不知道大家的结果怎么样

不过如果结果不好，也不要太伤心。

ECCV今年在佛罗伦萨，3.5的deadline，大家加油吧

分享给大家

压缩感知届怪兽级人物陶哲轩（因为就看过压缩感知的工作 ）获得了克拉福德奖。此同为瑞典皇家科学院颁奖的，为诺贝尔不喜欢的数学、地球科学、生物科学和天文学颁发的，因为比较穷的关系，每年只发一个领域的（今年发了俩个领域）。陶哲轩和同获得过菲尔茨奖的普林斯顿高等研究院的比利时数学家Jean Bourgain分享了这个奖。奖金，在2011年是600,000美刀，陶哲轩获得的是2012年的奖，Marr Prize貌似没奖金。

The Royal Swedish Academy of Sciences has decided to award the Crafoord Prize in Mathematics 2012 to Jean Bourgain, Institute for Advanced Study, Princeton, USA and Terence Tao, University of California, Los Angeles, USA,

“for their brilliant and groundbreaking work in harmonic analysis, partial differential equations, ergodic theory, number theory, combinatorics, functional analysis and theoretical computer science”.

今天看到一个很好玩的iOS应用GroupShot。点子是很早微软推出的Photo Fuse，将连拍的数张照片合成一张完美合照。

有设备的可以把玩一下。

Via

主要思路：

用Kmeans做特征的粗索引。

根据统计数据对feature进行压缩。

检索时使用非对称的方式计算索引特征与查询特征之间的距离。

算法：

训练：

1. 使用Kmeans对欲索引的特征进行聚类，得到K个中心。对二进制形式的feature做聚类时，类别中心更新方式为：对于每一个bit，统计所有落在该类别的特征的对应bit上的1,0频率，并取高者。
2. 对于每个cluster，统计所有落在该类别的特征的每个bit位的1,0频率，取1或者0频率靠近50%的前M个bits。（越靠近50%，熵越大）

经过训练，我们得到两组数据：

• K个特征类别中心。
• 对于每个类别中心，都有一组“M个bit位置标示符”。这些标示符构成一个对原始feature进行压缩的依据。（本文以后将其称为投影向量）

索引：

1. 建立倒排表
2. 对于每一个欲索引的特征，计算其类别中心，并用该类别的投影向量对特征进行投影，得到一个M位的signature。记为sig_templ, 将该signature插入到倒排表中。

ANN检索：

1. 计算query特征的类别中心query_cluster，并用该类别的投影向量对特征进行投影，得到一个M位的sig_query。
2. 计算query特征与类别中心的，除去该类别投影向量对应的bit位的距离，记为dist_base.
3. 遍历query_cluster对应的倒排表项，计算sig_query于表项中的sig_templ的距离，记为dist_sig。那么query特征与索引特征的距离dist = dist_base + dist_sig. 如此得到的最小距离，如小于某阈值，即可认为找到一个ANN。

关于时间复杂度的一点分析：

假设K = 40， M = 64，特征为32Byte ORB（假设有1k的供索引的特征）：

那么每次ANN检索只用做

40次32Byte的Hamming Distance计算 +  约25次8Byte的Hamming Distance计算。

对比穷举检索：

1000次32Byte的Hamming Distance计算

速度提升：20x

示例：

实验配置：

• 特征：Orb
• nn/ann匹配阈值：50
• K = 40

下图为穷举检索，图像匹配的结果：（无ransac）

After Ransac：

下图为使用上述ANN检索方式，图像匹配的结果：（无ransac）

After Ransac：

可以看出，匹配点数会有明显下降。原因是两级索引方式固有的缺陷。（两个本身很近的feature，会被分类到不同的cluster）。也许使用Multi Assignment会有所改善。

等代码整理好再发part2.

之前介绍过一个反人脸检测的东西，作者Adam Harvey又将其发扬光大了，整出一个CV Dazzle。最早的版本没有考虑到审美上的因素，纯粹只是为了干掉人脸检测器（OpenCV based），这次Adam Harvey试图弄得。。好看点？

对比一下以前的图像：

Adam Harvey还得意洋洋的调戏了一把face.com的phototagger：

CV Dazzle vs PhotoTagger from Adam Harvey on Vimeo.

官方网站

媒体报道

CamDictionary [Android, free or $4.99.] 号称支持50种语言。基于视频。

Word Lens [iPhone, price varies.] 大概是最有名的AR翻译APP了吧。

Babelshot[iPhone, $4.99.] 拍照识别，需要选定ROI，交互比较复杂。

BabelCam [Windows Phone, free.] 情况不详。WP是亮点。

QQ慧眼[iPhone, free]腾讯的AR翻译APP。

链接

Researcher responsible for developing algorithms and prototype of research projects related to video and audio. The candidate will work with the multimedia technology lab in Hangzhou or Shenzhen.

