Pixelwise-adaptive blind optical flow assuming nonstationary statistics

    Authors

    H. Foroosh

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Image Process.

    Keywords

    blind estimation; generalized cross validation (GCV); motion estimation; nonstationary statistic; optical flow; COMPUTATION; MOTION; DISCONTINUITIES; SEGMENTATION; CONSTRAINTS; PERFORMANCE; RELAXATION; FILTERS; MODELS; Computer Science, Artificial Intelligence; Engineering, Electrical &; Electronic

    Abstract

    In this paper, we address some of the major issues in optical flow within a new framework assuming nonstationary statistics for the motion field and for the errors. Problems addressed include the preservation of discontinuities, model/data errors, outliers, confidence measures, and performance evaluation. In solving these problems, we assume that the statistics of the motion field and the errors are not only spatially varying, but also unknown. We, thus, derive a blind adaptive technique based on generalized cross validation for estimating an independent regularization parameter for each pixel. Our formulation is pixelwise and combines existing first- and second-order constraints with a new second-order temporal constraint. We derive a new confidence measure for an adaptive rejection of erroneous and outlying motion vectors, and compare our results to other techniques in the literature. A new performance measure is also derived for estimating the signal-to-noise ratio for real sequences when the ground truth is unknown.

    Journal Title

    Ieee Transactions on Image Processing

    Volume

    14

    Issue/Number

    2

    Publication Date

    1-1-2005

    Document Type

    Article

    Language

    English

    First Page

    222

    Last Page

    230

    WOS Identifier

    WOS:000226400200008

    ISSN

    1057-7149

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