Publication: Tensorial Template Matching for Fast Cross-Correlation with Rotations and Its Application for Tomography
Authors
Martinez-Sanchez, Antonio ; Homberg, Ulrike ; Almira Picazo, Jose María ; Phelippeau, Harold
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Publisher
Springer, Cham
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DOI
https://doi.org/10.1007/978-3-031-73383-3_2
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info:eu-repo/semantics/lecture
Description
© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This document is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0
This document is the submitted version of a published work that appeared in final form in
Lecture Notes in Computer Science
To access the final work, see DOI: https://doi.org/10.1007/978-3-031-73383-3_2
Abstract
Object detection is a main task in computer vision. Template matching is the reference method for detecting objects with arbitrary templates. However, template matching computational complexity depends on the rotation accuracy, being a limiting factor for large 3D images (tomograms). Here, we implement a new algorithm called tensorial template matching, based on a mathematical framework that represents all rotations of a template with a tensor field. Contrary to standard template matching, the computational complexity of the presented algorithm is independent of the rotation accuracy. Using both, synthetic and real data from tomography, we demonstrate that tensorial template matching is much faster than template matching and has the potential to improve its accuracy.
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Citation
Martinez-Sanchez, A., Homberg, U., Almira, J.M., Phelippeau, H. (2025). Tensorial Template Matching for Fast Cross-Correlation with Rotations and Its Application for Tomography. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15085. Springer, Cham. https://doi.org/10.1007/978-3-031-73383-3_2
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