In this dissertation we propose priors and learning based methods for super- resolution and other video processing applications. We also propose efficient al- gorithms for global motion estimation and projection on L1 ball under box con- straints. We
Multivariate Nonparametric Methods with R: An Approach Based on Spatial Signs and Ranks 多变量非参数方法与R This book introduces a new way to analyze multivariate data. The analysis of data based on multivariate spatial signs and ranks proceeds very much as
This paper propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models.
1 Introduction 1 1.1 Classical and robust approaches to statistics 1 1.2 Mean and standard deviation 2 1.3 The “three-sigma edit” rule 5 1.4 Linear regression 7 1.4.1 Straight-line regression 7 1.4.2 Multiple linear regression 9 1.5 Correlation coef
题目：Robust Visual Tracking using L1 Minimization 翻译：基于L1范数最小化的鲁棒性视觉追踪 Author 作者：Xue Mei 凌海滨 本文要解决的问题： occlusion, corruption and other challenging issues视觉追踪中存在的遮挡、腐蚀和其他调整性问题 方法：prosposed a robust visual tracking by casting tracking as a sparse approx
第三版的英文原文feedback system：An Introduction for Scientists and Engineers SECOND EDITION
Version v3.0i (2018-09-30)
Karl Johan °Astr¨om
Richard M. Murray
注：自己下载拼合而成Contents
Preface to the second edition
Preface to the first edition
X
Chapter 1. Introd
The RPCA model has achieved good performances in various applications. However, two defects limit its effectiveness. Firstly, it is designed for dealing with data in matrix form, which fails to exploit the structure information of higher order tensor
