The Fourth Dimension Space

枯叶北风寒,忽然年以残,念往昔,语默心酸。二十光阴无一物,韶光贱,寐难安; 不畏形影单,道途阻且慢,哪曲折,如渡飞湍。斩浪劈波酬壮志,同把酒,共言欢! -如梦令

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独立口语第一题 分类表述技巧[转]

1、关于套话表述:

for starters 第一点,用于代替常用的firstly, first of all等等

more importantly 更重要的是, 用于代替second, for another thing...等等

the icing on the cake 更棒的是,超级加分用法!一定要掌握的说法

E.g. describe a job you would like to pursue in the future. Use specific details and examples to illustrate why you want to get this job.

 

 

Speaking of my future job, I would like to be a marketing director in a global top company.

For starters, it is definitely a chanllenging job which can make me feel fulfilled! This job will make me completely understand the ture meaning of " a sense of satisfaction and achievement." The icing on the cake is that the high annual salary, the tempting bonus and satisfying welfare benefits will meet my material demands! And I can also build up a network of professinal contacts when I work with PR agency and institutes, which is quite important in this whole industry!

On the top of it, this field has great career prospects! These are what I love about my job and give me strong incentive to work even harder. And I believe this job helps me to realize my full potential!

注意我用红色笔标注的加分内容,这些都是非常地道的口语说法,在下面我会罗列

for starters 第一点

sense of satisfaction and achievement 成就感和满足感

The icing on the cake 更棒的是

high annual salary, the tempting bonus and satisfying welfare benefits 高收入,丰厚的年终奖和诱人的福利待遇

has great career prospects 很棒的职业前景

give sb strong incentive to .强烈的驱使某人做某事

realize one's full potential 实现某人全部潜能

 

今天的加分用法记下来了么?哈哈,希望大家在遇上职业描述类的时候可以用上,这些加分词汇同样可以用在major等描述里面
转自:http://blog.sina.com.cn/s/blog_5d874c650100h8nm.html

posted @ 2012-09-18 21:54 abilitytao 阅读(141) | 评论 (0)编辑 收藏

我的几个托福写作模板


正能量(Rip it up,the radically new

approach to changing your life)
参考亚马逊书评.

//大致介绍
the Richard Wiseman'new book-rip it up,the radically new approach to changing your life-bring a whole heap of revolutionary psychology studies that turn your idea about how to change upside down.
//主题思想
it express a key idea that something so simple can be effective in changing someone's life.
//主题思想展开
The idea is that we have confused the horse with the cart(习语,混淆因果关系)-
compared with the theory which tells us how to change the way we think, it's far easier to change the way we act in simple & subtle ways.
//具体例证
Want to feel happier? Force yourself to smile & you will actually feel better.
Want to be more confident? Stand in a confident pose & it will effect how you see yourself.


马斯洛需求金字塔(Maslow's hierarchy of

needs.)
参考wiki.

physio logical needs:food,water
safety:health,body
love:friendship,family
esteem:confidence,respect of others.
self-actualization:creativity,morality



乔布斯(Steven Jobs) 7加t,工作s.
参考乔布斯在斯坦福大学演讲。

//关键词:谦虚,进取
Key:as the proverb goes ,stay hungry , stay foolish

dropped out of college after the first 6 months.
following my curiosity and intuition turned to be priceless in the future.

//关键词:机遇,兴趣
first:calligraphy class
if Jobs never dropped in on that single course in college, the Mac would  never have multiple typefaces.

//关键词:挫折

//陈述背景
second story:love and loss
Jobs started Apple in his parent's garage when he was 20.They worked hard and in 10 years Apple had grown from just the two people in a garage into a 2 billion company with over 4000 employees.And he had just turned 30 and then he got fired.
(Jobs got fired by the company he started)
//转折原因
he had been rejected but he was still in love.
it is dream and love that drive him to start over.
one of the most creative period of his life.He started another company named NeXT.

//哲学总结
It was awful tasting medicine but the patient needed it.
don't lose faith. Do what you love.Don't settle.
persistence.


