Ayşın Ertuzun - Academia.edu (original) (raw)

Papers by Ayşın Ertuzun

Proceedings of 3rd IEEE International Conference on Image Processing, 1996

In this paper, the eight parameter two-dimensional adaptive lattice filter is used to detect defe... more In this paper, the eight parameter two-dimensional adaptive lattice filter is used to detect defects in textures corresponding to raw textile fabrics. A novel histogram modification technique is also applied for pre-processing the grey level texture image. Moreover, with the proposed scheme, it is possible to detect defects using the defective image only.

IEEE Transactions on Geoscience and Remote Sensing

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in de... more In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data.

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009

We investigate a hybrid method which improves the quality of state inference and parameter estima... more We investigate a hybrid method which improves the quality of state inference and parameter estimation in blind deconvolution of a sparse source modeled by a Bernoulli-Gaussian process. In this problem, when both the signal and the filter are jointly estimated, the true posterior is typically highly multimodal. Therefore, when not properly initialized, standard stochastic inference methods, (MCEM, SEM or SAEM), tend to get stuck and suffer from poor convergence. In our approach, we first relax the Bernoulli-Gaussian prior model by a Student-t model. Our simulations suggest that deterministic inference in the relaxed model is not only efficient, but also provides a very good initialization for the Bernoulli-Gaussian model. We provide simulation studies that compare the results obtained with and without our initialization method for several combinations of state inference and parameter estimation methods used for the Bernoulli-Gaussian model.

IEEE Transactions on Geoscience and Remote Sensing, 2000

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in de... more In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data.

Proceedings of 3rd IEEE International Conference on Image Processing, 1996

In this paper, the eight parameter two-dimensional adaptive lattice filter is used to detect defe... more In this paper, the eight parameter two-dimensional adaptive lattice filter is used to detect defects in textures corresponding to raw textile fabrics. A novel histogram modification technique is also applied for pre-processing the grey level texture image. Moreover, with the proposed scheme, it is possible to detect defects using the defective image only.

IEEE Transactions on Geoscience and Remote Sensing

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in de... more In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data.

2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009

We investigate a hybrid method which improves the quality of state inference and parameter estima... more We investigate a hybrid method which improves the quality of state inference and parameter estimation in blind deconvolution of a sparse source modeled by a Bernoulli-Gaussian process. In this problem, when both the signal and the filter are jointly estimated, the true posterior is typically highly multimodal. Therefore, when not properly initialized, standard stochastic inference methods, (MCEM, SEM or SAEM), tend to get stuck and suffer from poor convergence. In our approach, we first relax the Bernoulli-Gaussian prior model by a Student-t model. Our simulations suggest that deterministic inference in the relaxed model is not only efficient, but also provides a very good initialization for the Bernoulli-Gaussian model. We provide simulation studies that compare the results obtained with and without our initialization method for several combinations of state inference and parameter estimation methods used for the Bernoulli-Gaussian model.

IEEE Transactions on Geoscience and Remote Sensing, 2000

In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in de... more In this paper, we propose a Bayesian methodology for receiver function analysis, a key tool in determining the deep structure of the Earth's crust. We exploit the assumption of sparsity for receiver functions to develop a Bayesian deconvolution method as an alternative to the widely used iterative deconvolution. We model samples of a sparse signal as i.i.d. Student-t random variables. Gibbs sampling and variational Bayes techniques are investigated for our specific posterior inference problem. We used those techniques within the expectation-maximization (EM) algorithm to estimate our unknown model parameters. The superiority of the Bayesian deconvolution is demonstrated by the experiments on both simulated and real earthquake data.