Principal component analysis (PCA) is a classical data analysis technique that finds linear transformations of data that retain the maximal amount of variance. We study a case whe...
This paper considers highly ill-posed surface recovery inverse problems, where the sought surface in 2D or 3D is piecewise constant with several possible level values. These level...
We analyze the Synchrosqueezing transform, a consistent and invertible time-frequency analysis tool that can identify and extract oscillating components (of time-varying frequency...
Eugene Brevdo, Neven S. Fuckar, Gaurav Thakur, Hau...
We present a novel stereo video disparity estimation method. The proposed method is a two-stage algorithm. During the first stage, initial disparity maps are computed in a frameb...
Ramsin Khoshabeh, Stanley H. Chan, Truong Q. Nguye...
—We present an alternative analysis of weighted 1 minimization for sparse signals with a nonuniform sparsity model, and extend our results to nuclear norm minimization for matric...