Machine Vision Handbook PDF
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Machine Vision Handbook Bruce G. Batchelor (Ed.) Machine Vision Handbook With1295Figuresand117Tables Editor BruceG.Batchelor ProfessorEmeritus SchoolofComputerScienceandInformatics CardiffUniversity Cardiff,Wales UK Pleasenotethatadditionalmaterialforthisbookcanbedownloadedfrom http://extras.springer.com ISBN978-1-84996-168-4 e-ISBN978-1-84996-169-1 DOI10.1007/978-1-84996-169-1 ISBNBundle978-1-84996-170-7 SpringerLondonDordrechtHeidelbergNewYork LibraryofCongressControlNumber:2011942208 ©Springer-VerlagLondonLimited2012 Apartfromanyfairdealingforthepurposesofresearchorprivatestudy,orcriticismorreview,aspermitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted,inanyformorbyanymeans,withthepriorpermissioninwritingofthepublishers,orinthecase ofreprographicreproductioninaccordancewiththetermsoflicensesissuedbytheCopyrightLicensingAgency. Enquiriesconcerningreproductionoutsidethosetermsshouldbesenttothepublishers. Theuseofregisterednames,trademarks,etc.,inthispublicationdoesnotimply,evenintheabsenceofaspecific statement,thatsuchnamesareexemptfromtherelevantlawsandregulationsandthereforefreeforgeneraluse. The publisher makes no representation, express or implied, with regard to the accuracy of the information containedinthisbookandcannotacceptanylegalresponsibilityorliabilityforanyerrorsoromissionsthatmay bemade. Printedonacid-freepaper SpringerispartofSpringerScienceþBusinessMedia(www.springer.com) RejoiceintheLordalways. Iwillsayitagain:Rejoice! Letyourgentlenessbeevidenttoall. TheLordisnear. Donotbeanxiousaboutanything, butineverything,byprayerandpetition, withthanksgiving,presentyourrequeststoGod. AndthepeaceofGod,whichtranscendsallunderstanding, willguardyourheartsandyourmindsinChristJesus. ForthoseIholddearest:mywifeEleanor,motherIngrid,daughterHelen, sonDavidandmysevengrandchildren. Preface WhenpeopleaskmewhatworkIdo,IsaythatIamanacademicengineer,studyingMachine VisionandthereforecallmyselfaMachineVisionSystemsEngineer(VisionEngineerforshort). Whenaskedtoexplainwhatthatmeans,Ireplyinthefollowingway: ‘‘Istudymachinesthatcan‘see’.Iuseavideocamera,connectedtoacomputer,tofinddefects inindustrialartifactsastheyarebeingmade.’’ Inmyexperience,thisisjustshortenoughtoavoidtotalboredominfellowguestsatdinner parties.However,itiswoefullyinadequate,sinceitdoesnotencompassmanytopicsthatare essentialforbuildingasuccessfulMachineVisionsystem.Comparethe‘‘dinner-partydefini- tion’’ with the following, more formal statement, which introduces the broader concept of ArtificialVision: Artificial Vision is concerned with the analysis and design of opto-mechanical-electronic systemsthatcansensetheirenvironmentbydetectingspatio-temporalpatternsofelectro-magnetic radiationandthenprocessthatdata,inordertoperformusefulpracticalfunctions. Machine Vision is the name given to the engineering of Artificial Vision systems. When browsing through the technical literature, the reader will soon encounter another term: Computer Vision (CV) and will quickly realise that it and Machine Vision (MV) are often usedsynonymously.ThisisapointonwhichIstronglydisagree!Indeed,thisbookisfounded onmyfirmbeliefthatthesesubjectsarefundamentallydifferent.Thereisacertainamountof academicsupportforthisview,althoughitmustbeadmittedthattheloudestvoicesofdissent comefromwithinuniversities.Ontheotherhand,mostdesignersofindustrialvisionsystems implicitly acknowledge this dichotomy, often by simply ignoring much of the academic researchinCV,onthegroundsthatitisnotrelevanttotheirimmediateneeds.Intheensuing pages, I and my co-authors argue that Machine Vision should be recognised as a distinct