Hasan Al-Marzouqi

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The procedure currently used for cochlear implementation requires wide surgical exposure to identify anatomic landmarks. At our institution a minimally invasive technique is being developed that will permit performing the procedure with a single pass of a surgical drill as opposed to the current wide-field technique [1]. This technique does, however,(More)
In this work we propose using the coefficient of variation as a cost function to improve seismic data representation in the curvelet domain. Performance improvement is demonstrated in denoising and compressed sensing data recovery. The demonstrated approach can be extended to other seismic applications and alternate transforms.
Curvelets were recently introduced as a popular extension of wavelets. In the curvelet domain the input image is represented by sets of coefficients representing signal energy in different scales and angular directions. In this paper an algorithm that searches for optimal tilings for use with the curvelet transform is introduced. We consider two(More)
The curvelet transform is a recently introduced non-adaptive multi-scale transform that have gained popularity in the image processing field. In this paper, we study the effect of customized tiling of frequency content in the curvelet transform. Specifically, we investigate the effect of the size of the coarsest level and its relationship to denoising(More)
This paper presents a new method for texture based image retrieval. The proposed algorithm uses a periodically extended variant of the curvelet transform. The sum of the absolute value of differences in the mean and standard deviation between curvelet wedges representing the query image and the test image is used as the distance index. Performance(More)
Sparse representations for signals and images have been used extensively in various image processing tasks. In this work, we use the curvelet transform as a sparsity inducing tool in neural networks. Nowadays, there is much interest in research and development of efficient algorithms that reduce the computational demands of training neural networks. We(More)
In this paper we propose a new density based clustering algorithm. As with other density based clustering algorithms our approach does not require the number of clusters as input. A modification of the Kuwahara filter, used in image processing, is used to generate a special density map in which the brightness of pixels is indicative of the density of the(More)
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