Tensorial template matching for fast cross-correlation with rotations and its application for tomography
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
Keywords
Cite
@article{arxiv.2408.02398,
title = {Tensorial template matching for fast cross-correlation with rotations and its application for tomography},
author = {Antonio Martinez-Sanchez and Ulrike Homberg and José María Almira and Harold Phelippeau},
journal= {arXiv preprint arXiv:2408.02398},
year = {2024}
}
Comments
Accepted in The 18th European Conference on Computer Vision ECCV 2024