academicandpracticalsubjectthatisfundamentallydifferentfromComputerVision. ComputerVision,ArtificialIntelligence,PatternRecognitionandDigitalImageProcessing (DIP)allcontributetoMV,whichmakesuseofalgorithmicandheuristictechniquesthatwere firstdevisedthroughresearchintheseotherfields.MachineVisionconcentrateson making them operate in a useful and practical way. This means that we have to consider all aspects of the system, not just techniques for representing, storing and processing images inside a computer.Withthisinmind,wecometothefollowingworkingdefinition. MachineVisionisconcernedwiththeengineeringofintegratedmechanical-optical-electronic- softwaresystemsforexaminingnaturalobjectsandmaterials,humanartifactsandmanufacturing processes,inordertodetectdefectsandimprovequality,operatingefficiencyandthesafetyofboth productsandprocesses.Itisalsousedtocontrolmachinesusedinmanufacturing.MachineVision requirestheharmoniousintegrationofelementsofthefollowingareasofstudy ● Mechanicalhandling ● Lighting ● Optics(conventional,fibreoptics,lasers,diffractiveoptics) ● Sensors(videocameras,UV,IRandX-raysensors,laserscanners) ● Electronics(digital,analogueandvideo) viii Preface ● Signalprocessing ● Imageprocessing ● Digitalsystemsarchitecture ● Software ● Industrialengineering ● Human-computerinterfacing ● Controlsystems ● Manufacturing ● ExistingworkpracticesandQAmethods. MachineVisionhasalsobeenappliedsuccessfullytoseveralhigh-volumenichemarkets, including: ● Readingautomobileregistrationplates ● Facerecognition(securitypurposes) ● Fingerprintrecognition ● Irisrecognition ● Documentprocessing ● Signatureverification ● Securitysurveillance ● Printinspection ● Fabricatingmicro-electronicdevices ● Karyotyping (chromosomeidentificationandclassification) ● Inspectingbareprintedcicuitboards ● Controlling/checkingtheplacementofcomponentsonprintedcircuitboards. However,weshallnotdiscussthesenicheapplicationareas,preferringinsteadtoconcen- trateonmanufacturingindustry,whichisdistinctiveinpresentingmanymillionsoflow-volume potentialapplications.Thesevaryfromtrivialtoextremelydifficult.Ratherthansolvingone applicationatatime,Ihavespentmyworkinglifedevisingtoolsthatareappropriateforavery widerangeofsituations.Theaimofmyworkoverthelast35yearshasbeentobuildversatile development systems that can be used in the study of newly defined industrial inspection, measurementorcontroltasks.Usingsuchsystemsenablesnewapplicationstobesolved,or dismissedasimpractical,inaveryshorttime.Thereisanever-endingstreamofnewindustrial applications.Inthe1980s,the3MCompany,withwhomIwasprivilegedtowork,introduced morethan10newproductseachday!Thechallengewefacedwastodevisetoolsallowingthe potentialbenefitsofMVtobeexploredquicklyandcheaply;wecouldspendnomorethan afewhoursevaluatingeachnewapplication.Contrastthiswiththenichemarketsmentioned above,wherethereisthepotentialforahighfinancialreturnforasingledesign.Unlikemost industrialMVprojects,thesenichemarketswereabletojustifythehighdesigncostsneeded. Letusbemorespecificaboutwhatweincludewithinthescopeofourdiscussioninthis book.Weconcentrateonmanufacturingindustry:‘‘metalbashing’’andprocessingmaterials suchasplastics, wood,glass,rubber,andceramics.Asimilarapproachisappropriateinthe manufacture and packagingof food, beverages, pharmaceuticals, toiletries, clothing and furniture.Manyofthesameideasarealsoapplicabletoprocessingfabrics,leather,minerals, plantmaterials,fruit,vegetables,nuts,timber,animalandbirdcarcasses,meat,fish,etc. TheproblemofnomenclaturearisesbecauseMV,CVandDIPareallconcernedwiththe processingandanalysisofpictureswithinelectronicequipment.Ididnotspecifycomputers Preface ix explicitly,becauseArtificialVisiondoesnotnecessarilyinvolveadevicethatisrecognizableas acomputer.Ofcourse,itisimplicitintheverynameofCVthatimageprocessingtakesplace