//例子没用,记住几个句型
//关键词:走自己的路
third story:death

If you live each day as if it was your last,someday you'll most certainly be right.

every thing - all external expectations, all pride, all fear of embarrassment or failure will fall away in the face of death.You are already naked so that there is no resson not to follow your heart.

Don't be trapped by dogma, don't let the noist of others' opinions drown out your own inner voice.

Have the courage to follow your heart and intuition.

情商
EQ(emotional quotient)
//参考google.

//EQ作用
EQ is sometimes described as more important than IQ since EQ helps us to understand our life, our values better.
//证据
plenty of experiments indicate that having better EQ is a must for making healthy choices in every aspects of life.
//再展开,一般用不到。
functions:
1.know and manage your own emotions.
2.motivate ourselves.
3.influence others'emotions.
4.handle relationship.

posted @ 2012-08-23 13:32 abilitytao 阅读(229) | 评论 (0)编辑 收藏

计算机视觉领域的一些牛人博客,超有实力的研究机构等的网站链接

     以下链接是本人整理的关于计算机视觉(ComputerVision, CV)相关领域的网站链接,其中有CV牛人的主页,CV研究小组的主页,CV领域的paper,代码,CV领域的最新动态,国内的应用情况等等。打算从事这个行业或者刚入门的朋友可以多关注这些网站,多了解一些CV的具体应用。搞研究的朋友也可以从中了解到很多牛人的研究动态、招生情况等。总之,我认为,知识只有分享才能产生更大的价值,真诚希望下面的链接能对朋友们有所帮助。
(1)googleResearch; http://research.google.com/index.html
(2)MIT博士,汤晓欧学生林达华; http://people.csail.mit.edu/dhlin/index.html
(3)MIT博士后Douglas Lanman; http://web.media.mit.edu/~dlanman/
(4)opencv中文网站; http://www.opencv.org.cn/index.php/%E9%A6%96%E9%A1%B5
(5)Stanford大学vision实验室; http://vision.stanford.edu/research.html
(6)Stanford大学博士崔靖宇; http://www.stanford.edu/~jycui/
(7)UCLA教授朱松纯; http://www.stat.ucla.edu/~sczhu/
(8)中国人工智能网; http://www.chinaai.org/
(9)中国视觉网; http://www.china-vision.net/
(10)中科院自动化所; http://www.ia.cas.cn/
(11)中科院自动化所李子青研究员; http://www.cbsr.ia.ac.cn/users/szli/
(12)中科院计算所山世光研究员; http://www.jdl.ac.cn/user/sgshan/
(13)人脸识别主页; http://www.face-rec.org/
(14)加州大学伯克利分校CV小组; http://www.eecs.berkeley.edu/Research/Projects/CS/vision/
(15)南加州大学CV实验室; http://iris.usc.edu/USC-Computer-Vision.html
(16)卡内基梅隆大学CV主页;
http://www.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html

(17)微软CV研究员Richard Szeliski;http://research.microsoft.com/en-us/um/people/szeliski/
(18)微软亚洲研究院计算机视觉研究组; http://research.microsoft.com/en-us/groups/vc/
(19)微软剑桥研究院ML与CV研究组; http://research.microsoft.com/en-us/groups/mlp/default.aspx

(20)研学论坛; http://bbs.matwav.com/
(21)美国Rutgers大学助理教授刘青山; http://www.research.rutgers.edu/~qsliu/
(22)计算机视觉最新资讯网; http://www.cvchina.info/
(23)运动检测、阴影、跟踪的测试视频下载; http://apps.hi.baidu.com/share/detail/18903287
(24)香港中文大学助理教授王晓刚; http://www.ee.cuhk.edu.hk/~xgwang/
(25)香港中文大学多媒体实验室(汤晓鸥); http://mmlab.ie.cuhk.edu.hk/
(26)U.C. San Diego. computer vision;http://vision.ucsd.edu/content/home
(27)CVonline; http://homepages.inf.ed.ac.uk/rbf/CVonline/
(28)computer vision software; http://peipa.essex.ac.uk/info/software.html
(29)Computer Vision Resource; http://www.cvpapers.com/
(30)computer vision research groups;http://peipa.essex.ac.uk/info/groups.html
(31)computer vision center; http://computervisioncentral.com/cvcnews

