA)
00.51
0
0.5
1
O2
00.5 1
0
0.5
1
O3
False alarms
Hits
Separable−
model
dimensions
Integral−
model
dimensions
Fig. 6. Measured vs. model hits and false alarms. Similar to Fig. 5, but probabilities of hits and false alarms are plotted instead of sensitivity.
O2
= −0.52
O3
= −0.81
O1
Internal response:
orientation contrast
Internal response: color contrast
= −0.62
Fig. 7. The best-fitting decision spaces for the three observers for the integral model. The distributions are shown as contour plots. The pairs of distributions lying on the axes
correspond to the single-cue conditions. The pair of distributions near the positive diagonal corresponds to the cue-combination condition, and the pair of distributions near
the negative diagonal corresponds to the cue-conflict condition. The covariance in each of the best-fitting models was non-zero and negative, resulting in an elliptical, ‘‘tilted’’
distribution. The non-zero, negative covariance effectively reduces the variance along the direction on which the cue-combination distributions lie, reflecting the greater
sensitivity in this condition. The same does not happen along the direction on which the cue-conflict distributions lie.
64
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Page 7

‘‘congruent’’ condition (squares) falls in or near the ‘‘mutual facil-
itation’’ region, indicating that observers benefited from having
the second cue present (for observer O1, performance in the color
task was improved, but performance in the texture task was not).
In the ‘‘conflict’’ condition, the second cue was also present but it
defined an edge orthogonal to the target cue. In this case, the sec-
ond cue generally impaired performance and the data points (tri-
angles) fall in or near the ‘‘mutual masking’’ region (again, with
the exception of O1 in the texture task).
We tested for significant differences between the congruent and
conflict conditions with a permutation test. We resampled the hits
and false alarms 10,000 times and computed d0for the congruent
and conflict trials (separately for color and texture), and took their
difference. From the resulting distribution of differences in d0val-
ues, we computed the 95% confidence interval and tested whether
this interval contained zero. We thus had six tests for the differ-
ence in d0(two tasks, color and texture, and three observers). The
difference was not significant for observer O1 in the texture task
(the x-axis locations of the square and triangle did not differ signif-
icantly in the first panel, Fig. 8; two-tailed p = 0.36). Due to the ob-
server’s time restrictions, O1 was able to complete fewer sessions
than the other two observers (with a total of 200 trials per d0esti-
mate, compared to 440 and 500 trials for O2 and O3, respectively).
In the five other cases—color task for O1 and both tasks for O2 and
O3—the difference between congruent and conflict conditions was
significant (two-tailed p < 0.05). Thus, the presence of a congruent
cue, as compared to a conflicting cue, improved performance, even
though the additional cue was not being judged.
4. Discussion
Human observers can integrate two different cues, texture and
color, to improve visual segmentation. Visual segmentation is sig-
nificantly better when both texture and color cues are available,
compared to conditions where only one cue is available. Perfor-
mance only improves, however, when the cues are spatially
aligned; when both texture and color edges are present but orthog-
onal to each other, observers perform similarly to the single-cue
conditions (Experiment 1). Further, observers cannot completely
discount the other cue when segmenting the stimulus based on
only one cue: they perform better when the second cue is congru-
ent with the target cue compared to when the second cue is in
conflict with the target cue (Experiment 2).
The results presented above argue against complete indepen-
dence of texture and color processing in visual segmentation.
Consider, first, the cue-combination and cue-conflict conditions
of Experiment 1. In both conditions, the stimuli to be discriminated
contained texture- and color-defined edges. The only difference
was the alignment: In the cue-combination condition, the texture
and color edges coincided, whereas in the cue-conflict condition,
the texture and color edges were orthogonal to each other. If tex-
ture and color were processed independently of each other, the
alignment should not matter, and performance should be equal
in these two conditions. The observers, however, did much better
in the cue-combination condition (aligned cues) than in the cue-
conflict condition (orthogonal cues). In fact, none of the observers
showed any improvement in the cue-conflict condition over the
single-cue conditions. Second, we compared the performance in
the cue-combination condition to predictions based on indepen-
dent processing of texture and color. The predictions were calcu-
latedfrom the performance
assuming independent processing and optimal integration of the
cues. All observers performed significantly better than predicted
with the cue-combination stimulus (and significantly worse than
predicted with the cue-conflict stimulus).
