This paper studies the problem of simultaneously aligning a batch of linearly correlated
images despite gross corruption (such as occlusion). Our method seeks an optimal set of …

We propose a new provable method for robust PCA, where the task is to recover a low-rank
matrix, which is corrupted with sparse perturbations. Our method consists of simple …

In this paper, we propose a new tool to efficiently extract a class of “low-rank textures” in a
3D scene from user-specified windows in 2D images despite significant corruptions and …

This paper reviews the basic theory and typical applications of compressed sensing, matrix
rank minimization, and low-rank matrix recovery. Compressed sensing based on convex …

In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate
its application for robust image restoration. In the literature, principal component pursuit …

We study robust PCA for the fully observed setting, which is about separating a low rank
matrix L and a sparse matrix S from their sum D= L+ S. In this paper, a new algorithm …

Transform invariant low-rank textures (TILT) is a novel and powerful tool that can effectively
rectify a rich class of low-rank textures in 3D scenes from 2D images despite significant …

In this paper, we present an attributed grammar for parsing man-made outdoor scenes into
semantic surfaces, and recovering its 3D model simultaneously. The grammar takes …

In this paper, we present an attribute grammar for solving two coupled tasks: i) parsing a 2D
image into semantic regions; and ii) recovering the 3D scene structures of all regions. The …

Background: Sparse representation has achieved tremendous success recently. Low-rank
representation is one of the successful methods. It is aimed to capture underlying low …