(32)浙江大学图像技术研究与应用(ITRA)团队:http://www.dvzju.com/

(33)自动识别网:http://www.autoid-china.com.cn/

(34)清华大学章毓晋教授:http://www.tsinghua.edu.cn/publish/ee/4157/2010/20101217173552339241557/20101217173552339241557_.html

(35)顶级民用机器人研究小组Porf.Gary领导的Willow Garage:http://www.willowgarage.com/

(36)上海交通大学图像处理与模式识别研究所:http://www.pami.sjtu.edu.cn/

(37)上海交通大学计算机视觉实验室刘允才教授:http://www.visionlab.sjtu.edu.cn/

(38)德克萨斯州大学奥斯汀分校助理教授Kristen Grauman :http://www.cs.utexas.edu/~grauman/

(39)清华大学电子工程系智能图文信息处理实验室(丁晓青教授):http://ocrserv.ee.tsinghua.edu.cn/auto/index.asp

(40)北京大学高文教授:http://www.jdl.ac.cn/htm-gaowen/

(41)清华大学艾海舟教授:http://media.cs.tsinghua.edu.cn/cn/aihz

(42)中科院生物识别与安全技术研究中心:http://www.cbsr.ia.ac.cn/china/index%20CH.asp

(43)瑞士巴塞尔大学 Thomas Vetter教授:http://informatik.unibas.ch/personen/vetter_t.html

(44)俄勒冈州立大学 Rob Hess博士:http://blogs.oregonstate.edu/hess/

(45)深圳大学 于仕祺副教授:http://yushiqi.cn/

(46)西安交通大学人工智能与机器人研究所:http://www.aiar.xjtu.edu.cn/

(47)卡内基梅隆大学研究员Robert T. Collins:http://www.cs.cmu.edu/~rcollins/home.html#Background

(48)MIT博士Chris Stauffer:http://people.csail.mit.edu/stauffer/Home/index.php

(49)美国密歇根州立大学生物识别研究组(Anil K. Jain教授):http://www.cse.msu.edu/rgroups/biometrics/

(50)美国伊利诺伊州立大学Thomas S. Huang:http://www.beckman.illinois.edu/directory/t-huang1

(51)武汉大学数字摄影测量与计算机视觉研究中心:http://www.whudpcv.cn/index.asp

(52)瑞士巴塞尔大学Sami Romdhani助理研究员:http://informatik.unibas.ch/personen/romdhani_sami/

(53)CMU大学研究员Yang Wang:http://www.cs.cmu.edu/~wangy/home.html

(54)英国曼彻斯特大学Tim Cootes教授:http://personalpages.manchester.ac.uk/staff/timothy.f.cootes/

(55)美国罗彻斯特大学教授Jiebo Luo:http://www.cs.rochester.edu/u/jluo/

(56)美国普渡大学机器人视觉实验室:https://engineering.purdue.edu/RVL/Welcome.html

(57)美国宾利州立大学感知、运动与认识实验室:http://vision.cse.psu.edu/home/home.shtml

(58)美国宾夕法尼亚大学GRASP实验室:https://www.grasp.upenn.edu/

(59)美国内达华大学里诺校区CV实验室:http://www.cse.unr.edu/CVL/index.php

(60)美国密西根大学vision实验室:http://www.eecs.umich.edu/vision/index.html

(61)University of Massachusetts(麻省大学),视觉实验室:http://vis-www.cs.umass.edu/index.html

