Normalized Cross-Correlation for Spherical Images
Abstract
Recent advances in vision systems have spawned a new generation of image modalities. Most of today’s robot vehicles are equipped with omnidirectional sensors which facilitate navigation as well as immersive visualization. When an omnidirectional camera with a single viewpoint is calibrated, the original image can be warped to a spherical image. In this paper, we study the problem of template matching in spherical images. The natural transformation of a pattern on the sphere is a 3D rotation and template matching is the localization of a target in any orientation. Cross-correlation on the sphere is a function of 3D-rotation and it can be computed in a space-invariant way through a 3D inverse DFT of a linear combination of spherical harmonics. However, if we intend to normalize the cross-correlation, the computation of the local image variance is a space variant operation. In this paper, we present a new cross-correlation measure that correlates the image-pattern cross-correlation with the autocorrelation of the template with respect to orientation. Experimental results on artificial as well as real data show accurate localization performance with a variety of targets.
Cite
Text
Sorgi and Daniilidis. "Normalized Cross-Correlation for Spherical Images." European Conference on Computer Vision, 2004. doi:10.1007/978-3-540-24671-8_43Markdown
[Sorgi and Daniilidis. "Normalized Cross-Correlation for Spherical Images." European Conference on Computer Vision, 2004.](https://mlanthology.org/eccv/2004/sorgi2004eccv-normalized/) doi:10.1007/978-3-540-24671-8_43BibTeX
@inproceedings{sorgi2004eccv-normalized,
title = {{Normalized Cross-Correlation for Spherical Images}},
author = {Sorgi, Lorenzo and Daniilidis, Kostas},
booktitle = {European Conference on Computer Vision},
year = {2004},
pages = {542-553},
doi = {10.1007/978-3-540-24671-8_43},
url = {https://mlanthology.org/eccv/2004/sorgi2004eccv-normalized/}
}