Industrial Image
Processing
Christian Demant ·Bernd Streicher-Abel
Carsten Garnica
Visual Quality Control in Manufacturing
2nd Edition
Industrial Image Processing
Christian Demant
•
Bernd Streicher-Abel
Carsten Garnica
Industrial Image Processing
Visual Quality Control in Manufacturing
Second Revised Edition
123
Dipl Ing. Christian Demant
Dipl Ing. Bernd Streicher-Abel
Dipl Ing. (FH) Carsten Garnica
NeuroCheck GmbH
Stuttgart
Germany
www.neurocheck.com
Authors of the first edition, 1999: Demant, Streicher-Abel, Waszkewitz
ISBN 978-3-642-33904-2 ISBN 978-3-642-33905-9 (eBook)
DOI 10.1007/978-3-642-33905-9
Springer Heidelberg New York Dordrecht London
Library of Congress Control Number: 2013935767
/>Ó Springer-Verlag Berlin Heidelberg 1999, 2013
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Foreword
As a student of ETH Zurich, I encountered image processing for the first time in
the mid-1980s. Then, the subject was primarily discussed from a scientific and
theoretical point of view (algorithms) and had no practical use in automation
technology. Expensive special hardware with weak, non-standardized processors
in combination with error-prone assembler programming resulted in poor
reliability for industrial processes and thus prevented its spread.
While writing my doctoral thesis in the early 1990s as one of the first Ph.D.
candidates at the Paul Scherrer Institute in Zurich (now the Centre Suisse
d’Electronique et de Microtechnique, abbreviated CSEM), I focused on this sub-
ject with research concerning the then novel CMOS image sensors. Since then,
image processing has become the central focus of my professional career.
In the wake of the rapid development of PC technology, the triumphant

progress of industrial image processing began in the mid-1990s and continues to
this date. Modern industrial production processes are inconceivable without image
processing systems. Many automation solutions are even made possible only by
using image processing. Industrial image processing has turned from an abstract
science into a still ambitious, yet also extremely useful key technology of modern
automation technology.
The authors deserve credit for providing the first edition of this book back in
1999, a reference book on the subject of industrial image processing for the first
time offering both beginners and advanced readers an ideal introduction and
reference. This is not an abstract work of academia but explains in an under-
standable way the methodical processes and mathematical foundations of important
image processing functions. It also deals with all vital aspects needed to implement
industrial image processing systems for quality control in industrial manufacturing
processes. From illumination to optics, cameras and image capturing hardware, the
fundamental software algorithms and automation interfaces, the relationships of all
relevant parts are presented.
What makes this book unique is the practical relevance. Using the professional
image processing software NeuroCheck developed by authors Demant and
Streicher-Abel, the reader is able to follow the many examples in the book taken
from practice using an intuitive, modern graphical interface, and parameterize
v
them anew interactively. From the viewpoint of my former academic work at the
institute this is a revolutionary approach.
Therefore, for many interested in image processing the book has rightly become
a standard reference within a short time after its publication. And it is still an
authority even if the image processing user of today usually does not need to
develop algorithms since standard software is available on the market enabling
him to implement even complex applications. The understanding of the interaction
of all components described in the book is still vital and valid.
Since the publication of the first edition in 1999, many things have changed in

the area of image processing hardware, e.g. imaging sensors. The availability of
modern digital cameras with ever faster CCD and CMOS sensors, and of modern
digital interfaces such as USB, IEEE 1394 (‘‘FireWire’’) and Gigabit Ethernet have
contributed to image processing becoming even faster and more productive.
Modern multi-core CPU technology allows for more comfortable and more reliable
image processing software—while simultaneously cost is decreasing.
The authors allow for this development in this heavily revised second edition,
and not least thanks to the new NeuroCheck software version 6.0 (available since
2009), they demonstrate what state-of-the-art image processing systems can look
like. This standard reference in its latest edition should have its place on every
bookshelf.
Frauenfeld, Switzerland, January 2013 Dr. Oliver Vietze
CEO & Chairman
Baumer Group
www.baumer.com
vi Foreword
Preface
Since the publication of the first edition of our book in 1999, machine vision has
enjoyed continuous strong growth as in the decade before. After machine vision
had crossed the 1 billion Deutsche Mark revenue line in Germany in 2000, the
same euro milestone was then reached in 2005. The average growth rate was
approximately 6.4 % between 2000 and 2010 [VDMA (German Engineering
Association)]. One has to look carefully to find industries with comparable growth
dynamics.
However, this glossy image has experienced its first setbacks. In 2009 in the
wake of the global economic crisis, companies in this industry suffered significant
losses (-21 %) for the first time.
In addition to this, machine vision had by now reached the phase of a ‘‘con-
solidated industry’’ in the life cycle of an economic sector. The spirit of optimism
from the 1990s has mostly evaporated, technological quantum leaps have become