(62)华盛顿大学博士后Iva Kemelmacher:http://www.cs.washington.edu/homes/kemelmi

(63)以色列魏茨曼科技大学Ronen Basri:http://www.wisdom.weizmann.ac.il/~ronen/index.html

(64)瑞士ETH-Zurich大学CV实验室:http://www.vision.ee.ethz.ch/boostingTrackers/index.htm

(65)微软CV研究员张正友:http://research.microsoft.com/en-us/um/people/zhang/

(66)中科院自动化所医学影像研究室:http://www.3dmed.net/

(67)中科院田捷研究员:http://www.3dmed.net/tian/

(68)微软Redmond研究院研究员Simon Baker:http://research.microsoft.com/en-us/people/sbaker/

(69)普林斯顿大学教授李凯:http://www.cs.princeton.edu/~li/
(70)普林斯顿大学博士贾登:http://www.cs.princeton.edu/~jiadeng/
(71)牛津大学教授Andrew Zisserman: http://www.robots.ox.ac.uk/~az/
(72)英国leeds大学研究员Mark Everingham:http://www.comp.leeds.ac.uk/me/
(73)英国爱丁堡大学教授Chris William: http://homepages.inf.ed.ac.uk/ckiw/
(74)微软剑桥研究院研究员John Winn: http://johnwinn.org/
(75)佐治亚理工学院教授Monson H.Hayes:http://savannah.gatech.edu/people/mhayes/index.html
(76)微软亚洲研究院研究员孙剑:http://research.microsoft.com/en-us/people/jiansun/
(77)微软亚洲研究院研究员马毅:http://research.microsoft.com/en-us/people/mayi/
(78)英国哥伦比亚大学教授David Lowe: http://www.cs.ubc.ca/~lowe/
(79)英国爱丁堡大学教授Bob Fisher: http://homepages.inf.ed.ac.uk/rbf/
(80)加州大学圣地亚哥分校教授Serge J.Belongie:http://cseweb.ucsd.edu/~sjb/
(81)威斯康星大学教授Charles R.Dyer: http://pages.cs.wisc.edu/~dyer/
(82)多伦多大学教授Allan.Jepson: http://www.cs.toronto.edu/~jepson/
(83)伦斯勒理工学院教授Qiang Ji: http://www.ecse.rpi.edu/~qji/
(84)CMU研究员Daniel Huber: http://www.ri.cmu.edu/person.html?person_id=123
(85)多伦多大学教授:David J.Fleet: http://www.cs.toronto.edu/~fleet/
(86)伦敦大学玛丽女王学院教授Andrea Cavallaro:http://www.eecs.qmul.ac.uk/~andrea/
(87)多伦多大学教授Kyros Kutulakos: http://www.cs.toronto.edu/~kyros/
(88)杜克大学教授Carlo Tomasi: http://www.cs.duke.edu/~tomasi/
(89)CMU教授Martial Hebert: http://www.cs.cmu.edu/~hebert/
(90)MIT助理教授Antonio Torralba: http://web.mit.edu/torralba/www/
(91)马里兰大学研究员Yasel Yacoob: http://www.umiacs.umd.edu/users/yaser/
(92)康奈尔大学教授Ramin Zabih: http://www.cs.cornell.edu/~rdz/

(93)CMU博士田渊栋: http://www.cs.cmu.edu/~yuandong/
(94)CMU副教授Srinivasa Narasimhan: http://www.cs.cmu.edu/~srinivas/
(95)CMU大学ILIM实验室:http://www.cs.cmu.edu/~ILIM/
(96)哥伦比亚大学教授Sheer K.Nayar: http://www.cs.columbia.edu/~nayar/
(97)三菱电子研究院研究员Fatih Porikli :http://www.porikli.com/
(98)康奈尔大学教授Daniel Huttenlocher:http://www.cs.cornell.edu/~dph/
(99)南京大学教授周志华:http://cs.nju.edu.cn/zhouzh/index.htm
(100)芝加哥丰田技术研究所助理教授Devi Parikh: http://ttic.uchicago.edu/~dparikh/index.html
(101)瑞士联邦理工学院博士后Helmut Grabner: http://www.vision.ee.ethz.ch/~hegrabne/#Short_CV

