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Fast template matching based on deformable best-buddies similarity measure

journal contribution
posted on 2019-01-01, 00:00 authored by H Xia, W Zhao, F Jiang, H Li, J Xin, Z Zhou
© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Accuracy and speed are the essential metrics for the template matching algorithms in solving object tracking problems. Since the method based on Best Buddies Similarity (BBS) has achieved the state-of-the-art performance in terms of accuracy, matching speed becomes the shortest piece of wood of the bucket. In this paper, we propose a fast template matching method based on our deformable BBS measure. The deformable BBS measure enables matching to be performed between the patches in varying sizes, and hence leads to even higher accuracy than the original BBS-based methods. More important, we develop a fast potential-area discovery algorithm based on proposal generation and selection. It significantly reduces the numbers of useless attempts on calculating and comparing similarities of impossible image patches. The experimental results show that, with the deformable BBS measure and the fast potential-area discovery, our template matching method outperforms the state-of-the-art methods in terms of accuracy, speed and robustness.

History

Journal

Multimedia Tools and Applications

Volume

78

Issue

9

Pagination

11905 - 11925

Publisher

Springer

Location

Berlin, Germany

ISSN

0942-4962

eISSN

1573-7721

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal