Machine Vision

453 Pages·2010·2.625 MB·English

by  Wesley E. Snyder, Hairong Qi

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MACHINE VISION
Thisbookisanaccessibleandcomprehensiveintroductiontomachinevision.Itprovidesall
thenecessarytheoreticaltoolsandshowshowtheyareappliedinactualimageprocessing
andmachinevisionsystems.Akeyfeatureistheinclusionofmanyprogrammingexercises
thatgiveinsightsintothedevelopmentofpracticalimageprocessingalgorithms.
The authors begin with a review of mathematical principles and go on to discuss key
issues in image processing such as the description and characterization of images, edge
detection, feature extraction, segmentation, texture, and shape. They also discuss image
matching,statisticalpatternrecognition,syntacticpatternrecognition,clustering,diffusion,
adaptive contours, parametric transforms, and consistent labeling. Important applications
aredescribed,includingautomatictargetrecognition.Tworecurrentthemesinthebookare
consistency(aprincipalphilosophicalconstructforsolvingmachinevisionproblems)and
optimization(themathematicaltoolusedtoimplementthosemethods).
Software and data used in the book can be found at www.cambridge.org/9780521830461.
Thebook isaimed atgraduatestudentsinelectricalengineering,computerscience,
and mathematics. It will also be a useful reference for practitioners.
wesleye. snyder receivedhisPh.D.fromtheUniversityofIllinois,andiscurrently
ProfessorofElectricalandComputerEngineeringatNorthCarolinaStateUniversity.Hehas
writtenover100scientificpapersandistheauthorofthebookIndustrialRobots.Hewas
a founderofboth the IEEE Roboticsand Automation Society and the IEEE Neural
Networks Council. He has served as an advisor to the National Science Foundation,
NASA, Sandia Laboratories, and the US Army Research Office.
hairong qi receivedherPh.D.fromNorthCarolinaStateUniversityandiscurrentlyan
Assistant Professor of Electrical and Computer Engineering at the University of
Tennessee,Knoxville.

MACHINE VISION
Wesley E. Snyder
NorthCarolinaStateUniversity,Raleigh
Hairong Qi
UniversityofTennessee,Knoxville
cambridge university press
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São Paulo, Delhi, Dubai, Tokyo, Mexico City
Cambridge University Press
The Edinburgh Building, Cambridge cb2 8ru, UK
Published in the United States of America by Cambridge University Press, New York
www.cambridge.org
Information on this title: www.cambridge.org/9780521169813
© Cambridge University Press 2004
This publication is in copyright. Subject to statutory exception
and to the provisions of relevant collective licensing agreements,
no reproduction of any part may take place without the written
permission of Cambridge University Press.
First published 2004
First paperback edition 2010
A catalogue record for this publication is available from the British Library
Library of Congress Cataloging in Publication Data
Snyder, Wesley E.
Machine vision / Wesley E. Snyder and Hairong Qi.
p. cm.
ISBN 0 521 83046 X
1. Computer vision. I. Qi, Hairong, 1970. II. Title.
TA1634.S69 2004
006.37 – dc21 2003053083
isbn 978-0-521-83046-1 Hardback
isbn 978-0-521-16981-3 Paperback
Cambridge University Press has no responsibility for the persistence or
accuracy of URLs for external or third-party internet websites referred to in
this publication, and does not guarantee that any content on such websites is,
or will remain, accurate or appropriate.
To Graham and Robert
WES
To my parents and Feiyi
HQ

Contents
Totheinstructor page xv
Acknowledgements xviii
1 Introduction 1
1.1 Concerningthisbook 1
1.2 Concerningprerequisites 2
1.3 Someterminology 3
1.4 Organizationofamachinevisionsystem 5
1.5 Thenatureofimages 6
1.6 Images:Operationsandanalysis 6
Reference 7
2 Reviewofmathematicalprinciples 8
2.1 Abriefreviewofprobability 8
2.2 Areviewoflinearalgebra 10
2.3 Introductiontofunctionminimization 15
2.4 Markovmodels 20
References 28
3 Writingprogramstoprocessimages 29
3.1 ImageFileSystem(IFS)software 29
3.2 Basicprogrammingstructureforimageprocessing 31
3.3 Goodprogrammingstyles 32
3.4 Exampleprograms 33
3.5 Makefiles 34
4 Images:Formationandrepresentation 38
4.1 Imagerepresentations 38
4.2 Thedigitalimage 42
vii
viii Contents
4.3 Describingimageformation 49
4.4 Theimageasasurface 51
4.5 Neighborhoodrelations 53
4.6 Conclusion 56
4.7 Vocabulary 56
Topic4A Imagerepresentations 57
4A.1 Avariationonsampling:Hexagonalpixels 57
4A.2 Othertypesoficonicrepresentations 60
References 62
5 Linearoperatorsandkernels 65
5.1 Whatisalinearoperator? 65
5.2 Applicationofkerneloperatorsindigitalimages 66
5.3 Derivativeestimationbyfunctionfitting 69
5.4 Vectorrepresentationsofimages 73
5.5 Basisvectorsforimages 75
5.6 Edgedetection 76
5.7 Akernelasasampleddifferentiablefunction 78
5.8 Computingconvolutions 83
5.9 Scalespace 85
5.10 Quantifyingtheaccuracyofanedgedetector 88
5.11 Sohowdopeopledoit? 90
5.12 Conclusion 92
5.13 Vocabulary 92
Topic5A Edgedetectors 97
5A.1 TheCannyedgedetector 97
5A.2 Improvementstoedgedetection 98
5A.3 Inferringlinesegmentsfromedgepoints 99
5A.4 Space/frequencyrepresentations 99
5A.5 Vocabulary 101
References 104
6 Imagerelaxation:Restorationandfeatureextraction 107
6.1 Relaxation 107
6.2 Restoration 108
6.3 TheMAPapproach 111
6.4 Meanfieldannealing 115