(102)香港中文大学教授贾佳亚:http://www.cse.cuhk.edu.hk/~leojia/index.html

(103)南洋理工大学副教授吴建鑫:http://c2inet.sce.ntu.edu.sg/Jianxin/index.html

(104)GE研究院研究员李关:http://www.cs.unc.edu/~lguan/

(105)佐治亚理工学院教授Monson Hayes:http://savannah.gatech.edu/people/mhayes/

(106)图片检索国际会议VOC(微软剑桥研究院组织): http://pascallin.ecs.soton.ac.uk/challenges/VOC/

(107)机器视觉开源处理库汇总:http://archive.cnblogs.com/a/2217609/

(108)布朗大学教授Benjamin Kimia: http://www.lems.brown.edu/kimia.html

(109)数据堂-图像处理相关的样本数据:http://www.datatang.com/data/list/602026/p1

(110)东软基于CV的汽车辅助驾驶系统:http://www.neusoft.com/cn/solutions/1047/

(111)马里兰大学教授Rema Chellappa:http://www.cfar.umd.edu/~rama/


(112)芝加哥丰田研究中心助理教授Devi Parikh:http://ttic.uchicago.edu/~dparikh/index.html

(113)宾夕法尼亚大学助理教授石建波:http://www.cis.upenn.edu/~jshi/


(114)比利时鲁汶大学教授Luc Van Gool:http://www.vision.ee.ethz.ch/members/get_member.cgi?id=1, http://www.vision.ee.ethz.ch/~vangool/

(115)行人检测主页:http://www.pedestrian-detection.com/

(116)法国学习算法与系统实验室Basilio Noris博士:http://lasa.epfl.ch/people/member.php?SCIPER=129576 http://mldemos.epfl.ch/

转自:http://blog.csdn.net/carson2005

 

 

posted @ 2012-07-17 14:17 abilitytao 阅读(669) | 评论 (0)编辑 收藏

opencv中访问像素点的方法


* Indirect access: (General, but inefficient, access to any type image)
效率低!
o For a single-channel byte image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,1);
CvScalar s;
s=cvGet2D(img,i,j); // get the (i,j) pixel value
printf("intensity=%f/n",s.val[0]);
s.val[0]=111;
cvSet2D(img,i,j,s); // set the (i,j) pixel value

o For a multi-channel float (or byte) image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_32F,3);
CvScalar s;
s=cvGet2D(img,i,j); // get the (i,j) pixel value
printf("B=%f, G=%f, R=%f/n",s.val[0],s.val[1],s.val[2]);
s.val[0]=111;
s.val[1]=111;
s.val[2]=111;
cvSet2D(img,i,j,s); // set the (i,j) pixel value

* Direct access: (Efficient access, but error prone)

o For a single-channel byte image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,1);
((uchar *)(img->imageData + i*img->widthStep))[j]=111;

o For a multi-channel byte image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,3);
((uchar *)(img->imageData + i*img->widthStep))[j*img->nChannels + 0]=111; // B
((uchar *)(img->imageData + i*img->widthStep))[j*img->nChannels + 1]=112; // G
((uchar *)(img->imageData + i*img->widthStep))[j*img->nChannels + 2]=113; // R

o For a multi-channel float image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_32F,3);
((float *)(img->imageData + i*img->widthStep))[j*img->nChannels + 0]=111; // B
((float *)(img->imageData + i*img->widthStep))[j*img->nChannels + 1]=112; // G
((float *)(img->imageData + i*img->widthStep))[j*img->nChannels + 2]=113; // R

* Direct access using a pointer: (Simplified and efficient access under limiting assumptions)

o For a single-channel byte image:

IplImage* img = cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,1);
int height = img->height;
int width = img->width;
int step = img->widthStep/sizeof(uchar);
uchar* data = (uchar *)img->imageData;
data[i*step+j] = 111;

o For a multi-channel byte image:

