Classification of defects using uncertainty techniques in industrial web inspection.

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CLASSIFICATION OF DEFECTS USING

UNCERTAINTY TECHNIQUES

IN

INDUSTRIAL WEB INSPECTION

by

Duncan John Wilson

A Thesis Submitted for the Degree o f Doctor o f Philosophy

in the

Faculty o f Engineering

UNIVERSITY OF LONDON

January 1998

Department o f Mechanical Engineering

University College London

Torrington Place

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ProQuest Number: U643020

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Abstract

This research investigated how Artificial Intelligence techniques could be applied to industrial web

inspection to improve the process of classifying defects. It focuses on applying uncertainty techniques to

aid the construction of a classification scheme.

In many industrial process control situations, the need to identify and classify defects is key to enabling

process improvements. Inspection is used for this task. Constructing a classification scheme to correctly

identify defects in a product is difficult. Many people assume that a description o f what is to be identified

exists. That is not necessarily true.

The domain knowledge is vague and nearly always incomplete, and the constraints on inspection

equipment means the data output often produces ambiguous results. A method was required which would

allow such uncertainties to be incorporated into an automated visual inspection system. Fuzzy Set Theory

and Dempster Shafer were used to aid the development of the classification scheme. Fuzzy Set Theory

provided an intuitive numerical method for capturing knowledge about defects (for example, concepts

such as ‘defect has small area’) and Dempster Shafer provided a framework for processing such heuristic

knowledge so that a classification could be made.

The result was a set of rules where each rule had a fuzzy antecedent and a consequent describing a set of

possible defect types. Dempster Shafer was used to combine the consequants of the rules to calculate the

most likely outcome. For comparison with the output generated using Dempster Shafer, decision criteria

proposed by Smets and Wesley were implemented. The accuracy of the classification scheme and an

analysis against five desirable criteria for an inspection system are the focus of the discussion.

An experimental rig was constructed using a linescan CCD camera to simulate a production line. Real

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Acknowledgements

First I would like to thank my supervisors, John Giiby, Alistair Greig and Robert Smith for their support

during my PhD years. John for the numerous discussions on inspection, uncertainty and object oriented

design, Alistair for his direction along the path of PhD research and Bob for always having an open door

at Sira.

I would also like to thank those people who made my working environment so pleasurable. In the Control

Lab at UCL, Jason for always being one step ahead of me in the PhD process, Alex for knowing when to

take me to the park to reflect on what I was doing, Szen for taking time to proof read this thesis, and

finally, for making me laugh, Susi, Nader, William, Hugh, Raju, Dave, Sarah, Habib and Patrick, who

complete the Control Lab social circle. At Sira I would like to thank Chris, Sarah, Robin, Mark and Neil

for the many suggestions they made over the years which have not necessarily had anything to do with

this thesis.

Since this PhD was undertaken as a collaboration between industry and academia I mention the staff at

Sira Technology Centre, Image Automation Ltd., the Centre for Advanced Instrumentation Systems, the

Department of Mechanical Engineering and UCL Graduate School for always supporting me and taking

time to listen to my queries.

Finally, thanks to my wife Imelda for her continual support and belief in me.

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2 . 2 Th ed i f f e r e n c eb e t w e e n d e t e c t i o na n d c l a s s i f i c a t i o n i nAVI...2 8 2 .3 Wh yc l a s s i f i c a t i o n i sd i f f i c u l t i ni n s p e c t i o n... 3 2 2 . 4 Th ew e b i n s p e c t i o n p r o c e s s... 3 5 2 .5 Th ed a t a p r o d u c e db y t h ei n s p e c t i o n s y s t e m... 4 1 2.5.1 Limitations in the data...44

2.5.2 Man and machine ‘se e ’ differently...45

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2 . 8 Su m m a r y... 5 0

MACHINE VISION AND CLASSIFICATION...52

3 .1 Cl a s s i f i c a t i o n T H E O R Y ...5 2 3.1.1 Statistical Techniques...53

3.1.2 Machine Learning...55

3.1.3 Neural Networks...57

3 . 2 Ex i s t i n gt e c h n o l o g yi n r e l a t i o nt o w e bi n s p e c t i o n...5 8 3 .3 Un c e r t a i n t y Ma n a g e m e n t Te c h n i q u e s... 5 9 3.3.1 Probability Theory...60

3.3.2 Dempster Shafer - Theory o f Evidence...62

3 .3 .2 .1 E x a m p le to sh o w b a sic c o n c e p ts o f D em p ster S h a fer T h e o r y ... 6 4 3 .3 .2 .2 W h y D em p ster S h afer is u se fu l fo r in sp e c tio n ...6 8

3.3.3 Fuzzy Set Theory...68

3 . 3 .3 .1 A n e x a m p le and th e b a sic c o n c e p ts o f fu z z y set th e o r y ...6 8

3 .3 .3 .2 F u z z y se t th e o ry in relation to th is t h e s is ...7 0 3 . 4 Su m m a r y...7 1

UNCERTAINTY TECHNIQUES AND CLASSIFICATION... 73

4 .1 Fu z z y l o g i c f o rr e p r e s e n t i n gv a g u e a n d i n c o m p l e t e i n f o r m a t i o n... 7 4 4.1.1 How Fuzzy Set Theory has been used....75

4 . 2 De m p s t e r Sh a f e rf o rr e p r e s e n t i n gi n c o m p l e t ea n d a m b i g u o u sd a t a... 7 8 4.2.1 How Dempster Shafer has been used....79

4 .3 A HYBRID APPROACH...8 3 4 . 4 Su m m a r y...8 7

EXPERIMENTAL METHOD...88

5 .1 A FRAMEWORK FOR AUTOM ATED VISUAL INSPECTION...8 8

5 .2 Th em a c h i n ev i s i o n s y s t e m - Ha r d w a r e...9 0 5.2.1 The design brief....90

5.2.2 Synthesis o f design...91

5 .2 .2 .1 T h e C C D L in e sca n C a m e r a ... 93 5 .2 .2 .2 T h e fram egrab ber... 9 4 5 .2 .2 .3 T h e p o sitio n in g actuator and in te r fa c e ...9 4 5 .2 .2 .4 T h e lig h t s o u r c e ...9 5 5 .2 .2 .5 T h e fra m e w o rk ... 9 6 5 .2 .2. 6 T h e d e fe c t sa m p le s...9 6

5 .2 .2 .7 T h e P C ... 9 6 5.2.3 Discussion o f performance o f hardware...96

5 .3 Th e m a c h i n ev i s i o n s y s t e m - So f t w a r e... 9 9

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5.3.1 Conceptual design...99

