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The goal of the MPLab is to develop systems that perceive and interact with humans in real time using natural communication channels. To this effect we are developing perceptual primitives to detect and track human faces and to recognize facial expressions. We are also developing algorithms for robots that develop and learn to interact with people on their own. Applications include personal robots, perceptive tutoring systems, and system for clinical assessment, monitoring, and intervention.

  • Introduction to the MPLab (PDF)
  • MPLAB 5 Year Progress Report (PDF)

  • NEWS


    Apple just released their iPhone SDK. Under the terms, we can distribute software freely through apple. We might want to consider making an application similar to “cheese” for the iphone that a) rates your smiles, and b) takes your picture when you are smiling.  Should we try to have something ready by the release of iPhone apps (June)? You can download the sdk at: http://developer.apple.com/iphone/ 

    iShowU (link: http://www.shinywhitebox.com/home/home.html ) is a really amazing piece of Mac software. It lets you record video from a region of your screen that you choose (or it can follow your mouse). As it’s recording, it compresses the video on-the-fly and makes really reasonably sized files.  I have been using this program to make videos of other software I have made for the purpose of demoing them in my keynote presentations, without having to exit the presentation and open another piece of software.The software is very reasonably priced ($20), and is really well put together. I was skeptical before I tried it, thinking I would have to manually crop or compress the video afterward. My expectations were hugely exceeded. 

    The NSF announced 14 grand engineering challenges, and the public gets to vote on how they should be ranked in terms of importance. Check out the article and vote for you favorite. Article.   www.networkworld.com/community/node/25219

    Small article about handling many camera feeds, but only displaying the important video. I thought it was interesting when considering how to mine large amounts of video for segments of interest. article

    http://vectormagic.stanford.edu/

    This site was recommended by a Cognitive Science student. It is a utility for converting images to vector graphics. It is useful for turning things like images of logos, figures, etc. that don’t scale well into vector graphics that do.

    I haven’t yet tried it out, but you may want to at some point.

    I recently discovered Matlab’s “sample” function, which I have implemented on my own several times before. There are always slightly annoying implementation details to work out, and it’s very nice to have a function that is standard to do it for me.

    The idea is to sample from a multinomial distribution, which is something you need to do from time to time for various reasons. Here is an example of usage:

    >> sample([.25, .5, .1, .15],1),  ans =  3
    >> sample([.25, .5, .1, .15],1),  ans =  4
    >> sample([.25, .5, .1, .15],1),  ans =  1
    >> sample([.25, .5, .1, .15],1),  ans =  4
    >> sample([.25, .5, .1, .15],1),  ans =  2
    >> sample([.25, .5, .1, .15],1),  ans =  1
    >> sample([.25, .5, .1, .15],1),  ans =  2
    >> sample([.25, .5, .1, .15],1),  ans =  2

    a = sample([.25, .5, .1, .15],10000);
    >> nnz(a == 1),  ans =  2483
    >> nnz(a == 2),  ans =  5027
    >> nnz(a == 3),  ans =  988
    >> nnz(a == 4),  ans =  1502

    from article “Accurate face recognition is critical for many security applications. Current automaticface-recognition systems are defeated by natural changes in lighting and pose, which often affect face images more profoundly than changes in identity. The only system that can reliably cope with such variability is a human observer who is familiar with the faces concerned. We modeled human familiarity by using image averaging to derive stable face representations from naturally varying photographs. This simple procedure increased the accuracy of an industry standardface-recognition algorithm from 54% to 100%, bringing the robust performance of a familiar human to an automated system.” Face Recognition Article

    This year is in Monterrey, California
    August 9th-12th, 2008.

    http://www.icdl08.org/

    Important dates:
    Feb. 15 Special session proposals due
    March 14 Full 6-page paper submissions due
    March 21 Tutorial proposals due
    April 14 Notification of accept/reject
    April 18 1-page poster abstracts due
    May 9 Camera-Ready Copy due


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