IplImage* img = cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,3);
int height = img->height;
int width = img->width;
int step = img->widthStep/sizeof(uchar);
int channels = img->nChannels;
uchar* data = (uchar *)img->imageData;
data[i*step+j*channels+k] = 111;

o For a multi-channel float image (assuming a 4-byte alignment):

IplImage* img = cvCreateImage(cvSize(640,480),IPL_DEPTH_32F,3);
int height = img->height;
int width = img->width;
int step = img->widthStep/sizeof(float);
int channels = img->nChannels;
float * data = (float *)img->imageData;
data[i*step+j*channels+k] = 111;

* Direct access using a c++ wrapper: (Simple and efficient access)

o Define a c++ wrapper for single-channel byte images, multi-channel byte images, and multi-channel float images:

template<class T> class Image
{
private:
IplImage* imgp;
public:
Image(IplImage* img=0) {imgp=img;}
~Image(){imgp=0;}
void operator=(IplImage* img) {imgp=img;}
inline T* operator[](const int rowIndx) {
return ((T *)(imgp->imageData + rowIndx*imgp->widthStep));}
};

typedef struct{
unsigned char b,g,r;
} RgbPixel;

typedef struct{
float b,g,r;
} RgbPixelFloat;

typedef Image<RgbPixel> RgbImage;
typedef Image<RgbPixelFloat> RgbImageFloat;
typedef Image<unsigned char> BwImage;
typedef Image<float> BwImageFloat;

o For a single-channel byte image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,1);
BwImage imgA(img);
imgA[i][j] = 111;

o For a multi-channel byte image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_8U,3);
RgbImage imgA(img);
imgA[i][j].b = 111;
imgA[i][j].g = 111;
imgA[i][j].r = 111;

o For a multi-channel float image:

IplImage* img=cvCreateImage(cvSize(640,480),IPL_DEPTH_32F,3);
RgbImageFloat imgA(img);
imgA[i][j].b = 111;
imgA[i][j].g = 111;
imgA[i][j].r = 111;

posted @ 2012-07-16 15:56 abilitytao 阅读(406) | 评论 (0)编辑 收藏

研究生生活我之见

第一年上课,除了尹一通的组合数学学到了不少实质性的东西,以及老板的课上了解到了许多最新的研究方向外,其余的课感觉收获不是很大(宋公的课确实好,可能是人太多了...),在基础理论上,越发感觉在理论数学和概率知识上的积累薄弱了,很多论文里的公式都来自概率论或者随机过程,但这些东西在之前的教育体系中恰恰是被忽略掉的。感觉研究生培养模式并不是阶梯式的,而学习是不断积累渐进式的过程,不能一蹴而就,应该扎扎实实地学好知识,学透,成为一个领域的领军人才,这才是王道!

posted @ 2012-07-13 19:04 abilitytao 阅读(416) | 评论 (0)编辑 收藏

细分曲面Catmull-Clark Subdivision算法[转]

最近在做ruoyuYang的作业,搜集了一些关于各种细分算法的介绍。
——————————————————————————————————————————————————随着Directx11的推出,细分曲面在游戏中得到了越来越大的关注。偶一开始觉得是一大堆复杂数学推导的东西,因为导师在中科院的博士几年就是在做细分曲面,听说一个很强的师兄三年也都是在做细分曲面。近来做了几天助教帮忙改作业才偶然看到原来细分曲面也有很简单的算法实现, 比如Catmull-Clark Subdivision算法,其可以对任意拓扑结构的多边形进行细分。下面简要介绍下。

 细分新的曲面,先求出新的曲面的顶点:

 Face point(位于原来多边形面里的新顶点)

 Edge point(在原来的边中点附近的新顶点)

  New vertex point (对原来的顶点进行调整得到新顶点)

 

 Face point:

 給定一個面F,有顶点V1,V2,……,Vn,那么新的Face point,VF计算公式如下 

 

  Edge point:

   假设一边E的两个顶点为vw,还有相邻的两个面为F1F2(其面顶点已经算出为VF1VF2)。那么对应这个边的新顶点VE

  New Vertex point:

  给定一个顶点v。假设Q是与v相邻的多边形的face point的平均值;vn条边相邻,R是与v相邻的边的中点的平均值,那么调整后得到的新顶点位置v'为。

  得到新的顶点后,边是如何产生?

