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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


    http://www.webmd.com/brain/autism/news/20090329/autism-new-clue-to-earlier-detection

    http://www.crunchgear.com/2009/01/30/say-cheese-the-smile-scan-makes-grumpy-employees-friendlier/

    http://www.sciencemag.org/cgi/content/full/322/5909/1800

    INC 2009 Rockwood Lecture

    Josh Bongard

    Department of Computer Science
    Vermont Advanced Computing Center
    University of Vermont

    Friday, March 20, 2009 11 am
    Cognitive Science Building, Room 003

    Abstract:

    Intelligent robots must be able to not only adapt an existing behavior on the fly in the
    face of environmental perturbation, but must also be able to generate new, compensating
    behavior after severe, unanticipated change such as body damage. In this talk I will
    describe a physical robot with this latter capability, a capability we refer to as resiliency.
    The robot achieves this by (1) creating an approximate simulation of itself;
    (2) optimizing a controller using this simulator; (3) using the controller in reality;
    (4) experiencing body damage; (5) indirectly inferring the damage and updating the simulator;
    (6) re-optimizing a new controller in the altered simulator; and (7) executing this compensatory
    controller in reality. I will also describe recent work generalizing this approach to robot teams.

    Host: Terry Sejnowski

    I was recently doing something like the following in Matlab:

    im1 = imread(‘im1.jpg’);

    im2 = imread(‘im2.jpg’);

    if sum((im1(:)-im2(:)).^2) < 1e-5, disp(‘Image 1 and 2 are duplicates!!!’), end

    So the point of these three lines of code is to compare image 1 and image 2, and if they are sufficiently close (exactly or nearly exactly), call them duplicates.

    The preceding code has an interesting non-obvious bug:  For integer-valued jpgs, Matlab loads them as type uint8. The above test will be passed not only if all pixels are the same, but also if every pixel in im2 is brighter than every pixel in im1. This is because, with unsigned integers, 1-2 = 0.

    So I had a very washed out image in my image set that was testing as a duplicate of many images, just because nearly all pixels in the saturated image were 255, and x-255=0 for all unsigned x.

    http://www.sciencemag.org/cgi/content/abstract/sci;323/5918/1222

    http://keepvid.com/?url=http%3A%2F%2Fwww.youtube.com%2Fwatch%3Fv%3DQbKw0_v2clo%26feature%3Drelated

    http://www.latimes.com/features/health/la-he-time9-2009mar09,0,748518.story


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