Title

Pixelwise-adaptive blind optical flow assuming nonstationary statistics

Authors

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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