Rapid handling of outliers in dense sampling descriptor correspondence fields

Chong Dong; Zhisheng Wang; Changda Xing; Yuan Xue

doi:10.1117/1.JEI.27.6.063025

19 December 2018 Rapid handling of outliers in dense sampling descriptor correspondence fields

Chong Dong, Zhisheng Wang, Changda Xing, Yuan Xue

Author Affiliations +

Journal of Electronic Imaging, Vol. 27, Issue 6, 063025 (December 2018). https://doi.org/10.1117/1.JEI.27.6.063025

Abstract

The sparse-to-dense approach is considered to be the standard method of capturing the long-range motion of small objects during large displacement optical flow. Despite progress in the matching and interpolation of this approach, little work has focused on improving the handling of outliers after dense sampling descriptor matching. We propose an improved grid-based statistical matching method that can quickly remove outliers without calculating backward flow. First, a multigrid statistical matching method is developed to remove the most outliers of the dense sampling descriptor correspondence field. Second, to improve the accuracy of outliers handling, the misjudgment match in the edge grid is corrected based on the statistical matching constraint. The results of extensive experiments on public optical flow datasets demonstrate the effectiveness of the proposed method.

Citation Download Citation

Chong Dong, Zhisheng Wang, Changda Xing, and Yuan Xue "Rapid handling of outliers in dense sampling descriptor correspondence fields," Journal of Electronic Imaging 27(6), 063025 (19 December 2018). https://doi.org/10.1117/1.JEI.27.6.063025

Received: 26 May 2018; Accepted: 27 November 2018; Published: 19 December 2018

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