外文翻译--基于批处理灰度图像的拼接方法COM组件技术.docx

外文翻译--基于批处理灰度图像的拼接方法COM组件技术.docx

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附录B引用外文文献及其译文

COMcomponenttechnologybasedbatchgrayimagesmosaicmethod

Abstract

Inthispaper,wepresentagrayimagemosaiccomponentdesignmethodbasedonthevectorrotatingrelaxmatchingalgorithm,whichcanhandlebatchimagesfastandwithhighquality.Wecomputethecoordinatetransformationmatrixforfull-sceneimagesplicingbyusingtheimagematchingalgorithm.Thealgorithm’smatrixoperationimplementationisbasedontheCOMcomponenttechnologytoolboxinMatlab7.0.WeapplybothfuzzyhumanvisualrestrictionconditionsforsplicingimagesandthemultithreadtechnologyintheVCdevelopmentenvironmenttoimprovethematchingalgorithmperformanceefficiency.Theexperimentpartdemonstratestheexecutionefficiencyoftheproposedmethod,whichshowsthatitcanmeetthereal-timedemandofthebatchgrayimagemosaicsoftwaresystem.

Keywords:

imagemosaic;imageregistration;relaxationmatching.

1.Introduction

Fullsceneimagesoftwarebasedonimagesplicingattractsgreatattentionrecently,suchasArcSoftPanoramaMaker,Photoshop8.0etc.Thissoftwareareoftenusedtoprocessimagecapturedbythecommercialcameraorpersonalcamera,whichhashighimagequality.However,thatsoftwareisnotsuitableforthecamerawhichisoftenusedinhighspeedmode,complexandharshshootingconditions,suchasindustrialcameras.Theimagequalityandimagefidelitycapturedbyindustrialcamerasarenotasgoodasthosecapturedbythepersonalcameraorcommercialcamera,whichmoreorlesshavedifferentdegreesofimagedistortionandreducetheaccuracyandstabilityoftheautomaticimagemosaicalgorithm（Liu,2007）.

Becausethemaindifficultyofimagesplicingliesonimageregistration,researchersputgreatattentiononimageregistrationtechnologyinthepastdecades,andhaveachievedsignificantresearchresults（Kanazawa&Kanatani,2004;Miranda-Luna,Daul,Blondel,Hernandez-Mier,WolfandGuillemin,2008;Zitová&Flusser,2003）.Toimprovetheimageregistrationaccuracy,alargesumofmatrixoperationsareperformedontheimages,whichishighcomputationcost,andreducestheefficiencyoftheimagesplicingsoftware.ThiscannotHence,thesealgorithmscannotextendtotherealisticapplications.

Imageregistrationalgorithmbasedonvectorrotatingrelaxhasbeenproventobeapreciseandrobustimagematchingalgorithm（Wang,Hou,Cong,andSun,2010）.However,inordertopursuetheabovetwocharacteristics,thiskindalgorithmalsoinvolvemanymatrixoperations,andtheexecutionefficiencyisnothigh.Toovercomethisbottleneck,inthispaper,weproposedamethodthatusesmultithreadtechnologytooptimizetheimageregistrationmatrix,andtoexecuteconcurrently,

whichcanmeetthereal-timedemandingoftheimageregistrationsystem.

2.RelatedTheories

2.1.Relaxmatchingalgorithmbasedonthevectorrotating

Wang,Hou,Cong,andSun（2010）proposedtherelaxmatchingalgorithmbasedonthevectorrotating,andthemainideaisasfollows:

First,evaluatethetwopairsofinitialmatchingcornerpointsextractedfromtwoimages;Second,involveanotherpairofcornerpoints,andifthevectorrotationanglesofthethispaircornersandthatoftheinitialpairsofcornersareverysimilar,supportdegreeofthispaircornerpointsandthetwoinitialpairsofcornerpointsishigh.Ifthesumthesupportdegreeofpairsofcornerpoints,whichareconstitutedbyonecornerpointwithallotherpoints,thiscornerpointiswrong,andwecandeleteit.Werepeatthisprocessuntilallselectedcornerpointsmeettheaboveconditions.