Skills/Experience

Must hold a Bachelors and/or a Masters/or a PhD in Electrical Engineering/or Computer Engineering with at least 2+ years experience in design, development, and integration of multimedia algorithms.

Strong Knowledge in computational camera, computational photography, 3D display and video/audio signal processing.

Knowledge in video standards on H.264, MPEG4, VP6/VP8, or graphics domain, Open GL standards is a definite plus.

Knowledge in audio standards on AMR, AMR-WB, G.711, G.719 and OpenAL.

Knowledge in optical lens design is a plus.

Strong C, C++, and MatLab development skills is required.

Experience in initiating a research proposal and conduct the research activities, developing deliverable software with a GUI interface is desired.

Should have good analytical ability, problem solving skills and be a self-starter.

Work well within a matrix organization and able to influence and collaborate with team members all over the world.

Responsibilities

Responsible for system design, algorithm development and verification of new multimedia technologies on mobile devices. Will work with Huawei Hi-silicon, User Centric Design and other cross functional teams for various milestones and deliverables.

Education Requirements:

BE with at least 4+ years experience, ME with at least 2+ years experience, PhD from a college of repute.

Contact person:

Dr. Zhou Jiong

Email: John.zhoujiong@huawei.com

额外声明： 需已经取得学位。

ViBe – a powerful technique for background detection and subtraction in video sequences

Executive summary

Description

ViBe is a powerful pixel-based technique that detects the background in video sequences. Many experiments have shown that it performs better than the state-of-the-art techniques known in the scientific literature. In addition the computational load is lower than simple background techniques implemented in commercial products. ViBe is the perfect solution for both software and hardware implementations.

Code and program for Windows and Linux

• A program for Windows and Linux. Download an archive zip archive [10 MB - updated on May 19, 2011] to use ViBe on Windows (or under Wine in Linux). Details on this page.
  The program allows you to: (1) save the result for your own images, (2) change the few parameters of ViBe to experiment with, and (3) reproduce our results.
• Linux: link a C/C++ object file to your own code. We provide the object (compiled) code of ViBe for non-commercial applications. Under Linux, download the 32 bits zip or compressed tar file, or the 64 bits zip or compressed tar file. Details on this page.

Licensing opportunities

ViBe is protected by several patents (patent track: WO2009007198 / Publication date: 2009-01-15; Priority number(s): EP20070112011 20070708) / US (granted): US 8009918 B2 (pdf) / Japan (granted): JP 2011 4699564 B2
The University of Liege holds all the rights on the technology and the innovations have been validated during the patent track.
For licensing the technology, please contact Prof. Van Droogenbroeck.

Major advantages:

• Very low computational load. Operations are limited to subtractions and operations on memory blocks. A downscaled version only needs one comparison per pixel and one byte of memory per pixel (see illustration below).
• Parameter-free method. There is no need to adapt parameter values to the content. Absolutely all the experiments have been led with the same set of values.
• Ready for direct inclusion in commercial products. Suited both for software and hardware inclusions.
• Faster and improved performances in comparison with state-the-art techniques (Mixture of Gaussians, Parametric methods, Sacon, etc).
• Pixel-based technique, ideal for any pre-processing step. ViBe allows you to model the objects in the videos as you like.
• Instantaneous initialization of the background model. ViBe is ready to operate from the second frame of any video sequence.
• Robust to noise.

References

• O. Barnich and M. Van Droogenbroeck. ViBe: A universal background subtraction algorithm for video sequences. In IEEE Transactions on Image Processing, 20(6):1709-1724, June 2011. Also available on the University site in PDF format: , and HTML format. This paper contains a very detailed pseudo-code description of the complete algorithm.
• O. Barnich and M. Van Droogenbroeck. ViBe: a powerful random technique to estimate the background in video sequences. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2009), pages 945-948, April 2009. Available as a IEEE publication or on the University site.
• Patent description at the "freepatentsonline" web site

Performance

An independant evaluation is available in S. Brutzer, B. Hoferlin, and G. Heidemann. Evaluation of background subtraction techniques for video surveillance. In IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), pages 1937-1944, Colorado Spring, USA, June 2011.
In their conclusions, they claim: "Considering these aspects, Barnich is a strong favorite, since it is simple and almost parameterless."

Contacts

For any question, please contact Prof. Van Droogenbroeck .
See also the site of the Interface, in charge of the partnerships between companies and the University of Liege.

项目主页

感谢wushuangseu的投递