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Publication - Professor Walterio Mayol-Cuevas

    Real-time Learning and Detection of 3D Texture-less Objects: A Scalable Approach

    Citation

    Damen, D, Bunnun, P, Calway, AD & Mayol-Cuevas, WW, 2012, ‘Real-time Learning and Detection of 3D Texture-less Objects: A Scalable Approach’. in: British Machine Vision Conference. BMVA, 2012, pp. 23.1-23.12

    Abstract

    We present a method for the learning and detection of multiple rigid texture-less 3D
    objects intended to operate at frame rate speeds for video input. The method is geared
    for fast and scalable learning and detection by combining tractable extraction of edgelet
    constellations with library lookup based on rotation- and scale-invariant descriptors. The
    approach learns object views in real-time, and is generative - enabling more objects to
    be learnt without the need for re-training. During testing, a random sample of edgelet
    constellations is tested for the presence of known objects. We perform testing of single
    and multi-object detection on a 30 objects dataset showing detections of any of them
    within milliseconds from the object’s visibility. The results show the scalability of the
    approach and its framerate performance.

    Full details in the University publications repository