Feature displacement interpolation

Mads Nielsen, Per Rønsholt Andresen

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    Abstract

    Given a sparse set of feature matches, we want to compute an interpolated dense displacement map. The application may be stereo disparity computation, flow
    computation, or non-rigid medical registration. Also estimation of missing image data, may be phrased in this framework. Since the features often are very
    sparse, the interpolation model becomes crucial. We show that a maximum likelihood estimation based on the covariance properties (Kriging) show properties
    more expedient than methods such as Gaussian interpolation or Tikhonov regularizations, also including scale-selection. The computational complexities
    are identical. We apply the maximum likelihood interpolation to growth analysis of the mandibular bone. Here, the features used are the crest-lines of the object
    surface.
    Original languageEnglish
    Title of host publicationIEEE 1998 International Conference on Image Processing (ICIP 98), Chigaco, USA
    Number of pages5
    PublisherIEEE Computer Society Press
    Publication date1998
    Publication statusPublished - 1998
    EventIEEE 1998 International Conference on Image Processing (ICIP 98), Chigaco, USA -
    Duration: 1 Jan 1998 → …

    Conference

    ConferenceIEEE 1998 International Conference on Image Processing (ICIP 98), Chigaco, USA
    Period01/01/1998 → …

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