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Machine Learning for Image Processing

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Machine Learning for Image Processing


Dong Hye Ye


In recent years, it has become increasingly easy to gather large quantities of images. Processing these large image databases is key to unlocking a wealth of information with the potential to be used. However, both interpretation of that big data and connecting it to downstream image processing is still challenging. To tackle this challenge, I unlock the valuable prior knowledge from large image databases via machine learning techniques and use it to improve image processing. In this talk, I will present how machine learning can help image processing such as CT Metal Artifact Reduction/ Reconstruction, microscopic imaging, and UAV detection/tracking.


Dr. Dong Hye Ye is an Assistant Professor in Electrical and Computer Engineering at Marquette University. His research interests are in advancing image processing via machine learning. His publications have been awarded Best Paper at MICCAI-MedIA 2010, Best Paper Runner-Up at ICIP 2015, and Best Paper at EI-IMAWM 2018. During his PhD, Dong Hye conducted research at Section of Biomedical Image Analysis (SBIA) in Hospital of the University of Pennsylvania (HUP) and Microsoft Research Cambridge (MSRC). He received Bachelor’s degree from Seoul National University in 2007 and Master's degree from Georgia Institute of Technology in 2008.


2019-07-22 at 2:30 pm (subject to variability)