DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR

CONSTANCY ACROSS CAMERA AND ILLUMINATION CHANGES

Hazem Wannous

IMS Laboratory, University of Bordeaux, Talence, France

Sylvie Treuillet, Yves Lucas

PRISME Institute, University of Orleans, Orleans, France

Alamin Mansouri, Yvon Voisin

Le2i Laboratory, University of Burgundy, Auxerre, France

Keywords:

Color imaging, Color checker design, Chromatic adaptation.

Abstract:

This paper adresses the problem of color constancy on a large image database acquired with varying digital

cameras and lighting conditions. Automatic white balance control proposed by an available commercial cam-

era is not sufﬁcient to provide reproducible color classiﬁcation. A device-independent color representation

may be obtained by applying a chromatic adaptation transform, from a calibrated color checker pattern in-

cluded in the ﬁeld of view. Instead of using the standard Macbeth color checker, we suggest to select judicious

colors to design a customized pattern from contextual information. A comparative study demonstrates that

this approach insures a stronger constancy of the interesting colors before the vision control.

1 INTRODUCTION

The human visual system ensures color constancy,

so that the perceived color of objects remains rela-

tively constant under varying illumination conditions.

This ability, described by the retinex theory, involves

both the eye and the brain (Land, 1977). But no

digital camera has this ability and color coordinates

of pixels are highly depending on acquisition condi-

tions and camera tuning (Barnard and Funt, 2002).

Color constancy generally addresses the illumination

changes, also called white balancing (Barnard et al.,

2002). But, a second problem is caused by chang-

ing the camera. While each digital camera provides a

device-dependent RGB color coordinates system and

embeds a custom color adjustment processing, a more

complete chromatic adaptation transform is required

for minimizing the color differences between sev-

eral cameras. The radiometric response function of

the camera is generally non linear, different for each

color channel and depends on exposure settings such

as aperture, focal length and shutter speed. In addi-

tion, each commercial digital camera embeds some

hidden secret color processing like color demosaic-

ing, white balance adjustment and illumination color

correction automatically applied in JPEG format. So,

some precautions have to be taken with commercial

digital cameras to ensure a consistent color analysis

from JPEG images.

In the case of our medical application (Wannous

et al., 2007; Wannous et al., 2008), images are ac-

quired in a free manner in several care centers with

different types of cameras. In routine, medical staff

have no time and no technical competence for using

RAW format. But, color constancy is the main cue

for a reliable monitoring of tissue classiﬁcation over

time. If a scene was recorded by a free handled cam-

era under several view points, there would be color

variations between images of the same object. In that

case color constancy may be improved by radiometric

alignment between images.

In some previous works algorithms are proposed

for estimating the radiometric response function from

differently illuminated images taken with the same

camera (Debevec and Jitendra, 1997; Mitsunaga and

Nayar, 1999; Grossberg and Nayar, 2002; Kim and

Wannous H., Treuillet S., Lucas Y., Mansouri A. and Voisin Y. (2010).

DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND ILLUMINATION CHANGES.

In Proceedings of the International Conference on Computer Vision Theory and Applications, pages 60-67

DOI: 10.5220/0002835700600067

 SciTePress

Pollefeys, 2004). Then, the color textures of im-

ages may be aligned by the estimated function. Most

of these methods require prior knowledge of expo-

sures on a static scene observed with a ﬁxed camera

(Debevec and Jitendra, 1997; Mitsunaga and Nayar,

1999). Extensions have been proposed for non static

scene (Grossberg and Nayar, 2002) and free move-

ment of the camera or adding vignetting correction

(Kim and Pollefeys, 2008). Such a radiometric align-

ment addresses images acquired with a single digital

camera but is not adapted to a large image dataset ac-

quired over time in several medical centers with dif-

ferent commercial digital cameras.

Indeed, automatic white balancing and color ad-

justments embedded in digital cameras do not ensure

color constancy as the applied algorithms differs from

a trademark to another. Each digital camera provides

a device-dependent RGB color coordinates system.

