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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

    Nick@CVPR: Bruce at Inria uses dist. of Harris detectors in Natural inputs to choose which corners to accept. A lot can still be done here.

    Nick@CVPR: Bolme et al. from Colorado have a way to learn good filters for eye detection by learning many okay filters and averaging them.

    Nick@CVPR: Both Fei-Fei Li and Jitendra Malik get a lot out of over-segmenting images & then inferring which segments go with which object.

    Nick@CVPR: Activity Recognition now is like Object Recognition 4 years ago: datasets are too easy and they’re turning to YouTube for data.

    Nick@CVPR: 2nd best student paper- Tensor based graph matching. Make graphs of feature points. Does it match an object in your training set?

    Nick@CVPR: Best student paper-Torralba’s student. Match pixels in two scenes using optical flow on sift features at every pixel & warping.

    Nick@CVPR: 2nd best paper: comparison of blind deconvolution algorithms: image blurred w/ unknown kernel, want to recover image and kernel.

    Nick@CVPR: Best paper: Haze Removal With a Dark Channel Prior. DCh is min(min(rgb) in local patch). Nat. Im.s have DCh ~ 0, hazy images >>0.

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