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Evaluation and Benchmark of Localization Algorithms for Stochastic Optical Localization Nanoscopy

Evaluation and Benchmark of Localization Algorithms for Stochastic Optical Localization Nanoscopy

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When Dec 11, 2019
from 01:00 pm to 02:00 pm
Speaker Yi Sun, Ph.D
Speaker Information Dr. Yi Sun obtained his Ph.D. in electrical engineering at the University of Minnesota, St. Paul-Minneapolis, MN. He is Associate Professor with Electrical Engineering Department at the City College of New York. Dr. Sun's research focuses on modeling, parameter estimation, algorithm development, and performance analysis with broad applications in neural networks, image processing, wireless communications, magnetic resonance angiography, robotics, bio-optics, and biomedical imaging.
Where 1311 HN
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Stochastic optical localization nanoscopy (SOLN), winning the 2014 Nobel Prize in Chemistry, has been significantly impacting biological research. A localization algorithm plays an important role in obtaining a high quality of SOLN images. About one hundred localization algorithms have been reported in the literature. To evaluate and benchmark the localization algorithms, we have done two studies. First, to evaluate the quality of SOLN images that are substantially different from the conventional images, we proposed the root mean square minimum distance (RMSMD) as a universal and objective quality metric for SOLN images. Second, to benchmark performance of a localization algorithm, we developed the information-achieving unbiased Gaussian (IAUG) estimator that achieves the Fisher information and the Cramer-Rao lower bound and therefore achieves the highest performance of unbiased estimators. On the basis of the RMSMD and the IAUG estimator, we have been developing a website to provide a means for evaluation and benchmark of the localization algorithms in various synthesized experiments.

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