Computer Vision: Theory and Industrial Applications

457 Pages·1992·13.73 MB·English

by  Josep Amat, Alícia Casals (auth.), Prof. Carme Torras (eds.)

Carme Torras
(Ed.)
Computer Vision:
Theory and Industrial
Applications
With 199 Figures
Springer-Verlag
Berlin Heidelberg New York
London Paris Tokyo
Hong Kong Barcelona Budapest
Prof. Carme Torras
Institut de Cibemetica
Consejo Superior de Investigaciones Cientificas
Universitat Poltecnica de Catalunya
Diagonal 647
08028 Barcelona
Spain
ISBN-13: 978-3-642-48677-7 e-ISBN-13: 978-3-642-48675-3
DOl: 10.1007/978-3-642-48675-3
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© Springer-Verlag Berlin Heidelberg 1992
Softcover reprint of the hardcover 1st edition 1992
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Preface
This book is the fruit of a very long and elaborate process. It was conceived as
a comprehensive solution to several deficiencies encountered while trying to teach
the essentials of Computer Vision in different contexts: to technicians from industry
looking for technological solutions to some of their problems, to students in search of
a good subject for a PhD thesis, and to researchers in other fields who believe that
Computer Vision techniques may help them to analyse their results.
The book was carefully planned with all these people in mind. Thus, it covers the
fundamentals of both 2D and 3D Computer Vision and their most widespread industrial
applications, such as automated inspection, robot guidance and workpiece acquisition.
The level of explanation is that of an expanded introductory text, in the sense that,
besides the basic material, some special advanced topics are included in each chapter,
together with an extensive bibliography for experts to follow up.
Well-known researchers on each of the topics were appointed to write a chapter
following several guidelines to ensure a consistent presentation throughout. I would
like to thank the authors for their patience, because some of them had to go through
several revisions of their chapters in order to avoid repetition and to improve the
homogeneity and coherence of the book. I hope they will find that the final result has
been worth their efforts.
In sum, the aim has been to produce a book unbiased towards any particular topic
or approach, with the rigour and balance of a single-author text, but without foregoing
the benefits derived from the variety of outlooks characteristic of multi-author volumes.
However, there is (at least) one aspect in which the book is unbalanced: Many
authors are from the same country, due to the fact that the book was originally to be
published in Spanish and by a Spanish publisher. In the end, this was not so due to a
series of difficulties. I wish to thank the Engineering Editorial of Springer-Verlag for
having made this book become a reality.
To conclude, I hope that, whether you are a student, a technician, a teacher or a
researcher in the field, you will find in this book the information you are looking for.
Carme Torras
Table of Contents
PART I - ACQUISITION AND 2D PROCESSING
1- Image Obtention and Preprocessing
J. Amat and A. Casals, Universitat Politecnica de Catalunya ................ .
2- Segmentation
C. Torras, Institut de Cibernetica (CSIC-UPC) ............................. 59
PART II - OBTAINING 3D INFORMATION
3- Active Methods for Obtaining Depth Maps
V. Llario and A.B. MartInez, Universitat Politecnica de Catalunya ........... 97
4- Motion and Stereopsis
P. Anandan, Yale University ............................................. 135
5- Shape from Shading, Occlusion and Texture
A. Yuille, Massachusetts Institute of Technology ........................... 185
PART III - REPRESENTATION AND INTERPRETATION
6- Statistical and Syntactic Models. Pattern Recognition Techniques
H. Bunke, Institutfur Informatik, Bern,' and
A. Sanfeliu, Institut de Cibernetica (CSIC-UPC) .......................... 215
7 - Geometric Object Models
J. Juan, Andersen Consulting, Barcelona ................................. 267
8- A Methodology for the Development of General Knowledge-based
Vision Systems
E.M. Riseman and AR. Hanson, University of Massachusetts, Amherst ...... 293
PART IV - INDUSTRIAL APPLICATIONS AND FUTURE TRENDS
9- Bin-picking Techniques
R.B. Kelley, Rensselaer Polytechnic Institute .............................. 337
10- Automated Visual Inspection Algorithms
R.T. Chin, University of Wisconsin, Madison .............................. 377
11- Commercial Vision Systems
B.G. Batchelor, University of Wales, Cardiff,' and
D. Braggins, Machine Vision Systems, Royston ........................... 405
Addresses of Authors ...................................................... 453
CHAPTER 1
IMAGE OBTENTION AND PREPROCESSING
Josep Amat and Alicia Casals
Universitat Politecnica de Catalunya
1.1 Introduction
Human sight processes are highly complex. In a few tenths of a second, an individual
can take in and process a large amount of information as well as interpret it, and can
recognize objects from very different angles and even from fragments. It is reckoned
that the human retina is capable of carrying out roughly ten billion operations per second
and the brain's visual cortex has an even greater capacity (Roberts 1965; Kirsch 1971).
