Table Of ContentIMAGE ANALYSIS
AND PROCESSING
IMAGE ANALYSIS
AND PROCESSING
Edited by
V. Contoni
Pavia University
Pavia, Italy
s.
Levioldi
Rome University
Rome, Italy
and
G. Musso
ELSAG
Genoa, Italy
PLENUM PRESS • NEW YORK AND LONDON
Library of Congress Cataloging in Publication Data
International Conference on Image Analysis and Processing (3rd: 1985: Rapallo, Italy)
Image analysis and processing.
"Proceedings of the Third International Conference on Image Analysis and Process
ing, held September 30-0ctober 2, 1985, in Rapallo, Italy" - T.p. verso.
Includes bibliographies and index.
1. Image processing-Congresses. I. Cantoni, V. II. Levialdi, S. III. Musso, G. IV.
Title.
TA1632.I552 1985 621.36'7 86-22668
ISBN-13: 978-1-4612-9312-5 e-ISBN-13:978-1-4613-2239-9
DOI:I0.1007/978-1-4613-2239-9
Proceedings of the Third International Conference on Image Analysis and
Processing, held September 30-0ctober 2, 1985, in Rapallo, Italy
© 1986 Plenum Press, New York
Softcover reprint of the hardcover 1s t editon 1986
A Division of Plenum Publishing Corporation
233 Spring Street, New York, N.Y. 10013
All rights reserved
No part of this book may be reproduced, stored in a retrieval system, or transmitted
in any form or by any means, electronic, mechanical, photocopying, microfilming,
recording, or otherwise, without written permission from the Publisher
PREFACE
For the third time the Italian Group on Pattern Recogni tion has
organized an International Conference on Image Analysis and Processing (lAP)
gathering together the most active groups working in this area in our
country. The first International Conference lAP was held in Pavia (1980)
and the second one in Selva di Fasano (1982).
A selected set of distinguished speakers has been invited to talk about
their personal experience and views on industrial applications (H Freeman),
the critical analysis of medical image processing (D Rutovitz), the advances
of robot vision languages (M Silva) and the availability of AI technology
for imI?roving the performance of PR and IP programs (J M Chassery).
Four different areas have been covered by the papers submitted and
refereed) to the conference first and to a scientific committee next, namely
IP Techniques, Multiprocessor Architectures, Robot Vision and IP
Applications. A final paper giving the results of a census of the Italian
groups is provided showing, with some detail, typical research lines as
pursued in working groups both at the University and Industry. About 39
groups are presently active in 12 different places of the peninsula.
We are pleased to announce the 4th International Conference on lAP which
will be held on September 25-28, 1987 at Palermo, (Sicily); let us all meet
there!
V. Cantoni
Pavia University
S. Levialdi
Rome University
G. Musso
Elsag SpA Genoa
v
CONTENTS
INVITED LECTURES
Survey of Image Processing Applications in Industry 1
H Freeman
Robot Programming and Robot Vision 11
M Silva, A Roy
Expert Systems and Image Processing 31
J M Chassery
MULTIPROCESSOR ARCHITECTURES - HARDWARE AND SOFTWARE
A Systolic Architecture for Cartesian-to-Polar Coordinate 43
Mapping
C Braccini, A Grattarola, A Maestrini, T Vernazza
Modular Architecture for a Fast 2D Convolver 55
L Borghesi, E Giuliano, G Musso, F Cabiati, P Ottonello
An Approach to Functional Testing of Array Processors 65
L Liotta, D Sciuto
The Papia Control System (Hardware Design) 75
o Catalano, G Gerardi, R Lombardi, A Machi
Parallel Image Processing Primitives 81
V Cantoni, L Carrioli, M Ferretti, L Cinque, S Levialdi
V Di Gesu
Some Examples of IP Algorithms for Pyramidal Architecture 91
V Di Gesu, A Machi
A Pyramidal Haar-Transform Implementation 99
L Carrioli
IMAGE PROCESSING TECHNIQUES
Form Feature Representation in a Structured Bounda~y III
Model
S Ansaldi, L De Floriani, B Facidieno
3D Recontruction from Multiple Stereo Views 121
P Morasso, G Sandini
A Stereo Algorithm for Flat Objects 129
F Masulli, M Straforini, G Sandini
Finding Multiple Pixels 137
C Arcelli, G Sanniti di Baja
On Edge Detection of Trihedrical Vertexes 145
E De Micheli, G Sandini
A Technique for Object Recongnition Base on Subpart 153
Classification
V Cappellini, M T Pareschi, C Raspollini