So, different cameras can exhibit radically different

color responses and can cause signiﬁcant errors in

scene interpretation (Ilie and Welch, 2005). Further-

more, digital cameras - even of the same type - do

not give consistent response. So, we need to homog-

enize the photos of the data base before the classi-

ﬁer learning stage. A reasonable consistency can be

obtained by pair-wise correlation for modeling trans-

fer function based on image color histogram (Porikli,

2003). But the complexity of this approach increases

quadratically with the number of cameras and may in-

troduce distortions and quantization errors

In this context, a good solution to achieve a color

alignment between images captured with different

cameras is given by introducing a small calibrated

color pattern in the ﬁeld of view during acquisition.

Then, color values may be converted in a device-

independent coordinate system by estimating the best

adaptation transform that maps the image color mea-

surements to the corresponding target reference co-

ordinates. This on-line calibration process reduces

the color variations due to illumination and camera

changes and consequently ensures the reproducibil-

ity of the automatic segmentation and classiﬁcation of

the textured color regions. A classical choice for the

color pattern is the 24-sample Macbeth color checker

(Barnard et al., 2002; Haeghen et al., 2000; Ilie and

Welch, 2005; Mansouri et al., 2005). This standard

pattern consists of 24 patches chosen to emulate com-

mon natural colors such as skin, foliage, and sky, in

addition to primary colors and a six step grey scale.

It covers an extensive gamut, adapted to a large range

of images. But a common drawback of a calibration

with such a standard pattern is the decrease in perfor-

mance of constancy when interesting colors are not

represented in the color checker. In fact, many surface

inspection problems are based on a limited palette of

colors. Then, a good strategy for color calibration

would be to minimize the mean square errors in some

judiciously selected areas of the color space, to ensure

stronger constancy of these interesting colors.

In this paper, we propose a methodology for ex-

tracting judicious colors to design a customized pat-

tern by analyzing contextual information in a large

image dataset. This issue deals with the color quanti-

zation problem for automatically extracting dominant

colors in images by some adaptive clustering algo-

rithm (Hsieh and Fan, 2000; Cheng and Yang, 2001;

Sirisathitkul et al., 2004). But here, the aim is not to

classify pixels into their corresponding palette colors,

but to design a speciﬁc color chart. So, the domi-

nant colors were selected from a set of images instead

of a single image. Next, a special sorting algorithm

was developed to reduce these colors to a very small

set while saving the best share-out in the color space.

A comparative study between correction results ob-

tained with the standard Macbeth color checker chart

and our customized one proves that the color distance

errors are minimized, to consequently provide a more

robust automatic classiﬁcation in the area of interest.

The organization of the paper is as follows: in the

next section, we present the proposed methodology

for designing a contextual checker chart. The adopted

correction process for insuring color constancy across

camera and illumination changes is detailed in section

3. In section 4, we present the results of the proposed

approach before to conclude in the last section.

2 DESIGN OF A CUSTOMIZED

COLOR CHECKER

Considering a given surface inspection problem, the

color consistency has to be particularly centered on a

limited palette of interesting colors. In this section,

we propose an automatic method to customize a color

checker pattern using contextual data scene statistics.

The ﬁrst thing to do is to collect a large dataset

of images under varying illuminations and cameras in

real conditions relative to the aimed application. All

the images have been captured with a small standard

Macbeth color checker pattern included in the ﬁeld

of view, to be ﬁrst normalized by applying a standard

color calibration detailed in the next section.

To reduce the combinatorial complexity of the

24-bit color representation, we apply a perceptual

color quantization for automatically extracting dom-

inant colors in this collection of images. The pre-

processing stage includes a Peer group ﬁltering algo-

rithm to remove impulsive noise in the images and to

DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND

ILLUMINATION CHANGES

compute a weighting index used after for color quan-

tization (Deng et al., 1999). Homogeneous neighbor-

ing pixels are favored by higher weights. Next, the

generalized-Lloyd algorithm (GLA) is applied in the

perceptive CIELUV space to drastically reduce the

number of colors by clustering. These choices are

justiﬁed by the use of a similar pre-processing stage

during color region segmentation (Deng and Manju-

nath, 2001). Among several advanced segmentation

methods, the JSEG algorithm proved to be the most

efﬁcient on our dermatological image database. Nev-

ertheless, this choice can be suited to a large range

of applications as a JSEG method includes a reliable

color quantization stage. The quantization results in

a codebook limited to some dominant colors for each

image (Fig. 1).

