Deep learning for particle tracking
| dc.contributor.author | Lundell, Adrian | |
| dc.contributor.author | Tonderski, David | |
| dc.contributor.author | Meisingseth, Fredrik | |
| dc.contributor.author | Kristiansson, Dennis | |
| dc.contributor.department | Chalmers tekniska högskola / Institutionen för fysik | sv |
| dc.contributor.examiner | Falk, Lena | |
| dc.contributor.supervisor | Midtvedt, Daniel | |
| dc.contributor.supervisor | Volpe, Giovanni | |
| dc.date.accessioned | 2020-06-22T12:15:05Z | |
| dc.date.available | 2020-06-22T12:15:05Z | |
| dc.date.issued | 2020 | sv |
| dc.date.submitted | 2020 | |
| dc.description.abstract | The use of machine learning for classifcation has in recent years spread into a wide range of disciplines, amongst them the detection of particles for particle tracking on microscopy data. We modifed the Python package DeepTrack, which makes use of deep learning to detect particles, creating a package called U-Track. By using a new network architecture based on a U-Net, better performance and higher computational effciency than DeepTrack was achieved on images with multiple particles. Furthermore, functionality to track detected particles over series of frames was developed. The application of U-Track on experimental data from two-dimensional ow nanometry produced tracks consistent with theory, as well as tracking larger quantities of particles over longer periods of time compared to a digital filter based benchmark algorithm. | sv |
| dc.identifier.coursecode | TIFX04 | sv |
| dc.identifier.uri | https://hdl.handle.net/20.500.12380/300955 | |
| dc.language.iso | eng | sv |
| dc.setspec.uppsok | PhysicsChemistryMaths | |
| dc.title | Deep learning for particle tracking | sv |
| dc.type.degree | Examensarbete för kandidatexamen | sv |
| dc.type.uppsok | M2 |