Computational Imaging for Sensing High-speed Phenomena
Despite recent advances in sensor technology, capturing high-speed video at high-spatial resolutionsremains a challenge. This is because, in a conventional camera, the available bandwidth limits either the maximum sampling frequency or thecaptured spatial resolution. In this talk, I am going to cover our recent works that use computational imaging to allow high-speed high-resolution imagingunder certain conditions. First I will describe Diffraction Line Imaging, a novel imaging principle that combines diffractive optics with 1D (line) sensorsto allow high-speed positioning of light sources (e.g., motion capture markers,car headlights) as well structured light 3D scanning with line illumination andline sensing. Second, I will present a recent work that generalizes Diffraction Line Imaging to handle a new class of scenes, resulting in new applicationdomains such as high-speed imaging for Particle Image Velocimetry and imaging combustible particles. Lastly, I will present a novel method for sensingvibrations at high speeds (up to 63kHz), for multiple scene sources a tonce, using sensors rated for only 130Hz operation. I will presentresults from our method that include capturing vibration caused by audio sources(e.g. speakers, human voice, and musical instruments) and analysing thevibration modes of a tuning fork.
Bio: Mark Sheinin is a Post-doctoral Research Associate at Carnegie Mellon University’s RoboticInstitute at the Illumination and Imaging Laboratory. He received his Ph.D. inElectrical Engineering from the Technion – Israel Institute of Technology in2019.
Hiswork has received a Best Student Paper Award at IEEE CVPR 2017. He is therecipient of the Porat Award for Outstanding Graduate Students, the Jacobs-QualcommFellowship in 2017, and the Jacobs Distinguished Publication Award in 2018. Hisresearch interests include computational photography and computer vision.