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ELSEVIER

White balance in inspection systelttLS
with a neural network
and Hiroshi Kan♂
Httime Yamashina*,Kiyoshi Fukushima†*D"α′″ηι″r9′ P″ cおJοれ ν

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Automatic inspection systems based on image processing have been used in manuFacturingsystemso However,in the case ofsystems using colour images,colour signals fcd into thc system
dil181y,it isthrough a camcra dcpcnd upon thc inspcctcd ollcCtS and oll thc light sourccso Accol・necessary to convert the colour signals received under the inunlination at the tilne of inspection
into those that would be received when using a standard light source to achieve a reliable andversatile inspection system that can operate stably under a wide variety of light sources.In thispaper,a colour image input system Mrith white balancing using a neural network is proposed,and compared with that used in ordinary video cameras by means of computer silnulationo This
systcnl is applicablc as thc prc‐ proccssOr OF colour signals for automatic inspcction systOms
based on colour illlagc proccssingo Copyright()1996 EIscvicr Scicncc Ltd.
vords:automatic inspcction,colour imagc proccssing,ncurai nctwork
Kcy、

In ordinary photography, the differcnccs bctwccn
thc colours perccived by thc human eye and thc actual
ln recent ycars,automatic inspection systcms bascd on
colours that would bc rcgistercd by a coloriincter arc
image proccssing havc become increasingly important,compcnsated by selccting a suitablc filin or adding
cspcciaHy for computer aidcd manufacturing,bccause
filtcrs to suit thc lighting conditions. Conventional
they can enhancc spccd of inspection and also rccord
vidco camcras usc a siinplc auto―
whitc balance function,
useful information to analy2e conditions of thc pro―、vith practicaHy acceptable pcrfOrmance for hobby
duction linc.Many vision systcms are constructcd withuscs3.
black and whitc cameras, and arc mainly uscd toSincc the colour adaptation ofthc human,ye results
Iowever, using colour
inspcct the shape of obiCCtS・ I‐
from processing carricd out within thc brain,it should
camcrasitis possible to make more inteHigentinspection
be possiblc to achicvc si:nilar results from a back‐systems that can analyZe, understand and inspcct
propagation ncural nctwork.Bascd on these idcas,wc
images of oblects With grcatcr precision.
have developcd a ncw white balance function that usesヽVhcn this sort oFinspcction is carried out by human
input,thrcc‐laycr,three―output ncural network
a four‐beings, thcy can determinc the actual colour of thc
to which arc input the co10ur temperature of thc light
ObieCt With rcasonable accuracy whethcr the obicct issourcc and thc thrce prilllary colour signals of thelit by an incandesccnt bulb, a fluorescent light, or
rccclved image4.
natural daylightl. This is bccausc human perception
This paper compares the whitc balancc corrcction
adapts to thc spcctral charactcristics of thc light sourcc;
performancc 9f thiS neurai nctwork with the con―′cthis characteristic of human vision is caHcd rο
cた α′
vcntional whitc balancing techniquc that is uscd, for
2.
jθ
αα9ρ′
α′
κ
examplc,in vidco camcras.
Howcvcr,thc colourinformation fed to an inspection
vith changcs in lightvili vary、
system fl・om a camcra 、
sourcc, cvcn if thc camera continucs to look at thc
White balancing
samc obicct.As a result,to acllicvc a more reliablc andvill first considcr thc basic principles of whitc
Wc 、versatilc inspcction systcm that can operate stably″ rbalancing as carricd out in vidco camcras.Fig
undcr a widc varicty ofllght sOurces,it is ncccssary toshows a modcl of the imaging process within a video
convcrtthecoloursignalsreccivcdundcrthcillunlination
camcra.In thc camera,light rettccted from thc oblectiSat the time 9f inspcction into those that would be
separatcd into thc thrcc p五 mary colours(red,grcen
reccived when using a standard light sourcc.