rare, and by now the continuous reduction of system cost is at a premium. Start-ups
can only establish themselves on the market with the help of huge grants and only
rarely do they leave the ‘‘small business’’ sector. On the other hand, the number of
co-operations is increasing and many market players are growing solely by pur-
posefully acquiring smaller businesses.
Where in the 1990s a complex algorithm was able to convince on the spot, today
software reliability during continuous production and trouble-free integration into
networked production structures are vital.
Since all industry partners feel the increasing time pressure, intelligent easy-to-
use functionality becomes more and more important. Wherever possible, system
providers have to use high performing hardware and software standards since the
development of proprietary systems is no longer acceptable to the market, neither
technologically nor financially.
However, the subject retains its fascination and there is a number of reasons
why, globally, machine vision will continue to grow successfully over the fol-
lowing years.
Ensuring quality is the top priority among manufacturers. Machines that are able
to ‘‘see’’ gauge high-precision parts, guide robot arms into the correct position, and
identify components during production flow from incoming to outgoing goods.
vii
Let us summarize: today, industrial production without machine vision is
unthinkable! Therefore, visual inspection systems can be found in businesses of all
sizes and industrial sectors.
Especially German industry with its strong ‘‘Mittelstand’’ (medium-sized busi-
nesses) again and again holds numerous very different and demanding tasks for
machine vision. Hence, German machine vision businesses are globally leading in
many areas, especially when it comes to versatility, flexibility and integration into
various production environments. Excellent competence with regard to solving the
image processing task is the fundamental requirement to be seriously taken into
consideration as a provider. With this background, a practical introduction into

image processing is now more needed than ever before.
This book is based on years of practical experience on the part of the authors in
development and integration of automated visual inspection systems into manu-
facturing industry. We have tried to use a different approach than most books
about (digital) image processing. Instead of introducing isolated methods in a
mathematically systematic sequence, we present applications taken with few
exceptions from industrial practice. These image processing problems then
motivate the presentation of the applied algorithms, which focuses less on theo-
retical considerations than on the practical applicability of algorithms and how to
make them work together in a consistently designed system. The mathematical
foundations will not be neglected, of course, but they will also not be the main
focus of attention.
We hope that this approach will give students and practitioners alike an
impression of the capabilities of digital image processing for the purposes of
industrial quality control. We also hope that it will create an understanding for the
prerequisites and methodology of its application.
We would like to thank Baumer Optronic, Radeberg, Germany, for the many
years of successful cooperation and constructive support in writing the chapter on
digital cameras.
We would also like to thank Industrial Vision Systems Ltd., Kingston Bag-
puize, UK, for providing and editing the vivid application examples in the chapter
‘‘Color Image Processing’’.
Furthermore, we want to thank all the people who have supported us in the past
years and have been, in one way or another, involved in the evolution of Neu-
roCheck. With their work and effort, our NeuroCheck brand has become a
resounding success and has thus enabled us to produce this book. We wish to
thank:
• Dipl Inf. Marcellus Buchheit, Edmonds/Seattle, U.S.A.
• Dipl Ing. (FH) Richard Herga, Süßen, Germany
• Bernd Marquardt, Dormagen, Germany

• Prof. Dr. Konrad Sandau, Darmstadt, Germany
• Dipl Ing. (FH) Anton Schmidt, Bernau, Germany
• Dipl Ing. (FH) Axel Springhoff, Metzingen, Germany
• European Patent Attorney Dipl Ing. Christoph Sturm, Wiesbaden, Germany
viii Preface
• Dr Ing. Peter Waszkewitz, Kornwestheim, Germany
• Earl Yardley, B.Eng. (Hons), Wantage, UK
Finally we would like to express our special thanks to Ms. Hestermann-Beyerle
and Ms. Kollmar-Thoni, Springer Publishing, for enabling us to publish this
second edition, and, last but in no way least, to our translator, Ms. Michaela Strick.
Stuttgart, spring of 2013 Dipl Ing. Christian Demant
Dipl Ing. Bernd Streicher-Abel
Dipl Ing. (FH) Carsten Garnica
NeuroCheck GmbH

www.neurocheck.com
Preface ix
Contents
1 Introduction 1
1.1 Why Write Another Book About Image Processing?. . . . . . . . 1
1.2 Possibilities and Limitations . . . . . . . . . . . . . . . . . . . . . . . . 3
1.3 Types of Inspection Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 Structure of Image Processing Systems . . . . . . . . . . . . . . . . . 6
1.4.1 Hardware. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4.2 Signal Flow in Process Environment. . . . . . . . . . . . . 9
1.4.3 Signal Flow Within the Image Processing System . . . 11
1.5 Process Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.6 Introductory Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.6.1 Optical Character Recognition . . . . . . . . . . . . . . . . . 16
1.6.2 Thread Depth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

1.6.3 Presence Verification . . . . . . . . . . . . . . . . . . . . . . . 20
1.7 From Here . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2 Overview: Image Preprocessing 25
2.1 Gray Scale Transformation . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.1.1 Look-Up Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.1.2 Linear Gray Level Scaling. . . . . . . . . . . . . . . . . . . . 28
2.1.3 Contrast Enhancement. . . . . . . . . . . . . . . . . . . . . . . 29
2.1.4 Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . 30
2.1.5 Local Contrast Enhancement . . . . . . . . . . . . . . . . . . 31
2.2 Image Arithmetic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.1 Image Addition and Averaging. . . . . . . . . . . . . . . . . 34
2.2.2 Image Subtraction. . . . . . . . . . . . . . . . . . . . . . . . . . 35
2.2.3 Minimum and Maximum of Two Images . . . . . . . . . 37
2.2.4 Shading Correction . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.3 Linear Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.3.1 Local Operations and Neighborhoods . . . . . . . . . . . . 39
2.3.2 Principle of Linear Filters . . . . . . . . . . . . . . . . . . . . 40
2.3.3 Smoothing Filters . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.3.4 Edge Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
xi
2.4 Median Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
2.5 Morphological Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.6 Other Non-linear Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
2.7 Coordinate Transformations . . . . . . . . . . . . . . . . . . . . . . . . . 58
2.8 Integral Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.9 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3 Positioning 65
3.1 Position of an Individual Object . . . . . . . . . . . . . . . . . . . . . . 65

