国际会议英文主持词

国际会议英文主持词
【篇一：国际会议主持词英语口语】
ladies and gentlemen:
good afternoon
welcome to nanjing! welcome to southeast university! i’m going to be the host of this management forum. please allow
me to extend our warmest congratulations to the conference
and the most cordial welcome to you. thank you very much for
your attention. management forum is devoted to expanding
academic communication, promoting mutual understanding
and following the advanced studies. this time, the forum will
focus on frontier international management. it includes the
frontier trend and the application of ne w theory. now, let’s clap our hands and welcome the chairman of the conference to
have an welcome speech.
thank you! it’s really a heart-warming speech.
well, ladies and gentlemen, please allow me to introduce our
speaker today, professor cui, and it a great’s pleasure for me
to introduce her to us all. she comes from harvard business
college and devoted to the study about frontier management.
the topic of her speech is”current management trends and issues ” . please join me in welcoming our guest speaker,cui. the next speaker is professor wang. since 1992, he has
published nearly 20 papers, of which more than 5 were
included in journal of management. at present, he is also the
regular member of society of business administration, which
is the most authoritative international organization in this field.
please join me in welcoming professor wang, whose topic is ”
leadership ”.
thanks for two professors’ excellent reports. do you have any questions? i hope you will participate in the discussion by
raising your hands.
thanks two professors again for their excellent explanation.
and thanks all of you for your attention and your time. i
appreciate it very much. but now, i’ m sorry to say that this conference will have to stop here. we will invite the
next conference organizer to give an speech.
【篇二：英文国际会议主持人稿】
opening remarks:
distinguished delegates and guests,ladies and gentlemen,it ’s a great privilege for me to start the conference.let me introduce
myself first. i am du ruimin from harbin engineering university.
and i am very honored to be the chair person for this
morning ’ s session. it is a great pleasure for me to share the chairmanship with professor lee guobin who is harvard
university. on behalf of the organizing committee of tcassp , i
would like to announce the session open.what we are going to
do this morning is to review the different aspects of signal
processing and their current research challenges.
we have some of the world ’s foremost professors and researchers, people at the forefront of this field. let me
introduce our first speaker professor lee guobin, who is the
director of information and communication engineering
apartment of harvard university.professor lee has published
extensively in sci and books on the subject of image, video,
and multidimenional signal processing. his presentation is
entitled “-noreference perceptual quality assessment of jpeg compressed images ” . let ’ s welcome professor lee~
thank you, prof. lee. your presentation is very convincing.
from your presentation, we know that (---)your speech is
indeed very useful, interesting and challengeable. thank you.
qa----------------------------------------------------------------------------------
dai jia comes from columbia university who is famous for his
study on signal processing theory and methods,and also make
its application have a practical significance.our speaker is also
co-author of five books and over 40 published articles. as a communications expert, he has been quoted in the seattle
times, the chicago tibune and the atlanta journal
constitution.now a lot of first-class books on this subject are
wrote by professor dai,and today we are very honored to have
prof.dai give us a speech entitled “ fractionalierfourtansform and its applications ” .let ’
-----------------------------------------------------------
thank you, prof.dai. your speech is the absolutely inspiring.
we are delighted to be able to share your new specific
strategies and techniques. (---- )will be greatly cherished by the
people present here. now, let ’ s welcome our next speaker, dr. cao qingming.dr.cao qingming is a professor and the
chairperson of the electrical engineering department at the
ohio state university in columbus, ohio.our speaker got his
ph.d. in ee at the university of california, berkeley, followed
by a series of teaching and research positions at harvard,
cambridge university, and princeton.for the past 6 years,he
published more than10 papers on journal.please join me in
welcoming our guest speaker today—dr.cao qingming, whose
topic is entitled signal processing for communications and
networking.
qa----------------------------------------------------------------------------------
（thank you very much for your worthwhile/
enlightening/informative presentation. let’ s welcome the next speaker prof. guo xiangchen with warm applause.）
xiangchen is from chongqing jiaotong university,who is co-
author of five books and over 40 published articles. as a
communications expert, he has been quoted in the
seattle times, the chicago tibune and the atlanta journal
constitution.our speaker has been honored many awards--2013
marconi prize paper award and a national book award.today,
guo xiangchen will address you on multicast scheduling and
resource allocation algorithms for ofdma-based systems: a
survey.
let ’s welcome prof.guo xiangchen.
qa----------------------------------------------------------------------------------
prof.guo xiangchen’s speech is highly useful, interesting and informative. we have learnt a lot from him. thank you again,
prof.guo xiangchen.
qa
ladies and gentlemen, our distinguished guest speakers have
finished their presentations. we now enter into discussion and
share with each other our different ideas. i hope that all here
present will feel free to express your ideas and exchange
various opinions, so as to make this discussion a real success.
yes, the young man in the second row, please.
closing speech:
i ’d like to pay my tribute to the speakers for their excellent
presentations and the audience for their attention this morning.
i declare the plenary session adjourned until 12 a.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。

