A. Ben Hamza

Works (5)

Updated: July 5th, 2023 15:59

2003 article

A generalized divergence measure for robust image registration

He, N. Y., Hamza, A. B., & Krim, H. (2003, April 23). IEEE Transactions on Signal Processing, Vol. 51, pp. 1211–1220.

By: N. He  n, A. Hamza n & H. Krim n 

topics (OpenAlex): Sparse and Compressive Sensing Techniques; Infrared Target Detection Methodologies; Advanced Image Fusion Techniques
TL;DR: This paper defines a new generalized divergence measure, namely, the Jensen-Renyi (1996, 1976) divergence, and proposes a new approach to the problem of image registration based on it, to measure the statistical dependence between consecutive ISAR image frames. (via Semantic Scholar)
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Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2003 article

Geodesic Object Representation and Recognition

Hamza, A. B., & Krim, H. (2003, January 1). (B. Hamza & H. Krim, Eds.). Lecture Notes in Computer Science, Vol. 2886, pp. 378–387.

By: A. Hamza n & H. Krim n 

Ed(s): B. Hamza n & H. Krim n 

topics (OpenAlex): 3D Shape Modeling and Analysis; Image Processing and 3D Reconstruction; Robotics and Sensor-Based Localization
TL;DR: A shape signature that captures the intrinsic geometric structure of 3D objects is described, based on a global geodesic distance defined on the object surface, and takes the form of a kernel density estimate. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2003 article

Image Registration and Segmentation by Maximizing the Jensen-Rényi Divergence

Hamza, A. B., & Krim, H. (2003, January 1). (M. F. A. Rangarajan & J. Zerubia, Eds.). Lecture Notes in Computer Science, Vol. 2683, pp. 147–163.

By: A. Hamza n & H. Krim n 

Ed(s): M. Rangarajan & J. Zerubia

topics (OpenAlex): Statistical Mechanics and Entropy; Bayesian Methods and Mixture Models; Advanced Statistical Methods and Models
TL;DR: This paper analyzes the theoretical properties of the Jensen-Renyi divergence which is defined between any arbitrary number of probability distributions and derives its maximum value, and also some performance upper bounds in terms of the Bayes risk and the asymptotic error of the nearest neighbor classifier. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

2002 article

Unifying probabilistic and variational estimation

Hamza, A. B., Krim, H., & Unal, G. B. (2002, September 1). IEEE Signal Processing Magazine, Vol. 19, pp. 37–47.

By: A. Hamza*, H. Krim*  & G. Unal*

topics (OpenAlex): Image and Signal Denoising Methods; Medical Image Segmentation Techniques; Sparse and Compressive Sensing Techniques
TL;DR: This article presents a variational approach to MAP estimation with a more qualitative and tutorial emphasis and proposes an information-theoretic gradient descent flow which is a result of minimizing a functional that is a hybrid between a negentropy variational integral and a total variation. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: August 6, 2018

2001 article

A Variational Approach to Maximum a Posteriori Estimation for Image Denoising

Hamza, A. B., & Krim, H. (2001, January 1). (J. Z. M. Figueiredo & A. K. Jain, Eds.). Lecture Notes in Computer Science, Vol. 2134, pp. 19–33.

By: A. Hamza n & H. Krim n 

Ed(s): J. Figueiredo & A. Jain

topics (OpenAlex): Image and Signal Denoising Methods; Medical Image Segmentation Techniques; Advanced Vision and Imaging
TL;DR: A variational filter called Huber gradient descent flow is proposed, which yields the solution to a Huber type functional subject to some noise constraints, and the resulting filter behaves like a total variation anisotropic diffusion for large gradient magnitudes and like an isotropic diffusion for small gradient Magnitudes. (via Semantic Scholar)
Sources: NC State University Libraries, NC State University Libraries
Added: August 6, 2018

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