3.1.1 Positioning Using the Entire Object . . . . . . . . . . . . . 66
3.1.2 Positioning Using an Edge. . . . . . . . . . . . . . . . . . . . 68
3.2 Orientation of an Individual Object. . . . . . . . . . . . . . . . . . . . 70
3.2.1 Orientation Computation Using Principal Axis . . . . . . 71
3.2.2 Distance-Versus-Angle Signature . . . . . . . . . . . . . . . 73
3.3 Robot Positioning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.3.1 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.3.2 Image Processing Components . . . . . . . . . . . . . . . . . 76
3.3.3 Position Determination on One Object . . . . . . . . . . . 78
3.3.4 Orientation of an Object Group . . . . . . . . . . . . . . . . 78
3.3.5 Comments Concerning Position Adjustment. . . . . . . . 79
3.4 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4 Overview: Segmentation 83
4.1 Regions of Interest (ROIs). . . . . . . . . . . . . . . . . . . . . . . . . . 83
4.2 Binary Segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
4.2.1 Thresholds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.2.2 Threshold Determination from Histogram
Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.2.3 Gray Level Histograms . . . . . . . . . . . . . . . . . . . . . . 87
4.2.4 Generalizations of Thresholding . . . . . . . . . . . . . . . . 90
4.3 Contour Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.3.1 Connectedness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.3.2 Generating Object Contours . . . . . . . . . . . . . . . . . . . 93
4.3.3 Contour Representation . . . . . . . . . . . . . . . . . . . . . . 95
4.4 Template Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.4.1 Basic Operation . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
4.4.2 Optimizing Template Matching . . . . . . . . . . . . . . . . 99
4.4.3 Comments on Template Matching . . . . . . . . . . . . . . 103
4.4.4 Edge-Based Object Search. . . . . . . . . . . . . . . . . . . . 104

4.5 Edge Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106
4.5.1 Edge Probing in Industrial Image Scenes. . . . . . . . . . 106
4.5.2 Edge Search with Subpixel Accuracy . . . . . . . . . . . . 108
xii Contents
4.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
5 Mark Identification 113
5.1 Bar Code Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.1.1 Principle of Gray-Level-Based Bar
Code Identification . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.1.2 Types of Bar Codes . . . . . . . . . . . . . . . . . . . . . . . . 115
5.1.3 Examples for Industrial Bar Code Identification . . . . . 117
5.1.4 Two-Dimensional Codes . . . . . . . . . . . . . . . . . . . . . 119
5.2 Character Recognition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.2.1 Laser-Etched Characters on an IC. . . . . . . . . . . . . . . 121
5.2.2 Basic Configuration of Character Recognition . . . . . . 123
5.2.3 Fundamental Structure of a Classifier Application . . . 126
5.2.4 Position Adjustment on the IC . . . . . . . . . . . . . . . . . 130
5.2.5 Improving Character Quality . . . . . . . . . . . . . . . . . . 135
5.2.6 Optimization in Operation . . . . . . . . . . . . . . . . . . . . 137
5.3 Recognition of Pin-Marked Digits on Metal. . . . . . . . . . . . . . 138
5.3.1 Illumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.3.2 Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
5.3.3 Segmentation and Classification . . . . . . . . . . . . . . . . 140
5.4 Block Codes on Rolls of Film . . . . . . . . . . . . . . . . . . . . . . . 142
5.5 Print Quality Inspection. . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
5.5.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145
5.5.2 Print Quality Inspection in Individual Regions . . . . . . 146
5.5.3 Print Quality Inspection with Automatic
Subdivision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

5.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
6 Overview: Classification 151
6.1 What is Classification? . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.2 Classification as Function Approximation . . . . . . . . . . . . . . . 153
6.2.1 Basic Terms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
6.2.2 Statistical Foundations. . . . . . . . . . . . . . . . . . . . . . . 155
6.2.3 Defining Classifiers . . . . . . . . . . . . . . . . . . . . . . . . 156
6.3 Instance-Based Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . 158
6.3.1 Nearest Neighbor Classifier . . . . . . . . . . . . . . . . . . . 158
6.3.2 RCE Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159
6.3.3 Vector Quantization . . . . . . . . . . . . . . . . . . . . . . . . 161
6.3.4 Template Matching . . . . . . . . . . . . . . . . . . . . . . . . . 161
6.3.5 Comments on Instance-Based Classifiers . . . . . . . . . . 162
6.4 Function-Based Classifiers. . . . . . . . . . . . . . . . . . . . . . . . . . 162
6.4.1 Polynomial Classifier . . . . . . . . . . . . . . . . . . . . . . . 163
Contents xiii
6.4.2 Multilayer Perceptron-Type Neural Networks. . . . . . . 164
6.5 Comments on the Application of Neural Networks . . . . . . . . . 167
6.5.1 Composition of the Training Set. . . . . . . . . . . . . . . . 167
6.5.2 Feature Scaling. . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
6.5.3 Rejection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
6.5.4 Differentiation from Other Classifiers . . . . . . . . . . . . 169
6.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
7 Gauging 173
7.1 Gauging Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
7.2 Simple Gauging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
7.2.1 Centroid Distances . . . . . . . . . . . . . . . . . . . . . . . . . 175
7.2.2 Contour Distances. . . . . . . . . . . . . . . . . . . . . . . . . . 178