5.3.2 Control o f actuator...101

5.3.3 Image capture...104

5.3.4 Feature detection...105

5.3.5 Output from inspection system...107

5 .4 Su m m a r y...10 8 THE CLASSIFICATION SYSTEM... 109

6 .1 Th ec l a s s i f i c a t i o n s y s t e m - s o f t w a r e...1 1 0 6.1.1 The inputs to the classification system...110

6 .1 .1 .1 M eth o d fo r c r e a tin g fu z z y n u m b ers (C R u le D a ta )... 112

6.1 .1 .2 M e th o d s fo r im p le m e n tin g D em p ster S h afer (C S e t) ... 115

6.1.2 Creating evidence fo r possible defect types (CeList)...117

6.1.3 Methods fo r Decision Making...118

6.1.4 Summary...122

6.2 Cr e a t i n ga c l a s s i f i c a t i o ns c h e m e...1 2 2 6.2.1 Sources o f information...124

6 .2 .1 .1 T rain in g d a ta ... 124

6 .2 .1 .2 E xp ert k n o w le d g e ...124

6 .2 .1 .3 T e st d a ta ...127

6.2.2 The ‘normal’ ruleset...127

6 .2 .2 .1 C o n stru ctin g th e r u le s e t ...128

6 .2 .2 .2 D if f ic u lt ie s ... 129

6 .2 .2 3 T h e R u l e s ... 130

6.2 .2 .4 A sa m p le ou tp u t fro m the ‘n o rm a l’ ru leset u sin g test d a t a ...133

6.2.3 The normal ruleset as a benchmark...137

6 .3 Ex p e r im e n t a l De s i g n... 1 3 9 6 .4 Su m m a r y... 1 4 7 RESULTS... 148

7 .1 Fo r m a t OF THE Re s u l t s... 14 8 7 .2 Su m m a r yo f Ex p e r i m e n t s... 1 4 9 7 .3 Ex p e r i m e n t a l Re s u l t s... 1 4 9 DISCUSSION...166

8.1 Th ep e r f o r m a n c eo ft h ei n s p e c t i o ns y s t e m...1 6 6 8 .2 Co m m e n t so n t h ee x p e r i m e n t s...1 7 0 8.2.1 Results o f adding information to ruleset...170

8.2.2 Results o f deleting information from ruleset...171

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8.2.4 Results o f not using fuzzy antecedents and changing structure o f consequents...173

8.2.5 Results from different people defining rules...174

8 .3 Co m m e n t so nt h eo v e r a l ls u c c e s so ft h e f i v e c r i t e r i a...1 7 5 8 .4 Re v i e w o f t h et h r e es o u r c e so f u n c e r t a i n t y... 1 7 8 8 .5 Su m m a r y...1 8 0 CONCLUSIONS...182

APPENDIX A - EQUIPMENT SPECIFICATIONS... 188

A . 1 Su r v e ya n d c o s ta n a l y s i so fh a r d w a r e...1 8 8 A . 2 Ca m e r a / i m a g ep r o p e r t i e s... 1 9 0 A .3 Bl o c k d i a g r a m sf o rc a m e r a a n d f r a m e g r a b b e r... 1 9 0 APPENDIX B - CODE USED TO GET IMAGE OF DEFECT...194

APPENDIX C - CODE USED IN CLASSIFICATION SCHEM E... 203

APPENDIX D - DETAILED TABLE OF RESULTS... 223

APPENDIX E - COMBINING EVIDENCE IN DEMPSTER SHAFER...230

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Figures

Fi g. 1 .0 Th ep r o d u c t i o np r o c e s s a n d t h ei n s p e c t i o n s y s t e m... 1 7 Fi g. 2 . 0 . De f e c t TYPE ‘b u b b l e’ i n g l a s s...3 0 Fi g. 2 . 1 . De f e c t TYPE ‘r e a m k n o t’ i ng l a s s...3 0 Fi g. 2 . 3 . Th ei n f o r m a t i o n w h i c hg o e si n t o M Y C I N ... 3 3 Fi g. 2 . 4 In f o r m a t i o ni na n i n s p e c t i o n e x p e r t s y s t e m...3 4 Fi g. 2 .5 On ew a ym a p p i n gb e t w e e n d e f e c tc h a r a c t e r i s t i c s a n d c l a s s i f i c a t i o n... 3 4 Fi g. 2 . 6 Th e F IO l a s e ri n s p e c t i o n s y s t e m (c o u r t e s y Im a g e Au t o m a t i o n Lt d. ) ...3 5 Fi g. 2 . 7 Co m p u t e rg e n e r a t e d i m a g eo fa n o n-l i n el a s e r i n s p e c t i o n s y s t e m... 3 6 Fi g. 2 .8 Cr e a t i n ga 2 D i m a g ew i t h al i n e s c a n C C D ... 3 7 Fi g. 2 . 9a Ov a li n b r i g h tf i e l d... 3 9 Fi g. 2 . 9b Ov a li n d a r k f i e l d... 3 9 Fi g. 2 . 9c Fi s h e y ei n b r i g h tf i e l d... 4 0 Fi g. 2 . 9d Fi s h e y ei n d a r kf i e l d...4 0 Fi g. 2 . 9e Du s ti n b r i g h tf i e l d...4 0 Fi g. 2 . 9f Du s ti n d a r kf i e l d...4 0 Fi g. 2 . 1 0 In t e n s i t ym a p, f i s h-e y e d e f e c ti n p l a s t i cf i l m...4 2 Fi g. 2 .1 1 Th r e s h o l di m a g e so ff i s h-e y e d e f e c t...4 3 Fi g. 2 . 1 2 In s p e c t i o nd a t af o rf i s h-e y ed e f e c t...4 3 Fi g. 2 . 1 3 a c l a s s i f i c a t i o no fu n c e r t a i n t y ( Kr a u s e a n d Cl a r k (p g. 7 ) [ 1 9 9 3 ] ) ... 4 7 Fi g. 3 . 0 Ve n nd i a g r a m o f Q a n d H , ... 6 5 F ig . 3 .1 V e n n d i a g r a m t o s h o w i n t r o d u c t i o n o f H2...6 6

F ig . 3 .2 V e n n d i a g r a m t o s h o w i n t r o d u c t i o n o f H4...6 6