2.2.Fuzzyhumanvisualrestrictionconditions

Wecanextractthreemainvisualrestrictionconditionsfortheimagestobespliced,fromthevectorrotatingrelaxmatchingalgorithm.

1.Proportionalbandoftheoverlappartsbetweenimages

2.Thesimilarityofthegreylevelorthethresholdbetweentheimages.

3.Subjectivevisualimagedistortiondegreeoftheimages.

Becauseofthedifferencesbetweentheimagesintherealworld,theabovethreeconditionsintherealimageprocessprojectcannotbeconsistentexactly,althoughwecangetapproximatevaluesbasedonanalysisofplentyofimagedata.However,thisstrategywillreducetheexecutionefficiencyofthesystem,whichisnotsuitablefortherealworldapplications.Onthisotherhand,inmanyindustrialconditions,thereissomecertainpatternfortheimagesequencesselected.Forthiskindofimagesequences,theabovethreeconditionareusuallyapplicable.

2.3.Softwaredesignationforthefastimageregistration

TheimageregistrationalgorithminSec.2.1containsthreemainsteps:

Firstly,extractHarriscornerpointmatrixforthetwoimagestobespliced;secondly,initialcircularprojectionmatching;thirdly,relaxoptimizationmatching.Thecomputingprocessofthethreestepsiscorrespondingtotheabovethreeconditions.Hence,wecanimprovetheexecutionefficiencyofthealgorithmandtherobustnessofthesystembyreasonablyusingthesefuzzyvisualrestrictionconditions.

TheflowchartoftheimageregistrationalgorithmproposedinSec.2.1isshowninFig.1（a）.WecanseefromFig.1（a）that,thereisnorelevancebetweenStep1andStep2,andeveryindividualextractionisbasedonthewholeimage.Thiswillproduceplentyofuselesscornerpoints,whichareawasteoftimeandwillcauseinterferencefortheinitialmatching.ForthecornerpointmatrixesselectedfromStep3andStep5,thereisrelevance,butthereisnorelevanceforthecornerpointswithinthematrix.Hence,thecomputationofgreysimilarityofthecornerpointsandthatofthesupportdegreesummationoftheoptimizationselectionalgorithmcanbeexecutedconcurrently.

Fig.1（a）Flowchartoftheimageregistrationalgorithm;（b）Softwareimplementationoftheimageregistrationalgorithm

TheflowchartofthesoftwarefortheimageregistrationproposedinSec.2.1is

showninFig.1（b）.Fig.1（b）showsthat,thecomputationtimeofStep1andStep2canbeoverlap,andthecornerpointssearchingfromtheun-overlapareacanbeavoidedbasedontherestrictioncondition1,whichcanimprovetheefficiencyofthefeaturepointssearchingalgorithm,andtheaccuracyoftheinitialmatching.Step3dividesthecornerpointmatrixgotfromStep1basedoncolumnsofthematrix.Inthispaper,wedivideitinto4blocksbasedontheheightofthesimulationimage（1280×1024）.Allthecornerpointmatrixblockscomputethegreysimilarityconcurrently,andselecttheinitialcornerpointmatchingpairsbasedontherestrictioncondition2.Step6clonestheinitialmatchingpairpositioncoordinatesetofthefirstimagetobesplicedgotfromStep5setinto4,andeachsetwillberelaxmatchingoptimizationselectedbasedonthefollowingstrategy.

Lettheelementsofthesetben,andtheelementsofthesetx（x=1,2,3）,theinitialmatchingpairspositioncoordinatesofwhichneedtobeselectedbasedrelaxmatchingoptimizationalgorithm,belongtotheregion:

（x-1）×[n/4]-[n/4]×x,andthat

oftheset4belongsto3×[n/4]-n.

