We propose a novel approach that jointly removes reflection or translucent layer from a scene and estimates scene depth. The input data are captured via light field imaging. The problem is couched as minimizing the rank of the transmitted scene layer via Robust Principle Component Analysis (RPCA). We also impose regularization based on piecewise smoothness, gradient sparsity, and layer independ...

Principal component analysis (PCA) is a popular technique to reduce the dimension of the data at hand. Since PCA is based on the empirical variance-covariance matrix, the estimates can be severely damaged by outliers. To reduce these effects, several robust methods were developed, mostly by replacing the classical variance-covariance matrix by a robust version. In this paper we focus on Stahel-...

We consider a decomposition method for compressive streaming data in the context of online compressive Robust Principle Component Analysis (RPCA). The proposed decomposition solves an n-`1 cluster-weighted minimization to decompose a sequence of frames (or vectors), into sparse and lowrank components, from compressive measurements. Our method processes a data vector of the stream per time insta...

The Dehaj area, located in the southern part of the Urumieh-Dokhtar magmatic belt, is a well-endowed terrain hosting a number of world-class porphyry copper deposits. These deposits are all hosted in an acidic to intermediate volcano-plutonic sequence greatly affected by various types of the hydrothermal alterations, whether argillic, phyllic or propylitic. Although there are a handful of hithe...

Foreground detection is the first step in video surveillance system to detect moving objects. Robust Principal Components Analysis (RPCA) shows a nice framework to separate moving objects from the background. The background sequence is then modeled by a low rank subspace that can gradually change over time, while the moving foreground objects constitute the correlated sparse outliers. In this p...

the aim of this study was to assess the environmental impact of socio-cultural practices on the water quality of river ganga at the foothills of the garhwal himalayas in uttarakhand state, india. the physico-chemical parameters that contributed to the temporal variation and pollution in the river were identified in this study. principal component analysis (pca) and cluster analysis (ca) were us...

We consider the dimensionality-reduction problem for a contaminated data set in a very high dimensional space, i.e., the problem of finding a subspace approximation of observed data, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some outlying observations. We propose a High-dimension Robust Principal Component...

Dictionary learning and component analysis models are fundamental in learning compact representations that are relevant to a given task (feature extraction, dimensionality reduction, denoising, etc.). The model complexity is encoded by means of specific structure, such as sparsity, low-rankness, or nonnegativity. Unfortunately, approaches like K-SVD that learn dictionaries for sparse coding via...

With the advent of high-throughput measurement techniques, scientists and engineers are starting to grapple with massive data sets and encountering challenges with how to organize, process and extract information into meaningful structures. Multidimensional spatio-temporal biological data sets such as time series gene expression with various perturbations over different cell lines, or neural sp...