(a) (b) (c)

Figure 1: Color quantization by generalized-Lloyd algo-

rithm (GLA): (a) original image, (b) image quantization (c)

color codebook.

We collected 132 colors from a base of 26 images

as the most representative ones for our application

(Fig. 2).

(a) (b)

Figure 2: Codebook of 132 colors selected by GLA on the

image data base compared the 24 Macbeth color patches

(a) corresponding coordinates in the CIELUV chromaticity

plan (b).

As it can be seen on the right part of the Fig. 2,

these dominant colors are not uniformly spread in

the CIELUV chromaticity plan (u*, v*). They form

a stretched cloud of points. A principal component

analysis of these data points shows that the principal

axis is deﬁned by the unit vector [0.97; 0.24]. This

axis of the largest variance (explicated inertia of 0.87)

is highly correlated to the u* axis. This observation

agrees with the examination of the colors of skin

tissues which present hue gradation from pink to

beefy red, yellow and brown. By comparison, the

gamut covered by the Macbeth color checker is

really extensive, but only ﬁve patches of the Macbeth

pattern are conﬁned in the area corresponding to the

dominant colors selected by GLA. So, the aim is

to select about twenty judicious colors among the

dominant ones to design a customized pattern. The

proposed selection algorithm is based on two basic

ideas: give greater importance to the colors with the

highest occurrences while preserving the spreading

in the chromaticity plan. The proposed approach for

sorting the most representative colors is the following

iterative algorithm.

Algorithm. Color checker design using ∆E uv dis-

tance between candidate colors.

Input: a data set of candidate colors D={L d,u d,v d,

d = 1,2,...,max} and a predeﬁned K number of repre-

sentative colors to be selected).

Output: a K-color codebook P = {(Lp,up,vp), p =

1,2,...,K}

Method:

1. keep all the candidate colors for u∗ and v∗ values

in range ∓100

2. compute the ∆E uv distance between all pairs of

the candidate colors

3. sort the candidate colors along the axis of their

increasing distance adjacent in an ordered whole.

Choose the pair, with the smallest distance ∆E uv,

among

∑

i = 1

(N − i) pairs of N colors as origin of

this axis. Let D j=d

(c j,c j − 1) be the squared Eu-

clidian distance of adjacent colors in CIELUV space

4. divide this whole of colors into (L < K) consecu-

tive cells where each cell contains the same number

of colors

5. for of each cell, compute the summation of

distances between the adjacent colors dsum i =

∑

j = 1

(D j)

6. if dsum i ≥ τ collect the median color in each cell

to produce m color codebook, else merge the remain-

ing cells in one and divide the latter in K − m cells

where the dsum i in each one equal to (N − m)/K

7. collect the median color in each cell to produce

(K − m) color codebook.

8. stop

Step 3 of the algorithm guarantee an extended re-

VISAPP 2010 - International Conference on Computer Vision Theory and Applications

covery of the workspace (samples), while step 6 takes

into account the distribution of samples (candidate

color) in the uniform perceptual space CIELUV.

We note ﬁnally that the splitting threshold τ and

the initial number of cells L have been tuned empiri-

cally in such a way as to obtain K-color codebook.

This algorithm has been applied to the previousset

of 132 candidate colors selected by GLA on the image

data base, with K=22. This choice enables to obtain

a target of similar size than MacBeth. The result is il-

lustrated in Fig.3. Black and white patches are added

to the 22 selected colors to design the customized pat-

tern. The latter has the advantage of proposing a large

palette of representative colors included in chronic

wound images while keeping a compact size. The

two color checkers (the standard Macbeth pattern and

the customized one) have been printed on a calibrated

printer with the coordinates assigned to each patch,

and next used in the following experimental tests.

(a) (b)

Figure 3: Customized color checker and the corresponding

coordinates in the CIELUV chromaticity plan.