Another extreme possibility, opposed to complete indepen-
dence, is complete cue-invariance with respect to texture and color
in visual segmentation. Complete cue-invariance predicts that sen-
sitivity (d0) in the cue-combination condition is a sum of the single-
cue sensitivities. We tested for cue-invariance by comparing the
cue-combination performance to this prediction. The observed d0
values were lower than predicted for all three observers (signifi-
cantly so for one), showing that processing is not completely
cue-invariant either.
Comparison of the two signal-detection models also speaks for
interdependence of texture and color. The ‘‘integral’’ model, which
allows for interactions between texture and color, fit the data sig-
nificantly better than the ‘‘separable’’ model, which assumes strict
perceptual independence. The separable model always predicts
equal performance in the cue-combination and cue-conflict condi-
tions. The integral model, on the other hand, accurately captures
the difference between these conditions with one additional
parameter, the covariance.
Two of the observers, O1 and O2, performed in the cue-conflict
condition in a roughly similar way to the single-cue conditions.
Thus, these observers might have used only one of the cues when
the cues were conflicting (that is, they might have attended to one
of the two cues alone). The third observer, on the other hand,
performed worse with the conflict stimulus than with either of
the single cues alone, as if the conflict induced cross-orientation
inthesingle-cueconditions,
1
d’texture
2
1
2
O1
d’color
Neutral
Mutual masking
Mutual facilitation
Congruent
Conflict
12
1
2
O2
Neutral
Congruent
Conflict
12
1
2
O3
Neutral
Congruent
Conflict
Fig. 8. Masking and facilitation between texture and color cues in the segmentation task. The x-axis shows the sensitivity (d0) when the observer used only the texture cue,
and the y-axis shows the sensitivity when the observer used the only color cue to do the task. Neutral trials: the second cue was absent. Congruent trials: the second cue
defined an edge in the same orientation as the target cue. Conflict trials: the second cue defined an orthogonal edge. The area labeled ‘‘mutual facilitation’’ indicates a region
where performance in both tasks (color and texture) tends to be better than in the neutral condition. A point falling inside this region indicates that color improved
performance in the texture blocks and texture improved color performance. The area labeled ‘‘mutual masking’’ indicates a region where performance in both tasks (color and
texture) tends to be weaker than in the neutral condition. A point falling inside this region indicates that color impaired performance in the texture blocks and vice versa.
Error bars show 95% confidence intervals and shaded areas are based on the error bars for the neutral condition (see text for details).
T.P. Saarela, M.S. Landy/Vision Research 58 (2012) 59–67
65

Page 8

masking between the two second-order gratings. With all observ-
ers, the signal-detection model with correlated cues accounts for
the performance in all four conditions simultaneously, with no
need to postulate attentional restrictions or a change in strat-
egy—cue exclusion in this case—in the cue-conflict condition.
In fact, the results from Experiment 2 make cue exclusion an
unlikely possibility. In this experiment, we directly tested whether
the observers are able to ignore a task-irrelevant cue when per-
forming the segmentation task. Observers knew that on one-third
of the trials the second cue would not be there (neutral trials), on
one third it would be in conflict with the target cue (conflict trials),
and only on one third of the trials it would be informative (congru-
ent trials). Nonetheless, their sensitivity tended to be lower on the
conflict trials and higher on congruent trials compared to the neu-
tral trials, as if the conflicting cue masked and the congruent cue
facilitated the detection of the edge. The observers seem to be un-
able to filter out the other cue even when they know it will not
help them do the task.
The non-zero covariance in the best-fitting signal-detection
model, together with the pattern of facilitation and masking in
Experiment 2, is consistent with non-independent processing of
texture and color. There are several possible underlying causes
for this interdependence. The non-zero covariance could reflect
correlated noise in two separate mechanisms, one responsive to
texture and the other for color. Second, these two separate mech-
anisms, one for texture and the other for color, could interact when
both are activated at the same time. Third, some mechanisms could
be tuned for both texture and color signals. Double-opponent neu-
rons that are jointly tuned for local orientation and chromaticity
(Johnson, Hawken, & Shapley, 2008) are one possible underlying
neural mechanism for our observations. This would also be reason-
ably in accord with the study by Pearson and Kingdom (2002), who
found subthreshold summation between color and luminance con-
trast in an orientation-modulated texture discrimination task.
