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20 August 2020 Digital video microscopy with deep learning
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From the start of digital video microscopy over 20 years ago, single particle tracking has been dominated by algorithmic approaches. These methods are successful at tracking well-defined particles in good imaging conditions but their performance degrades severely in more challenging conditions. To overcome the limitations of traditional algorithmic approaches, data-driven methods using deep learning have been introduced. They managed to successfully track colloidal particles as well as non-spherical biological objects, even in unsteady imaging conditions.
Conference Presentation
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Saga Helgadottir, Aykut Argun, and Giovanni Volpe "Digital video microscopy with deep learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 1146918 (20 August 2020);

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