Introduction

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White balancing with a neural network

全6色 L i g h t s o u r c el ( λ)Red Filter

ObieCtR ( λ)/ □r () λヽVhitc balancc correction is thought of as consisting of
two sttigcs:(:)cStillllltion or tilc ligilt sourcc cllttr
istics;and(2)corrCCtiOn or the c。
1。ur signals bascd on
thcsc c、tinlを
ltcd chitractcristics6. Thc chartlctcristics or
thc light sourcc should ideaHy bc rё prcscnted by nlcans
oFits spectral distribution,butin practicc they arc Oftcn
reprcsented morc silnply as a colour tcmpcrature.
In this paper, wc considcr thc quality of whiteblllancc tllttt is achievcd 、
vhcn tllc colour signals arc
processcd arter scparation.ヽ Vc compare thc usc oftwo
colour signal processing nlcthods: using a neuralnctwork,and adiuSting the RGB gain hctors.Wc
assumcd the light sourcc to bc a Fu‖radiator,and thatits colour tc:ηpcraturc had alrcady bcen dctcr■ lincd.

r醗
fr
拳 風
△＼
Blue Finer｀b ( λ)□￨(λ):Relattve spectra!distribu‖
onR(λ):Spectral relectance of oblectr(λ),9(λ),b(λ):Spectra:sensnivity for each color
Figurc l MOdcl oF colour canlcrtl

Experilnental inethod
′
イ
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・
・ ′′
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̀̀′
a n d b l u c ) , w h i C h a r c o u t p u t s c P a r a t C l y . I t c p r c s c n t i n g′ ̀ノ'′ wing
cxpcrirncnts「hc r。‖。
、
vcrc an carricd Out as
λ,、vc conSidCr thc casc or an
the、vavclcngth oflight as
CO:llputCr sinlulations. Thc raw data uscd for thcsco b j e c t o f r e f l c c t a n c c R)(iλn a light sourcc with aexperiments was prcparcd as fo‖ ows.
s p c c t r a l d i s t r i b u t i o n o f) . fL (i λght from thc obicCt iS
converted into rcd,grcen and bluc outputs ofr,g and b
んな力′s ο″κι・T h e D 6 5 i S S p c c i f i c d i n t h c C I E ( C o m m i S ‐vit1l corrcsponding rcsponscs oF
by an inlagi1lg systcnl、sion I:ltcrnationtll dc I'Ecllliragc)Standard and JISr(λ) , g ( λ) a n d b ( λ) . T h C V a l u c s o f r , g a n d b c a n b c(」apan lndustrial Standard)Z8720 as a standard lightving set oF equations:expressed by the foHo、sourcc of which thc co10ur tcnlpcrature is 6500 K. In
our cxpcrilncnts,、
vc chOSC thc standard light sourcc as(λ/=∫r(λ
λ
) R(λ
)ノ
)′
a fuH radiator with a colour tempcraturc of 650tl Ko We
(λ
g=∫′
λ
)グ
)R(λ
)g(λ
´
(1)
also assumcd that observation was pcrformcd using
(λ
λ
b=∫ ′
)b(λ
)′
)R(λ
non―standard illunlination fronl fu‖ radiator lightvitll colour tcmpcraturcs of 2000 K, 3000K,
sourccs 、
These cquations arc solved by intcgratin3 0VCr thc
4()()OK,5()()OK,600()K,7()00 K and 8000 Ko Spectral
ve assunlc
vavclcngths.In this inodcl,、
rangc of visibic、
charactcristics of thc main light sources uscd thcscin′that inspection is performcd using a light source with ac x p c r i m c n t s a r c s h o w n i n t t″g J .
s p ёc t r d d ヽt 」b u d o n o f f l ( λ) , r C S u l ●n g h r c d , g r c c n
and blue outputs of rl,gi and bl.
οri′
, 7̀′
C()′
ζ
ッ。We sclccted 35 standard colours from theヽVhitc balancing is achicvcd by using tilc t1lrcc
v i:lg t、
vo groups()「 colour cards:
R)llo、
outputs rl,gl a1ldわ i to cstinllttc thosc or′1),8。211ldわo
7:24 colour patchcs.
、
vith a standard Hght sourccthat would be obtaillcd
1, Macbcth Color Chcckcl・
having a spectral distribution orム )(λ2. Ma:lscl Book8: 1l c。lour patchcs.
).
Thc Macbcth Color Chcckcr contains six shadcs ofWhite balancing in video cameras

