Image Analysis and Processing PDF
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IMAGE 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- 3
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