7.2.3 Angle Measurements. . . . . . . . . . . . . . . . . . . . . . . . 182
7.3 Shape Checking on a Punched Part. . . . . . . . . . . . . . . . . . . . 183
7.3.1 Inspection Task . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.3.2 Modeling Contours by Lines . . . . . . . . . . . . . . . . . . 184
7.3.3 Measuring the Contour Angle . . . . . . . . . . . . . . . . . 186
7.4 Angle Gauging on Toothed Belt. . . . . . . . . . . . . . . . . . . . . . 186
7.4.1 Illumination Setup . . . . . . . . . . . . . . . . . . . . . . . . . 187
7.4.2 Edge Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
7.5 Shape Checking on Injection-Molded Part . . . . . . . . . . . . . . . 191
7.5.1 Computing Radii . . . . . . . . . . . . . . . . . . . . . . . . . . 191
7.5.2 Comments on Model Circle Computation . . . . . . . . . 194
7.6 High Accuracy Gauging on Thread Flange . . . . . . . . . . . . . . 194
7.6.1 Illumination and Image Capture . . . . . . . . . . . . . . . . 195
7.6.2 Subpixel-Accurate Gauging of the Thread Depth . . . . 196
7.7 Calibration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
7.7.1 Calibration Mode . . . . . . . . . . . . . . . . . . . . . . . . . . 197
7.7.2 Inspection-Related Calibration . . . . . . . . . . . . . . . . . 199
7.8 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
8 Overview: Image Acquisition and Illumination 203
8.1 Solid-State Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203
8.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
8.1.2 CCD Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
8.1.3 CMOS Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
8.1.4 Special Types. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
8.1.5 Color Sensors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
8.1.6 Properties of Sensors. . . . . . . . . . . . . . . . . . . . . . . . 213
8.2 Digital Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217
8.2.1 Control of Image Capture . . . . . . . . . . . . . . . . . . . . 217
xiv Contents

8.2.2 Capturing Color Images. . . . . . . . . . . . . . . . . . . . . . 219
8.2.3 Characteristic Values of Digital Cameras. . . . . . . . . . 221
8.2.4 Operating Conditions in Industrial Environments . . . . 222
8.3 Image Data Transfer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
8.3.1 CameraLink
Ò
224
8.3.2 FireWire
Ò
226
8.3.3 USB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228
8.3.4 Gigabit Ethernet . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
8.4 Line-Scan Cameras . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
8.4.1 Types of Line-Scan Camera Applications . . . . . . . . . 232
8.4.2 Spatial Resolution of Line-Scan Cameras . . . . . . . . . 234
8.4.3 Illumination for Line-Scan Cameras . . . . . . . . . . . . . 234
8.4.4 Control of Line-Scan Cameras . . . . . . . . . . . . . . . . . 235
8.5 Optical Foundations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
8.5.1 f-number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236
8.5.2 Laws of Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . 238
8.5.3 Depth of Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242
8.5.4 Typical Capturing Situations . . . . . . . . . . . . . . . . . . 246
8.5.5 Aberrations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
8.5.6 Lens Determination. . . . . . . . . . . . . . . . . . . . . . . . . 249
8.5.7 Special Lens Types. . . . . . . . . . . . . . . . . . . . . . . . . 251
8.6 Illumination Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
8.6.1 Light Sources. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 253
8.6.2 Front Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
8.6.3 Back Lighting . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
8.7 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
9 Presence Verification 265
9.1 Presence Verification Using PTZ Cameras . . . . . . . . . . . . . . 266
9.1.1 Inspection Part Geometry . . . . . . . . . . . . . . . . . . . . 266
9.1.2 Illumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 267
9.1.3 Positioning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
9.1.4 Object Detection. . . . . . . . . . . . . . . . . . . . . . . . . . . 268
9.1.5 Verification of Results . . . . . . . . . . . . . . . . . . . . . . 269
9.2 Simple Gauging for Assembly Verification . . . . . . . . . . . . . . 270
9.2.1 Illumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
9.2.2 Inspection Criteria . . . . . . . . . . . . . . . . . . . . . . . . . 273
9.2.3 Object Creation and Measurement Computation . . . . . 275
9.2.4 Position Adjustment . . . . . . . . . . . . . . . . . . . . . . . . 276
9.3 Presence Verification Using Classifiers . . . . . . . . . . . . . . . . . 277
9.3.1 Illumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 279
9.3.2 Check of Crimping . . . . . . . . . . . . . . . . . . . . . . . . . 281
9.3.3 Type Verification of the Flange . . . . . . . . . . . . . . . . 286
Contents xv
9.4 Contrast-Free Presence Verification . . . . . . . . . . . . . . . . . . . 290
9.5 Presence Verification Using Line-Scan Cameras . . . . . . . . . . 292
9.5.1 Inspection of Cylindrical Parts
with Area-Scan Cameras . . . . . . . . . . . . . . . . . . . . . 292
9.5.2 Inspection of a Valve Body . . . . . . . . . . . . . . . . . . . 295
9.5.3 Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
9.6 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301
10 Overview: Object Features 303
10.1 Basic Geometric Features . . . . . . . . . . . . . . . . . . . . . . . . . . 303
10.1.1 Enclosing Rectangle . . . . . . . . . . . . . . . . . . . . . . . . 303
10.1.2 Area and Perimeter . . . . . . . . . . . . . . . . . . . . . . . . . 304
10.1.3 Centroid Coordinates. . . . . . . . . . . . . . . . . . . . . . . . 308