3 CORRECTION STRATEGY

Color constancy generally addresses the illumination

changes by so-called white balancing, but this does

not takes into consideration the changing of the cam-

era. While each digital camera provides a device-

dependent RGB color coordinates system and em-

beds a custom color adjustment processing, a more

complete chromatic adaptation transform is required

for minimizing the color differences between several

cameras. The correction strategy is then composed of

two steps.

3.1 White Balancing

The colorimetric data provided by a digital camera

can not be dissociated from the illuminant coordi-

nates. Many algorithms of computer vision have been

developed to model color constancy (McCann, 2004)

under illumination changes. Most of them adopt

retinex models and the assumption of the indepen-

dence of each color channel. The dependence to il-

luminant may then be corrected by a 3x3 linear trans-

form on the RGB coordinates, as follows:

R c

G c

B c

0 0

0 G

0 0 B m/B W

where (R W, G W, B W) are the white reference co-

ordinates, (R m, G m, B m) the measured coordinates

and (R c, G c, B c) the corrected ones.

White balancing control is realized by digital im-

age processor embedded in cameras. Digital cameras

often propose two modes: automatic or manual white

balancing. The manual mode of cameras requires to

capture an image of a white paper sheet under the am-

bient lighting to store the white source coordinates.

But this is not a convenient use for a routine visit in a

patient room. The automatic white balance control is

based on stored reference coordinates of some stan-

dard illuminants, which are generally considered as

the maximal RGB values present in the image. The

ambient lighting in the patient rooms, composed of

several varying sources, may be indeed far away from

the stored standard illuminants, and because of the

centering, the maximal RGB values in the image may

not be localized on really white object.

For this reason, the channel ratios applied on

the color coordinates of each pixel will be based on

the white patch coordinates (R m, G m, B m) mea-

sured on the checker and the reference ones (R W,

G W, B W) deﬁned during calibration stage under

D65 standard illuminant (CIE, 2008). The above lin-

ear correction allows to obtain white balanced images

whatever the lighting conditions.

3.2 Chromatic Adaptation Transform

In order to ensure the color constancy across mul-

tiple cameras, we apply an on-line calibration pro-

cess based on the known color target included in the

ﬁeld of view. This calibration allows converting the

measured color values in a device-independent coor-

dinates system (as in sRGB space) by estimating the

following transform Φ with m terms. It can be written

as:

R c

G c

B c

= Φ

RGB→sRGB









(1)





a 1 ·· · a m

b 1 ·· · b m

c 1 · ·· c m





















DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND

ILLUMINATION CHANGES

Our transformation is based on a polynomial

model. The transformation coefﬁcients are computed

by the closest match between the reference coordi-

nates of the target patches and the measured coor-

dinates in the images. The reference coordinates of

the 24 color patches of the target are provided by

a spectrophotometer (Minolta CS 1000 SPM), under

the standardized D65 illuminant for the 10

◦

ﬁeld of

view.

The best chromatic adaptation was obtained using

the lower order (m = 3) which is in good agreement

with other works (Haeghen et al., 2000). More gen-

eral polynomial functions have also been tested but

they do not give signiﬁcant enhancement while they

cause largest distortions and higher computation com-

plexity. Each reference color patch provides 3 equa-

tions, allowing to estimate 3 by N parameters for N

patches. With lower order and 22 patches, the linear

system is over determined and a least mean squares

solution is provided.

Even if the standard calibration with Macbeth sta-

bilizes the segmentation and classiﬁcation steps, as il-

lustrated in Fig.4, it can be seen that some interesting

colors for medical diagnosis are not optimally cor-

rected. That can be explained by the fact that the mini-

mization of errors is constrained only at some selected

points of the color space represented on the pattern. A

better strategy would be to use a customized checker

by selecting judicious colors to insure the closest con-

stancy of these interesting colors, as presented in the

next section.

4 EXPERIMENTAL RESULTS

In this section, we show the effectiveness of the pro-

posed correction by several experiments. We use two

sets of real images: the ﬁrst is composed of images

taken under controlled lighting conditions in labora-

tory; the second one includes in vivo images taken

in hospital environment. The Macbeth and the cus-

tomized color charts have been calibrated with the

same spectrophotometer.