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Fi g. 4 .1 A Fu z z y REGION t o i l l u s t r a t e N O T h e d g e... 7 7 Fi g. 4 . 2 Ou t p u tf r o m c l a s s i f i c a t i o n c o d et oh i g h l i g h t De m p s t e r Sh a f e rr e s u l t...8 0 Fi g. 4 . 3 Sy s t e m v i e w f l o w d i a g r a m - d e f i n i t i o no fr u l e s... 8 3 Fi g. 4 . 4 Sy s t e m v i e w f l o w d i a g r a m - i m p l e m e n t a t i o n o fr u l e s... 8 4 Fi g. 4 . 5 Ap p l y i n gt h e c a m e r a d a t at ot h er u l e s... 8 5 Fi g. 5 . 0 A FRAMEWORK FOR AUTOM ATED INSPECTION... 8 9 Fi g. 5 .1 Sc h e m a t i co fe x p e r i m e n t a l r i g... 9 1 Fi g. 5 . 2 Ph o t o g r a p h o fe x p e r i m e n t a lr i g...9 2 Fi g. 5 .3 C C D Ca m e r aw i t hl e n s i n p l a c e (l e f t) a n d s h o w i n gs e n s o r (r i g h t) ... 9 3 Fi g. 5 . 4 Po s i t i o n i n g a c t u a t o ra n d s a m p l em o u n t...9 5 Fi g. 5 .5 Cr o s s s e c t i o no fr i gt o s h o w d a r ka n db r i g h t f i e l dc o n f i g u r a t i o n s... 9 7 Fi g. 5 . 6 In t e r f a c et oe x p e r i m e n t a lr i g...9 8 Fi g. 5 . 7 Al i g n m e n to fd a r k f i e l dm a s k...9 8 Fi g. 5 . 8 Fl o w d i a g r a m f o re x p e r i m e n t a l r i gs o f t w a r e... 1 0 0 Fi g. 5 . 9 Pr o c e d u r ef o ro b t a i n i n g r a w i m a g e...1 0 2 F iG . 5 . 1 0 In t e n s i t ym a po fd e f e c t u s e df o r r e g i s t r a t i o nt e s t... 1 0 3 Fi g. 5 .1 1 Gr a p ht os h o w p o s i t i o n i n ge r r o r i na c t u a t o r...1 0 4 Fi g. 5 . 1 2 Bi t m a p TO i l l u s t r a t e A R E A m e a s u r e m e n t... 1 0 5 Fi g. 5 . 1 3 Bi t m a p TO i l l u s t r a t e W I D T H m e a s u r e m e n t...1 0 6 Fi g. 5 . 1 4 Bi t m a pt oi l l u s t r a t e L E N G T H m e a s u r e m e n t... 1 0 6 Fi g. 5 . 1 5 Bi t m a p TO i l l u s t r a t e % A R E A m e a s u r e m e n t...1 0 6 Fi g. 5 . 1 6 Bi t m a p TO ILLUSTRATE W I D T H : L E N G T H RATIO...1 0 6 Fi g. 5 . 1 7 Sa m p l eo u t p u tf r o m m a c h i n ev i s i o n s y s t e m... 1 0 7 Fi g. 6 . 0 Th ef o u re l e m e n t s o ft h e c l a s s i f i c a t i o n s o f t w a r e... 1 1 0 Fi g. 6 .1 Th ei n p u t st ot h ec l a s s i f i c a t i o ns o f t w a r e... 1 1 2 Fi g. 6 . 2 Th es h a p e o ff u z z yr e g i o n sa v a i l a b l ei n C Fu z z yNu m b e r... 1 1 3 Fi g. 6 . 3 Da t a Ty p e C Ru l eDa t a... 1 1 3 Fi g. 6 . 4 Fu z z yr e g i o n f o rd a r kf i e l dp e r c e n t a g ea r e a...1 1 4 Fi g. 6 . 5 Fu z z yr e g i o n f o rb r i g h tf i e l dw i d t h / l e n g t h r a t i o... 1 1 4 Fi g. 6 . 6 Cl a s s s t r u c t u r ef o rd e s c r i b i n g s e t s...1 1 6 Fi g. 6 . 7 Cl a s s e s CeLi s ta n d C Ev i d e n c e... 1 1 7 Fi g. 6 . 8 Co m b i n e d e v i d e n c ei n a l i n k e d l i s t...1 1 9 Fi g. 6 . 9 Fl o w d i a g r a m t o s h o w i n p u t st oc l a s s i f i c a t i o ns o f t w a r e...1 2 3 Fi g. 6 . 1 0 Da t a s h e e t f r o m t r a i n i n g d a t af o rf i s h e y e d e f e c t (u n i t si n p i x e l s) ... 1 2 5 Fi g. 6 .1 1 Th r e s h o l d e di m a g e so ff i s h e y ed e f e c t f r o m t r a i n i n gd a t a... 1 2 6 Fi g. 6 . 1 2 Fu z z y Re g i o n f o r Br i g h t Fi e l d Wi d t h/ Le n g t h r a t i ow i t h e x a m p l ei m a g e...13 1 Fi g. 6 . 1 3 Fu z z y Re g i o n f o r Da r k Fi e l d Wi d t h/ Le n g t hr a t i o... 1 3 2 Fi g. 6 . 1 4 Fu z z y Re g i o n f o r Da r k Fi e l d % Ar e aw i t h e x a m p l ei m a g e...1 3 2

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Fi g. 6 .1 5 Fu z z y Re g i o nf o r Br ig h t Fie l d % Ar e a... 13 2

Fi g. 6 .1 6 Lis t i n go fr e s u l t sf o r Fis h_e y ed e f e c tu s i n gn o r m a l r u l e s e t...13 4

Fi g. 6 .1 7 Po s s i b l eo u t c o m e s w h e nc l a s s i f y i n g Ge l s... 1 35

Fi g. 6 .1 8 Po s s i b l eo u t c o m e s w h e nc l a s s i f y i n gf i b r e s... 13 6

Fi g. 6 .1 9 Cl a s s i f i c a t i o nd a t a u s i n gn o r m a lr u l e s e tw i t ht e s td a t a... 1 3 6

Fi g. 6 .2 0 Cl a s s i f i c a t i o nd a t a u s i n gn o r m a lr u l e s e tw i t ht r a i n i n gd a t a... 13 7

Fi g. 6 .2 1 Re s u l t s s h o w i n gp r o b l e me n c o u n t e r e dw i t hi n s u f f i c i e n tr e a s o n p r i n c i p l e...13 8

Fi g. 6 .2 2 Gr a p h st o s h o w c h a n g ef r o mf u z z yt o c r is pn u m b e r a tm i d-p o i n t... 141

Fi g. 6 .2 3 Gr a p h st o s h o w c h a n g ef r o mf u z z yt oc r i s pn u m b e rn e a r t r u e...143

Fi g. 6 .2 4 Gr a p h st o s h o w c h a n g ef r o mf u z z yt oc r i s pn u m b e rn e a r f a l s e... 1 4 4

