《中国图象图形学报》论文投稿模板.docx

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1、中藻分突（此号在中国图书馆分类法中杳）力WMRe1.A文Moo6896（年I-修文引用格式I融合深度模型和传统模型的显著性检测（标题中不建议使用缩写词，居中排）作者I”,作者2（4号，各作者之间用逗号分隔，居中排）I.单上所在省市版箱,2.单位,所在褥市邮箱（6号一体.单位名解务必与金称.J：中M要,目的显杵性校测站图像和视觉久!域个收砒计题.传统极小时卜骁为性物体的边界保用较好,但是对必汇性目标的自信度不妙高,召回车岐，而深度学习模生对于是斤性物体的自信度跖，但是共结果边界粗IS,准痛率较低,针对这两种模型各自的优或点，提出了种H方性模里以煤介利用两种方法的优点并抑制各自的不足.方法田先改造

2、域族的宓生卷积网络.训练了个Mj该网络的全卷税网络（FCN）显普性根本.同时选取个现有的基于是像总的显著性别归慑型，在得到两种模型的W杆性结果图后,提出一-种融合辑法，岫合两种方法的菇果以得到很终忧化结;K.PiJJdii1.U行性结果Hd11iM积和像发何是#性位的对非线性哄射，物FCN结!K5传统模里的结果相融合，的果实段在4个数期上与最新的10种方法进行了比较.在HK1.MS数据集中.相比于性能第2的粳型.F（itf72.6%.在MSRA故据集中.桶比F性能第2的根小F值捉Mr2.2%,MAEWtt/5.6%!在DUTOMRoN数妪集中,相比于性能第2的模型，F值提高5.6%.MAEHf

3、KT17.4,同时也HMSRA数据架中进行了对比实验以验证融合H法的有效性，对I匕实段结果证明提出（!,1.J-Uf!P.I。绪攀、焦点检索词啸方面精选关位词）Sa1.iencydetectionviafusionofdeepmode1.andtraditiona1.mode1.（首词首字母大写，其他小写，居中排）作者-2,作者2（4号，各作者之间用逗号分隔，居中期h单位,所在省市邮编.期箝2、单位,所在省市邮址,国京（6号斜付,KqoAh%tract:Objcc1.iveSx1.icncydetectionifund;IECnuI1.PrehICnIincomputervi%inn11n!i

4、11cprocessing,whichAims1.identifythemoMconspicuousobjectsOCregionsinunimage.SaJicncydetectionhasbeenwide1.yUSCdinsevera1.visua1.a1.icaiMns.inc1.udingObjZ1.rctafgctn.scenec1.asMaiiovisua1.tracking,imgeretrieva1.,andscancscgmenuion.Inmost（11d1.iu1.UPPnKId1.eXSa1.ien1.（bjec1.xavderivedIxxwdmiI1.ieextru

5、dedtea1.urefmniP1.Rekorrvgiut.Fina1.%a1.iencymapsconsistofthcnmcthxis;indthese1.ectionoffeatures.TbcwnproadccannotPrOdUCCsaisfactoresu1.tsWhEimageswithmu1.tip1.esa1.ientobjectsorIoWYomnistcoiMcnuarecounicrcd.Tradiixma1.apnx1.utionncu11works(CNNs)havebeenintroducedinpixe1.-wisepredictionb1.enH.suchas

6、sa!iencydcicciion.duetotheiroutstandingPC1.fUmUnNinimagec1.assificaiiontasks.CNNsredefine(beSaJiCnCyprob1.emasa1.abe1.ingpbkmWherVIheIcaiurvSeIeeIiOnbc(*rnsa1.icinandnosa1.icnObjCC1.Sisaxomaica1.1.yPernEnedt1.vughgradie1.decen1.ACNNcantMXbedirect1.yusedtoHumaKi1.ieneymode1.,axiaCNNcunbeU1.i1.izcx1.i

7、nii)icncydc1.ec1.nbyextractingmihicpatchamundeachpixe1.andbyuingthepatchtoPnRiCtthecenterpixe1.sCiaMPaichesarcfrequent1.yobtainedfromdifferentrcsoicnsoftheinputimugctocaptureg1.oba1.information.AnothermethodIstheadditionofup-samp1.ed1.ayc11*intheCNN.AmodifiedCNNisc1.1.cdafu1.1.yconneccdIKIWOfk(FCN).

8、v*tichisfi11*proposedforenan(icenenU1.iu.Miw1.Xa1.iCnCyde1.tx*txnCNN11dehueFVNtocapturecrtierab1.eg1.oba1.and1.oca1.InkKina1.iDi1.FCNisaPnPU1.armx!c1.thatfncx1.i11cvtheCNNtofitdensePfvdiCtionPrnWCe、whichrep1.aces(heSoHMaxandfi1.1.yCnnneCCCd1.ayc11iintheCNNintoconvo1.utionanddeconvo1.ution1.ayers.Com

9、Parcdwithtraditiona1.11thods.FCNscanaccurate1.y1.nIU1.CSare1.owerthanIhCUudi1.iona1.UPP“融be、dueIoIheJuimmphiig!(u.tuinKN%.Todea1.vi1.hI1.iehmiU1.iaiisIhe2kix1.%of3ecy11xxck.WCPmPOHCpositeIicncymode1.thatcombines(headv;InUigc%andrcstninthedrawbacksCfIWoMi1.icncynxx1.c1.s.MethodInIhKstudy,anewFCNbased

10、ondenseCanvo1.uiiona1.ncwidIuyerisaPP1.1.CdIdub1.anUUrM1.ienCynq%.MtheIKIinmgPrOCeStIheSaIig1.Cyt1.workb(aifuasquaredEuc1.it11inourMier*mode1.Ourtrainingsetconsistsof3900imagesthatUrcrandom1.yse1.ectedfrom5sa1.ic,pub1.icdataset,name1.y.ECSSD.SOD.HKU-IS.MSRA.andICOSEG.Oursa1.icncyworkisimp1.ementedin

11、Caffctoo1.box.Theinputimagesandund-(thmapsarcresized105OOr5OOforItuining.thenunen1.umxruw1.erisMrtto0.99.IbeIeannngru1.eisietIo10,0.ax1.theweightdecayxO.(MX5.1heSGDIBninjJPfnCed1.IrCiXeCICnUCdKinIIaNVIDIAGTXTTTANXGPudevice,hihtakesapproximate1.yonedayin2(X(KM)ic11nions.Then,weuseaImdi1.iona1.sa1.icn

12、cymx1.c1.TbcSdZcdmode1.adopsmu1.ti-1.eve1.wgnntaiontoprx1.uccsevera1.SCgmenuiiiosofanimage,whereeachhueixc1.isrvsencdbyafeatureVeC1.(M1.hd1.containsdifferentkindsofimagefeatures.AIundoInfores!istrainedbyIhusefeaturevector%Idfeivehecymap%.OnIhebasisft1.x;2nuk1.s.pop0ieafu%inAp11ithmthatcombinesthead、FnofInuh1.inna1.APProaChGanddeepkamingmebnxoftheimngcareproduced,andthesa1.icncymapsofu1.1.segmentationsarcderivedbythe11ndomforw(.Then,weIKCFCNcoPfVdUCCanothertypeofsa1.icncynwoftheimage.Thefusiona1.pofithmapp1.iestheHadamaidP(OdUC1.onthe2typesofsa1