The history of artificial vision systems began over 25 years ago with enormous
difficulties and limitations due to their complexity and the limited computing power
available.
The classic architectures of von Newman-type computers, characterized by their
operating sequentially, have hampered the development of computer vision systems, in
which a certain parallelism for the processing of images is required.
The hardware set-up for a computer vision system consists basically of the
following:
- a system for acquiring images
- a digitizer
- a processing system
The early image acquisition systems were matrices of photosensitive elements,
which provided a very low resolution, acceptable for only very limited applications. At
present, TV cameras -whether monochrome or colour-are used and they provide a
much higher resolution.
A system for processing images generally consists of two levels. In a first
preprocessing stage, low-level operations are carried out, such as filtering, feature
highlighting, feature extraction, and so on. In a second stage, the analysis and
interpretation of the scene required for each application is undertaken.
The capacity and services that can be obtained from a computer vision system will
therefore depend on each one of the three parts mentioned above, as well as on a
2 J. Amat and A. Casals
certain compromise achieved between resolution and volume of information available,
cost and processing time.
1.2 Acquiring the Signal
In computer vision systems, various types of receivers based on the different existing
technologies are used to convert the optical image of a scene into an electrical signal
containing the information corresponding to each point. The technology of the receiver
conditions the main features of the signal supplied, namely resolution and format, as
well as the form and speed of the sweep.
Image sensors can be classified according to their structure as point, linear and
two-dimensional. According to the configuration of the acquisition system, the latter
-independently of the type of sensor employed- may supply point by point, two
dimensional or three-dimensional information on the scene visualized.
At present, most of the computer vision systems developed employ TV cameras as
their pick-up system, due to the large production scale of these components and their
relatively high resolution.
1.2.1 Light·Point Sensors
On a first level, a point sensor can be employed to get the information from a scene by
using a suitable optical system enabling us to explore the scene sequentially and extract
the information point by point. The sensor element employed may be a photodiode or
a phototransistor which supplies a signal corresponding to the intensity of the light at
each point explored.
Given the difficulties involved in getting the sweep and focus for each point, this
problem can be solved for particular scenes with the help of a laser-sensor combine,
which causes a loss of vision of the luminous point in those areas of the plane occupied
by an object. In this way, it proves possible to locate certain types of objects in a
plane and find their positions, but, in general, the use of point sensors imposes severe
limitations.
It is also possible to form a three-dimensional image from a light-point sensor by
using, for instance, a laser range-finder system (see Chapter 3).
Image Obtention and Preprocessing 3
1.2.2 Linear Sensors
Linear sensors are composed of a juxtaposition of photosensitive elements.
Phototransistors were originally used for this type of sensors but CCD devices are
used today.
CCD-type linear sensors exist with resolutions of 256,512, 1024, 2048, and 4098
bits or else with a Facsimile standard format of 1728, 2592, 3456, and 5184 bits.
These sensors enable us to achieve a signal/noise ratio of 2500: 1 or even 5000: 1,
with which resolutions in grey levels of up to 12 bits can be achieved. However,
these sensors are frequently used to obtain binary signals by employing an adjustable
threshold.
