继QCAR之后，高通近日发布了FastCV：一个针对移动平台的计算机视觉库。FastCV1.0是专为android设备设计的。但是可以预见后续的版本会支持iOS和WP设备。
FastCV可以运行在任何基于ARM的处理器，但是对于高通自家的Snapdragon是做了特别优化的。
FastCV1.0包括如下几个模块
要求：
1. 脖子以上部分能够重建出来。
2. 头发部分需要重建。
3. 效果逼真，精度1mm以内。
4. 具体方法不做要求，可以采用双目或基于深度摄像头。
5. 如果选用基于双目的方法，可以只做视差图求取与后处理部分，本人负责标定以及由视差图求三维信息部分。
项目经费：
1.5-2.0万元人民币
联系方式：
有点像video lecture，但是界面更清爽，内容更新一点。会把最新的一些会议的视频和slides发布出来。
如：CVPR2011，
列几个视频：
一个非常棒的各种descriptor的评测。全文转载如下：（美中不足的没有特征匹配的评测）
(一句话总结，综合考虑速度和性能，ORB是最好的。）
For this test i have written special test framework, which allows me to easily add the new kind of descriptors and test cases and generate report data in CSV-like format. Than i upload it in Google docs and create this awesome charts. Five quality and one performance test was done for each kind of descriptor.
Kinect数据集越来越多了，上一篇Random Forests for Real Time Head Pose Estimation [Form ETHZ]中就有一个头部姿态的数据集，TomBone’s blog总结了几个kinect object dataset，原文转载如下。
The Kinect, made by Microsoft, is starting to become quite a common item in Robotics and Computer Vision research. While the Robotics community has been using the Kinect as a cheap laser sensor which can be used for obstacle avoidance, the vision community has been excited about using the 2.5D data associated with the Kinect for object detection and recognition. The possibility of building object recognition systems which have access to pixel features as well as 2.5D features is truly exciting for the vision hacker community!
使用kinect捕捉的深度信息，利用hough forest实时估计头部姿态。
Fast and reliable algorithms for estimating the head pose are essential for many applications and higher-level face analysis tasks. We address the problem of head pose estimation from depth data, which can be captured using the ever more affordable 3D sensing technologies available today.
To achieve robustness, we formulate pose estimation as a regression problem. While detecting specific face parts like the nose is sensitive to occlusions, we learn the regression on rather generic face surface patches. We propose to use random regression forests for the task at hand, given their capability to handle large training datasets.
In this page, our research work on head pose estimation is presented, source code is made available and an annotated database can be downloaded for evaluating other methods trying to tackle the same problem.
微软正在进行一个名为KinectFusion的项目。目标是利用一台围绕物体移动的kinect实时重建物体的三维模型。（是不是可以理解为Kinect版本的SLAM？）不同于的简单的三维点云的拼接，该项目的另外的吸引人的特性在于：如果对物体进行持续的扫描，三维重建精度可以由粗到细的逐渐提高。（类似superresolution？）演示视频（必看！！！）很给力。
The tracking problem is handled using a bag-of-pixels representation and comprises a rigid registration between frames, a segmentation and online appearance learning. The registration compensates for rigid motion, segmentation models any residual shape deformation and the online appearance learning provides continual refinement of both the object and background appearance models. The key to the success of our method is the use of pixel-wise posteriors, as opposed to likelihoods.
说说Android和iOS里面自带的人脸检测API。
Android提供了一个人脸检测类 android.media.FaceDetector，调用findFaces函数，它可以返回图片中的找到的人脸。人脸的属性封装在Face类，包括x, y, z三轴的人脸pose，还有两眼距离eyesDistance()。这个API从Android刚推出时Level 1就已经有了。
iOS以前是没有人脸检测模块的。去年苹果收购了Polar Rose，把他们人脸检测技术融入到新推出的iOS 5当中：CIDetector是个通用的检测器，构造时可以指定感兴趣的目标，目前只能检测人脸CIDetectorTypeFace。返回的人脸信息封装在CIFaceFeature类中，包含了：leftEyePosition, rightEyePosition, iOS 5现在还在Beta测试版中，只对付费developer开放，估计很快就要正式发布了。mouthPosition。
从功能上来看两者差不多。至于效果，我只看过WWDC上iOS的Demo，可以做实时Avatar。Android的API从初代Level 1就有了，如果用的人不多的话，猜测可能效果不能保证。
VIA 老杨
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