10.1.4 Axes and Radii. . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
10.2 Shape Descriptors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310
10.2.1 Contour Curvature . . . . . . . . . . . . . . . . . . . . . . . . . 310
10.2.2 Fiber Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 312
10.2.3 Euler Number . . . . . . . . . . . . . . . . . . . . . . . . . . . . 313
10.2.4 Moments and Fourier Descriptors . . . . . . . . . . . . . . . 314
10.3 Gray Level Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
10.3.1 First-Order Statistics . . . . . . . . . . . . . . . . . . . . . . . . 315
10.3.2 Textural Features . . . . . . . . . . . . . . . . . . . . . . . . . . 315
10.4 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
11 Color Image Processing 319
11.1 Color Identification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 320
11.1.1 Evaluation Strategy. . . . . . . . . . . . . . . . . . . . . . . . . 321
11.1.2 Illumination and Image Capture . . . . . . . . . . . . . . . . 322
11.1.3 Color Classification . . . . . . . . . . . . . . . . . . . . . . . . 324
11.1.4 Selecting a Camera Image for Character
Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 327
11.1.5 Recognition of Writing . . . . . . . . . . . . . . . . . . . . . . 329
11.2 Color Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
11.2.1 Illumination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 332
11.2.2 Color Classification . . . . . . . . . . . . . . . . . . . . . . . . 333
11.2.3 Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 336
11.2.4 Presence Verification . . . . . . . . . . . . . . . . . . . . . . . 336
11.3 Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
12 Implementation of Industrial Image Processing Applications 339
12.1 Image Processing Projects . . . . . . . . . . . . . . . . . . . . . . . . . . 339
12.2 Process Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 341
xvi Contents

12.3 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 344
Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
Appendix A: Mathematical Notes 347
Appendix B: Software Download 359
Appendix C: Weblinks to Industrial Image Processing 361
Index 363
Contents xvii
Chapter 1
Introduction
With ever increasing demands regarding product quality and documentation,
industrial vision has become a key technology. Meanwhile the use of industrial
vision systems in automated manufacturing goes without saying. However, there is
in many cases a lack of understanding for this modern technology. This book was
written in order to remedy this condition, which was in part created by the vision
industry itself. As with all areas in which PCs are increasingly used, a trend to give
the user more possibilities for application development became apparent in image
processing. This makes it also necessary to equip the user with adequate know-how.
In this introductory chapter we will present the typical application areas for
vision systems in industry and their basic structure, describe the object-oriented
model on which our method is based, and illustrate this model using a simple
example. But before doing this, we want to explain why we thought it necessary to
add another book on industrial vision to those that are already available.
1.1 Why Write Another Book About Image Processing?
There are a number of books available on digital image processing. It is therefore
justified to ask: why add another one? From our experience, the books available
can be divided into three categories:
• Most books introduce methods and algorithms, one after the other, in a more or
less mathematical fashion. These books are mainly written by (and for) aca-
demics and document the ongoing research in the field of image processing. As
such they are of immeasurable value to the developers of image processing

software. To the end-user, however, who needs to solve a specific visual
inspection task, they are of no great help. He starts out with a description of his
problem rather than with isolated methods of whose existence he, as a non-
expert, may not even know. Furthermore, the methods are usually discussed
independently, whereas a solution for an inspection problem will always require
the collaboration of several algorithms—which may sometimes yield surprising
results.
C. Demant et al., Industrial Image Processing,
DOI: 10.1007/978-3-642-33905-9_1, Ó Springer-Verlag Berlin Heidelberg 2013
1
• Some books deal with the practical development and implementation of image
processing software, usually in the form of algorithm libraries. Again, very
important for the software developer, they are probably even less useful for the
end-user who should not have to concern himself with implementation details of
the software he uses for solving his problem.
• A small number of books present real-world industrial applications, which is
just what the industrial user needs. Most of the time, though, these books
describe only very superficially how the experts arrived at the final solution. The
reason for this is that the manufacturers of inspection systems base their com-
petitive advantage on hiding the solution approach in a black box, offering only
an extremely limited interface to the end-user. The end-user will typically not be
able to get detailed information about the structure and inner workings of the
application he bought.
In contrast to this, we are convinced that industrial image processing will only
be able to meet expectations if it emerges from its present state as some kind of
‘‘occult science’’ only mastered by a select few and becomes a generally recog-
nized and familiar technology. This book was written to further such a develop-
ment by describing functioning solutions of real-world inspection problems to
show how the various well-known algorithms can actually be used in such a way
that they support and enhance each other. Our approach assumes a certain scenario