Fi g. 6 .2 5 Gr a p ht os h o w f u n c t i o nu s e df o r Mr. Op t im is t icv i e w p o i n t...145

Fi g. 6 .2 6 Gr a p ht os h o wf u n c t i o nu s e d f o r Mr. Pe s s im i s t i cv i e w p o i n t...1 4 6

Fi g. 7 .0 La y o u to fr e s u l t sf o r m...1 4 8

Fi g. 7 .1 Re s u l t s - No r m a l Ru l e s e t...1 5 0

Fi g. 7 .2 Re s u l t s - Ex p e r im e n t 1 ... 151

Fi g. 7 .3 Re s u l t s - Ex p e r im e n t 2 ... 1 5 2

Fi g. 7 .4 Re s u l t s - Ex p e r im e n t 3 ... 1 53

Fi g. 7 .8 Re s u l t s - Ex p e r i m e n t 7 .... i... 1 5 7

Fi g. 7 .1 0 Re s u l t s - Ex p e r im e n t... 9 ...1 5 9

Fig. 7 .1 1 Re s u l t s - Ex p e r im e n t 1 0 ...1 6 0

Fi g. 7 .1 2 Re s u l t s - Ex p e r im e n t 1 1 ...161

Fi g. 7 .1 3 Re s u l t s - Ex p e r im e n t 1 2 ...1 6 2

Fi g. 7 .1 5 Re s u l t s - Ex p e r im e n t 1 4 ...1 6 4

Fi g. 8 .0 We s l e ya n d De m p s t e r Sh a f e rr e s u l t sf r o me x p e r i m e n t 5 ... 16 8

Fi g. a. 1 Ca m e r a / p r o d u c tp ix e ls i z e... 1 9 0

Fi g. A .2 Fu n c t i o n a lb l o c kd i a g r a m o f A M - D I G 1 6 D d ig it a la c q u i s i t i o nb o a r d... 191

Fi g. A .3 Fu n c t i o n a lb l o c kd i a g r a m o f C L -C 3 l i n es c a nc a m e r a... 1 9 2

Fi g. a .4 Fu n c t i o n a lb l o c kd i a g r a mo f M F G f r a m e g r a b b e r [M F G 1 9 9 3b] ... 1 93

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Tables

Ta b l e 5 .0 Op e r a t i o n su s e di n C Ta b l e...10 1 Ta b l e 5 .1 Op e r a t i o n s u s e di n C Gr a b b e r...1 0 2 Ta b l e 6 . 0 Da t at y p e su s e dt o c o n s t r u c ta r u l e... 1 1 1 Ta b l e A . 1 Co s t a n a l y s i so ff r a m e g r a b b e ra n d c a m e r a...1 8 9

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Notation

In general italicised roman letters are used to represent variables such as lengths and capitalised roman

letters denote an object which is a member of a set. The use of the courier font in the running text denotes

an object defined in software. Larger sections of code are clearly separated from the running text. The

character * is used as a wildcard.

Roman symbols

A ,B ,C

a

w

I

Hi

U

M eaning

Reference names for threshold images

Area in slice A, B, C

Width in slice A, B, C

Length in slice A, B, C

hypothesis (a set o f possible defect types)

universe of discourse (Fuzzy Set Theory)

G reek symbols

Q

0

Meaning

frame o f discernment (Dempster Shafer)

empty set (Dempster Shafer)

Miscellaneous

%Area

w/1

UMT

AVI

expert

OHP

b.p.a.

M eaning

Percentage area measured in slice A, B, C

Width divided by length measured in slice A, B, C

Uncertainty Management Technique

Automated Visual Inspection

a person who uses / develops the inspection system

Overhead Projector

Basic Probability Assignment (Dempster Shafer)

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Chapter 1 Introduction

The aim of this research is to investigate techniques for classifying objects of interest into different

classes where the data describing the objects is prone to the uncertainties of the real world. Two aspects

o f classification were identified in this problem domain. The first describes what input data is used, the

second describes the algorithms used to make the classification. The aim of this research is not to select

the best classification algorithm available given some input data. This thesis proposes that, to improve the

method by which classification schemes can be generated using real world data, there is a need to allow

for the representation of the uncertainties in the data used as input.

The objective of the research is to:

• analyse the techniques which can be used to represent uncertain data.

• analyse how this benefits the generation o f a classification scheme.

The research does not:

• identify the best classification algorithm for web inspection.

To satisfy the objectives of this research an experimental rig was constructed to test the proposed

techniques. A real inspection problem was analysed and three types of uncertainties in the input data

highlighted. This enabled the description o f the properties required to generate a classification scheme for

this application o f web inspection. The five desirable properties for web inspection are described later in

this chapter. Using these properties an assessment was made to identify how the techniques used for

representing uncertainty made the task of generating a classification scheme easier. The remainder of this

chapter introduces inspection.

In many industrial process control situations, the need to identify and classify defects is key to enabling

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Chapter 1 - Introduction

to be gained from increased throughput, improved product quality and unit cost reduction. In other

words, providing more of a better product at a lower price.

As modem manufacturing processes become more advanced, the inspection tasks required to monitor

them become more complex. As a result automated inspection is required. The benefits of automating

processes are well known; humans typically suffer from fatigue, inconsistency and slowness, whereas

machines offer repeatability, accuracy and efficiency [Bayro-Corrochano 1993b, Rao 1996, Smith and

Hopkins 1994]. This thesis will concentrate on inspection using machine vision or Automated Visual

Inspection (AVI).

One o f the major obstacles in the development of AVI systems has been making them robust enough to

work in different situations. Indeed many of the successful applications to date have been bespoke

systems working in highly constrained environments. There is a need to reduce this specialisation and

aim to make AVI systems more flexible [Newman and Jain 1995]. The difficulty lies in developing

methods which enable inspection systems to reason and make decisions within the world they are

situated. As inspection system designers try to make their systems more able to make finer distinctions

between defect categories, and applicable to a wider range o f inspection tasks, they need better ways in

which to use the information detected by their inspection systems.

Inspection tasks require substantial visual abilities which humans quite often take for granted. Inspectors

find it very difficult to express what characterises a defect. Invariably they will just ‘know’ what type it is

from experience. It is very difficult to capture this experienced knowledge in a form that could be

developed in an AVI system. There is a false common assumption that precise descriptions o f defects

exist. As a result, the information available to engineers setting-up AVI systems is uncertain. This thesis

investigates how uncertainty management techniques (UMTs) can be applied to model such uncertainty.

The thesis also describes how the techniques have been applied to a real inspection problem.