The location of the color checker is automatically

detected in the images, and the checker is replaced

in a facial position by applying a robust homographic

transform to remove the perspective effects. Hence

the choice of a speciﬁc shape of the color checker tar-

get with four colored balls facilitates automatic detec-

tion. The average RGB coordinates are then extracted

in the squared area centered in each patch.

The color correction is generally applied in sRGB

space for its convergence with the PC-world, but to

make quantitative evaluation, we must use a percep-

(a) (b) (c)

Figure 4: Segmentation of a wound image captured under

automatic white balance control (a) and after color correc-

tion using the calibrated color checker included in the ﬁeld

of view: (b) Macbeth pattern (c) customized pattern.

tually uniform space such as CIELAB. The individ-

ual inter-sample deviation is computed by the Euclid-

ian distance between the measured coordinates and

the reference ones in CIELAB space for each patch.

Then, we take the average color difference of the 24

patches to measure the differences between the im-

ages and the reference ones.

4.1 Measures under Controlled

Illumination

For comparison purpose, Macbeth and customized

targets have been jointly placed in a lighting test box.

Images has been captured under three different con-

trolled lighting: a cool ﬂuorescent light (Illuminant F

- 4150 K), an incandescent light (Illuminant A - 2856

K), and a daylight (Illuminant D65 - 6500 K). Two

different models of cameras have been tested (canon

EOS 350D, Leica D-Lux 3). The images were stored

in RAW format to avoid embedded custom color pro-

cessing and next to be compared to JPEG compressed

images. The scene has been observed from ﬁve dif-

ferent points of view: a centering front view and four

cardinal views with angle about 25

◦

, corresponding to

our clinical protocol for 3D reconstruction (Wannous

et al., 2008). Each view has been repeated 5 times,

i.e. a total of 600 measures (24 patches x 5 views x 5

acquisitions) to compute the average CIE∆E ab dis-

tance for each camera and each illuminant, before and

after correction. Results are presented in Table 1.

CIELAB color distances are measured simultane-

ously on the Macbeth color checker and on the cus-

tomized pattern before and after correction. The ’stan-

dard correction’ indicates that the chromatic adapta-

tion is computed from the MacBeth pattern; the ’pro-

posed correction’ corresponds to a chromatic adapta-

tion computed from the customized pattern.

Before correction, we observed very large differ-

ences between the two types of digital cameras under

different illuminants, with an average around 25 units.

A smaller standard deviation has also been observed

on the customized pattern, which may be explained

by a more restricted range of colors.

VISAPP 2010 - International Conference on Computer Vision Theory and Applications

Table 1: CIELAB color distances measured on the 24 patches after correction with the standard Macbeth color checker and

the customized designed one under controlled illumination.

CIELAB distances illuminant Canon EOS 350D Leica D-Lux 3

Max Average Std Max Average Std

Macbeth target Cool 39.78 23.35 4.18 51.13 23.11 1.39

before correction A 36.17 26.82 2.19 48.78 21.45 1.59

D65 39.50 28.32 2.21 46.59 24.57 4.44

Customized target Cool 31.40 22.82 3.26 21.08 14.28 0.84

before correction A 32.34 26.21 1.44 23.69 14.12 1.14

D65 36.80 28.93 1.64 45.21 16.52 4.04

Macbeth target Cool 8.45 3.94 0.12 9.89 3.04 0.30

standard correction A 8.97 4.01 0.13 11.05 3.36 0.41

D65 7.57 3.88 0.14 7.89 3.26 0.20

customized target Cool 15.67 5.39 0.24 22.41 5.25 0.58

standard correction A 13.51 5.22 0.29 20.01 5.15 0.54

D65 12.71 5.24 0.33 17.97 6.05 0.35

customized target Cool 5.87 2.76 0.22 5.40 2.00 0.26

proposed correction A 5.46 2.86 0.13 10.15 2.04 0.36

D65 8.90 2.94 0.17 10.37 1.90 0.23

Table 2: CIELAB color distances measured on the 24 patches of the customized color checker, in JPEG and RAW format.