of the future development in the field of image processing, which we will briefly
describe in the following paragraphs.
Generally recognized and observed standards are a sine qua non for the
widespread distribution of a technology. The most important tool for industrial
vision is the computer, and the most commonly used standard in this area is a PC
with a Windows
Ò
operating system by Microsoft (Redmond, WA, U.S.A.). Of
course there will always be tasks that exceed the limits of a PC system, but the
better part of industrial vision tasks can be solved by a PC. The fact that PCs are
widespread in private, administrative and industrial areas serves as an additional
acceleration factor since most people are familiar with handling mostly stan-
dardized user interfaces.
In this we agree with Jähne et al. (1995) regarding the development of image
processing systems: ‘‘falling prices and the possibility of using familiar computer
systems for image processing will make it a tool as generally and easily used as
data acquisition is today.’’
Image processing software went the same way as software for data acquisition:
towards user-friendly, interactive systems, which can be configured and re-tooled
by the end-user. This has removed one of the most important obstacles to the
application of industrial image processing, especially in small companies. These
companies frequently manufacture varying pieces in small series. In this situation,
the maintenance cost of an inspection system requiring outside knowledge and an
expensive expert to adapt the system to a change in production would be
intolerable.
2 1 Introduction
However, improvements in the handling of inspection systems must not obscure
the fact that industrial image processing is not and will not be a simple field. Too
many factors influence the results: the interactions of test piece, environment and
software are too complex. As always in engineering, nothing can replace experi-

ence. The expert will still be needed, especially for the initial design and instal-
lation of an inspection system. We hope that this book will be a first step for
practitioners and students to become vision experts themselves. A second goal of
this book is to give an overview of digital image processing enabling decision-
makers to understand the technical problems and the process of implementing a
visual inspection system even if they do not intend to get so deeply involved with
details as would be necessary to design their own vision systems.
Digital image processing is a vast field of work. Examples are the best way of
learning in such an area and therefore constitute the core of this book, motivating
both the theoretical explanations and the descriptions of algorithms. You can
download all you need in order to carry out these examples on an off-the-shelf PC
with current Windows operating systems using exactly the same software system
employed for the industrial solutions—this should probably be a unique oppor-
tunity. Because of this example-oriented, ‘‘intuitive’’ approach, you will not find
the most arcane details of every algorithm in this book. We will of course present
the essential methods and their mathematical foundations, but our aim is to
illustrate the use, application, and effect of the algorithms, not to prove their
mathematical validity.
To illustrate our intentions with a handy example: this book does not try to
answer the question ‘‘What is a hammer, how do I make one and how do I pound
in a nail with it?’’ but encourages the reader to ask himself/herself: ‘‘I have a box
with a hammer, nails and other tools, how do I use this to build a table or perhaps
even a log cabin?’’ Sometimes we will have to jump ahead of the theory and use
methods which will only later be described in detail, but we think this is justified
by the possibility of using realistic examples.
1.2 Possibilities and Limitations
It is due to its very visual nature, of all things, that industrial vision is sometimes in
a less than enviable situation compared to related areas. Most potential users of
automated inspection systems are perfectly willing to accept the difficulties of
interpreting endless series of measurements. Even for acoustic data—for which

humans also have built-in sensory equipment—these mathematical difficulties are
usually appreciated. Manufacturers of image processing systems, however, will
frequently hear the argument ‘‘But I can easily see that!’’ What is forgotten is that
we humans have learned vision through millions of years of evolution. What is
easy for us, is anything but for a machine. One of the main problems in the
implementation of automated visual inspection systems is therefore understanding
1.1 Why Write Another Book About Image Processing? 3
the way in which the machine ‘‘sees’’ and the conditions that have to be created for
it to perform its task optimally.
Directly related to this problem is another difficulty encountered when one tries
to introduce image processing systems on the production line: they will inevitably
be compared to the peak performance of humans. Of course it is true that people
can in general recognize characters without errors, even hardly legible handwriting
after adequate practice. It is therefore justified to speak of a recognition rate of
100 %. However, no-one can keep up this performance over the course of a full
working day. Although printed characters are easier to recognize, it is fair to
assume that the error rate for this kind of visual inspection in industry is even
higher than for the reading of handwritten texts because of the failing concen-
tration due to the monotony of the work.
One could easily write several books on the capabilities of the human visual
system and how it differs from the processing of image information by a computer.
This cannot be the task of this practically-oriented introduction to image pro-
cessing, which is why we will restrict ourselves to a core statement: automated
visual inspection systems are able to deliver excellent recognition results contin-
uously and reliably, equal to the average performance of humans over time, even
better in some areas, provided the following basic rules are observed:
• The inspection task has been described precisely and in detail, in a way
appropriate for the special characteristics of machine ‘‘vision’’.
• All permissible variants of test pieces (with regard to shape, color, surface etc.)
and all types of errors have been taken into account.