grcy, 12 colours that closcly rescmblc thc spcctral

cctivity oF natural objccts such as skin, sky a
rcΠ

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lcns(1lL)whitc
Fig ″ι2 i l l u s t r a t c s t h e t h r o u tg h c‐‐
lcavcs, plus additivc and subtractivc prilllary colours,balancing method used in most vidco cameras.In thismcthod, thc averaged output voltage of the ilnaging
elcments is used as an indication of thc colour
Signal Processing
Matrix
Encoder
tempcraturc of thc light sourcc, and variablc― gain
fy the red and bluc signals areamplificrs that anlp‖
adiuStCd accordingly.Thc colour tempcraturc oF thc
ical rule
light sourcc is esti11latcd bascd on thc cinpil・
that,ir onc avcragcs aH thc inlagc signals obtaincd rrolllthc cntirc obicct under vicw,thc rcsulting colour wi‖
raging of thc signals in an
be grey.Ho、vcver,silllple avё
iinage can rcsult in largc errors in cstirnating thc colour
temperaturc whcn largc parts ofthc imagc are takcn up
Detector of Color
by ObiCCtS Of a particular colour.Thc wcighted avcrage
Temperature
bascd on fussy infcrcncc has becn reportcd to beeffcctivc ror improving accuracy in thc cstirnation5。 Figurc 2 Automatic whitc balancc of TV camcra

Ｌ一′lt'frノ:ドッ∫′′″1.ゞ
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sカル′″ctモ11̀わ̀′ ̀ル

Ｃ〇一
一コ０一
わ ∽一
Ｑ 一

200

150

5

Hcrc,χ(λ) , y ( λ) a n d Z ( λ) a r e t h C C 0 1 o u r m a t c h i n gfunctions for tri‐
stimulus valucs,r!。
,y:。 and zl。.

‐‐‐・1'42888K――‑ 5 0 0 0 K‑6500K― ・― ・8 0 0 0 K

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ο ″ε
οrrι
ε ″.sys′
ι
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̀わ

Colour cOrrcction is pcrformё
d by convcrting the tri―s t i m u l u s v a l u c s ( χ, y , z ) 。
f thC light sourcc that

二
i二
l二
l二
i:i:
̀fil「

iliuminatcs thc Objcct during imaging intO thc t

ｏ①Ｑ∽ ①＞

100
´

´

´

゛
゛

50

´

゛
゛
゛

1■
1 ゛・
「
゛

・ :

stinlulus valucs corrcspOnding to a c010ur temperaturcof 65()OK(scc Eig ″ ).
̀イ

W/1′
ノ
α′
77g ″″
ιε″′
力 α 77ι″α′rl̀′″ο/た。Thc ncural
̀̀ bα
nct、
vork
of back prOpagation algorithm is uscd for thc

::.゛

①∝
一

whitc balancing.As shown in ag ″ 5,thc colour
tcmpcraturc of thc light sourcc illuminating thc object

0

400

500
600
700
Wave Length(nrll)

Figurc 3 1ヽclそ
ttivc spcctrai disti‐
ibuti。
:l oFfu‖radiator
rcd, grccn, bluc, cyan, magcnta and yc‖o、v. Thc
vidcly uscd as a sourcc of colour
ManscI Book is、sanlplcs, and 10 standard colours Froln thc hue circlc
ttnci tl、
htt(lc()「grcy wc:・c ch()、cn:l、c()l()llr i〕
Wc̀ltChCS・
nlcasurcd thc spectra1 1・ cricctancc oF thcsc 35 colour
patchcs usi1lg a ⅣFinolta Cヽ/1‑100 spcctral colorillletcr,、
vhich can nlcasurc thc rcl:lё ctancc spcctra over thc
ra1lgc from 400‑700 nm.
vcre
Thc colours fronl the Macbeth Color Chcckcr、
uscd to train thc ncural nctwork,1:ld thc Manscl Book
colours、 vcrc uscd to cvaluatc thc pcrfornlancc or thc
tl・
vork
aillcd nct、

a n d t h c t n ―s t i luη
l u s v a l u c s (,χy , z ) 。f t h e C O r r e s p o n d i n g
irnage arc input to thc net、
vOrk,and thc correctcd tri―sti:ηu lus valucs for a colour tcmpcraturc of 6500 K(χ
65,Z65)arC Output.Thc nctwork Was constructed
̀5,γ)ti r:nPllt、、
vitil負1(l:t hiddcn itlycr 30
o「,l1lrcc()llt pl:ts:‖
cicnlcnts.
B c 「o l c・ t h e n c u l ・
al nctwork was madc to perform
colour correction calculations, it was trained using
known input and output signals. For this purposc、vc
̲1‐
adiatorlight sources with colourtemperaturcs
uscd ru‖or 2()(X)K,30()()K,4()()()K,5()()()K,60()OK,7()()OKand 8000 K,and the corrcspondingχ ,y and z values
ofthc 24 colour patchcs of the lMacbeth cOlor Checker
as input signals,and」(65, f65 and Z65 ValuCs obtained、vith a 6500 K fuH― radiator light source as output
signals.Each Ofthcり て,y and z valucs ofcolour patchcs
can be dctcrmincd from thc sct of cquations(3)bas,don thc spccti‐
そll rcricctivity mcasurcd in advancc.

′
′
77ag′
″g sysrι″1. T1lc colour of thc light rcflcctcd froln

α4 εゴ′
W/1J′ ノ: g b y c/θ
g ′/ 2 ̀ R C 3 g αj れ1″
θ〃J れ
ο浴. Í n′7 6 ′
̀b′、vhite balancing in ordinary'idco camcras,the gains ofthc colour patchcs was dctcctcd by thrcc(RGB)
inlaging systcnls which havc spcciric spectral charac―「
cPort,｀VC uscd thc col()urtcristics,rcsPcCtiVCly.In thi、イ,y andmatchillg runctions For thc tri―
stinlulus valucsブ

thc R and B channels arc controHcd such that total
input signtlls for an i:nagc is vays
al、 maintained at an c u t lこ‐ll slladc or grcy, i.c.R = C= B.In thcse
CXPCrimcnts,wc adiusted thC scttings for each light

Z or χ l。
,yIo and Z:()laid down in thc standard oF CIEor」IS Z8701.Thc colour illatching functions rcprescnt
thc spcctral scnsitivity of thc hunlan cyc, and can bc
regarded as colour filters in the siFnulation. Thcir
charactcristics arc defillcd such that the tri―stilnulus、
vhich arc based on t1lc physio―
valucs R, C and B,
logical priinary colours,arc related to the tri―stimulus
vを
llucsジr, y tind z as folo、vs:χ =1.85995× R‑1.12939×
G+0.21990×γ〓 0.36119× R+0.63881×G‐(2)
Z=1.08906×3

Color Correctlon
System

′
｀
65

3
Z65

′
「hc nght rcFicctcd rronl an obicct or reFicctivityR ( λ) W h C n i l l u m i n a t c d b y a l i g h t s o u r c c w i t h a s p c c t r a l
d i s t r i b u t i o n o(Fλ′)can bc convcrtcd intχo ,y and Z
components using the foHo、ving cquation:

Color ttemperature
ｈ
ｇ ｆ
＝¨ ０

(λ
(λ
χ=∫′
λ
)R(λ
)ご
)χ
y=.fr(λ
λ
)R(λ
) y(λ
)ご
Z=.′′
(λ
λ
)R(λ
) Z(λ
)″

X65

(3)

X , Y , Z : T sr li m‐u l u s v a l u e u n d e r t h e a m b i e n t
lmulus value underthe llght
x s , Y 6 5 ,5乙: T nS‐
6500K fu‖radlator
Figurc 4 Colour corrcctiO:l systcinn

W/1ピrιbα″′
ι ll J4,r,ι
C″οrl sysrc2″s″ルヵα7VⅣr∴r yα″αsぁj4αct al.
̀̀′

／ 〆
X●

3

̲Y6S

fornling thc c010r circlc and a standard grcy from thc
Manscl B00k.
ミヽι 7shows thc averagc colour dirFcrenccs orゝ ″white balancc cOrrection for each of thc 24 standard
colours ofthc Macbcth Color Chccker used fortraining
thc ncural nctwork.Thc information actuaHy lcarncd
by thc ncural nctwork is indicatcd by thc cight points

Layer

Figure 5 Ncurai nctwork for whitc ba:ancc

sourcc such thatthe RGB valucs wcrc thc samc forthc
four grcy colour patchcs ofthcⅣlacbcth Color Chcckcr.

shown in Fig
6,but thc nctwork was ablc to opcratc
cffectively ovcr the cntirc rangc of colours, in01uding
colour tcmpcraturcs that、verc noticarncd.In addition,
the white balance corrcction obtaincd fronl the ncural
network rcsulted in a colour diffcrcncc△ E of about lover thc range of colour temperatures fron1 2000‑
89()OK.