insideacomputer.Ontheotherhand,MVdoesnotimposesuchalimitation.Forexample, it also allows the processing of images to take place in specialised electronic or electro- opticalhardware. AlthoughMV,CVandDIPshareagreatmanyterms,conceptsandalgorithmictechniques, they requireacompletelydifferentsetofpriorities, attitudesandmentalskills.Thedivision betweenMVandCVreflectsthedivisionbetweenengineeringandscience.Engineershavelong struggledtoestablishacleardistinctionbetweenengineeringandscience.Thisbooktriesto dothesameforMVSystemsEngineeringanditsscientific-mathematicalcounterpart:Com- puterVision. CVisusuallyconcernedwithgeneralquestions,suchas‘‘Whatisthis?’’.Ontheotherhand, MValmostalwaysaddressesveryspecificquestions,forexample‘‘Isthisawellmadewidget?’’. Thisreflectsoneverysimplefact:inafactory,weknowwhatproductisbeingmadeatanygiven moment,soweknowwhatanobjectshouldlooklike.Asaresult,MVsystemsareusuallyused forverification,notrecognition.ThereverseisoftentrueforCV.Asweshallseeinthefollowing pages,thishasprofoundimplications. The people responsible for generating MV data (i.e. images) are usually cooperative. A manufacturing engineer wants to produce a good product and will, as far as is practical, modifytheinspectionmillieu,oreventheproductitself,toimprovematters.Contrastthiswith the task of designing systems for recognising military targets. The enemy does not want to cooperate and actually tries to obscure his presence and actions! Similar remarks apply to fingerprint identification and forensic image analysis. Not surprisingly, criminals showlittle interest in cooperating with those people who are trying to catch them! In medicine, the situation is little better; tumours do not cooperate with equipment designers, although a battery of techniques (e.g. staining) has been developed for highlighting the presence of cancercells.Onacontinuumscalerecordingthedegreeofcooperationthatwecanexpect,MV applicationsareclusteredatoneendandCVapplicationsneartheother. In MV, the allowed unit cost for analysing each image is likely to be very low. In CV applications,suchasmilitarytargetrecognitionandoncology,thecostofmakingamistakecan beveryhighindeed,soahigherunitcostpersceneinspectedcanoftenbetolerated.MostCV problemsare‘‘difficult’’intermsofartificialintelligence,whileMVisusually‘‘easy’’. ThedistinctionbetweenMVandCVcanbeillustratedbyconsideringaseeminglysimple task:determiningwhetheracoinhasitsobverse,orreverse,faceuppermost.(Thismightwell beacomponentinthebiggerprocessofinspecting proof-qualitycoins.)CVbeginswithan image that was obtained by somebody else, not the CV specialist. On the other hand, MV beginswithanembossedmetaldisc.DerivinganimagefromitrequiresthattheMVengineer designs the lighting, optics and mechanical handling of the coins, as well as choosing an appropriatecamera.Thecrucialimportanceoflightingandopticsisobviousfromthefactthat very different images of the same coin can be produced, simply by moving the lights very slightly. IntheMVengineer’smind,considerationoftherepresentation,storage,processing andanalysisofimagesfollowsonceagoodimagehasbeenproduced.Computerprocessing ofimagesforMVisimportantbutitisnotthevisionengineer’sonlyconcern.Hecannotafford toconcentrateonthis,oranyotherpartof asystem,andthenneglectallothers.Thisisthe primaryandinviolateruleforMachineVisionsystemdesign. Duringthe1970sand1980s,Japantaughttherestoftheworldtheimportanceofensuring thehighestqualityinmanufacturedgoods.TheWestlearnedthehardway:marketswerelostto x Preface companieswhose nameswerehitherto unknown.Manylong-establishedandwell-respected companieswereunabletomeetthechallengeandfailedtosurvive.Thosethatdidwereoften faced with difficult years, as their