58 citations · 59 across the 5 of their papers we have counts for
6 papers
Investigation of moving objects through atmospheric turbulence from a non-stationary platform
Nicholas Ferrante, Jerome Gilles, Shibin Parameswaran
In this work, we extract the optical flow field corresponding to moving objects from an image sequence of a scene impacted by atmospheric turbulence \emph{and} captured from a movi…
Out-of-Distribution Learning with Human Feedback
Haoyue Bai, Xuefeng Du, Katie Rainey +2
Out-of-distribution (OOD) learning often relies heavily on statistical approaches or predefined assumptions about OOD data distributions, hindering their efficacy in addressing mul…
Real-Time Event-Based Tracking and Detection for Maritime Environments
Stephanie Aelmore, Richard C. Ordonez, Shibin Parameswaran +1
Event cameras are ideal for object tracking applications due to their ability to capture fast-moving objects while mitigating latency and data redundancy. Existing event-based clus…
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)
Chris M. Ward, Josh Harguess, Brendan Crabb +1
Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to understanding the capabilities and limitations of modern image processing m…
Image denoising with generalized Gaussian mixture model patch priors
Charles-Alban Deledalle, Shibin Parameswaran, Truong Q. Nguyen
Patch priors have become an important component of image restoration. A powerful approach in this category of restoration algorithms is the popular Expected Patch Log-Likelihood (E…
Accelerating GMM-based patch priors for image restoration: Three ingredients for a 100 speed-up
Shibin Parameswaran, Charles-Alban Deledalle, Loïc Denis +1
Image restoration methods aim to recover the underlying clean image from corrupted observations. The Expected Patch Log-likelihood (EPLL) algorithm is a powerful image restoration…