增强视觉 | 计算机视觉 增强现实

Job Function:

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.

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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.

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WillowGarage的猛士再次发力，实现了今年ISMAR上炫目的KinectFusion。

The preliminary source code is currently available in our SVN repository’s trunk in the CUDA/KinFu module. Since this code is still unreleased and under active development, we won’t be able to provide support via our forums yet; however, advanced users are free to check out the code and give it a try. Be advised that this code relies heavily on the NVidia CUDA development libraries for GPU optimizations and will require a compatible GPU for best results.

Moving forward, we continue to refine and improve the system, and we are hoping to improve upon the original algorithm in order to model larger scale environments in the near future. We are targeting a stable release date to coincide with the upcoming PCL 2.0 release next year. (Please note there is no planned release in the 1.x branch.)

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一对对偶操作：分离颜色通道，合并颜色通道。

分离颜色通道：使用-channel 和 –separete 参数

convert ..\SampleImages\Lena.png -channel R -separate Lena_R.png
convert ..\SampleImages\Lena.png -channel G -separate Lena_G.png
convert ..\SampleImages\Lena.png -channel B -separate Lena_B.png

另外一种分离方式：同时提取三个通道。

convert ..\SampleImages\Lena.png  -separate Lena_RGB_%d.png

合并颜色通道：使用-combine 参数

convert Lena_R.png Lena_G.png Lena_B.png -combine Lena2.png

来自老杨的一篇评测。

好久没更新博客了，最近都在忙找工作，目前来看形势还不错。感觉我基本已经跟computer vision没什么关系了，面了很多公司，只有一个是视觉的，其它都是纯码农活儿了。

这学期上了个计算机架构的水课，最后的大作业我就benchmark了一下OpenCV在x86和ARM下面的性能，我的笔记本的CPU是Intel Core i7 620M，iOS测试我用的是iPod Touch，和iPhone 4是一样的CPU, Apple A4。最新的iPhone 4s和iPad2上的Apple A5没机会测，因为这俩都不能完全越狱。x86比ARM快那是肯定的，不过这个benchmark可以看出来到底差多少。

编译：在x86上我用的是64位编译的，因为Apple A4分别支持ARM v6和v7两个版本，我就分别都编译了不同的静态库。

测试：我使用了不同的数据类型，8/16/32位整形，32/64位浮点；不同的输入矩阵大小，4*4/8*8/…/256*256/512*512；不同的操作，加、乘、转置、求逆、SVD，还有一组图像处理的比较。

下面是一些比较结果：

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两个AR的视频：一个是利用kinect的魔镜，二是“三维立体导航”。

来自Andol的hand detector。

Flow by Amazon，结合了Barcode和物体识别。

非常浅显易懂的tf-idf导论I, II。

来自csk的低成本3d scanner。本文的前传在此。

1. 设备设计

核心元件原型

在第一篇文章的原理介绍[1]中，已经大致提到了本次制作的核心元件：摄像头、激光器以及进行扫描的伺服电机的 选型要求。

对于我期望的精度和性能，一般市面常见的USB VGA摄像头即可满足要求。

图：本制作使用的USB摄像头(已经拆除外壳)

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来自CSK的低成本3D scanner。Very Impressive！

在开始介绍原理前，先给出一些扫描得到的3D模型以及演示视频，给大家一个直观的认识。

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ImageMagick是拥有批处理能力的，比如如下命令：

montage “*.jpg” -geometry 24×24+2+2 thumbnail.png

就是将当前目录下所有的jpg图像，缩小到24×24大小，并排列到一张图像上去。

除了上面这种通配符的方式，另外一种批处理方式是使用@，如:

dir /B  *.png > filelist.txt

montage @filelist.txt -geometry 24×24+2+2 thumbnail.png

同样也可以达到上述效果。这也意味着我们可以使用一个文件名列表来完成批处理工作。

更进阶的方式是使用@-，@-接受从通道传递过来的参数，如下：

echo ‘a.jpg b.jpg c.jpg’ | montage @- -geometry 24×24+2+2 thumbnail.png

但是貌似上述方式只有在linux的console下才有效，在dos下我没能实验成功。

使用ImageMagick实现老照片效果只需一行命令：

convert %1 -sepia-tone 75%% ( %1 -fill #FFFFFF -colorize 100%% +noise Random -colorspace gray -alpha on -channel A -evaluate Set 100 ) -compose overlay -composite %2

上述命令完成如下工作：

1：将输入图像使用sepia-tone滤镜处理

2：生成一个白色蒙版，填充随机噪声，转化为灰度，并加上alpha通道

3：步骤1和步骤2的结果使用overlay的方式compose