Phase corrections based on readout-asymmetry and navigatorechoes can effectively reduce the motion-related phase errors in multi-shot EPI for diffusion weighted imaging studies. However, occasional involuntary motions might be too severe to be corrected by the navigator echoes. To minimize this effect, kspace data swapping technique is proposed in this study. By replacing the corrupted k-space ...

The goal of machine learning is to develop predictors that generalize well to test data. Ideally, this is achieved by training on an almost infinitely large training data set that captures all variations in the data distribution. In practical learning settings, however, we do not have infinite data and our predictors may overfit. Overfitting may be combatted, for example, by adding a regularize...

This paper proposes a method of automatic facial reconstruction from a facial image partially corrupted by noise or occlusion. There are two key features of this method; the one is the automatic extraction of the correspondences ∗ corresponding author Facial Image Reconstruction from a Corrupted Image by SVDD 1213 between the corrupted input face and reference face without additional manual tas...

List Decoding and Property Testing of Error Correcting Codes Atri Rudra Chair of the Supervisory Committee: Associate Professor Venkatesan Guruswami Department of Computer Science and Engineering Error correcting codes systematically introduce redundancy into data so that the original information can be recovered when parts of the redundant data are corrupted. Error correcting codes are used ub...

Blood donation is a rather simple procedure, and most people have done so and feel that they can make it at any time. However, it is not uncommon for the public, blood donors and blood service operators to overlook the health and safety issues that could carry impact to both donors and blood donation. In this short review, we try to present an overview of the interaction between donor health an...

We consider the dimensionality-reduction problem (finding a subspace approximation of observed data) for contaminated data in the high dimensional regime, where the the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some (arbitrarily) corrupted observations. We propose a High-dimensional Robust Principal Component Analys...

Kernel Principal Component Analysis (KPCA) is a widely used technique for visualisation and feature extraction. Despite its success and flexibility, the lack of a probabilistic interpretation means that some problems, such as handling missing or corrupted data, are very hard to deal with. In this paper we exploit the probabilistic interpretation of linear PCA together with recent results on lat...

Missing data in corrupted audio recordings poses a challenging problem for audio signal processing. In this paper we present an approach that allows us to estimate missing values in the time-frequency domain of audio signals. The proposed approach, based on the Nonnegative Hidden Markov Model, enables more temporally coherent estimation for the missing data by taking into account both the spect...

Principal component analysis is a fundamental operation in computational data analysis, with myriad applications ranging from web search to bioinformatics to computer vision and image analysis. However, its performance and applicability in real scenarios are limited by a lack of robustness to outlying or corrupted observations. This paper considers the idealized “robust principal component anal...

The restoration of blurred images corrupted with impulse noise is a difficult problem which has been considered in a series of recent papers. These papers tackle the problem by using variational methods involving an L1shaped data-fidelity term. Because of this term, the relevant methods exhibit systematic errors at the corrupted pixel locations and require a cumbersome optimization stage. In th...