The following sections discuss AVI in the context of an industrial setting and declares three forms of

uncertainty present in this application. In the next section, five desirable properties for an inspection

system are introduced. It is argued that for these five criteria to be met it is necessary to develop systems

that will allow the expert to represent the three forms of uncertainty in their inspection system. The

remainder of this chapter expands this argument and proposes a way forward.

1.1 A framework fo r automated visual inspection

This research focused on the application of Automated Visual Inspection. In particular the thesis looks at

web inspection, the process used to inspect continuously manufactured products such as paper, textiles,

glass and plastic films. Web inspection shall be discussed in detail in chapter 2. To appreciate how and

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why this research has been applied to this application it is necessary to investigate the environment in

which web inspection is used.

Inspection is the task o f examining and reporting. The former task, also referred to as detection, involves

analysing an object to identify characteristics or features which could be used for identification. The latter

task uses the features obtained through detection to return some information about the object being

analysed. The information returned classifies the object into a predefined category. The objective of this

report, or classification, is to assess the state o f the product being analysed. The reason for wanting the

assessment is simply to satisfy customers.

The customers who want the classification may not only be the end users of the product (i.e. external

customers), but also sections within the same factory (i.e. internal customers). For example. Production

might want to know the status of the materials they are producing, in order to assess their machines, or

Sales might want to know what quality Production are achieving in order to target customers. Inspection

provides manufacturers with vital information about the product they are making and the process by

which it is made. Figure 1.0 shows a flow diagram for a typical production process.

At the lower left comer o f figure 1.0 raw material is input to the production process. The result is the

product being manufactured. The product is then classified, graded, and shipped to the customer. The aim

o f the AVI system is to automate the classifying process. Given that an inspection system can be used in

parallel to the production process, the information gathered on the classified product can also be used to

control the process. Figure 1.0 illustrates this feedback loop. Whilst the figure represents the overall

production process, it should be noted that inspection is also applied to smaller parts of the process.

Assuring the quality at intermediate stages has the benefit that it can simplify later stages of production

and avoids the company adding value to something which will ultimately be scrapped. For example, in

the production of plastic films for use on overhead projectors (OHPs), if the film is going to be coated for

high quality inkjet printing, then one doesn’t want to coat defective product.

By using an inspection system, there are two main impacts on the production process. As a result of

classifying defects, information can be fed back to production leading to improvements in the process.

This greater control over the production process implies engineers can identify the optimum operating

point of their production facility, and then, through control of the process, ensure they operate at that

point. The second benefit is the ability to know the quality of the product being produced. Through

monitoring the product being manufactured it should be possible to highlight areas and ways in which the

process could be improved. In turn this implies improvements to the end product.

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Chapter I - Introduction

« Automated Visual Inspection System

. . j _____

Raw Material

as input to system

Production Process

1

Control Feedback Y -W..- Classified o f process ^ f ’ t to process Defect

Production Process

High Low Reject

-► Customer

Fig. 1.0 The production process and the inspection system.

W hilst th e m o tiv a tio n s fo r u sin g in sp ectio n sy stem s are clea r, it is also n e c e ssa ry to in v e stig a te th e e n v iro n m e n t in w h ich an A V I sy stem is situ ated . A n im p o rta n t fe a tu re on the flo w d ia g ra m is the in teractio n o f th e in sp ectio n sy stem w ith th e p ro d u c tio n p ro cess. It sh o w s th e A V I system m u st w o rk w ith in th e co n stra in ts o f th e fa c to ry e n v iro n m e n t. T h is m e a n s th at fo r th e sy stem to be u sefu l it m u st tak e into c o n sid e ra tio n facto rs su ch as; th e o p e ra tin g p a ra m e te rs o f th e p ro d u c tio n line, th e ty p e s o f p ro d u c t to be inspected an d v a ria tio n s w h ic h o c c u r in th e e n v iro n m e n t (e.g. clim a tic ch a n g e s, c h a n g e s to th e p ro d u c tio n facility, sh ift c h a n g e s etc.). A fa c to ry e n v iro n m e n t is c o n tin u a lly ev o lv in g .

A n o th e r im p o rtan t in teractio n w h ich is no t m a rk ed on th e flow d ia g ra m is th e in teractio n w ith th e cu sto m e r. T he c u sto m e r sp e c ifie s th e q u a lity to w h ich a p ro d u c t m u s t be m ad e. T h e sp e c ific a tio n w ill o fte n v ary acc o rd in g to th e c u sto m e rs en d use o f th e p ro d u c t. F o r e x a m p le , as a re su lt o f cu ltu ral d iffe re n c e s , Ja p a n e se c u sto m e rs m ay e x p e c t a h ig h e r sta n d a rd o f q u a lity th an th e ir W estern c o n te m p o ra rie s. T h u s, if a c u sto m e r is su p p ly in g into d iffe re n t m a rk e ts th ey m ig h t h av e d iffe re n t in te rp re ta tio n s o f q u a lity levels. A lte rn a tiv e ly , th e level o f a c c e p ta b le q u a lity m ay d e p e n d on th e a m o u n t o f p ro d u c t av ailab le in th e o p en m ark et. T h is m e a n s th at a c u sto m e r m ay fin d it h a rd to p ro v id e a d e ta ile d d e sc rip tio n o f the ty p e s o f d e fe c t th e y w ish to h av e classified . T h e p ro c e ss o f c a p tu rin g k n o w le d g e ab o u t d e fe c ts is d ifficu lt. T h is h ig h lig h ts th e false a ss u m p tio n , id en tified e a rlie r, th a t a d esc rip tio n o f w h a t is to be id en tified exists. C h a p te r 2 d is c u sse s w h y th is k n o w le d g e a c q u isitio n ta sk is d ifficu lt.

1.2 Three sources o f uncertainty

T h is sectio n shall d efin e w h a t is m e a n t by u n c e rta in ty an d w ill in tro d u c e th e th ree m ain so u rc e s o f u n c e rta in ty w hich are in h e re n t in th is ap p licatio n .

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Chapter 1 - Introduction

The Oxford English Dictionary suggests that if something is uncertain then it is “not certainly known, not

to be depended on,” or is “changeable”. This thesis suggests that much of the information used to

describe a real industrial environment and industrial inspection can be categorised in the above manner.

Most businesses are continually under pressure to improve and new products are reaching the market

place with shorter and shorter lead times. A production environment is never standing still, it changes.

The motivation for this work is to incorporate this change by recognising that the information used to

model the inspection environment is uncertain. The goal of this research is to develop an inspection

system which takes into account these forms of uncertainty in an AVI system.

