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thetopicofherspeechis”currentmanagementtrendsand
issues”.pleasejoinmeinwelcomingourguestspeaker,cui.
thenextspeakerisprofessorwang.since1992,hehaspublishednearly20papers,ofwhichmorethan5wereincludedinjournalofmanagement.atpresent,heisalsotheregularmemberofsocietyofbusinessadministration,whichisthemostauthoritativeinternationalorganizationinthisfield.pleasejoinmeinwelcomingprofessorwang,whosetopicis”leadership”.
thanksfortwoprofessors’excellentreports.doyouhaveany
questions?
ihopeyouwillparticipateinthediscussionby
raisingyourhands.
thankstwoprofessorsagainfortheirexcellentexplanation.
andthanksallofyouforyourattentionandyourtime.i
appreciateitverymuch.butnow,i’msorrytosaythatthis
conferencewillhavetostophere.wewillinvitethenextconferenceorganizertogiveanspeech.
【篇二：
英文国际会议主持人稿】
openingremarks:
distinguisheddelegatesandguests,ladiesandgentlemen,it
’s
agreatprivilegeformetostarttheconference.letmeintroduce
myselffirst.iamduruiminfromharbinengineeringuniversity.
andiamveryhonoredtobethechairpersonforthis
morning’ssession.itisagreatpleasureformetosharethe
chairmanshipwithprofessorleeguobinwhoisharvard
university.onbehalfoftheorganizingcommitteeoftcassp,i
wouldliketoannouncethesessionopen.whatwearegoingto
dothismorningistoreviewthedifferentaspectsofsignal
processingandtheircurrentresearchchallenges.
wehavesomeoftheworld
’sforemostprofessorsand
researchers,peopleattheforefrontofthisfield.letme
introduceourfirstspeakerprofessorleeguobin,whoisthe
directorofinformationandcommunicationengineering
apartmentofharvarduniversity.professorleehaspublished
extensivelyinsciandbooksonthesubjectofimage,video,
andmultidimenionalsignalprocessing.hispresentationis
entitled
“-noreferenceperceptualqualityassessmentofjpeg
compressedimages
”.let
’swelcomeprofessorlee~
thankyou,prof.lee.yourpresentationisveryconvincing.
fromyourpresentation,weknowthat（---）yourspeechis
indeedveryuseful,interestingandchallengeable.thankyou.
qa----------------------------------------------------------------------------------
daijiacomesfromcolumbiauniversitywhoisfamousforhis
studyonsignalprocessingtheoryandmethods,andalsomake
itsapplicationhaveapracticalsignificance.ourspeakerisalso
co-authoroffivebooksandover40publishedarticles.asa
communicationsexpert,hehasbeenquotedintheseattle
times,thechicagotibuneandtheatlantajournal
constitution.nowalotoffirst-classbooksonthissubjectare
wrotebyprofessordai,andtodayweareveryhonoredtohave
prof.daigiveusaspeechentitled
“fractionalierfourtansform
anditsapplications
”.let
’
-----------------------------------------------------------
thankyou,prof.dai.yourspeechistheabsolutelyinspiring.
wearedelightedtobeabletoshareyournewspecific
strategiesandtechniques.（----
）willbegreatlycherishedbythe
peoplepresenthere.now,let’swelcomeournextspeaker,dr.caoqingming.dr.caoqingmingisaprofessorandthe
chairpersonoftheelectricalengineeringdepartmentattheohiostateuniversityincolumbus,ohio.ourspeakergothisph.d.ineeattheuniversityofcalifornia,berkeley,followedbyaseriesofteachingandresearchpositionsatharvard,cambridgeuniversity,andprinceton.forthepast6years,hepublishedmorethan10papersonjournal.pleasejoinmein
welcomingourguestspeakertoday—dr.caoqingming,whose
topicisentitledsignalprocessingforcommunicationsand
networking.
（thankyouverymuchforyourworthwhile/
enlightening/informativepresentation.let’swelcomethenext
speakerprof.guoxiangchenwithwarmapplause.）
xiangchenisfromchongqingjiaotonguniversity,whoisco-
authoroffivebooksandover40publishedarticles.asa
communicationsexpert,hehasbeenquotedinthe
seattletimes,thechicagotibuneandtheatlantajournal
constitution.ourspeakerhasbeenhonoredmanyawards--2013
marconiprizepaperawardandanationalbookaward.today,
guoxiangchenwilladdressyouonmulticastschedulingand
resourceallocationalgorithmsforofdma-basedsystems:
a
survey.
let’swelcomeprof.guoxiangchen.
prof.guoxiangchen’sspeechishighlyuseful,interestingand
informative.wehavelearntalotfromhim.thankyouagain,
prof.guoxiangchen.
qa
ladiesandgentlemen,ourdistinguishedguestspeakershave
finishedtheirpresentations.wenowenterintodiscussionand
sharewitheachotherourdifferentideas.ihopethatallhere
presentwillfeelfreetoexpressyourideasandexchange
variousopinions,soastomakethisdiscussionarealsuccess.
yes,theyoungmaninthesecondrow,please.
closingspeech:
i’dliketopaymytributetothespeakersfortheirexcellent
presentationsandtheaudiencefortheirattentionthismorning.
ideclaretheplenarysessionadjourneduntil12a.m.