In order to obtain two-dimensional images, linear sensors are used on moving
scenes, by employing, for example, conveyor belts. In 1977, the "Consight-I" system
was developed at General Motors for recognition of parts on conveyor belts, by
employing a linear sensor of 256 pixels (Rossol 1981). In order to binarize the
signal, a source of structured light was used (Fig. 1.1), consisting of an oblique plane,
which projects a beam of light onto the area focused by the sensor -a beam which is
interrupted by the passing of the different parts on the conveyor.
Figure 1.1. Use of a linear sensor to obtain binary two-dimensional images.
1.2.3 2-D Sensors
Matrix sensors directly provide the two-dimensional images required for a large number
of the industrial applications of computer vision.
4 J. Arnat and A. Casals
The early 2-D sensors used were the ones based on a matrix of discrete components.
The sensor device is made up of a photodiode matrix and a multiplexor system to make
the reading and generate the output signal. This signal may be analogical or directly
binarized by the multiplexor circuit itself. In this case, strongly contrasted images are
required. These sensors began to be used in the sixties to carry out the recognition of
printed characters.
The greater signal processing capacity which it is possible to achieve today enables
us to use conventional TV cameras based on both Vidicon and MOS technologies as
sensor elements.
1.2.3.1 Vacuum Tube Cameras
The use of conventional TV cameras as sensor devices has the advantage of their
relatively high resolution and low cost, due to large-scale production.
Initially, the TV cameras were vacuum tube technology. The sensor device was a
photosensitive surface onto which the image was projected. This surface behaved as
a set of sensitive elements formed by a capacity and a resistance in parallel, which
depends on the lighting at each point.
When the photoconductive surface is not lit, it makes a good insulator, and
when it is swept by a beam of electrons, a potential corresponding to the supply
current is stabilized in the condensers (Fig. 1.2). When a luminous signal strikes the
photoconductive surface, the resistance of each point decreases in relation to the light
received, and this potential is reduced.
Deflective electric coil
:r -·-7 .... '·'··,·,'",·"""""'''''', .. "",,,,,,, .. ,'-----
Photosensitive
surface L Beam of electrons r----.....- ........- --C~
/
mfHnmnmmmmm
fffitl!
mtmflHflfWHffilJ! Htftttttltltnltl mE
Figure 1.2. Vidicon-type image exploration tube.
When this surface is continually swept, the potential of each point is restored through
the beam and a recharge current is produced, which varies according to its lighting.
Image Obtention and Preprocessing 5
The corresponding variations in current caused in the charge resistance at each point
make up the video signal.
The Vidicon image sensor is a vacuum tube whose photosensitive layer is largely
composed of trisulphide of antimony (Sb2S3). This particular material has the drawback
of its relatively high persistence and time of response, as well as its vulnerability to
over-illumination, which reduces its useful life.
Newvicon-type vacuum image tubes are characterized by their photosensitive
surface being made of selenide of zinc (ZnSe) and a mixture of telenide of zinc
and telenide of cadmium (CdTe). These tubes also have a high sensitivity with a
wider band pass.
Plumbicon-type tubes are based on a layer of photoconductive oxide forming a
continuous matrix of inversely polarized diodes. This has the advantage of lower
persistence, but a narrower band pass.
Figure 1.3 displays the spectral response of the different types of image cameras
of the vacuum tube type. The resolution of these cameras depends on the band pass
of the video signal obtained when the photosensitive surface is swept by the electron
beam. This resolution enables us to appreciate up to 530 vertical lines when the image
is swept at a velocity of 5211-8 per line, the standard for commercial TV .
......
~---+----~-~-+----+---~--+-~--~--~
A-
Figure 1.3. Spectral response of the different types of image cameras
using vacuum tubes.
Greater resistance is achieved to over-exposures and persistence is reduced with
the use of solid state sensitive elements. An MOS capacity matrix is employed as the
photosensitive element, whose charges are restored by the electron beam making its