To illustrate why information in the inspection environment is uncertain an example of the type o f defect

to be classified shall be introduced. A gel is a defect which occurs in plastic films such as those used in

the manufacture o f OHP acetates, plastic bags, food wrap etc. The defect itself is the result of a local area

o f resin with different physical properties to the surrounding product and looks like a small spot. A gel in

an OHP acetate can quite often go unnoticed until it is viewed under the magnified display of the OHP.

Since the change in physical properties of the gel causes a lens like structure, the changes in the light

patterns as light is projected through the defect is quite noticeable on the projector screen. The

concentration of light through the lens shape o f the gel causes bright or dark spots to be visible on the

screen.

When people describe defects their interpretation is subjective since it relies upon what they see. This

leads to a very simple question.

What is and what is not a defect?

Whilst the question appears to be straightforward, the answer is invariably quite difficult. Using the

example of the gel in the OHP acetate, when questioned about the size of defects people will normally

respond with vague descriptions such as; ‘about 2mm’. The defect in this example generates this vague

response since it is very difficult to distinguish the point where the defect starts or stops diffracting the

light which causes the light pattern on the OHP screen. In addition, since the point at which this transfer

occurs is subjective, people are free to make their own interpretations as to the defining point between

defect and non-defect. It is therefore difficult to answer the general question raised above since there is

no sharp dividing line between defect and non-defect and different people work to different tolerances.

The purpose o f an AVI system is to try and make a distinction between defect and non-defect. In current

systems, a design decision is made which defines the boundaries of a class of defect. The definition of

such boundaries is very difficult since you don’t want the regions to miss a defect but you also don’t want

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the regions to be so broad that they catch other types of defect. A method for making the definition of the

boundaries more intuitive would be useful.

The problem described above is a result o f the natural variation in the product being inspected. Gels are

always present at some level in the product since they are caused by the method o f manufacture. The

severity o f the gels is dictated by the quality of the production method and the quality o f the raw material.

As a consequence the nature of gels observed at a company producing premium quality OHP acetates and

those at a company producing economy OHP acetates will be different. Both companies will have

different opinions as to what severity of gel constitutes a defect. The problem in this domain is not to test

whether a discrete item, such as a nut or a bolt, is on a production line, rather it is to make an assessment

of the product quality. In the case of gels, this might mean distinguishing when the volume of gels creates

defective product. For premium quality OHP acetates one would expect a low volume of gels. For

economy OHP acetates one would accept a higher volume o f gels. The borders between classes, such as

defect and non-defect, are not always obvious or discrete. The variation in the defects possible is

continuous.

The end use of the product also has an impact on the way it should be classified. The location o f the

defect in the plastic film may change one’s perception on whether it is a defect or not. For example,

imagine an outdoor scene was to be printed on one of the OHP acetates. A defect in the clear blue sky

might be visible whereas the same defect if situated in the dense foliage might not be. Taking into

consideration the end use of the product potentially enables one to allow some defects to be passed, hence

reducing the amount of scrap.

The above paragraphs have described an environment where the information available for making

classifications of defects is vague. This type o f uncertainty is a result of the heuristics which humans tend

to use to make classifications. Another source of uncertainty is the inability to describe a complete

domain model. The fact that the production environment is continually evolving means that any model of

the system will soon become obsolete, however using samples to define a model has been the primary

method by which automated inspection systems have been set-up. Variation in customer requirements

according to the current market climate also influence what constitutes a defect. For example, if there is

little product available on the market they will be more inclined to settle for lower quality product.

Conversely if they have the option of buying from several suppliers they will opt for high quality

product. Sample data is not enough to design a classification scheme. Data on the characteristics o f the

product and the process are also required. The experienced knowledge o f an inspector should be defined

within the classification scheme.

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The level of data available for classifying defects highlights the final source of uncertainty. It is possible

to get inspection data from any product moving at any production speed, but as the area o f product

inspected per second goes up then the amount of information for each defect goes down. One could

throw more sensing hardware at the problem however there are usually limits on the resources available.

A general trade-off should be evident between the volume of product which can be inspected and the

precision of the information returned. When trying to identify defects less than 1mm in size across a

product 2 metres wide moving at up to 200 metres per minute, the information available to make the

classification will be quite basic. This means that what the machine can ‘see’ and what a human can see is

quite different and a characteristic which is visibly different to the human eye may not necessarily be

different when measured by a machine. The machine may therefore recognise that a feature is present

without being able to determine exactly what defect the feature describes. The fact that a measurement

can imply more than one defect type means the results from the AVI system are ambiguous.

To summarise, three sources o f uncertainty have been described which are inherent in an AVI system.

They can be summarised as: vagueness, incompleteness, and ambiguity. This section introduced why we

need to look at uncertainty and gave some examples of the uncertainty in the inspection domain. Chapter

2 investigates the inspection problem further and shall probe deeper into the sources of uncertainty.

Chapters 3 and 4 explore the theoretical techniques which are available for representing uncertain

information. These first few chapters represent an important task in this thesis. To develop an AVI system

which properly takes into account those types of uncertainty present, it was important to identify which

formalisms could be used to model the types of uncertainty specified and how the formalisms could be

applied in a robust AVI system. While the technology available appeared promising, the task was to

implement the theory to overcome the sources o f uncertainty in this application. The following section

shall introduce how the proposed ideas were tested.

1.3 Five criteria for an inspection system

The previous section raised the issue of uncertain information. This section uses that uncertain

information to specify five desirable criteria for an inspection system. As previously touched upon, one

of the difficulties in the assessment of how well a classification scheme performs is that there is no real

model of what is and is not a defect. Making a right or wrong judgement is therefore difficult. The

performance o f a classification scheme is subjective, hence the reasoning behind the five criteria was to

construct a measure to analyse the model used to construct the classification scheme. Rather than

assessing the on-line performance o f an actual classification scheme, the objective of the research was to

study the method by which a classification scheme could be developed. The study analysed the

development o f a classification scheme with respect to a ‘wish list’ of five desirable properties.

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The five criteria were generated by identifying the properties o f an AVI system which would enable the

construction of a robust classification scheme. The five criteria are summarised as:

1. It should be possible to classify defects w ithout a complete model of the process. The development

of a classification scheme is a continuous process and should evolve with the production environment.

It is not reasonable to assume that a complete model of the domain can be constructed which will be

valid for the lifetime o f the classification scheme due to the incompleteness o f the data described in

the previous section. It is therefore desirable that the classification scheme should function correctly

as an open ended model. It is important for the experts developing the system to define the

information which can be extracted at the initial stages o f the project. Whilst it is beneficial to include

as much information as possible regarding the domain, the expert should also be aware that the initial

set-up is a snapshot o f the typical production environment.

2. It should be possible to introduce new inform ation when it becomes available. The incorporation

of new information with the existing data should not invalidate the previous knowledge. The new

information should blend with the old without the need to regenerate the total classification scheme.