Earlier studies on visual segmentation have investigated the
processing of texture and first-order color cues. The findings are
compatible with our observations. Reaction times are faster and
performance improves when both texture and color cues are avail-
able (Callaghan, Lasaga, & Garner, 1986; Gorea & Papathomas,
1991; Zhaoping & May, 2007). It is, however, difficult to judge
whether the improvement is great enough to suggest non-inde-
pendent processing of the cues. Reaction times increase and per-
formance worsens in a texture-segmentation task in the presence
of task-irrelevant color variability (Callaghan, Lasaga, & Garner,
1986; Gorea & Papathomas, 1991; Morgan, Adam, & Mollon,
1992; Snowden, 1998; Zhaoping & Snowden, 2006; Zhaoping &
May, 2007, but see Gorea & Papathomas, 1993). This interference
seems to be asymmetrical: color segmentation is not similarly af-
fected by texture (orientation) noise (Snowden, 1998; Zhaoping
& May, 2007; Zhaoping & Snowden, 2006), although the results
are mixed (Callaghan, Lasaga, & Garner, 1986). Similarly, in visual
search, having both orientation and first-order color cues available
speeds up responses (Koene & Zhaoping, 2007; Poom, 2009) and
improves performance (Monnier, 2006). As to the independence
of orientation and color cues in visual search, the results are
mixed: although some studies suggest summation of orientation
and color (Koene & Zhaoping, 2007), others have found no evidence
for it (Monnier, 2006; Poom, 2009).
Rivest and Cavanagh (1996) studied the localization of edges
defined either by single cues (luminance, color, or texture) or their
combination. Their results were consistent with independent pro-
cessing followed by cue integration with equal weights for each
cue, whereas our results suggest non-independent processing of
texture and color. A somewhat similar discrepancy seems to hold
for the combination of two texture cues, orientation and spatial
frequency: Edge-localization performance is consistent with
independent processing and subsequent (optimal) integration
(Landy & Kojima, 2001), whereas performance in a coarser task
of texture detection indicates non-independent processing (Mein-
hardt et al., 2004). The ‘‘feature synergy’’—the extent to which the
cues summate—also depends on the strength of the cues. The
weaker the cues are, the higher the advantage of having several
cues (Meinhardt et al., 2004; Persike & Meinhardt, 2006). The cues
in our tasks had a low contrast, which would make them more
likely to reveal cue-interaction effects. Also, in edge-localization
experiments the different cues are often mis-aligned, whereas
our results suggest that cue-integration is best with aligned cues.
Recent neuro-imaging evidence suggests that color and texture
are processed either partially (Cant, Arnott, & Goodale, 2009; Cant
& Goodale, 2007) or completely (Cavina-Pratesi et al., 2010) sepa-
rately of each other when discriminating or identifying surface
properties of objects. This is backed by behavioral data by Cant
et al. (2008): Texture does not interfere with surface color identifi-
cation, and color does not interfere with texture identification. This
difference—Cant et al. (2008) found independence whereas we
found interdependence of texture and color—can probably be ex-
plained by the tasks used: the studies mentioned above were inter-
ested in the identification of the surface properties themselves, not
in how those properties are used to detect edges or shapes. For
example, our cue-combination condition is very different from
the experiment by Cant et al. (2008): In the cue-combination con-
dition the observers can and should make use of both color and
texture information to do the task, whereas in their experiment
the task was to identify the color or texture and ignore the other.
Our observations are also consistent with psychophysical and
imaging studies demonstrating joint selectivity in the processing
of stimulus properties when they are used for segmentation or
shape recognition. Møller and Hurlbert (1997) demonstrated inter-
actions between color and motion signals in visual segmentation.
Self and Zeki (2005) found cue-invariance for motion and color,
and Grill-Spector et al. (1998) found cue-invariance for luminance,
texture, and motion in shape processing in area LOC.
In summary, we find that texture-segmentation performance
improves for edges signaled by two cues as compared to single-
cue boundaries, but only if the two cues signal identical bound-
aries. When the texture and color edges are not spatially aligned,
performance does not improve. This, together with the amount of
improvement with aligned cues suggests that texture and color are
not processed independently of each other in visual segmentation.
Acknowledgments
This work was supported in part by NIH Grant EY16165. TS was
supported by the Swiss National Science Foundation fellowship
PBELP1-125415. We would like to acknowledge the help of Angel
Patel and the helpful comments of John Ackermann and Zack Wes-
trick on earlier drafts of this manuscript.
References
Alais, D., & Burr, D. (2004). The ventriloquist effect results from near-optimal
bimodal integration. Current Biology, 14, 257–262.
