Single-shot self-supervised particle tracking (Conference Presentation)

Benjamin Midtvedt; Jesús Pineda; Fredrik Skärberg; Erik Olsén; Harshith Bachimanchi; Emelie V. Wesén; Elin K Esbjörner; Erik Selander; Fredrik Höök; Daniel Midtvedt; Giovanni Volpe

doi:10.1117/12.2633355

4 October 2022 Single-shot self-supervised particle tracking (Conference Presentation)

Benjamin Midtvedt, Jesús Pineda, Fredrik Skärberg, Erik Olsén, Harshith Bachimanchi, Emelie V. Wesén, Elin K Esbjörner, Erik Selander, Fredrik Höök, Daniel Midtvedt, Giovanni Volpe

Author Affiliations +

Proceedings Volume PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022; PC122040K (2022) https://doi.org/10.1117/12.2633355
Event: SPIE Nanoscience + Engineering, 2022, San Diego, California, United States

Abstract

We present LodeSTAR, a label-free, single-shot particle tracker. We design a method for exploiting the symmetries of problem statements to train neural networks using extremely small datasets and without ground truth. We demonstrate that LodeSTAR outperforms traditional methods in terms of accuracy and that it reliably tracks experimental data of packed cells. Finally, we show that LodeSTAR can exploit additional symmetries to extend the measurable particle properties to the axial position of objects and particle polarizability.

Conference Presentation

Citation Download Citation

Benjamin Midtvedt, Jesús Pineda, Fredrik Skärberg, Erik Olsén, Harshith Bachimanchi, Emelie V. Wesén, Elin K Esbjörner, Erik Selander, Fredrik Höök, Daniel Midtvedt, and Giovanni Volpe "Single-shot self-supervised particle tracking (Conference Presentation)", Proc. SPIE PC12204, Emerging Topics in Artificial Intelligence (ETAI) 2022, PC122040K (4 October 2022); https://doi.org/10.1117/12.2633355

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