• The environmental conditions (illumination, image capturing, mechanics etc.)
have been designed in such a way that the objects or defects to be recognized
stand out in an automatically identifiable way.
• These environmental conditions are kept stable.
There must be no doubt that an automatic visual inspection system like any
other machine has specifications outside of which one cannot expect the machine
to function without fault. It is surprising how often this simple rule is ignored for
primarily software-based systems. No-one would use a drilling machine equipped
with a wood bit to work his way through reinforced concrete, but a program is
expected to deal with input data practically unrelated to its original task. Of course,
one of the reasons for this is that the users of image processing systems typically
do not take the trouble to specify the tasks of the system and the possible variations
of the pieces to be inspected in necessary detail and with appropriate precision—
although on these issues there are specific and far-reaching requirements con-
cerning the cooperation between the customer who orders such a system and the
contractor.
4 1 Introduction
1.3 Types of Inspection Tasks
You can categorize inspection tasks for image processing systems according to the
intended goal or the process structure.
Categorization according to intended goal: Steger et al. (2008) subdivide the
tasks for image processing systems in industrial manufacturing into the following
categories:
• Object recognition
• Positioning
• Completeness check
• Shape and dimension check
• Surface inspection
We basically agree with this categorization. It should be noted, however, that
object recognition is a component of many applications without being the actual

objective of the respective inspection task. Therefore, we have changed the above
categorization, focusing on the basic technology used for marking an object
expressly for identification purposes. We will complement this list with two areas
that have come into focus over the past years due to rapid technological progress:
color image processing and 3D image processing. These are not different tasks
rather a different kind of information whose evaluation and capture necessitate
special methods. We have also added the category image and object comparison
because certain types of completeness checks are easier to describe in this way.
This leads to the following categorization:
• Positioning
• Mark identification
• Shape and dimensions check, gauging
• Completeness check
• Color processing
• Image and object comparison
• Surface inspection
• 3D image processing
The application areas are listed above in the sequence in which they will be
discussed in this book. We will start with position recognition because this type of
application has a quite simple structure: as soon as the object has been found, only
a single step is left to be done: the position determination. In contrast, we discuss
the completeness check towards the end of the book because, notwithstanding the
simple name, it can be a very complex application in practice.
In the interest of a coherent presentation and to avoid going beyond the scope of
this volume, we will restrict ourselves to the first five application areas which
PC-based vision systems are typically used for. A special case of image comparison,
print quality inspection, will be briefly discussed in connection with identification.
We will glance at surface inspection in the chapter on presence verification. Over the
1.3 Types of Inspection Tasks 5
past years, 3D image processing has been the object of much attention; however, this

area is still characterized by a variety of capturing techniques, each with its specific
advantages and disadvantages, one of which we will discuss as an example in the
overview chapter on image capturing and illumination. Usually 3D image data is
evaluated using the methods of classical two-dimensional image processing
substituting brightness information with distance information thus the evaluation
strategies presented in this book can also be used for 3D images.
Between the chapters on application areas we have inserted overview chapters
that discuss certain aspects from the preceding application chapter in greater detail.
The overview chapters thus serve to explain the algorithms which are often simply
taken for granted in the application chapters.
1.4 Structure of Image Processing Systems
This section gives a short overview of the fundamental setup of image processing
systems in industrial manufacturing. This overview is only intended as a first
introduction and will therefore not go into details like lighting equipment, prop-
erties of cameras or communication with higher-level production control systems.
These aspects will be covered more comprehensively in Chaps. 8 and 12.
1.4.1 Hardware
Practically every image processing system can be roughly divided into three parts:
sensors, computer, and communication interfaces, as depicted in Fig. 1.1. One area
has been omitted, although it is often the decisive factor for the success of image
processing applications: lighting, which is too difficult to generalize for a self-
contained description of the system setup. We will try to make up for this in Chap. 8.
Sensors: The sensors of a system for visual quality control are typically
cameras, as shown in Fig. 1.1, although other image-producing sensors can also be
used, e.g. laser and ultrasonic sensors. Scanners of the kind used in graphics design
and for the analysis of photographic material, e.g., satellite images, are rarely used
in industrial applications, above all because of their slowness. Camera technology
is discussed in detail in Chap. 8.
The connection between sensors (i.e. cameras) and computer is usually
achieved via digital media such as FireWire, Gigabit-Ethernet or USB. These PC

mass market technologies have established themselves in industrial applications
over the past years thus proving to be the logical extension of an effect typical of
the PC sector: profits from the mass market are used to drive the development in
the industrial high tech sector.
Computer: Depending on the application, very different types of computers
may be used. Parallel computers are often used for the extremely data-intensive
6 1 Introduction
inspections of continuous manufacturing processes like steel, paper or textile
production, because workstations or PC systems do not provide sufficient memory
bandwidth and computation speed to handle the data rates of such applications.
The bulk of industrial inspection tasks can easily be handled with PCs and standard
components, though. By using modern multi-core CPUs, industrial vision profits
immensely from a quantum leap in PC system performance. Especially the time-
consuming computation of image data can be distributed over the various pro-
cessor cores—proper, intelligent multi-threaded implementation provided—thus
frequently leading to significantly shorter evaluation times.
Until the mid-1990s, PC systems were not a serious competitor in industrial
image processing, mainly because of insufficient bandwidth of their bus systems.
VME bus systems and specialized vision processors dominated the market. No
other segment of information technology has developed as rapidly over the past
decades as the PC sector. The increase in performance with a simultaneous
decrease in prices allows for the solving of demanding image processing tasks with
the help of PCs. This is a kind of positive feedback, a self-accelerating effect: the
widespread use of the PC architecture makes expensive hardware and software
development worthwhile, which opens up new performance and application ran-
ges; this in turn increases the attractiveness of the PC platform, and so on. Also the
high level of standardization with regard to hardware and software interfaces
contributed to the fact that PC systems today play an important role in all areas of
industry, from manufacturing control to quality inspection. Another example is the
frequent use of PC technology in the area of programmable logic control (PLC)