Anstis, S. M., & Cavanagh, P. (1983). A minimum motion technique for judging
equiluminance. In J. D. Mollon & L. T. Sharpe (Eds.), Colour vision: Psychophysics
and physiology (pp. 155–166). New York, NY: Academic Press.
Braddick, O. (1993). Segmentation versus integration in visual motion processing.
Trends in Neurosciences, 16, 263–268.
Callaghan, T. C., Lasaga, M. I., & Garner, W. R. (1986). Visual texture segregation
based on orientation and hue. Perception and Psychophysics, 39, 32–38.
Cant, J. S., Arnott, S. R., & Goodale, M. A. (2009). fMR-adaptation reveals separate
processing regions for the perception of form and texture in the human ventral
stream. Experimental Brain Research, 192, 391–405.
Cant, J. S., & Goodale, M. A. (2007). Attention to form or surface properties
modulates different regions of human occipitotemporal cortex. Cerebral Cortex,
17, 713–731.
66
T.P. Saarela, M.S. Landy/Vision Research 58 (2012) 59–67

Page 9

Cant, J. S., Large, M.-E., McCall, L., & Goodale, M. A. (2008). Independent processing
of form, colour, and texture in object perception. Perception, 37, 57–78.
Cavina-Pratesi, C., Kentridge, R. W., Heywood, C. A., & Milner, A. D. (2010). Separate
channels for processing form, texture, and color: Evidence from fMRI adaptation
and visual object agnosia. Cerebral Cortex, 20, 2319–2332.
Cole, G. R., Hine, T., & McIlhagga, W. (1993). Detection mechanisms in l-, m-, and s-
cone contrast space. Journal of the Optical Society of America A, 10, 38–51.
Dubois, M., Poeppel, D., & Pelli, D. G. (in preparation). The cost of combining features
to see and hear a word.
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. New York, NY:
Wiley.
Ernst, M. O., & Banks, M. S. (2002). Humans integrate visual and haptic information
in a statistically optimal fashion. Nature, 415, 429–433.
Gepshtein, S., & Banks, M. S. (2003). Viewing geometry determines how vision and
haptics combine in size perception. Current Biology, 13, 483–488.
Gorea, A., & Papathomas, T. V. (1991). Texture segregation by chromatic and
achromatic visual pathways: An analogy with motion processing. Journal of the
Optical Society of America A, 8, 386–393.
Gorea, A., & Papathomas, T. V. (1993). Double opponency as a generalized concept in
texture segregation illustrated with stimuli defined by color, luminance, and
orientation. Journal of the Optical Society of America A, 10, 1450–1462.
Green, D. M., & Swets, J. A. (1988). Signal detection theory and psychophysics. Los
Altos Hills, CA: Peninsula Publishing.
Grill-Spector, K., Kushnir, T., Edelman, S., Itzchak, Y., & Malach, R. (1998). Cue-
invariant activation in object-related areas of the human occipital lobe. Neuron,
21, 191–202.
Hansen, T., & Gegenfurtner, K. R. (2006). Higher level chromatic mechanisms for
image segmentation. Journal of Vision, 6, 239–259.
Hillis, J. M., Ernst, M. O., Banks, M. S., & Landy, M. S. (2002). Combining sensory
information: Mandatory fusion within, but not between, senses. Science, 298,
1627–1630.
Hillis, J. M., Watt, S. J., Landy, M. S., & Banks, M. S. (2004). Slant from texture and
disparity cues: Optimal cue combination. Journal of Vision, 4, 967–992.
Johnson, E. N., Hawken, M. J., & Shapley, R. (2008). The orientation selectivity of
color-responsive neuronsinmacaque
8096–8106.
Knill,D.C.,&Saunders,J.A.(2003).Dohumansoptimallyintegratestereoandtexture
information for judgments of surface slant? Vision Research, 43, 2539–2558.
Koene, A. R., & Zhaoping, L. (2007). Feature-specific interactions in salience from
combined feature contrasts: Evidence for a bottom-up saliency map in V1.
Journal of Vision, 7(7), 1–14. 6.
Landy, M. S., & Bergen, J. R. (1991). Texture segregation and orientation gradient.
Vision Research, 31, 679–691.
Landy, M. S., & Graham, N. (2004). Visual perception of texture. In L. M. Chalupa & J.
Werner (Eds.), The visual neurosciences (pp. 1106–1118). Cambridge, MA: MIT
press.