share of the market shrank. Most companies in Europe and America have come to terms with this now and realize that quality has a vital role in establishingandmaintainingcustomerloyalty.Hence,anytechnologythatimprovesorsimply guarantees product quality is welcome. Machine Vision has much to offer manufacturing industryinimprovingproductqualityandsafety,aswellasenhancingprocessefficiencyand operationalsafety.MachineVisionowesitsrisingpopularitytothefactthatopticalsensingis inherentlyclean,safe,andversatile.Itispossibletodocertainthingsusingvisionthatnoother knownsensingmethodcanachieve.Imaginetryingtodetectstains,rustorsurfacecorrosion overalargeareabyanymeansotherthanvision! TherecentgrowthofinterestinindustrialapplicationsofMachineVisionisdue,inlarge part,tothefallingcostofcomputingpower.Thishasledtoaproliferationofvisionproducts and industrial installations. Ithas also enabled the development of cheaper and fastercom- putingmachines,withincreasedprocessingpower.Inmanymanufacturingcompanies,serious consideration is being given now to applying Machine Vision to such tasks as inspecting grading,sorting,counting,monitoring,measuring,gauging,controllingandguiding. Auto- matedVisualInspectionsystemsallowmanufacturerstokeepcontrolofproductquality,thus maintaining / enhancing their competitive position. Machine Vision has also been used to ensuregreatersafetyandreliabilityofthemanufacturingprocesses.Thewholeareaofflexible automation is an important and growing one, and Machine Vision will continue to be an essential element in future Flexible Manufacturing Systems. There are numerous situations where human inspection is unable to meet production demands. The advent of Machine Vision has often enabled the development of entirely new products and processes. Many companies now realise that Machine Vision forms an integral and necessary part of their long-termplansforautomation.This,combinedwiththelegalimplicationsofsellingdefective anddangerousproducts,highlightsthecaseforusingMachineVisioninautomatedinspec- tion. A similar argument applies to the application of vision to robotics and automated assembly, where human operators were previously exposed to dangerous, unhealthy / or unpleasantworkingconditions.MachineVisionisavaluabletoolinhelpingtoavoidthis. No Machine Vision system existing today, or among those planned for the foreseeable future, approaches the interpretative powers of a human being. However, current Machine Vision systems are better than people at some repetitive quantitative tasks, such as making measurementsundertightlycontrolledconditions.MachineVisionsystemscanout-perform people,incertainlimitedcircumstances.Industrialvisionsystemscangenerallyinspectsimple, well-definedmass-producedproductsatveryhighspeeds,whereaspeoplehaveconsiderable difficulty making consistent inspection judgments in these circumstances. Machine Vision systemsexistthatcaninspectseveralthousandobjectsperminute,whichiswellbeyondhuman ability. Studies have shown that, at best, a human inspector can only expect to be 70–80% efficient, even under ideal conditions. On many routine inspection tasks, a Machine Vision systemcanimproveefficiencysubstantially,comparedtoahumaninspector.Amachinecan, theoretically,dothisfor24hours/day,365days/year.MachineVisioncanalsobeusefulin detectinggradualchangesincontinuousprocesses.Trackinggradualcolour,shadeortexture variationsisnoteasyfor aperson. Currently, the main application areas for industrial vision systems occur in automated inspectionandmeasurementand,toalesserextent,robotvision.AutomatedVisualInspection and measurement devices have, in the past, tended to develop in advance of robot vision
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