CIELAB distances illuminant JPEG format RAW format

Max Average Std Max Average Std

Cool 25.32 16.83 1.57 21.08 14.28 1.84

before correction A 23.98 17.93 1.00 23.69 14.12 1.14

D65 41.99 21.21 3.18 45.21 16.51 4.09

correction Cool 6.95 2.68 0.34 5.40 2.00 0.26

with A 7.19 2.73 0.44 10.15 2.04 0.36

customized target D65 7.99 2.76 0.30 10.37 1.90 0.23

The correction based on the Macbeth pattern re-

duces the averageCIE∆E ab by a factor between 5 to

6, reducing the gap to about 3,5-4. But, for the inter-

esting colors on the customized pattern, the color con-

stancy is not sufﬁcient: the differences remain high

with an average around 5-6 and some maximum val-

ues between 12 to 22. Logically, the color constancy

is clearly improved by the proposed correction on the

customized pattern, as the minimization of errors is

constrained on judicious colors conﬁned in the inter-

esting area of the color space.

A second test concerns the inﬂuence on the re-

sults of color processing and JPEG compression car-

ried out by the digital camera (see Table 2). The JPEG

compressing format is more convenient for storing a

large image data base, but it implies no control on

the embedded color processing, such as demosaic-

ing or color white balancing speciﬁc to each camera

type. However, the test proves that the correction per-

formances are not signiﬁcantly modiﬁed by all these

transformations.

4.2 In Vivo Images

To complete these laboratory experiments, we anal-

ysed in a similar way color shifts on photos taken dur-

ing wound patient examination in several hospitals.

To evaluate the proposed correction in real conditions,

the same tests have been applied to a collection of in

vivo images taken in hospital environment (see Table

3). It conﬁrms that the contextual color correction on

the customized pattern results in a higher degree of

constancy for interesting colors. In last analysis, we

can evaluate the real impact of the color correction at

DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND

ILLUMINATION CHANGES

Table 3: CIELAB color distances measured on the 24 patches of the standard Macbeth color checker and the customized one

designed for in vivo images.

CIELAB distances in vivo images (JPEG)

Max Average Std

Macbeth before correction 29.28 15.07 6.56

customized before correction 34.82 20.02 4.05

Macbeth standard correction 9.74 4.24 0.25

customized standard correction 27.88 10.68 1.74

customized proposed correction 8.47 3.37 0.47

the end of the processing chain: the performance of

the classiﬁcation of the skin tissues is improved and a

better agreement is obtained with the medical expert

(about 15% on classiﬁcation rate) when including the

correction step (Wannous et al., 2007).

5 CONCLUSIONS

This paper deals with color constancy on a large im-

age database acquired with varying digital cameras

and lighting conditions. Automatic white balance

control proposed by the commercial cameras is not

sufﬁcient to provide reproducible surface inspection

or classiﬁcation results. The instability in color ren-

dering may be signiﬁcantly reduced by applying a

color calibration with a color checker pattern included

in the ﬁeld of view. A device-independent color rep-

resentation may then be obtained by applying a chro-

matic adaptation transform, without the necessity of

re-calibrating the cameras when the lighting condi-

tions change. Instead of using the standard Macbeth

color checker, we suggest to select judicious colors to

design a customized chart using contextual informa-

tion. A comparative study demonstrates that this ap-

proach insures a closest constancy of the interesting

colors for the vision control. We conclude that using

heterogeneous cameras and varying ambient illumi-

nation in patient room is possible for computer aided

diagnosis of skin lesions. While designed for opti-

mum color constancy in cutaneous imaging, the pro-

posed approach to customize a color checker can be

applied to many surface inspection problems requir-

ing a limited palette of colors. Future works will be

centered on the adaptation of the number of patches

inside the color pattern to the quality of the desired

precision.

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DESIGN OF A CUSTOMIZED PATTERN FOR IMPROVING COLOR CONSTANCY ACROSS CAMERA AND

ILLUMINATION CHANGES