    1:每个面顶点(Face PointVF与包围它的边对应的边顶点(Edge Point)VE相连。

    2:每个顶点调整后得到的新顶点(new vertex pointv’与它相邻的边上的点(edge pointVE相连。


    细分结果示例可以看下图

 

   



转自:http://blog.csdn.net/qiul12345/article/details/5938771

posted @ 2012-07-07 15:07 abilitytao 阅读(2759) | 评论 (1)编辑 收藏

图形图像领域的著名期刊会议.

一. 图形学、可视化领域的会议:

(一)高级别会议

    1. Siggraph  (图形学领域最高级别会议,不知SCI收录否。国内研究者除非结果特
                  牛,轻易别投)

    2. Eurograph (作为Computer Graphics Forum一期发表,SCI收录,影响不断增长

    3. IEEE proceeding of Visualization (可视化领域最高级别会议,EI收录,声誉
       很好)

    4. IEEE Symposium of Volume visualization(会议3的一个伴随的会议,EI收录,
       声誉很好)

(二)一般的会议

    1. Pacific Graphics(EI收录)
    2. CGI:   Computer Graphics International (EI是否收录不清楚)
    3. WSCG:  Int.Conf.on Computer Graphics, Visualization and Computer
              Vision
    4. Rendering
    5. Visualization and Data Analysis----SPIE Electronic Imaging系列会议之一
       (EI收录,容易接受)

    6. Visualization, Image-Guided Procedures, and Display-----SPIE Medical
       Imaging系列会议之一         (EI收录,容易接受)

    7.Joint Eurographics - IEEE TCVG Symposium on Visualization (估计EI收录)

二. 三维医学图像的可视化与分析的会议

  (一)高级别会议
     1. MICCAI----Medical Image Computing and Computer-Assisted Intervention
       (医学图像的计算与分析领域最高级别会议,Springer出版,论文(Oral,Poster)
         被SCI收录,声誉相当好. 不过国内研究者似乎未发表过。特别今年在日本召
         开,但国内无人投中. MICCAI接受的论文数很多,长文超过100篇,短文也有
         100篇,短文可能不被SCI收录。也接受医学可视化的论文)
     2. IPMI----Information processing in Medical imaging (医学图像分析领域非
        常有影响受尊重的会议,属于Workshop。许多新结果先在这里报告。Springer出
        版,估计被SCI收录。但接受论文很少,约40篇左右吧)
     3. CVPR-----Computer Vision & Pattern Recognition(属于计算机视觉领域的两
        个最高级别会议中的一个。有一个专题是医学图像分析。基于图像分析的思路处理
        三维医学图像的特别有意义的结果可以投这个会议。该会议声誉非常好,EI收录,但很
        不好投)
     4. ICCV----IEEE International Conference on Computer Vision(计算机视觉领
        域的两个最高级别会议中的另一个。今年在北京召开。EI收录。三维医学图像分析的很好
        结果可以投这个会议。不好投)
 
     注: 在上述几个会议中,每年都有各个方向的牛人参加,报告各个领域的最新进展
          。因此,这样的会很有意义。在同行的眼中,这些会议发表的论文不比低级别的外文期
          刊的论文差。