Throughthisway,eachsetjustneedstokeeponebestcornerpointsmatchingpair,andalso,inthenextimagesplicingstep,thecornerpointmatchingpairsofSVDcoordinatetransformationcancoverthewholeimageintheimagespace.Inaddition,thismethodcanguaranteethecoordinatetransformationofimagesplicingmatrixtobeaccurate.Basedontherestrictioncondition3,userscansettheimagedistortiontolerancedegreevaluesthemselvesthroughfuzzyvisualfeelings,whichcanimprovetherobustnessofthesystemsignificantly.

3.SimulationResults

Inthispaper,thepairofimagestospliceisrandomlyselectedfromtheHeilongjiangProvincehighwayconcretepavementsplicingsamples.Thesesamplesarecapturedbyanautomaticroaddetectionvehicle,whichrunsatthespeedof70km/h.Thissizeoftheimageis1280×1024.ConcretepavementforreferenceisshowninFig.2（a）,andconcretepavementtoberegisteredisshowninFig.2（b）.

Fig.2（a）Concretepavementforreference;（b）Concretepavementtoberegistered

Fromtable1wecanseethat,undertheaboveexperimentalsettings,theproposedalgorithmcanfinishtheimageregistrationinaboutanaverage20s,whichisacceptableforthecustomers.AnotherconvincingpointisthatthemultithreadtechnologycanmakefulluseoftheCUPresource.AsthemulticoreCUPisonadailybroadeningscale,multithreadtechnologywhichexecutesdataoperationconcurrentlywillbethetrend.TheexecutionefficiencyoftheimagesplicingalgorithmbasedonmultithreadtechnologywillcontinuetoimproveasthecontinuousupgradeofCUPtechnology.

4.Conclusion

Inthispaper,weproposedaCOMcomponentdesignmethodforamultithreadbasedimagesplicingalgorithm.Thismethodincreasesthestabilityofthesplicingsystembyinvolvingfuzzyvisualrestrictionconditionsand,insomedegrees,optimizesthealgorithmstructureandincreasestheexecutionefficiencyofthealgorithm.Theproposedmethodisvaluabletopromoteforrealindustrialfull-sceneimagesplicing.

基于批处理灰度图像的拼接方法COM组件技术

摘要

在本文中，我们提出了一种以矢量旋转的松弛匹配算法为根据的灰度图像拼接构件设计方法，可以快速地、高效地处理批处理图像。

我们利用图像匹配算法来计算全景图像拼接的坐标变换矩阵。

该算法的矩阵运算的实现是基于Matlab7当中的COM组件技术工具箱。

我们应用模糊的视觉限制条件进行拼接图像，在VC开发环境下利用多线程技术提高匹配算法的效率。

实验完全证实了所提出方法的执行效率，表明它能满足批量的灰度图像拼接软件系统实时性的要求。

关键词：

图像拼接；图像配准；松弛匹配。

1.引言

近年来，基于图像拼接的全景图像软件受到了极大的关注，如虹软全景图像拼接大师、PS图像处理软件 8.0 等。

这些软件经常被用来处理具有高质量的商业相机或个人相机拍摄的图像。

然而，这些软件并不适合经常用于高速模式、复杂和恶劣的拍摄条件的相机，如工业相机。

由工业摄像机拍摄的图像质量和图像保真度没有那些由个人或商业相机拍摄的那样好，这或多或少会降低图像自动拼接的准确性和稳定性。

由于图像拼接的主要困难是图像配准，因此在过去的几十年，研究者特别重视图像配准技术，并取得了显著的研究成果。

为了提高图像配准的精度，大量的矩阵运算应用于图像，这需要很高的计算成本，降低了图像拼接软件的效率。

还不仅如此，这些算法不能扩展到现实中。

基于矢量旋转的松弛的图像配准算法已被证明是准确和鲁棒性的图像匹配算法。

然而，为了追求上述两个