good morning everyone.
my report is about a motion detection system based
on improved lbp operator.
automatic motion detection can reduce the human cost of
video surveillance and improve efficiency [?f??(?)ns?] ，it is also the fundament of object extraction, tracking and
recognition
[rek?gn??(?)n]. in this work, efforts [ef?ts] were made to
establish the background model which is resistance to the
variation of illumination. and our video surveillance
system was realized on a fpga based platform.
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, optic flow, background subtraction and inter-frame difference are regard as the three mainstream algorithms to
detect moving object.
inter-frame difference based method need not model [m?dl]
the background. it detects moving objects based on the frame difference between two continuous frames. the method is
easy to be implemented and can realize real-time detection,
but it cannot extract the full shape of the moving objects [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。

the basic principle of background removal method is building
a background model and providing a classification of the
pixels into either foreground or background [3-5]. in a
complex and dynamic environment, it is difficult to build a
robust [r?(?)b?st] background model.
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。

the algorithms we have discussed above are all based on
grayscale. in practical applications especially outdoor
environment, the grayscales of each pixel are unpredictably
shifty because of the variations in the intensity and angle of 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。

in order to solve the above problems, we proposed an
improved lbp algorithm which is resistance to the variations
of illumination.
local binary pattern (lbp) is widely used in machine vision
applications such as face detection, face recognition and
moving object detection [9-11]. lbp represents a relatively
simple yet powerful texture descriptor which can describe
the relationship of a pixel with its immediate neighborhood.
the fundamental of lbp operator is showed in fig 1. the basic
version of lbp produces 256 texture patterns based on a 9
pixels neighborhood. the neighboring pixel is set to 1 or 0
according to the grayscale value of the pixel is larger than the
value of centric pixel or not. for example, in fig1 7 is larger
than 6, so the pixel in first row first column is set to 1.
arranging the 8 binary numbers in certain order, we get an 8
bits binary number, which is the lbp pattern we need. for
example in fig.1, the lbp is 10001111. lbp is tolerant
[t?l(?)r(?)nt] against illumination changing. when the
grayscales of pixels in a 9 pixels window are shifted due to
illumination changing, the lbp value will keep unchanged.
ppt(6)
图中的一些常有的纹理，都能用一些简单的 lbp 向量表示，对于每个像
素快，只要要用一个 8 比特的 lbp 值来表示。

there are some textures , and they can be represent by some
simple 8bit lbp patterns. ppt(7)
从这幅图也能够看出，固然灰度发生了很大的变化，可是纹理特点
并无改变， lbp 值也没有变化。

you can see, in these picture , although the grayscale
change alot, but the lbp patterns keep it value. ppt(8)
上述的算法是 lbp 算法的基本形式，可是这类基本算法不合适直策
应用在视频监控系统中。

主要有两个原由：第一，在常用的视频监
控系统中，特别是在高清视频监控系统中，9 个像素点覆盖的地区很
小，在这样小的地区内，各个像素点的灰度值十分靠近，甚至是相
同的，纹理特点不显然，没法在lbp 值上表现。

第二，因为以像素
为单位计算 lbp 值，像素噪声会造成 lbp 值的噪声。

这两个原由致使计
算出的 lbp 值存在较大的随机性，甚至在静止的图像中，相邻两
帧对应地点的lbp 值也可能存在差别，进而惹起的误检测。

the
typical lbp cannot meet the need of practical application of
video surveillance for two reasons: firstly, a“ window”
which only contains 9 pixels is a small area in which the grayscales
of pixels are similar or same to each other, and the texture
feature in such a small area is too weak to be reflected by a lbp.
secondly, pixel noise will immediately cause the noise of lbp,
which may lead to a large number of wrong detection. in order to
obtain a better performance, we proposed an improved lbp
based on the mean value of “ block ” . in our algorithm, one block
contains 9 pixels. compared with original lbp pattern
calculated in a local 9 neighborhood between pixels, the
improved lbp operator is defined by comparing the mean
grayscale value of central block
with those of its neighborhood blocks (see fig.2).by replacing
the grayscales of pixels with the mean value of blocks, the
effect of the pixel noise is reduced. the texture feature in such
a bigger area is more significant to be described by lbp pattern.
ppt(9)
运用 lbp 描述背景，其实质上也是背景差分法的一种。

背景差分法
应用在复杂的视频监控场景中时，要解决成立强健的背景模型的问
题。

驶入并停靠在监控画面中的汽车，被搬移出监控画面的箱子等，
都会造成背景的改变。

而正确的背景模型是正确检测出运动目标并
提取完好目标轮廓的基础。

假如系统能准时更新背景模型，将已经
挪动出监控画面的物体“剔除”出背景模型，将进入监控画面而且稳
定逗留在画面中的物体“增添”入背景模型，会减少好多因为背景改
变而造成的误检测。

