The theory of compressed sensing tells a dramatic story that sparse signals can be reconstructed near-perfectly from a small number of random measurements. However, recent work ha...
In this paper, we focus on the use of context-aware, collaborative filtering, machine-learning techniques that leverage automatically sensed and inferred contextual metadata toget...
Marc Davis, Michael Smith, John F. Canny, Nathan G...
This paper addresses the problem of video anomaly recovery from a sequence of spectrally compressed video frames. Analysis of anomalies occurring in both time and spectrum is impo...
Exact recovery from contaminated visual data plays an important role in various tasks. By assuming the observed data matrix as the addition of a low-rank matrix and a sparse matri...
Yadong Mu, Jian Dong, Xiaotong Yuan, Shuicheng Yan
We propose a novel method for reconstructing a complete 3D model of a given anatomy from minimal information. This reconstruction provides an appropriate intra-operative 3D visual...