Landy, M. S., & Kojima, H. (2001). Ideal cue combination for localizing texture-
defined edges. Journal of the Optical Society of America A, 18, 2307–2320.
Landy, M. S., Maloney, L. T., Johnston, E. B., & Young, M. (1995). Measurement and
modeling of depth cue combination: In defense of weak fusion. Vision Research,
35, 389–412.
Li, A., & Lennie, P. (2001). Importance of color in the segmentation of variegated
surfaces. Journal of the Optical Society of America A, 18, 1240–1251.
V1.
JournalofNeuroscience, 28,
Meinhardt, G., Schmidt, M., Persike, M., & Röers, B. (2004). Feature synergy depends
on feature contrast and objecthood. Vision Research, 44, 1843–1850.
Møller, P., & Hurlbert, A. (1997). Interactions between colour and motion in image
segmentation. Current Biology, 7, 105–111.
Monnier, P. (2006). Detection of multidimensional targets in visual search. Vision
Research, 46, 4083–4090.
Mood, A. M., & Graybill, F. A. (1963). Introduction to the theory of statistics (2nd ed.).
New York: McGraw-Hill Book Company.
Morgan, M. J., Adam, A., & Mollon, J. D. (1992). Dichromats detect colour-
camouflaged objects that are not detected by trichromats. Proceedings of the
Royal Society of London, Series B, 248, 291–295.
Nakayama, K., Shimojo, S., & Silverman, G. H. (1989). Stereoscopic depth: Its relation
to image segmentation, grouping, and the recognition of occluded objects.
Perception, 18, 55–68.
Nothdurft, H. C. (1985). Sensitivity for structure gradient in texture discrimination
tasks. Vision Research, 25, 1957–1968.
Olzak, L. A., & Thomas, J. P. (1991). When orthogonal orientations are not processed
independently. Vision Research, 31, 51–57.
Pearson, P. M., & Kingdom, F. A. A. (2002). Texture-orientation mechanisms pool
colour and luminance contrast. Vision Research, 42, 1547–1558.
Persike, M., & Meinhardt, G. (2006). Synergy of features enables detection of texture
defined figures. Spatial Vision, 19, 77–102.
Poom, L. (2009). Integration of colour, motion, orientation, and spatial frequency in
visual search. Perception, 38, 708–718.
Rivest, J., & Cavanagh, P. (1996). Localizing contours defined by more than one
attribute. Vision Research, 36, 53–66.
Sankeralli, M. J., & Mullen, K. T. (1996). Estimation of the L-, M-, and S-cone weights
of the postreceptoral detection mechanisms. Journal of the Optical Society of
America A, 13, 906–915.
Sankeralli, M. J., & Mullen, K. T. (1997). Postreceptoral chromatic detection
mechanisms revealed by noise masking in three-dimensional cone contrast
space. Journal of the Optical Society of America A, 14, 2633–2646.
Schofield, A. J. (2000). What does second-order vision see in an image? Perception,
29, 1071–1086.
Self, M. W., & Zeki, S. (2005). The integration of colour and motion by the human
visual brain. Cerebral Cortex, 15, 1270–1279.
Snowden, R. J. (1998). Texture segregation and visual search: A comparison of the
effectsof random variationsalong
Experimental Psychology: Human Perception and Performance, 24, 1354–1367.
Stockman, A., & Sharpe, L. T. (2000). The spectral sensitivities of the middle- and
long-wavelength-sensitive cones derived from measurements in observers of
known genotype. Vision Research, 40, 1711–1737.
Wichmann, F. A., & Hill, N. J. (2001). The psychometric function: I. Fitting, sampling,
and goodness of fit. Perception and Psychophysics, 63, 1293–1313.
Wolfson, S. S., & Landy, M. S. (1995). Discrimination of orientation-defined texture
edges. Vision Research, 35, 2863–2877.
Zhaoping, L., & May, K. A. (2007). Psychophysical tests of the hypothesis of a
bottom-up saliency map in primary visual cortex. PLoS Computational Biology, 3,
e62.
Zhaoping, L., & Snowden, R. J. (2006). A theory of a saliency map in primary visual
cortex (V1) tested by psychophysics of colour-orientation interference in
texture segmentation. Visual Cognition, 14, 911–933.
irrelevantdimensions.
Journal of
T.P. Saarela, M.S. Landy/Vision Research 58 (2012) 59–67
67