(二)一般的会议
     1. Medicai imaging----SPIE举办的系列会议,共7个,主题分别是:
          Visualization, Image-Guided Procedures, and Display
          Physics of Medical Imaging
          Physiology and Function: Methods, Systems, and Applications
          Image Processing
          PACS and Integrated Medical Information Systems: Design and Evalua
          tion
          Image Perception, Observer Performance, and Technology Assessment
          Ultrasonic Imaging and Signal Processing
         (SPIE会议相对容易接受,而且EI收录。不过EI收录的慢,因为会议论文集在会
          议10个月后才能出版)
     2. CARS------Computer Aided Radiology and Surgery:  分多个不同的主题会议

posted @ 2012-06-29 16:11 abilitytao 阅读(568) | 评论 (0)编辑 收藏

再探多线程经典生产者与消费者问题

     摘要: 所谓进步就是与知识的缘分与不期而遇,本来选嵌入式课程是为了学习嵌入式应用方面的知识,没想到竟然把生产者消费者问题学懂了。其实程序中最核心的部分是读者与写着在临界区部分的代码,用三个信号量锁住线程使得同一时刻只能有一个线程进入临界区。本程序中写者与写者互斥,读者与读者互斥,写者与读者也互斥。其实这个程序还可以提高效率,让读者与写着不互斥,实现时只需在读者与写者线程中使用独立的二值信号量即可。本程序在...  阅读全文

posted @ 2012-06-04 20:26 abilitytao 阅读(1841) | 评论 (3)编辑 收藏

组合数学作业题测试程序

原题为:

对于第二问,经过演算得到答案为pow(e,-1/k),下面用程序验证一下(k=1)的情况,n从1到20
#include<iostream>
using namespace std;

#define e 2.718281828459 
double g(double k)
{

    
return pow(e,-1.0/k);
}


#define bint __int64

bint f(bint n)
{
    
if(n==1||n==0return 1;

    
else return n*f(n-1);
}


bint Com(bint n,bint k)
{
    
return f(n)/f(n-k)/f(k);
}

bint process(bint n,bint k)
{
    bint ans 
= f(n);
    
for(int i=1;i<=n/k;i++)
    
{
        bint tem 
= 1;
        
for(int j=1;j<=i;j++)
            tem 
*= Com(n-k*j+k,k)*f(k-1);
        tem 
*= f(n-i*k);
        tem 
/= f(i);
        
if(i&1)ans -= tem;
        
else ans += tem;
    }

    
return ans;
}


int main()
{
    bint n,k;
    

    
for(int i=1;i<=20;i++)
    
{
        
//printf("fk(n)为:%.20lf\n",(double)process(n,k));
        printf("当n=%02d时,fk(n)/n!为:%.20lf\n",i,(double)process(i,1)/f(i));

    }

    printf(
"pow(e,-1/k)为:      %.20lf\n",g(1));


    
return 0;
}
测试结果如下图:

可见当k=1,n从1-20变化时,fk(n)/n!逼近pow(e,-1/k);

posted @ 2011-10-07 19:46 abilitytao 阅读(1478) | 评论 (0)编辑 收藏

PKU 2409 polya定理

原来rotation的时候也会形成环的,环的数量等于Gcd(n,i),n为珠子的数目,i为旋转步长。
其他就没什么了,只是求最大公约数那一步只是感觉出来的,不知道该怎么证明。

#include<iostream>
using namespace std;


int pow(int c,int x)
{
    
int ans = 1;
    
for(int i=0;i<x;i++)
        ans 
= ans * c;
    
return ans;
}


int Gcd(int a, int b)

    
return a == 0 ? b : Gcd(b % a, a);
}
 

int main()
{
    
int c,s;
    
int G;//表示置换群的大小
    while(scanf("%d%d",&c,&s)!=EOF)
    
{
        
if(c==0&&s==0)
            
break;

        G 
= s<<1;
        
int ans = pow(c,s);
        
//考虑rotation的情况
        for(int i =1 ;i< s ;i ++)
            ans 
+= pow ( c , Gcd(s, i));
        
//分奇偶考虑reflection的情况
        if(s&1)
            ans 
+= s*c*pow(c,(s-1)>>1);

        
else
        
{
            ans 
+= s/2 * pow(c,s/2);
            ans 
+= s/2 * c * c * pow(c,s/2-1);
        }

        printf(
"%d\n",ans/G);
    }

    
return 0;
}




 

posted @ 2011-10-02 19:05 abilitytao 阅读(1353) | 评论 (3)编辑 收藏

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