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


    Nice Talk on: Learning to combine foveal glimpses with a third-order Boltzmann machine. Has images of our own Josh Susskind in the paper

    Martin Banks on an issue for 3D broadcasting of NFL: 3D at a distance requires wide-baseline which makes players look tiny. No easy fix.

    Poster session Wed. Very cool work: On a Connection between Importance Sampling and the Likelihood Ratio Policy Gradient (see NiPS proc.)

    Twitter at MPLab is working again

    Learning To Count Objects in Images (Victor Lempitsky, Andrew Zisserman)
    http://books.nips.cc/papers/files/nips23/NIPS2010_0330.pdf

    2D for training 3D for test. Size of objects is determined from image metadata http://books.nips.cc/papers/files/nips23/NIPS2010_1221.pdf

    Francis Bach: Structured sparsity-inducing norms through submodular functions http://books.nips.cc/papers/files/nips23/NIPS2010_0875.pdf

    Modeling paper on the role of fixations in decision making
    http://www.nature.com/neuro/journal/v13/n10/abs/nn.2635.html


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