About Area of Figure Components 161
L P CordelIa, G Sanniti di Baja
Experiments Using the Generalized Hough Transform for 169
Detecting Cortespondences in a Sequence of Shapes
M F Costabile, G G Pieroni
A Procedure for Knowledge Elicitation in Image 177
Interpretation Experiments
U Cugini, P Mauri, P Mussio~ M Protti
IMAGE PROCESSING - APPLICATIONS
Knowledge Acquisition for Automatic Interpretation of Radar Images 189
A Della Ventura, A Maggioni, P Mussio, A Pawlina
A System for Morphological Analysis and Classification of 197
Astronomical Objects
F Pasian, M Pucillo, P Santin
Preliminary Results on Stress Inspection by Image Processing 205
R A Fiorini, P Coppa, 0 Salvatore
Cluster Analysis and Representation for Typology of 215
Mechanically Worked Surfaces
A Bruzzone, P M Lonardo, G Vernazza
A Computer Vision Model for the Analysis of Classes of Handwritten 225
Plane Curves
S Impedovo, M Castellano
Extending Prolog for a Robotic Environment 235
A De Sanctis, A Guercio, S Levialdi, G Tortora
Density Curves of Prometaphase Chromosomes by Semi- 241
Automatic Image Processing
R Bolzani, P Battaglia, A Forabosco
Cluster Analysis of Nuclear Pores in Mammalian Cells 245
G Vernazza, S B Serpico
Industrial Parts Indentification by Moment Invariants 251
G Biall0, L Caponetti, A Distante
Image Processing in Italy: A First Report 259
S Levialdi, A Fabbri
Index 269
viii
INVITED LECTUJRES
SURVEY OF
IMAGE PROCESSING APPLICATIONS IN INDUSTRY
Herbert Freeman
Rutgers University
New Brunswick, New Jersey
INTRODUCTION
Although computers have been used to process images for more than 25
years, it is only recently that we are able to find any significant number
·of image processing applications appearing in industry. The reason for
this is the strict economic test that any image processing application must
pass before ipdustryis willing to accept it: there must bea clear, dem
onstratable economic benefit over alternative methods. The benefit may be
direct, such as a reduction in production costs, or it may be indirect,
such as a less hazardous or physically less strenuous work envirOlrment.
Until recently not many industrial applications of image processing were
able to pass such ecqnomic scrutiny.
The severe requirement for economic viability has also been the
reason why virtually all of the pace-setting developments in image proces
;sing have originated outside of industry. They have occurred in (1) the
military (e.g., aerial reconnaissance), (2) the sciences (e.g" high-energy
physics, astronomy, chemistry), (3) non--military governmental areas (e.g,
;remote sensing, cartography, environmental protection), (4) medicine (e.g.,
radiography, tomography, cytology, and cytogenetics), and (5) commerce
(e.g., character recognition, picture transmission, image recording, and
television) • In all of these -,- except for the commercial applications -
economic viability just did not play an overriding role, and in the com
mercial area, the potential economic benefits were so obvious that applica-
tions ther-e were pursued without hesitation from the very beginning.
During the last few years the steady lowering of the cost of image
sensing and storage, together with the continuing reduction in computing
costs, has finally brought a number of industrial image processing applica
tions into the range of economic viability. We shall review here the cur
rent state of industrial image processing and attempt to give some indica-
tions where future developments in this field are likely to lead us.
IMAGE PROCESSING IN INDUSTRY
Industrial applications of image processing can be broadly classified
into two groups - those where computer image processing is a substitute for
human vision, and those where it is a replacement for other picture proces-
sing methods. The former is commonly referred to as machine V1Slon or
computer vision, and involves tasks that were previously performed (or at
least could have been performed) by a human. The latter is found prima
rily in the graphics arts field and in the processing of photographic
films, where computer image processing is increasingly being introduced as
a replacement for traditional manual and optical methods.
