Event cameras are ideal for object tracking applications due to their ability to capture fast-moving objects while mitigating latency and data redundancy.
Traditional metrics for evaluating the efficacy of image processing techniques do not lend themselves to understanding the capabilities and limitations of modern image processing methods - particularly those enabled by deep learning.
In this paper, we show that a generalized Gaussian mixture model (GGMM) captures the underlying distribution of patches better than a GMM.
Image restoration methods aim to recover the underlying clean image from corrupted observations.
Multi-task learning (MTL) improves the prediction performance on multiple, different but related, learning problems through shared parameters or representations.