ScholarBank@NUShttps://scholarbank.nus.edu.sgThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sat, 19 Oct 2019 20:09:27 GMT2019-10-19T20:09:27Z5061- An accelerated Gauss-Seidel method for inverse modelinghttps://scholarbank.nus.edu.sg/handle/10635/54977Title: An accelerated Gauss-Seidel method for inverse modeling
Authors: Ng, T.M.; Farhang-Boroujeny, B.; Garg, H.K.
Abstract: Inverse modeling is an application for adaptive filters that has found extensive use in many engineering disciplines. In this paper, we consider the problem of finding inverse models in the area of channel equalization, and adaptive control systems. First, the problem is formulated in a general setting as a standard least squares problem. With this, the inverse model can be found using any one of the many well established least squares methods. One such method is the classical Gauss-Seidel method. As the Gauss-Seidel method has the limitation of being slow in converging to the required solution when applied to inverse modeling, we propose a new acceleration technique to speed up its convergence. © 2002 Elsevier Science B.V. All rights reserved.
Sat, 01 Mar 2003 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/549772003-03-01T00:00:00Z
- Wavelet domain watermarking using maximum-likelihood detectionhttps://scholarbank.nus.edu.sg/handle/10635/72182Title: Wavelet domain watermarking using maximum-likelihood detection
Authors: Ming, N.T.; Garg, H.K.
Abstract: A digital watermark is an imperceptible mark placed on multimedia content for a variety of applications including copyright protection, fingerprinting, broadcast monitoring, etc. Traditionally, watermark detection algorithms are based on the correlation between the watermark and the media the watermark is embedded in. Although simple to use, correlation detection is only optimal when the watermark embedding process follows an additive rule and when the media is drawn from Gaussian distributions. More recent works on watermark detection are based on decision theory. In this paper, a maximum-likelihood (ML) detection scheme based on Bayes' decision theory is proposed for image watermarking in wavelet transform domain. The decision threshold is derived using the Neyman-Pearson criterion to minimize the missed detection probability subject to a given false alarm probability. The detection performance depends on choosing a probability distribution function (PDF) that can accurately model the distribution of the wavelet transform coefficients. The generalized Gaussian PDF is adopted here. Previously, the Gaussian PDF, which is a special case, has been considered for such detection scheme. Using extensive experimentation, the generalized Gaussian PDF is shown to be a better model.
Thu, 01 Jan 2004 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/721822004-01-01T00:00:00Z
- Wavelet domain watermarking using maximum likelihood detectionhttps://scholarbank.nus.edu.sg/handle/10635/57798Title: Wavelet domain watermarking using maximum likelihood detection
Authors: Ng, T.M.; Garg, H.K.
Abstract: Traditionally, digital watermark detection algorithms are based on the correlation between the watermark and the media the watermark is embedded in. Although simple to use, correlation detection is optimal only when the watermark embedding process follows an additive rule and when the medium is drawn from Gaussian distributions. More recent works on watermark detection are based on decision theory. In this article, a maximum likelihood detection scheme based on Bayes' decision theory is proposed for image watermarking in the wavelet transform domain. The decision threshold is derived using the Neyman-Pearson criterion to minimize the missed detection probability subject to a given false alarm probability. The detection performance depends on choosing a probability distribution function (PDF) that can accurately model the distribution of the wavelet transform coefficients. The generalized Gaussian PDF is adopted here. Previously, the Gaussian PDF, which is a special case, has been considered for such detection scheme. Using extensive experimentation, the generalized Gaussian PDF is shown to be a better model. ©2005, IS&T-The Society for Imaging Science and Technology.
Sun, 01 May 2005 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/577982005-05-01T00:00:00Z
- Maximum likelihood detection in image watermarking using generalized gamma modelhttps://scholarbank.nus.edu.sg/handle/10635/70891Title: Maximum likelihood detection in image watermarking using generalized gamma model
Authors: Ng, T.M.; Garg, H.K.
Abstract: Digital image watermark can be detected in transform domain using maximum likelihood (ML) detection, whereby the decision threshold is obtained using the NeymanPearson criterion. A probability distribution function (PDF) is required to correctly model the statistical behavior of the transform coefficients. In the literature, this detection method has been considered by modeling magnitude of a set of discrete Fourier transform (DFT) coefficients using a Weibull PDF. In this paper, we propose extending the Weibull model to a generalized gamma model. For the work here, we also propose new estimators for parameters of the generalized gamma PDF. © 2005 IEEE.
Sat, 01 Jan 2005 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/708912005-01-01T00:00:00Z
- A maximum a-posteriori identification criterion for wavelet domain watermarkinghttps://scholarbank.nus.edu.sg/handle/10635/54338Title: A maximum a-posteriori identification criterion for wavelet domain watermarking
Authors: Ng, T.M.; Garg, H.K.
Abstract: A detector based on Maximum A-posteriori Probability (MAP) criterion has been introduced for identifying image watermark in the Discrete Cosine Transform (DCT) domain. This type of detector has been shown to be optimum and robust to common image processing operations. In this paper, an MAP detector in the Discrete Wavelet Transform (DWT) domain is proposed. It is based on modelling the wavelet coefficients by a generalised Gaussian distribution. Simulations are used to compare the proposed detector with the conventional correlation detector in terms of detection effectiveness. Copyright © 2009 Inderscience Enterprises Ltd.
Sun, 01 Nov 2009 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/543382009-11-01T00:00:00Z
- Maximum-likelihood detection in DWT domain image watermarking using Laplacian modelinghttps://scholarbank.nus.edu.sg/handle/10635/56595Title: Maximum-likelihood detection in DWT domain image watermarking using Laplacian modeling
Authors: Ng, T.M.; Garg, H.K.
Abstract: Digital image watermarks can be detected in the transform domain using maximum-likelihood detection, whereby the decision threshold is obtained using the Neyman-Pearson criterion. A probability distribution function is required to correctly model the statistical behavior of the transform coefficients. Earlier work has considered modeling the discrete wavelet transform coefficients using a Gaussian distribution. Here, we introduce a Laplacian model and establish via simulation that it can result in a better performance than the Gaussian model. © 2005 IEEE.
Fri, 01 Apr 2005 00:00:00 GMThttps://scholarbank.nus.edu.sg/handle/10635/565952005-04-01T00:00:00Z