依据前一节的介绍，帧间差分法固然没法提取完好的运动目标，但
是它是一种不依靠背景模型就能进行运动目标检测的算法。

所以，
能够利用帧间差分法作为目前监控画面中能否有运动目标的依照。

假如画面中没有运动目标，就按期对背景模型进行更新。

假如画面
中有运动目标，就推延更新背景模型。

这样就能防止把运动目标错
误地“增添”到背景模型中。

in practical application, the
background is changing randomly. for traditional background
subtraction algorithm the incapability of updating background
timely will cause wrong detection. in order to solve this
problem, we propose an algorithm with dynamic self updating
background model. as we know, inter-frame difference method
can detect moving object without a background model, but this
method cannot extract the full shape. background subtraction
method can extract the full shape but needs a background
model. the basic principle of our algorithm is running a frame
difference moving object detection process concurrently
’ s [k?nk?r?ntli] with the background subtraction process. what
time to update the background is according to the result of
frame difference detection.
ppt(10)
if lbp algorithm is implemented in a software way, it will be
very slow. fpga have features of concurrent computation,
reconfiguration and large data throughput. it is suitable to be built an embedded surveillance system. the algorithm
introduced above is implemented on a fpga board. ppt(11)
这就是我们硬件实现的系统构造图。

第一输入系统的 的滤波、灰度计算及 lbp 计算，获取各个像素块的
lbp
rgb 像素信号
值。

而后背景
更新控制模块利用帧差模块的检测结果控制背景缓存的更新。

地区判断模块依据背景差模块的输出结果，联合像素块的坐标信息，对远景像素块进行地区判断。

the structure of the system is showed in this figure. in this system, a vga signal is input to the development board. and the lbp pattern is calculated , frame difference module also compares the current frame and the previous frame to
determine whether there is a moving object in the surveillance vision. if the surveillance vision is static for a certain amount of frame, the background model will be updated. ppt(12)
进而在最短的延时内提拿出相邻 9 个像素点的灰度值。

行缓存的大 小等于每一行图像包括的像素个数减 1。

将 9 个像素点的灰度值经过 求均值模块，能够求出一个像素块的像素均值。

to achieve real time computation of the lbp, a circuit structure is put forward as showed in fig.5. two line buffers and nine resisters are connected in the way showed in the figure. nine neighbor pixels are extracted with minimum [m?n?m?m] delay, and the mean value of this block is calculated by the mean value calculate module which contains some adders and
shifters. the mean values of the blocks are inputted to a similar structure and extracted in a similar way, and the lbp is calculated by the consequence lbp calculate module. ppt(13) ppt(14)
如下图，块均值计算模块计算出的 8 个块均值被图 3-11 中的窗口提取模块提拿出来，并作为比较器阵列的输入，比较器的输出结果
用 0 和 1 表示。

最后的比较结果按必定的次序摆列，从头拼接成一
个 8 位的二进制数，即 lbp 值。

lbp 计算电路没有采纳流水构造，在一个时钟周期内就能获取计算结果。

ppt(15)
这个是在系统测试中，实现对多个目标的检测。

in this system test ,we achieve a multi-object detection.
ppt(16)
这个图是对动向背景更新的测试，在监控地区中划定一个目标地区，
把一个静止的物体搁置到目标地区中。

在前 3 分钟内，系统会将其
当成远景目标，矩形窗口会以闪耀的形式发出报警信号。

3 分钟事后，因为物体向来处于静止状态，系统检测到了10800 个静止帧，于是
更新背景模型。

静止的物体被当成背景的一部分，今后窗口不再闪
耀。

经考证，该系统能够正的确现背景模型更新算法。

this is the test for the auto background update. we put a
statics object in the surveillance area ，at the beginning this is
trusted as a moving object . after 3 minutes , the system
receive ten thousand static frames ,and then update the
background model. then this object is regard as a part of the background.
ppt(17)
别的为了考证系统对室外光照变化克制能力，我们选用了大批有光
照变化，而且有运动目标的视频对系统进行了测试。

in order to verify the resistance to the varation of illumination ,
a certification experiment is designed, and the roc curves of
the two algorithms based on lbp and grayscale are plotted and compared. a number of short video clips with shifty and fixed illumination, including positive
samples with moving objects and negative samples without
moving objects .
ppt(18)测试平台如下图。

用一台pc机作为测试信号的输出源，而后在 pc 机中播放视频，并将视频vga 信号发送给运动目标检测系统，模拟真切的监控环境。

fpga 将输入信号和地区边框图形相叠加
后在 lcd 上显示。

the picture of the certification experiment is showed in this
picture . a pc acts as the source of the test signal which is
input to the fpga in the form of vga. passing through the fpga
board, video signal is displayed on a lcd screen.
ppt(19)
并最后描述了系统的 roc 特征曲线。

在没有光照强度变化的状况下，采
纳鉴于灰度的运动目标检测算法的性能略优于鉴于 lbp 值的运动目标检测
算法，两种算法都能获得较好的检测成效。

可是在图 5-15 中（测试集2），也就是在光照强度变化的状况下，画面整体灰度发生较大的改变，
鉴于灰度的检测算法的性能大幅度降落，靠近于失
效。

而采纳 lbp 值的检测算法却能保持较好的性能。

可见鉴于 lbp 的检测算法对克制光照强度变化造成的误检测有较好的成效。

ppt(20)
感谢大家！
thanks for your attention。