给大家介绍一个公司,Accurate3D，http://www.acute3d.com/ 。它是利用影像获取具有高清晰度的真三维场景。它采用影像匹配得到密集点云，建三角网，最后贴纹理。这家公司做的东西和c3technology 非常像，估计采用的技术也差不多。不过相对c3technology，Accurate3D已经有关于街景的三维模型展示。(c3technology已被apple购买，介绍请看http://www.pcmag.com/article2/0,2817,2395555,00.asp)。Google的street view，只能展示相片，谈不上三维场景，可能过不了几年就会被这东东取代。具体技术尚不清楚，有兴趣的同学可以好好研究下。
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关于征求2012年“龙星计划”讲者和承办单位的通知
2011年，“龙星计划”在中国科学院的支持下，通过“龙星计划”委员会，“龙星计划”办公室，讲者，承办单位和学员的努力，取得了圆满成功。2011年，“龙星计划”开课7门，共邀请8位讲者授课，听课人数达526人。
经“龙星计划”委员会研究，决定启动2012年“龙星计划”讲者和承办单位申请工作，两种申请同时进行。现将申请办法公布如下:
1.讲者：
欢迎北美杰出华人教授申请回国讲授“龙星计划”课程。有意者请与“龙星计划”委员会主任，美国俄亥俄州立大学张晓东教授联系http://www.cse.ohio-state.edu/~zhang/。
2.承办单位：
欢迎国内高等院校或科研院所申请承办“龙星计划”课程。详情见：http://dragonstar.ict.ac.cn/DS_application.htm。
请申请者/申请单位于2012年2月29日前提交申请至“龙星计划”办公室。申请过程中如有问题请与“龙星计划”办公室联系。
“龙星计划”委员会
2011年2月3日
Computer vision seeks to develop algorithms that replicate one of the most amazing capabilities of the human brain – inferring properties of the external world purely by means of the light reflected from various objects to the eyes. We can determine how far away these objects are, how they are oriented with respect to us, and in relationship to various other objects. We reliably guess their colors and textures, and we can recognize them – this is a chair, this is my dog Fido, this is a picture of Bill Clinton smiling. We can segment out regions of space corresponding to particular objects and track them over time, such as a basketball player weaving through the court.
| Editor’s note: the following is an anonymized letter from a Machine Learning researcher who decided to withdraw his submission from CVPR 2012. The submission received ratings of “Definitely Reject,” “Borderline” and “Weakly Reject.” The letter and the paper reviews are posted here with his permission. |
Hi Serge,
We decided to withdraw our paper #[ID no.] from CVPR “[Paper Title]” by [Author Name] et al.
We posted it on ArXiv: http://arxiv.org/ [ Paper ID] .
We are withdrawing it for three reasons: 1) the scores are so low, and the reviews so ridiculous, that I don’t know how to begin writing a rebuttal without insulting the reviewers; 2) we prefer to submit the paper to ICML where it might be better received; 3) with all the fuss I made, leaving the paper in would have looked like I might have tried to bully the program committee into giving it special treatment.
Getting papers about feature learning accepted at vision conference has always been a struggle, and I’ve had more than my share of bad reviews over the years. Thankfully, quite a few of my papers were rescued by area chairs.
This time though, the reviewers were particularly clueless, or negatively biased, or both. I was very sure that this paper was going to get good reviews because: 1) it has two simple and generally applicable ideas for segmentation (“purity tree” and “optimal cover”); 2) it uses no hand-crafted features (it’s all learned all the way through. Incredibly, this was seen as a negative point by the reviewers!); 3) it beats all published results on 3 standard datasets for scene parsing; 4) it’s an order of magnitude faster than the competing methods.
google在ICML2011上做的关于goggles的介绍，非常值得一看。
演讲人是google的Hartmut Neven，是负责visual search的老大？
欢迎大家努力发掘相关信息。
视频最后提到了用到了量子计算。（表示完全不懂，求扫盲）。
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