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- Dmitry M. Malioutov, Müjdat Çetin, Alan S. Willsky
- IEEE Transactions on Signal Processing
- 2005

We present a source localization method based on a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing penalties based on the /spl lscr//sub 1/-norm. A number of recent theoretical results on sparsifying properties of /spl lscr//sub 1/ penalties justify this… (More)

- Müjdat Çetin, W. Clem Karl
- IEEE Trans. Image Processing
- 2001

We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Compared to conventional… (More)

- Junmo Kim, John W. Fisher, Anthony J. Yezzi, Müjdat Çetin, Alan S. Willsky
- IEEE Transactions on Image Processing
- 2005

In this paper, we present a new information-theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the… (More)

- Dmitry M. Malioutov, Müjdat Çetin, Alan S. Willsky
- ICASSP
- 2005

We explore the application of a homotopy continuation-based method for sparse signal representation in overcomplete dictionaries. Our problem setup is based on the basis pursuit framework, which involves a convex optimization problem consisting of terms enforcing data fidelity and sparsity, balanced by a regularization parameter. Choosing a good… (More)

In this paper, we present a novel information theoretic approach to image segmentation. We cast the segmentation problem as the maximization of the mutual information between the region labels and the image pixel intensities, subject to a constraint on the total length of the region boundaries. We assume that the probability densities associated with the… (More)

- Lee C. Potter, Emre Ertin, Jason T. Parker, Müjdat Çetin
- Proceedings of the IEEE
- 2010

| Remote sensing with radar is typically an ill-posed linear inverse problem: a scene is to be inferred from limited measurements of scattered electric fields. Parsimonious models provide a compressed representation of the unknown scene and offer a means for regularizing the inversion task. The emerging field of compressed sensing combines nonlinear… (More)

- Müjdat Çetin, Lei Chen, +4 authors A. Willsky
- IEEE Signal Processing Magazine
- 2006

This paper presents an overview of research conducted to bridge the rich field of graphical models with the emerging field of data fusion for sensor networks. Both theoretical issues and prototyping applications are discussed in addition to suggesting new lines of reasoning.

- Junmo Kim, Müjdat Çetin, Alan S. Willsky
- 2005 13th European Signal Processing Conference
- 2005

When segmenting images of low quality or with missing data, statistical prior information about the shapes of the objects to be segmented can significantly aid the segmentation process. However, defining probability densities in the space of shapes is an open and challenging problem. In this paper, we propose a nonparametric shape prior model for image… (More)

- Walter Sun, Müjdat Çetin, +4 authors Alan S. Willsky
- IPMI
- 2005

Having accurate left ventricle (LV) segmentations across a cardiac cycle provides useful quantitative (e.g. ejection fraction) and qualitative information for diagnosis of certain heart conditions. Existing LV segmentation techniques are founded mostly upon algorithms for segmenting static images. In order to exploit the dynamic structure of the heart in a… (More)

We present a source localization method based upon a sparse representation of sensor measurements with an overcomplete basis composed of samples from the array manifold. We enforce sparsity by imposing an `1-norm penalty; this can also be viewed as an estimation problem with a Laplacian prior. Explicitly enforcing the sparsity of the representation is… (More)