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

Research output: Contribution to conference typesPaperpeer-review

Original languageEnglish
Pages333-344
Number of pages12
DOIs
Published1 Jan 2022
EventInternational Conference on Image, Vision and Intelligent Systems -
Duration: 19 Jun 202120 Jun 2021
http://www.icivis.net/icivis-2021/

Conference

ConferenceInternational Conference on Image, Vision and Intelligent Systems
Abbreviated titleICIVIS
Period19/06/202120/06/2021
Internet address

Bibliographical note

Funding Information: 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. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

King's Authors

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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