Statistical Tomography of Microscopic Life
Abstract
We achieve tomography of 3D volumetric natural objects, where each projected 2D image corresponds to a different specimen. Each specimen has unknown random 3D orientation, location, and scale. This imaging scenario is relevant to microscopic and mesoscopic organisms, aerosols and hydrosols viewed naturally by a microscope. In-class scale variation inhibits prior single-particle reconstruction methods. We thus generalize tomographic recovery to account for all degrees of freedom of a similarity transformation. This enables geometric self-calibration in imaging of transparent objects. We make the computational load manageable and reach good quality reconstruction in a short time. This enables extraction of statistics that are important for a scientific study of specimen populations, specifically size distribution parameters. We apply the method to study of plankton.
Cite
Text
Levis et al. "Statistical Tomography of Microscopic Life." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00671Markdown
[Levis et al. "Statistical Tomography of Microscopic Life." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/levis2018cvpr-statistical/) doi:10.1109/CVPR.2018.00671BibTeX
@inproceedings{levis2018cvpr-statistical,
title = {{Statistical Tomography of Microscopic Life}},
author = {Levis, Aviad and Schechner, Yoav Y. and Talmon, Ronen},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00671},
url = {https://mlanthology.org/cvpr/2018/levis2018cvpr-statistical/}
}