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| Título: | Tensorial Template Matching for Fast Cross-Correlation with Rotations and Its Application for Tomography |
| Fecha de publicación: | 3-nov-2024 |
| Editorial: | Springer, Cham |
| Cita bibliográfica: | 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 |
| ISBN: | Print: 978-3-031-73382-6 Electronic:978-3-031-73383-3 |
| Resumen: | 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. |
| Autor/es principal/es: | Martinez-Sanchez, Antonio Homberg, Ulrike Almira Picazo, Jose María Phelippeau, Harold |
| Forma parte de: | Lecture Notes in Computer Science ((LNCS,volume 15085)) |
| Versión del editor: | https://link.springer.com/chapter/10.1007/978-3-031-73383-3_2 |
| URI: | http://hdl.handle.net/10201/146120 |
| DOI: | https://doi.org/10.1007/978-3-031-73383-3_2 |
| Tipo de documento: | info:eu-repo/semantics/lecture |
| Derechos: | info:eu-repo/semantics/openAccess Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
| Descripción: | © 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 |
| Aparece en las colecciones: | Artículos |
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| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| TTM_ECCV_2024.pdf | 4,62 MB | Adobe PDF |  Visualizar/Abrir |
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