systems.
Fig. 1.1 Industrial vision system
1.4 Structure of Image Processing Systems 7
Communication: An image processing system for industrial quality control has
to work in step with the manufacturing process, i.e. it must be possible to control
the system from the outside. The system, on the other hand, must be able to
transmit its results to an external control in such a way that they can be processed
in automated production and quality control systems. The image processing system
must therefore be capable of communicating with other devices.
For remote control and immediate evaluation of final results (test passed or
failed), image processing systems are often connected to programmable logic
controls using digital interfaces or a Fieldbus. The system can also be connected
directly to a master computer using a network or serial communication. All these
means of communication can coexist. Usually the PLC is directly responsible for
the synchronization of inspection system and production process whereas the
master computer is responsible for global control and logging of quality data. Of
course, the image processing system itself can record quality-relevant data, like
measurements and the like, in files for further processing. By using standardized
file formats, this data can be evaluated practically everywhere—another advantage
stemming from the widespread use of PC systems. Taking this idea a step further,
we come to the concept of remote maintenance of inspection systems, e.g., over
the Internet. This part of customer support is of great importance when supporting
image processing systems, enabling suppliers to support their clients over large
distances within minutes. Despite these obvious advantages, visual inspection lines
without external network access are still installed because of security concerns
thus consciously forgoing the option of remote control maintenance. Because of
the decisive economic advantages, it can be presumed that in the medium term
most systems will be equipped with a remote control maintenance option.
Intelligent cameras: Beginning in the middle of the last decade (circa 2005)
there was a trend towards the development and use of intelligent cameras. By the

start of the current decade this market seems in decline, a point that we would like
to comment on. In principle, intelligent cameras follow the hardware setup out-
lined above, but the computer is integrated into the camera casing. The advantages
of this type of system are the small size and low cost of purchase. This lets them
appear attractive as a first step into the world of image processing, in particular for
small and medium-sized companies. On the other hand, computation performance
and especially the memory capacity of these cameras are limited due to their small
size, so that they are only suitable for relatively simple applications. Depending on
the camera type, the application has to be programmed, usually in C, or has only a
very limited set of user-adjustable parameters. In effect, this is a miniaturization of
the old ‘‘black box’’ concept to withhold information from the user. Building
powerful, object-oriented inspection applications in this way is very difficult. Also,
these systems can often visualize the inspection process and results only in a
limited way.
As an added advantage, simplified operation in comparison with a PC system is
often mentioned. This has to be taken with a grain of salt since the configuration of
inspection applications can usually not be carried out directly on the camera, but
often requires an additional PC as a terminal. The inspection application will then
8 1 Introduction
be configured on the PC—by programming it or by setting parameters of prede-
fined routines available on the processor of the camera—and downloaded to the
camera, usually by Ethernet or serial interface. Consequently, frequent
re-configuration and optimization—as is typical for the initial operation of a
production process, but also occurring later due to changes in the product spectrum
or simply because of drifting production parameters—are rather tedious.
This is not to deny the usefulness of intelligent cameras. One should be very
clear, however, on the present capabilities of such systems and their limitations
compared to those of PCs that we have grown accustomed to. The calculation of
the economic efficiency of intelligent cameras must usually be repeated when
using two or three cameras. On the other hand, a PC equipped with the proper

software can evaluate the images of a dozen cameras or more without difficulty.
1.4.2 Signal Flow in Process Environment
The purpose of an industrial image processing system is to derive a quality
statement from an image scene, i.e. something that exists in the real world.
Simplified as far as possible, the signal flow of an image processing system can be
represented by Fig. 1.2. Figure 1.2 shows that an image processing system is
connected to the outside world via at least two interfaces. Of course, further
interfaces are possible for remote or manual control of the system, but the two
interfaces illustrated above are indispensable: on the input side of the system the
real-world scene is translated into an image to be processed by the computer; on
the output side the processing result is transferred to the environment as a quality
statement.
Output interface: The quality statement can be made in very different ways.
This holds for content as well as for signal technology. It can be a numerical value,
a good/bad statement, a string of characters or even something totally different; it
can be transferred over a data line, printed, stored in a file or displayed as a light
signal. All this depends entirely on the task itself and on the process environment.
In any case, some kind of symbolic representation within the image processing
system has to precede transfer of a statement to the outside world.
Input interface: As we have already mentioned in the previous paragraph, very
different types of sensors can be used to provide the image information on the
input side. Basically, the result is always the same: a digital image encoding the
brightness of the image scene as numerical values (this also applies to color
images, but then each of the base colors red, green and blue will require its own
brightness value). A digital image forms a matrix of brightness values. Of course,
scene IP system statement
Fig. 1.2 Schematic signal
flow of an image processing
system
1.4 Structure of Image Processing Systems 9