* Multiple face detection and recognition in real time Download FaceRecPro Optimized Version: Source code, Demo: All binaries references, algorithm enhanced - 6.5 MB Introduction The facial recognition has been a problem very worked around the world for many persons; this problem has emerged in multiple fields and sciences, especially in computer science, others fields that are very interested In this technology are: Mechatronic, Robotic, criminalistics, etc. In this article I work in this interesting topic using EmguCV cross platform .Net wrapper to the Intel OpenCV image processing library and C# .Net, these library’s allow me capture and process image of a capture device in real time. Background Traditional Some facial recognition algorithms identify faces by extracting landmarks, or features, from an image of the subject's face. text taken from  An example of EigenFaces: A newly emerging trend, claimed to achieve previously unseen accuracies, is three-dimensional face recognition. image taken from  Skin texture analysis EmguCV Parameters:
WebPlatform.org — Your Web, documented Kinect + Emgu « Geek-Press Sorry about the inactivity but having and a daytime job and girlfriend sometimes leaves us without time. Started to play with Computer Vision and Kinect, didn't done a big thing but I was completly unaware of how CV works and after reading a while about it I hear that Emgu is a great wrapper for openCV library. In fact you can do some face detection without any worries at all. So let's do a hands-on on this library For this example you need This last one is great for helping to pass the kinect raw data to bitmaps I strongly recommend to read the Emgu documentation before using it... it may be tricky So let's start by creating a new windows project, after that it's time to add the references. Add the Microsoft.Research.Kinect reference that can be found on the .NET references and then the Coding4Fun dll, and all the dlls needed for Emgu. As you can see Emgu as lots of references and on the bin folder you should add the unmanaged libraries like cvextern.dll, opencv_calib3d220 and so on... so
Google's Mind-Blowing Big-Data Tool Grows Open Source Twin | Wired Enterprise Silicon Valley startup MapR has launched an open source project called Drill, which seeks to mimic a shocking effectively data-analysis tool built by Google Mike Olson and John Schroeder shared a stage at a recent meeting of Silicon Valley’s celebrated Churchill Club, and they didn’t exactly see eye to eye. Olson is the CEO of a Valley startup called Cloudera, and Schroeder is the boss at MapR, a conspicuous Cloudera rival. Both outfits deal in Hadoop — a sweeping open source software platform based on data center technologies that underpinned the rise of Google’s web-dominating search engine — but in building their particular businesses, the two startups approached Hadoop from two very different directions. Whereas Cloudera worked closely with the open source Hadoop project to enhance the software code that’s freely available to the world at large, MapR decided to rebuild the platform from the ground up, and when that was done, it sold the new code as proprietary software. — Tomer Shiran
KINECT Algorithms - Apache Mahout One-shot learning One-shot learning is an object categorization problem of current research interest in computer vision. Whereas most machine learning based object categorization algorithms require training on hundreds or thousands of images and very large datasets, one-shot learning aims to learn information about object categories from one, or only a few, training images. The primary focus of this article will be on the solution to this problem presented by L. Motivation The ability to learn object categories from few examples, and at a rapid pace, has been demonstrated in humans, and it is estimated that a child has learned almost of all the 10 ~ 30 thousand object categories in the world by the age of six. Yet this achievement of the human mind is due not only to its computational power, but also to its ability to synthesize and learn new object classes from existing information about different, previously learned classes. Background Theory Bayesian framework . and . to .
Running Hadoop on Windows « Hayes Davis What is Hadoop? Hadoop is a an open source Apache project written in Java and designed to provide users with two things: a distributed file system (HDFS) and a method for distributed computation. It’s based on Google’s published Google File System and MapReduce concept which discuss how to build a framework capable of executing intensive computations across tons of computers. What’s the big deal about running it on Windows? Hadoop’s key design goal is to provide storage and computation on lots of homogenous “commodity” machines; usually a fairly beefy machine running Linux. Caveat Emptor I’m one of the few that has invested the time to setup an actual distributed Hadoop installation on Windows. This guide uses Hadoop v0.17 and assumes that you don’t have any previous Hadoop installation. Bottom line: your mileage may vary, but this guide should get you started running Hadoop on Windows. A quick note on distributed Hadoop Hadoop runs in one of three modes: Pre-Requisites Java Cygwin Hadoop <?
COMPUTER VISION wiki Computer vision is a field that includes methods for acquiring, processing, analyzing, and understanding images and, in general, high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the forms of decisions. A theme in the development of this field has been to duplicate the abilities of human vision by electronically perceiving and understanding an image. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory. Computer vision has also been described as the enterprise of automating and integrating a wide range of processes and representations for vision perception. As a scientific discipline, computer vision is concerned with the theory behind artificial systems that extract information from images. Related fields Applications for computer vision Recognition
How MySpace Tested Their Live Site with 1 Million Concurrent Users This is a guest post by Dan Bartow, VP of SOASTA, talking about how they pelted MySpace with 1 million concurrent users using 800 EC2 instances. I thought this was an interesting story because: that's a lot of users, it takes big cajones to test your live site like that, and not everything worked out quite as expected. I'd like to thank Dan for taking the time to write and share this article. In December of 2009 MySpace launched a new wave of streaming music video offerings in New Zealand, building on the previous success of MySpace music. These new features included the ability to watch music videos, search for artist’s videos, create lists of favorites, and more. The anticipated load increase from a feature like this on a popular site like MySpace is huge, and they wanted to test these features before making them live. If you manage the infrastructure that sits behind a high traffic application you don’t want any surprises. Test Environment Architecture figure 1. Challenges
Vision Lab; Prof. Fei-Fei Li (Please cite all of Fei-Fei’s papers with the name L. Fei-Fei.) Large-Scale Video Classification with Convolutional Neural Networks Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei Socially-aware Large-scale Crowd Forecasting Alexandre Alahi, Vignesh Ramanathan, and Li Fei-Fei Co-localization in Real-World Images Kevin Tang, Armand Joulin, Li-Jia Li, Li Fei-Fei Scalable Multi-Label Annotation Jia Deng, Olga Russakovsky, Jonathan Krause, Michael Bernstein, Alexander C. Visual Categorization is Automatic and Obligatory: Evidence from a Stroop-like Paradigm Michelle Greene, Li Fei-Fei Journal of Vision, 2014 3D Object Representations for Fine-Grained Categorization Jonathan Krause, Michael Stark, Jia Deng, Li Fei-Fei ICCV 2013, Workshop on 3D Representation and Recognition Combining the Right Features for Complex Event Recognition Kevin Tang, Bangpeng Yao, Li Fei-Fei, Daphne Koller Video Event Understanding using Natural Language Descriptions O. B. L. J. V. K. L.