Share and Export
Share and Export
Pitkäaho, Tomi, Manninen, Aki and Naughton, Thomas J. (2017) Deep convolutional neural networks and digital holographic microscopy for in-focus depth estimation of microscopic objects. In: Irish Machine Vision and Image Processing Conference Proceedings 2017. Irish Pattern Recognition & Classification Society, pp. 52-59. ISBN 978-0-9934207-2-6
Preview
Naughton_Deep_OIBCP_2017.pdf
Download (317kB) | Preview
Abstract
Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its poten- tial in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of an arbitrary epithelial cell cluster encoded in a digital hologram. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, with- out performing numerical propagation. To our knowledge, this is the first application of deep learning in the field of digital holographic microscopy.
| Item Type: | Book Section |
|---|---|
| Additional Information: | This paper was presented at the 19th Irish Machine Vision and Image Processing conference (IMVIP 2017) Aug 30th-Sept 1st, 2017, Maynooth, Ireland. |
| Keywords: | Imaging; Digital Holographic Microscopy; Autofocusing; Deep Learning; Machine Learning; |
| Academic Unit: | Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
| Item ID: | 12049 |
| Depositing User: | Thomas Naughton |
| Date Deposited: | 18 Dec 2019 16:16 |
| Publisher: | Irish Pattern Recognition & Classification Society |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/12049 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
- Item control page
Downloads
Downloads per month over past year
