Dr. Larry Pearlstein, Associate Professor of Electrical and Computer Engineering, and five undergraduate students (Skyler Maxwell, Matthew Kilcher, Alex Benasutti, Warren Solo, and Oliva Shanley) working in his Intelligent Media Processing Laboratory (IMPL) published research about a neural network solution they successfully developed and trained to address a color conversion problem within the digital video and imagery industry. Their neural networks help pinpoint if color from images were converted by the appropriate method that is expected to lead to more accurate color rendering for users of digital imaging and video systems.
Benasutti ’21 shared that “being able to contribute to that paper allowed me to realize just how interested I was in deep learning and neural network applications and inspired me to want to learn more about the machine learning field”. IMPL is one of many research laboratories found within the School of Engineering providing underclassmen the unique ability to get involved in real-world research, publications, and presentations early on in their academic careers. Dr. Pearlstein’s students presented a poster of their findings at the Applied Imagery Pattern Recognition (AIPR) Workshop in DC. The conference is known for gathering researchers in government, industry and academia to help broaden the vision of the applicability of image analysis and machine learning technologies.
The group’s paper is now published online by IEEE, the Institute of Electrical and Electronic Engineers, and extension of the work will be published in Int’l J. Machine Learning & Computing, vol. 9, no. 4 (with 5 students as co-authors). You can read their research at https://ieeexplore.ieee.org/document/8707374/