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Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN

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The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 813))

Abstract

Finding a template in a search image is an important task underlying many computer vision applications. Recent approaches perform template matching in a deep feature-space, produced by a convolutional neural network (CNN), which is found to provide more tolerance to changes in appearance. In this article, we investigate whether enhancing the CNN’s encoding of shape information can produce more distinguishable features, so as to improve the performance of template matching. This investigation results in a new template matching method that produces state-of-the-art results in a standard benchmark. To confirm these results, we also create a new benchmark and show that the proposed method also outperforms existing techniques on this new dataset. Our code and dataset is available at: https://github.com/iminfine/Deep-DIM.

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Acknowledgements

The authors acknowledge use of the research computing facility at King’s College London, Rosalind (https://rosalind.kcl.ac.uk), and the Joint Academic Data science Endeavour (JADE) facility. This research was funded by China Scholarship Council.

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Authors and Affiliations

  1. Department of Informatics, King’s College London, London, UK

    Bo Gao & Michael W. Spratling

Authors
  1. Bo Gao
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  2. Michael W. Spratling
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Correspondence to Bo Gao .

Editor information

Editors and Affiliations

  1. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China

    Jian Yao

  2. Department of Computer Science, University of Alabama, Tuscaloosa, AL, USA

    Yang Xiao

  3. National University of Defense Technology, Changsha, Hunan, China

    Peng You

  4. Big Data Institute, Hunan University of Finance and Economics, Changsha, Hunan, China

    Guang Sun

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Gao, B., Spratling, M.W. (2022). Robust Template Matching via Hierarchical Convolutional Features from a Shape Biased CNN. In: Yao, J., Xiao, Y., You, P., Sun, G. (eds) The International Conference on Image, Vision and Intelligent Systems (ICIVIS 2021). Lecture Notes in Electrical Engineering, vol 813. Springer, Singapore. https://doi.org/10.1007/978-981-16-6963-7_31

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  • DOI: https://doi.org/10.1007/978-981-16-6963-7_31

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6962-0

  • Online ISBN: 978-981-16-6963-7

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