As an extension to criteria 1 this is particularly important for on-line systems situated in changing

environments. This criteria is necessary to test the situation where, for example, the expert will be

required to include information regarding new defects if a new product is introduced.

3. It should be possible to am end descriptions of defect classes. A direct consequence of the

uncertainty in the inspection domain is that much of the information available for describing defects is

vague. As a result initial descriptions are either incomplete or not expressive enough. It is therefore

necessary to be able to make changes to the information in the classification scheme when required.

As data becomes available which changes one’s perspective of a previously defined class the

modifications to the class should not disrupt the classification scheme as a whole. The expert should

assume that the production process will change over time. Another example is, changes in production

equipment might mean that previously defined information becomes invalid or not completely true. It

is necessary to be able to modify the existing information to update one’s view.

4. There should be an intuitive way for experts to express their knowledge to define defect classes.

It is very important that those developing a classification scheme to be able to represent their

knowledge in a manner which is natural to them. The alternative is that they must input their

knowledge in terms which are familiar to the system designer. This is often not intuitive to those who

actually use the inspection system since there is a mismatch between the way the two groups of

people think. A system designer may tend to think in terms of image processing algorithms which

may be useful for differentiating between two images to be classified, whereas the inspector would

view the images in terms o f the defect characteristics. Since it is the inspector who will take

ownership o f the system when it is on-line and it is their knowledge which shall be used to develop

the classification scheme, it is sensible for the system to function in a way that is natural to them. For

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example, previous approaches using production rules provided a logical method for expressing

knowledge, but the data types available were too rigid. By defining the antecedents as crisp numbers,

defects which were just outside the recognised limit would be classified incorrectly. The customer

does not always appreciate the difference between a 2.9mm and 3.0mm defect. A more intuitive data

type is required which does not rely on ‘black or white’ conditions.

5. The classification scheme should be understandable to the expert. To gain acceptance by those

who use the classification scheme an interface is required which enables the user to define their

knowledge. Furthermore, it should be clear how this knowledge is being used to make decisions and

classify defects. Such information is invaluable for justifying decisions in the event o f disputes.

These five criteria are used in the remainder of this thesis when assessing both the performance of a

classification scheme and the suitability of uncertainty formalisms for the task of inspection. In order to

make such an assessment, it was necessary to construct a classification scheme. For reasons discussed in

chapter 2, it was not practical to use a real on-line inspection system. Instead, an experimental rig was

constructed. In developing the test rig, it was important to design the system to emulate the sources of

uncertainty described in the preceding sections. The experimental AVI system was set-up to classify

seven types of defect in plastic film similar to OHP acetates. The images output from the camera system

provided the training and test data used to create and assess the classification scheme. The images

obtained were typical o f those which would be output from an on-line inspection system and as such the

experimental rig provided a suitable test bed for this research.

1.4 Sum mary

Automated inspection was introduced as having two component parts, detection and classification. This

thesis focuses on the task o f classification. Automated inspection, and in particular defect classification in

web product, is a process where the data used to model the environment is uncertain. A framework for

Automated Visual Inspection was introduced and typical problems experienced in defining a

classification scheme were described using the example o f a gel defect. The main sources o f uncertainty

were highlighted and five desirable properties for an inspection system were defined. These criteria were

proposed as a benchmark for testing the adequacy of applying uncertainty management techniques to

improve the development of inspection systems.

The motivation for this research is the benefits which could be gained from developing a system which

could take into consideration the types of uncertainty described. The result would be a more robust AVI

system which would provide more explicit classifications o f defects. By improving the representations

available for defining defects, the information in the classification scheme should also become more

intuitive to those who are responsible for managing the system. This transfer of ownership from the

system designer to those who use the equipment means the AVI system should evolve with the

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Chapter 1 - Introduction

production process. Just as control o f the production process is improved using an AVI system, better

control over the inspection system will improve one’s ultimate ability to manufacture better product.

To conclude, theoretical techniques for managing uncertainty do exist, however the method for applying

them to this problem is not intuitive. This thesis illustrates how uncertainty management techniques have

been applied to the domain of industrial inspection. The remainder of the thesis is structured as follows:

Chapter 2 - Machine vision and definition o f problem

Machine vision, and in particular web inspection, is discussed as a process with reference to the product

used for experimentation in this thesis. Examples of the data output from an inspection system are

analysed to illustrate the nature of the uncertain information.

Chapter 3 - Machine vision and classification

Classification theory is introduced with respect to machine vision to introduce aspects o f current

applications and techniques which appear useful for this research. An introduction to uncertainty

management techniques is then made and potential technologies proposed.

Chapter 4 - Uncertainty techniques and classification

The two formalisms used in this research are investigated with respect to classification. A method by

which they could be applied to the task of classification is proposed and the method by which the

techniques could be assessed is stated.

Chapter 5 -Acquisition o f experimental data

A test environment was developed with the same characteristics as the real one to provide controlled and

repeatable conditions. A review is given of the experimental rig used to collect data on defects. The

hardware and software used to create the data used as input to the classification scheme is described.

Chapter 6 -The classification scheme

To implement the technology in a form which could be tried out in the test environment, bespoke

software was written. The structure of this software using Fuzzy Logic and Dempster Shafer is described.

The ruleset used for comparison o f the experimental results is defined, with examples, to illustrate the

concept of the software. To conclude, the experimental design to test the thesis is described.

Chapter 7 -Results

A summary of results for the 15 experiments is given.

Chapter 8 -Discussion

A description of the general results observed over all the experiments is presented followed by comments

on each of the experiments. The results are then discussed in relation to the five desirable properties

discussed previously. The discussion then concludes by analysing how successfully the sources of

uncertainty have been modelled within the AVI system.

Chapter 9 - Conclusion

A statement of the major observations is made along with suggestions for further work.

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Chapter 2 Machine vision and definition of problem

The aims of this chapter are:

1. To introduce machine vision.

2. To define the nature of this inspection problem with regard to detection and classification.

3. Highlight why this classification task is different to others.

4. To introduce the web inspection problem.

5. To describe by example the data produced by the inspection system.

6. To define the limitations in this data and review the sources of uncertainty.

7. To state how this thesis shall be tested using the five desirable criteria for an inspection system.

The technology behind a machine vision system is introduced and the components o f a traditional vision

system are described. This is then extended to go beyond conventional systems and identifies approaches

being made to introduce intelligence into the inspection task.

