外文翻譯-基于批處理灰度圖像的拼接方法com組件技術(shù)

附錄B引用外文文獻及其譯文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.基于批處理灰度圖像的拼接方法COM組件技術(shù)摘要在本文中，我們提出了一種以矢量旋轉(zhuǎn)的松弛匹配算法為根據(jù)的灰度圖像拼接構(gòu)件設(shè)計方法，可以快速地、高效地處理批處理圖像。我們利用圖像匹配算法來計算全景圖像拼接的坐標變換矩陣。該算法的矩陣運算的實現(xiàn)是基于Matlab7當(dāng)中的COM組件技術(shù)工具箱。我們應(yīng)用模糊的視覺限制條件進行拼接圖像，在VC開發(fā)環(huán)境下利用多線程技術(shù)提高匹配算法的效率。實驗完全證實了所提出方法的執(zhí)行效率，表明它能滿足批量的灰度圖像拼接軟件系統(tǒng)實時性的要求。關(guān)鍵詞：圖像拼接；圖像配準；松弛匹配。1.引言近年來，基于圖像拼接的全景圖像軟件受到了極大的關(guān)注，如虹軟全景圖像拼接大師、PS圖像處理軟件8.0等。這些軟件經(jīng)常被用來處理具有高質(zhì)量的商業(yè)相機或個人相機拍攝的圖像。然而，這些軟件并不適合經(jīng)常用于高速模式、復(fù)雜和惡劣的拍攝條件的相機，如工業(yè)相機。由工業(yè)攝像機拍攝的圖像質(zhì)量和圖像保真度沒有那些由個人或商業(yè)相機拍攝的那樣好，這或多或少會降低圖像自動拼接的準確性和穩(wěn)定性。由于圖像拼接的主要困難是圖像配準，因此在過去的幾十年，研究者特別重視圖像配準技術(shù)，并取得了顯著的研究成果。為了提高圖像配準的精度，大量的矩陣運算應(yīng)用于圖像，這需要很高的計算成本，降低了圖像拼接軟件的效率。還不僅如此，這些算法不能擴展到現(xiàn)實中?；谑噶啃D(zhuǎn)的松弛的圖像配準算法已被證明是準確和魯棒性的圖像匹配算法。然而，為了追求上述兩個特點，這種算法還涉及到很多的矩陣運算，且執(zhí)行效率不高。為了克服這一瓶頸，在本文中，我們提出了一種利用多線程技術(shù)來優(yōu)化圖像配準矩陣的方法，可以滿足圖像配準系統(tǒng)的實時性的要求。2.相關(guān)理論2.1基于矢量旋轉(zhuǎn)的松弛匹配算法Wang，Hou，CongandSun提出了基于矢量旋轉(zhuǎn)的松弛匹配算法，其主要思想是：首先，評價兩組從兩幅圖像中提取的初始角點；第二，如果這兩組角點的向量旋轉(zhuǎn)角度非常相似，那么這兩組角點的相似度就很高。如果這對角點和其他角點都相似，那這個點就是錯誤的，我們可以刪除它。我們重復(fù)這個過程，知道所選定的角點都滿足上述條件。2.2模糊的視覺限制條件我們可以從矢量旋轉(zhuǎn)松弛匹配算法中為待拼接圖像提取三個主要視覺限制條件。圖像之間的重疊部分的比例帶。圖像之間的閥值和灰度級的相似度。圖像的主觀視覺圖像的失真度。雖然我們可以通過大量的圖像數(shù)據(jù)的分析獲得近似值，但因在現(xiàn)實世界圖像之間的差異，在實際的圖像處理中，上面三個條件不能夠準確一致。然而，這種策略會降低系統(tǒng)的執(zhí)行效率，這是不適合在現(xiàn)實世界中應(yīng)用的。在另一方面，在許多工業(yè)的情況下，可以選擇一些特定模式的圖像序列。這類圖像序列，以上三個條件通常是適用的。2