外文翻译-基于批处理灰度图像的拼接方法com组件技术

附录B引用外文文献及其译文COMcomponenttechnologybasedbatchgrayimagesmosaicmethodAbstractInthispaper,wepresentagrayimagemosaiccomponentdesignmethodbasedonthevectorrotatingrelaxmatchingalgorithm,whichcanhandlebatchimagesfastandwithhighquality.Wecomputethecoordinatetransformationmatrixforfull-sceneimagesplicingbyusingtheimagematchingalgorithm.Thealgorithm’smatrixoperationimplementationisbasedontheCOMcomponenttechnologytoolboxinMatlab7.0.WeapplybothfuzzyhumanvisualrestrictionconditionsforsplicingimagesandthemultithreadtechnologyintheVCdevelopmentenvironmenttoimprovethematchingalgorithmperformanceefficiency.Theexperimentpartdemonstratestheexecutionefficiencyoftheproposedmethod,whichshowsthatitcanmeetthereal-timedemandofthebatchgrayimagemosaicsoftwaresystem.Keywords:imagemosaic;imageregistration;relaxationmatching.1.IntroductionFullsceneimagesoftwarebasedonimagesplicingattractsgreatattentionrecently,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.RelatedTheories2.1.RelaxmatchingalgorithmbasedonthevectorrotatingWang,Hou,Cong,andSun(2010)proposedtherelaxmatchingalgorithmbasedonthevectorrotating,andthemainideaisasfollows:First,evaluatethetwopairsofinitialmatchingcornerpointsextractedfromtwoimages;Second,involveanotherpairofcornerpoints,andifthevectorrotationanglesofthethispaircornersandthatoftheinitialpairsofcornersareverysimilar,supportdegreeofthispaircornerpointsandthetwoinitialpairsofcornerpointsishigh.Ifthesumthesupportdegreeofpairsofcornerpoints,whichareconstitutedbyonecornerpointwithallotherpoints,thiscornerpointiswrong,andwecandeleteit.Werepeatthisprocessuntilallselectedcornerpointsmeettheaboveconditions.2.2.FuzzyhumanvisualrestrictionconditionsWecanextractthreemainvisualrestrictionconditionsfortheimagestobespliced,fromthevectorrotatingrelaxmatchingalgorithm.1.Proportionalbandoftheoverlappartsbetweenimages2.Thesimilarityofthegreylevelorthethresholdbetweentheimages.3.Subjectivevisualimagedistortiondegreeoftheimages.Becauseofthedifferencesbetweentheimagesintherealworld,theabovethreeconditionsintherealimageprocessprojectcannotbeconsistentexactly,althoughwecangetapproximatevaluesbasedonanalysisofplentyofimagedata.However,thisstrategywillreducetheexecutionefficiencyofthesystem,whichisnotsuitablefortherealworldapplications.Onthisotherhand,inmanyindustrialconditions,thereissomecertainpatternfortheimagesequencesselected.Forthiskindofimagesequences,theabovethreeconditionareusuallyapplicable.2.3.SoftwaredesignationforthefastimageregistrationTheimageregistrationalgorithminSec.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)SoftwareimplementationoftheimageregistrationalgorithmTheflowchartofthesoftwarefortheimageregistrationproposedinSec.2.1isshowninFig.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,andthatoftheset4belongsto3×[n/4]-n.Throughthisway,eachsetjustneedstokeeponebestcornerpointsmatchingpair,andalso,inthenextimagesplicingstep,thecornerpointmatchingpairsofSVDcoordinatetransformationcancoverthewholeimageintheimagespace.Inaddition,thismethodcanguaranteethecoordinatetransformationofimagesplicingmatrixtobeaccurate.Basedontherestrictioncondition3,userscansettheimagedistortiontolerancedegreevaluesthemselvesthroughfuzzyvisualfeelings,whichcanimprovetherobustnessofthesystemsignificantly.3.SimulationResultsInthispaper,thepairofimagestospliceisrandomlyselectedfromtheHeilongjiangFig.2(a)Concretepavementforreference;(b)ConcretepavementtoberegisteredFromtable1wecanseethat,undertheaboveexperimentalsettings,theproposedalgorithmcanfinishtheimageregistrationinaboutanaverage20s,whichisacceptableforthecustomers.AnotherconvincingpointisthatthemultithreadtechnologycanmakefulluseoftheCUPresource.AsthemulticoreCUPisonadailybroadeningscale,multithreadtechnologywhichexecutesdataoperationconcurrentlywillbethetrend.TheexecutionefficiencyoftheimagesplicingalgorithmbasedonmultithreadtechnologywillcontinuetoimproveasthecontinuousupgradeofCUPtechnology.4.ConclusionInthispaper,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，为了追求上述两个特点，这种算法还涉及到很多的矩阵运算，且执行效率不高。为了克服这一瓶颈，在本文中，我们提出了一种利用多线程技术来优化图像配准矩阵的方法，可以满足图像配准系统的实时性的要求。2.相关理论2.1基于矢量旋转的松弛匹配算法Wang，Hou，CongandSun提出了基于矢量旋转的松弛匹配算法，其主要思想是：首先，评价两组从两幅图像中提取的初始角点；第二，如果这两组角点的向量旋转角度非常相似，那么这两组角点的相似度就很高。如果这对角点和其他角点都相似，那这个点就是错误的，我们可以删除它。我们重复这个过程，知道所选定的角点都满足上述条件。2.2模糊的视觉限制条件我们可以从矢量旋转松弛匹配算法中为待拼接图像提取三个主要视觉限制条件。图像之间的重叠部分的比例带。图像之间的阀值和灰度级的相似度。图像的主观视觉图像的失真度。虽然我们可以通过大量的图像数据的分析获得近似值，但因在现实世界图像之间的差异，在实际的图像处理中，上面三个条件不能够准确一致。然而，这种策略会降低系统的执行效率，这是不适合在现实世界中应用的。在另一方面，在许多工业的情况下，可以选择一些特定模式的图像序列。这类图像序列，以上三个条件通常是适用的。2