ThC Si3nttis c()nl:)cnsatCd k)r whitclitt:lcc
l)〔l includcst‖colour dil・lcrcilccs.In thCsc cxPCri11lcnts,thc dillcrc:lccs
were ёvaluatcd in the L*α *わ* ulliform colour spacc

150

100

b*
ι* ′
αl̀′

50

600
700
Wave Length(nrn)( 臨5 / y l ) ) l‑ β
16
100°

3‑(臨
500・
5/X〕
)″
[(為5/為)ν
刊
3̲(z65/ZO)V旬
200・[(y65/yl))ν
10()。
(γ′
65/ylD:/3̲16
(4)
′
′
50()・
65/χ
)):/3‑(γ65/yl))1/31
I(χ′31
2()()・1(γ65/KD1/3̲(z,65/Zlll:′

Figurc 6 Rclativc spcctral
supplc:1lcntary lights* ― ι* ' ) 1 / 2 + ( α*̲α *′):/2+[(ι2( b オー b * ′):/2]:′

Hcrc,れ ,yl)and zo arc thc ―stt‖i n l u l u s v a l u c s o r afu‖y diffusc surfacc.

Experilnental results①＞＜︵①ｏｃ①﹂①﹂
ヒ∩ ﹂ｐＯＯ Φｏ ﹂
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一 一
二 一 一
一 一
一 一
一

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arC'X65,1165 and Z65,and thc corrcsponding valucs forstandard iHunlination cstimated fronl thc actuallighting
conditions after white balancc corrcction arc Йr′
65,y′65
ctwccn thcsc two
and Z′
6 5 , t h e C o l o u r d i f f c r e nEc cb△
vi:lg
0、
colorirnctric valucs can bc Found using t1lc ro‖
cquations:

一″

defincd in CIE(1976)and JIS Z 8729.This colour
spacc is designed so that colour diffcrcnccs sho、 vcd
closc agrecment bct、veen the numcrical and pcrccivcd
dirfcrcnccs.If thc tri¨
sti:1lulus valucs obtaincd rrom tilcvith a standard light sourcc
colour chart inunlinatcl 、A
一・・・・‐・D 5 0‑― ― D55‑D65‑0‑=D75

辞″

Ｃ〇一
﹂一∽一
一⊃０一
∩ 一 わ Ｏ①Ｑ∽ ①＞

θ ″グJと″
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′
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ln this section,wc comparc the pcrformancc of whitc
balancing achicved using a neural hetwork、 vith that ofwhite balancing by RGB gain adiustment,and compare
w hitc―balanccd
thc cffccts of both relativc to a non―signal.Thcse comparisons wcrc madc using two groups
oflight sources;a total of 69 fuH radiators rangi1lg from210(卜8 9 0 ( ) K i n s t c p s o f l ( ) ( ) K , a n d t,hDcЛス) , D 5 5 ,D65 and D75 Standard colorilnetry light sourccs deFinedin CIE(1964)and」 IS Z 8720.Thc latter group oflightsourccs llad colour tclllperaturcs of 2856 K, 5003K,
5503 K and 7504 K, respcctively. Their spectral

distribution of standard

100

and

Neurai network training points
O

Neuraineh″
ork non・training points

O Gain adlusiment□ No wHЮ
bJandng
10

｀◇
、、
、。
、

0.1
2000

4000
6000
8000
Color ttemperature of Llght S o u r c e ( K )6.As a colour tablc,
charactcristics arc shown inκag
Figurc 7 ヽ
Vhitc balancc colour diF「crcncc for training pattcrns
wc also used ll patchcs,nanlcly thc 10 colour patchcs

7//1′′
̀b滋

7κιル:プ″,′,̀αlο″Sysrι
771s″′
r/1 rJノVハ′rJり yrI″lαs/1′″αct al.

⌒①ＯＣ①﹂①﹂ｍ＜コШ一〇︶﹂０ ﹂ｐ ００ ｏｏ ﹂①＞＜

100

● Neurai notwork non・
:ralning points◇ Gain adiusiment□ No whle bJandng

10

1

0.1
2000

4000

6000

8000

Color ttemperature of Light Source(K)7Figr/κ8shows thc avcrage colour difference of whitc
balancc corrcction on the eleven unlearncd colours int h c M a n s c I B O o k . T h i s A g u r c c l o s c l y r e s c m b l c″s Fig
ι7, cspccia‖y i:l tilc casc of the ncurai network
balancing, 、vhich produccd cxtremely siinilar results.
his confirills that thc ncural nctwork was capablc of
1｀
opcrating effectivcly evcn in situations not cncountcrcd
during icarning.
Fig夕r‐ 9 and′θ shOw the avcragc colour diffcrences
obtained using the standard light sources dcfincd in
CIE and」 IS Z 8720 1o illuminatc thc Macbeth Color
Checkcr and Mansel Chart,rcspcctively.Thcsc figures
dcnlollsti・titc tllc icFibctivcncss 、
oF
vhitc
balancc cor―
rcction 、
vitll rcal light sourccs. Thc avcragc colour
differcnce of the signals correctcd with thc ncural
nct、
vork incrcascd tO bctwecn 2 and 3, but this was
bccause evcn though thc colour tcmperaturc of thelight sourcc was thc same,thcrc was a diffcrence in thc
SPCCtral distributions Of thc fu‖ radiator and thc(l light、stti nc!(tl‐ou rccs.

Figurc 8 Whitc baLl:lcc C010ur difitrcncc「or non―training
pattcrns

Conclusion

臥合壽:1識器 ng
、、

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、
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臥。 D65
ヽ

▲ 。

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A

４ １ Ａ

１

Color ttemperature of Light Source(K)

口︑

D75

↓
ロ

●

■４１ ５
Ｄ

０
５
Ｄ

５

diffcrent colours. Conscqucntly, it is csscntial for thecailleras uscd in illspcction cquiphlcnt to be propcrly
adiuStCd fOr thc light sourcc.

・
E〕

４ １

nique must bc used.
Whcn nO whitc balancing is pcrformed at aH, thecolour difFcrencc
vas natura‖ y O at 6500 K, but△g 、rosc abovc 10 at colour tenlpcraturcs below 5500 K.
Thc hunlan eyc can pcrccive a colour diffcrence of 5,
and colour dirfercnccs of 10 or nlorc resultin markcdly

A.

１

Whitc balancing with RGB gain adiustmcnt is very
sirnple to achieve,but rcsultcd in a satisfactory colour
differcncc Of、
vithin 3 only at tempcraturcs abovc
4000K. At lowcr colour tempcratures, anothcr tcch‐● Neural network non‐training points

口、.IPG乱
喘き
∬鳳dng
０

‖ghts

RGB gain componcnts.When using thc tri―
stimulus
Values Of a standard cOlour chart and a fuH radiatorlight source for training the nctwork, this approachrcsults in highly effcctivc whitc balancc compcnsation
for light sourccs witil dirrercnt colour tcmperaturcs andstandard light sOurccs. It should thus bc vcry uscful
as prc―processor for application to actual inspection
systcrns.
´
Howcvcr,sincc thc spcctral distribution of thc lightsourcc is not considcrcd in this tcchniquc,to achievc、
vhitc balancc cOrrcction with higher performancc it is⌒００ＣＯ﹄①一〇０ ０００﹂①＞＜〇︶
ヒ０ ﹄０一ｍくコШ一3000 4000 5000 6000 7000′
Figure 9 ヽヽhitc balancc colour diffcrcncc for CIE(training pattcrn)

Wc htlvc considcrcd whitc balancc correction using ancurill :lctwork, :ind conlpttrcd its Pcrro:・:lltlnCC With

that of white balance correction by adjustment of

５
７←
Ｄ

0□

０

︵
〇０ ０００﹂Φ＞くｍ＜コШ一〇 ︶ΦＯＣｏ﹂①一七∩ ﹂０一● Neural network training points

・ °・・
・
▲

3000 4000 5000 6000 7000

Colorttemperature of Light Source(K)Figure 10 Whitc balancc co10ur diffcrcncc for CIE lights(nOn‐traini113 pattcr:1)

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essential to base thc whitc balancc corrcction on tllc

spectral charactcristics oF the light sourcc.
Acknowledgements
The authors would likc to thank thc mcmbcrs or thc
human sciclicc laboratory of Sanyo EIcctric Co., Ltd
pleting this、
vork.
for their hclp in col■2 0ta,N Cο ノ
ο″″′
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Cノ
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ο々ィy,Asakurashotcn lnc,Tokyo(1986):ta,K̀Vidco camcra
3 Kikuchi,K,Haruki,T,TsuJ:no,K and FЦ
〕g f u z z y l o g i,cS｀s y s t c n l s un●ι..No 48(1991)
ス Ⅳy ο 7 セc 力″J c a F Rッ
pp 8‑19
4 Kallo,Ha:ld lruklishillla, K・
Whitc b::Li:lcc by nCttral:lctw()rks',

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Rёferences
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pp 591‑593
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7 McCal■ y, CS・ A colour― rcndition chart',′ Иr9P′
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