【篇三：
英文国际会议讲稿】
ppt
（1）
大家上午好！
今日我报告的主题是：
鉴于改良型lbp算法的运动目
标检测系统。
运动目标检测技术能降低视频监控的人力成本，提升
监控效率，同时也是运动目标提取、追踪及辨别算法的基础。
图像
信号拥有数据量大，及时性要求高等特点。
跟着算法的复杂度和图
像清楚度的提升，需要的办理速度也愈来愈高。
好运的是，图像处
理的固有特征是并行的，特别是低层和中间层算法。
这一特征使这
些算法，比较简单在fpga等并行运算器件上实现，今日报告的主题
就是对于改良型lbp算法在硬件上的实现。
goodmorningeveryone.
myreportisaboutamotiondetectionsystembasedonimprovedlbpoperator.
automaticmotiondetectioncanreducethehumancostof
videosurveillanceandimproveefficiency[?
f?
?
（?
）ns?
]，itisalsothefundamentofobjectextraction,trackingand
recognition
[rek?
gn?
）n].inthiswork,efforts[ef?
ts]weremadetoestablishthebackgroundmodelwhichisresistancetothevariationofillumination.andourvideosurveillancesystemwasrealizedonafpgabasedplatform.
ppt
（2）
目前，常用的运动目标检测算法有背景差分法、帧间差分法等。
帧间差分法的基来源理是将相邻两帧图像的对应像素点的灰度值进行减法运算，若获取的差值的绝对值大于阈值，则将该点判断为运动点。
可是帧间差分检测的结果常常是运动物体的轮廓，没法获取目标的完好形态。
currently,opticflow,backgroundsubtractionandinter-framedifferenceareregardasthethreemainstreamalgorithmstodetectmovingobject.
inter-framedifferencebasedmethodneednotmodel[m?
dl]thebackground.itdetectsmovingobjectsbasedontheframedifferencebetweentwocontinuousframes.themethodiseasytobeimplementedandcanrealizereal-timedetection,butitcannotextractthefullshapeofthemovingobjects[6].ppt（3）
在摄像头固定的状况下，背景差分法较为简单，且易于实现。
若背景已知，并能供给完好的特点数据，该方法能较正确地检测出运动目标。
但在实质的应用中，正确的背景模型很难成立。
假如背景模型假如没有很好地适应场景的变化，将大大影响目标检测结果的正确性。
像这副图中，背景模型没有及时更新，致使了检测的错误。
thebasicprincipleofbackgroundremovalmethodisbuildingabackgroundmodelandprovidingaclassificationofthepixelsintoeitherforegroundorbackground[3-5].inacomplexanddynamicenvironment,itisdifficulttobuildarobust[r?
）b?
st]backgroundmodel.
ppt（4）
上述的帧间差分法和背景差分法都是鉴于灰度的。
鉴于灰度的算法在光照条件改变的状况下，性能会大大地降低，甚至失掉作用。
thealgorithmswehavediscussedaboveareallbasedon
grayscale.inpracticalapplicationsespeciallyoutdoor
environment,thegrayscalesofeachpixelareunpredictably
shiftybecauseofthevariationsintheintensityandangleof
illumination.
ppt（5）
为认识决光照改变带来的鉴于灰度的算法无效的问题，我们考虑用
纹理特点来检测运动目标。
而lbp算法是目前最常用的表征纹理特
征的算法之一。
第一在图像中提取相邻9个像素点的灰度值。
而后
对9个像素中除中心像素之外的其余8个像素做二值化办理。
大于
等于中心点像素的，标志为1，小于的则标志为0。
最后将中心像素
点四周的标志值按一致的次序摆列，获取lbp值，图上当算出的lbp
值为10001111。
当某地区内全部像素的灰度都同时增大或减小必定
的数值时，该地区内的lbp值是不会改变的，这就是lbp对灰度的平
移不变特征。
它能够很好地解决灰度受光照影响的问题。
inordertosolvetheaboveproblems,weproposedanimprovedlbpalgorithmwhichisresistancetothevariationsofillumination.
localbinarypattern（lbp）iswidelyusedinmachinevisionapplicationssuchasfacedetection,facerecognitionandmovingobjectdetection[9-11].lbprepresentsarelativelysimpleyetpowerfultexturedescriptorwhichcandescribetherelationshipofapixelwithitsimmediateneighborhood.thefundamentaloflbpoperatorisshowedinfig1.thebasicversionoflbpproduces256texturepatternsbasedona9
pixelsneighborhood.theneighboringpixelissetto1or0accordingtothegrayscalevalueofthepixelislargerthanthevalueofcentricpixelornot.forexample,infig17islargerthan6,sothepixelinfirstrowfirstcolumnissetto1.arrangingthe8binarynumbersincertainorder,wegetan8bitsbinarynumber,whichisthelbppatternweneed.forexampleinfig.1,thelbpis10001111.lbpistolerant[t?
l（?
）r（?
）nt]againstilluminationchanging.whenthegrayscalesofpixelsina9pi
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