Shivkumar Chandrasekaran

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During the past few years several interesting applications of eigenspace representation of images have been proposed. These include face recognition, video coding, and pose estimation. However, the vision research community has largely overlooked parallel developments in signal processing and numerical linear algebra concerning efficient eigenspace updating(More)
In this paper we develop a fast direct solver for large discretized linear systems using the supernodal multifrontal method together with low-rank approximations. For linear systems arising from certain partial differential equations such as elliptic equations, during the Gaussian elimination of the matrices with proper ordering, the fill-in has a low-rank(More)
Information-theoretic analyses for data hiding prescribe embedding the hidden data in the choice of quantizer for the host data. We propose practical realizations of this prescription for data hiding in images, with a view to hiding large volumes of data with low perceptual degradation. The hidden data can be recovered reliably under attacks, such as(More)
In this paper we develop a new superfast solver for Toeplitz systems of linear equations. To solve Toeplitz systems many people use displacement equation methods. With displacement structures, Toeplitz matrices can be transformed into Cauchy-like matrices using the FFT or other trigonometric transformations. These Cauchy-like matrices have a special(More)
Semiseparable matrices and many other rank-structured matrices have been widely used in developing new fast matrix algorithms. In this paper, we generalize the hierarchically semiseparable (HSS) matrix representations and propose some fast algorithms for HSS matrices. We represent HSS matrices in terms of general binary HSS trees and use simplified(More)
In this paper we present a fast direct solver for certain classes of dense structured linear systems that works by first converting the given dense system to a larger system of block sparse equations and then uses standard sparse direct solvers. The kind of matrix structures that we consider are induced by numerical low rank in the off-diagonal blocks of(More)
In this paper we study steganalysis, the detection of hidden data. Specifically we focus on detecting data hidden in grayscale images with spread spectrum hiding. To accomplish this we use a statistical model of images and estimate the detectability of a few basic spread spectrum methods. To verify the results of these findings, we create a tool to(More)
In this paper, we apply the theory of hypothesis testing to the steganalysis, or detection of hidden data, in the least significant bit (LSB) of a host image. The hiding rate (if data is hidden) and host probability mass function (PMF) are unknown. Our main results are as follows. a) Two types of tests are derived: a universal (over choices of host PMF)(More)
Quantization index modulation (QIM) techniques have been gaining popularity in the data hiding community because of their robustness and information-theoretic optimally against a large class of attacks. In this paper, we consider detecting the presence of QIM hidden data, which is an important consideration when data hiding is used for covert communication,(More)