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    Mean shift algorithm for robust rigid registration between Gaussian Mixture Models


    Arellano, Claudia and Dahyot, Rozenn (2012) Mean shift algorithm for robust rigid registration between Gaussian Mixture Models. 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO). ISSN 2076-1465

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    Abstract

    We present a Mean shift (MS) algorithm for solving the rigid point set transformation estimation [1]. Our registration algorithm minimises exactly the Euclidean distance between Gaussian Mixture Models (GMMs). We show experimentally that our algorithm is more robust than previous implementations [1], thanks to both using an annealing framework (to avoid local extrema) and using variable bandwidths in our density estimates. Our approach is applied to 3D real data sets captured with a Lidar scanner and Kinect sensor.

    Item Type: Article
    Keywords: Mean Shift; Registration; Gaussian Mixture Models; Rigid Transformation;
    Academic Unit: Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 15271
    Depositing User: Rozenn Dahyot
    Date Deposited: 18 Jan 2022 16:42
    Journal or Publication Title: 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)
    Publisher: IEEE
    Refereed: Yes
    URI:

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