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A novel approach for scale and rotation adaptive estimation based on time series alignment

  • Delong Zhao
  • Fuzhou Du
Original Article
  • 11 Downloads

Abstract

This paper proposes a novel approach for target scale and rotation adaptive estimation based on template matching, which is robust to undergo brightness, contrast invariance, and noise corruption. Improved features based on ring projection transform are extracted, which can not only improve the matching ability of some special scenes by taking into account changes of pixel intensity and structure information, but also automatically recommend the sampling rings involved in the angle estimation. Moreover, treating image features from the perspective of signal time series, we have designed a hierarchical adaptive estimation strategy to solve the problem of scale invariance while reconstructing the transformation of brightness and contrast. Eliminating the limitations of the pre-prepared fixed-scale vertex template, the proposed approach implements an adaptive estimation of the scale. Additionally, rotation angle calculation based on the normalization cross-correlation can be used as the secondary verification of the candidate solution to further improve the matching accuracy. Numerical evaluation shows that the method enjoys attractive results.

Keywords

Template matching Time series alignment Scale adaptive estimation Brightness–contrast reconstruction Rotation invariance 

Notes

Acknowledgements

The State Key Laboratory of Precision Measurement Technology and Instruments provided research facilities for this work.

Funding

This study was funded by The State Key Laboratory of Precision Measurement Technology and Instruments (No. PIL1404).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical Engineering and AutomationBeihang UniversityBeijingChina

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