The two aspects of inspection introduced in chapter 1, detection and classification or examining and

reporting, are described further and their current role in an AVI system is discussed. The difficulties in

making classifications in real world inspection systems are highlighted. The second section continues this

argument by analysing why the classification task in inspection is different to that in other environments,

and makes a comparison with an existing approach which has had successful implementations (an

example from medical diagnosis is used).

The next section focuses on the real world problem by introducing the web inspection process. The type

o f defects to be classified in this research are highlighted to show the information which is available for

making classifications. Using this information about defects, the next section investigates the output data

from the experimental rig used to test this thesis. It highlights the basic measurements used to describe

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the problem. Chapter 5 shall go into greater depth on the performance of the actual inspection system

used in this research.

The next section highlights the limitations in the data available and analyses the sources of uncertainty.

The final section returns to the five desirable criteria for an inspection system introduced in chapter 1 and

describes how the inspection system created in this research can be assessed.

2.1 The machine vision industry

Machine vision is a well established research field which has developed over the past 25-30 years. There

have been several comprehensive surveys over the years which track this progression [Batchelor et al

1985, Bayro-Corrachano 1993a,b, Chin 1982, 1988, Rao 1996, Rosenfeld 1989, Thomas et al 1995]. The

survey by Bayro-Corrachano divided machine vision into two areas, conventional and intelligent systems.

This discussion will follow a similar design. The first section on conventional systems describes the usual

components which would in whole, or in part, be used to construct a machine vision system. The second

section looks at how artificial intelligence has been introduced into machine vision systems and discusses

applications of such systems.

2.1.1 Conventional machine vision systems

Sira [1995] describe the technology required for machine vision as four subsystems.

1. Subject interrogation - getting information out of the subject in the form o f an image

2. Image processing - performing operations on the acquired image to reveal information of interest and

to prepare the image for subsequent analysis.

3. Image analysis - extracting the required information from the image and drawing conclusions or

making decisions.

4. User interaction - a two way communication of information between the system and the user to

enable the completion of the inspection task.

Whilst these four subsystems go from low to high level processes, (subject interrogation being a low level

process and user interaction a high level), they are not necessarily sequential. In practice they can often

be interleaved.

The first subsystem is dependent upon the hardware used to obtain an image o f the product under

investigation. The hardware used in this research was a linescan CCD (Charged Coupled Device) which

will be discussed in section 2.4. The camera basically functions by recording the amount o f light at the

subject. The method for illuminating the object to be inspected therefore plays an important role. The

most common method for illuminating an object, and the one used in this research, is to use artificial

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light. Several lighting techniques are possible and include, front lighting, back lighting and structured

lighting [Awcock and Thomas 1996].

The second subsystem basically transforms the input image into another image with desirable properties.

Standard techniques used for this low level processing can be found in most image processing literature

[e.g. Gonzalez and Woods 1993], however the important processes are i) image correction, the process of

minimising or removing image degradations introduced in the image acquisition process, and ii) image

enhancement, the process o f accentuating the required information so as to produce an image that is more

suitable than the original for a specific application. This process of accentuation involves retaining the

important information whilst discarding the superfluous. In practical real-time applications this low level

processing must be fast, limiting the range o f techniques that are typically applied. The amount of

information to be processed can be calculated by identifying three properties; the pixel resolution

required, the product width, and the speed of the production line. Based upon a 2 metre wide product

manufactured at a line speed of 5 metres per second and a pixel resolution o f 1 millimetre, the data rate

required is 10 megahertz. The practical constraints of real systems usually mean that one has less

information at the end of the image processing task than one would ideally like.

The third process is the analysis of the pre-processed image to extract the information o f interest. This

section is conventionally subdivided into three areas [Bayro-Corrochano 1993a]: segmentation, feature

extraction, and classification.

Segmentation is the process of dividing the image space into meaningful regions. Numerous

segmentation techniques have been proposed however they fall into the broad categories o f region and

boundary segmentation. The former divides the image into regions within which pixel values have

distinguishing characteristics and the latter forms lines of pixels having characteristics that delineate

segments. A simple example of segmentation is thresholding. That is classifying pixels according to

whether their greyscale value is above or below a given level.

Feature extraction is the process of extracting features and their characteristics from the segmented

image. The description of an object is normally expressed as a composition of relevant object features,

hence the process o f transforming information from the spatial image domain (single pixel information)

to a feature domain (information about a pixel relevant to its surrounding). Examples of features are

points (a distinct pixel value within a region) and lines (a sequence of pixels connected one by one in a

path through the image). This process enables the reduction o f data to a manageable form. For example,

images may be reduced from 512 by 512 arrays to a few well defined objects.

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Chapter 2 - Machine vision and definition o f problem

Classification techniques are used to sort images into predetermined categories. The sorting process

requires that conclusions can be made about the subject being inspected. Various techniques have been

proposed including template matching, deterministic and statistical classification, and feature space

classification. This will be discussed further in section 3.1.

The last of the four subsystems, user interaction, provides a method for the user to communicate with the

system. This stage should allow the input o f user requirements (e.g. objects to be classified) and the

output o f the results (e.g. the actual classification of objects). The user interaction subsystem should be an

interface between the user and the system.

Examples of conventional applications can be found in dimensional verification, surface detection and

inspection for completeness [Bayro-Corrochano 1993a]. One of the more mature fields of industrial

inspection is in the semiconductor industry. Here the applications normally aim to detect errors such as

over-etching (open circuits), under etching (short circuits), cracks, holes, and contamination. In many of

these traditional inspection applications, the approach has two stages: first detection using automated

detection tools, then second, classification by a human operator. As previously stated the detection is

accepted as being a fairly well developed technology. Given the existing limitations of detection and

processing power, the problem still remains in making an automatic classification [Chou et al 1993, Luria

et al 1994]. This suggests that if practical solutions to the inspection problems are to be solved extensions

to the conventional methods are required. In a review of real-time industrial visual inspection Thomas et

al [1995] stated that many o f the successful conventional products performed little more than simple

pixel counting and intensity thresholding operations. A method for using the information which results

from the pixel counting and thresholding operations is required so that classifications can be generated

automatically.

2.1.2 Beyond conventional systems

The comparatively slow progress o f automated visual inspection in manufacturing may be due to the

reasoning required to make a classification decision [Newman and Jain 1995]. Human inspection is

characterised by substantial visual abilities and the flexibility to do different tasks. In contrast most

industrial machine vision applications are specialised. They are normally constrained to one particular

application hence require a great deal o f development work every time a new installation is set-up. In

order to increase the viability of industrial applications it is necessary to increase the flexibility of

machine vision systems.

In a paper aimed to provoke discussion amongst researchers in computer vision, Jain and Binford [1991]

highlight the problem which many conventional systems do not address;

Classification of defects using uncertainty techniques in industrial web inspection.