Machine vision in turn can be divided into four categories: (1) robot
vision, (2) inspection, (3) line-drawing conversion, and (4) object model
ing, of which the first two are of especial importance. In each of these
categories the primary motivation is improvement in productivity or the
assumption of a task that for humans is either too hazardous, overly stren
uous, or undesirable in some other way. A classification of machine vision
is shown in Table 1.
There are some significant differences between the image processing
applications typically found in industry and those found in other fields.
Foremost among the differences is that -- for reasons of economy -- indus
trial applications tend to involve only a modest amount of computer proces
sing. (Complex processing tasks require an expensive comput.er or consume
too much time on a low-cost computer). The images tend to be of good qual
ity, relatively free of noise, since the camera location and illumination
can be carefully controlled. For some applications, the illumination may
even be structured (i.e., patterned) to simplify the image int.erpretation
task. To hold the computation requirements down, only moderate resolution
(256 or 512 square) is normally used [11,31,33,36].
COMPUTER VISION FOR MATERIALS HANDLING
One particularly import.ant application for comput.er VISIon is in the
area of materials handling. Typically, the problem here involves three
specific operations: (1) to identify an object from among a limited set of
known objects, (2) to determine the object's position in two- or three
dimensional space, and (3) to determine the object.'s orientation [3,7,
8,15]. The object may be on a moving conveyor, on a stationary platform,
or freely placed in a bin. The last-named is the most difficult case
because the object can assume virtually any position and orientation, and
may, in fact, be partially overlapped by other objects.
Although materials handling normally requires object identification,
position determination, and orientation determination, there are instances
where only position and orientation determination (or perhaps even only one
of these) may be required, thereby simplifying the task [6]. Clearly, if
all objects on a conveyor belt are assumed to be identical, object identi
fication is not required. Also, if the objects are always positioned or
oriented in a fixed manner, position determination or orientation deter
mination will not be required. Some degree of simplification is achieved
in the case where an object is placed on a flat horizontal surface, away
from other objects. The object then a~sumes a stable position, of which
there are usually only a small number for any object. In contrast, when an
object is placed in a bin with many other similar (or different) objects,
the object can usually appear in any position and orientation, and there
will be no a priori limitation on the possible views that the object may
present to the image sensor. This is one of the reasons why the bin
picking problem is still largely an unsolved machine vision problem [20].
Generally machine vision systems work well if certain simplifying
constraints can be embedded. One system that has been successfully applied
is the General Motors' CONSIGHT system [17]. A line source of light illu
minates a conveyor belt at. an angle. The image sensor is directly above
2
Table 1. A Classification of Machine Vision
1. Robot Vision
a. Materials Handling
(i) Identification
(ii) Position and orientation determination
b. Process Control
c. Assembly
d. Motion Control
(i) Navigation and path control
(ii) Collision avoidance
2. Inspection and Quality Control
a. Go/No-go inspection
b. Quantitative visual inspection
3. Line-Drawing Conversion
a. Engineering drawing conversion
b. Map conversion
c. Free-hand sketch conversion
4. Object Modeling
a. Image templates
b. 3D modeling
the place where the projected line of light normally impinges on the belt.
When an object passes under the light, the line of light is distorted, with
the line deflected toward the left by an amount that is proportional to the
height of the object above the belt. The image sensed by the camera thus
contains both shape information as well as height information about the
object in view. The system has been successfully applied in materials han
dling applications, where unknown objects are identified and their position
and orientation are determined.
Most materials handling systems have been designed for handling rigid
parts. One notable exception is the WIRESIGHT system which was designed
for locating and identifying flexible wire leads on an electrical component
[35]. The system uses sideways illumination to obtain images that contain
both object and and object--shadow information. The use of shadows makes it
possible to extract the precise location and shape of the wireleads.
APPLICATIONS IN PROCESS CONTROL
Machine vision applications for process control distinguish them-
selves from those in materials handling in that they tend to operate con
tinuously and are usually used as sensors in a feedback control loop. They
tend to be highly specialized and the vision involved is really more in the
nature of optical sensing, with relatively little image interpretation or
pattern recognition involved. In most applications the image processing
must be very fast (much faster t.han for materials handling) to be compati
ble with the dynamics of the controlled process [23J.
Compared with traditional automation, machine vision may be able to
cope with greater variability in the process and provide higher precision
at reduced cost. Some successful industrial applications are in vision
guided spray painting [11], in seam welding [24J, in automatic gauge rea-
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