Works (5)

Updated: July 5th, 2023 15:59

2003 journal article

A generalized divergence measure for robust image registration

IEEE Transactions on Signal Processing, 51(5), 1211–1220.

By: Y. He n, A. Hamza n & H. Krim n

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)
Source: NC State University Libraries
Added: August 6, 2018

2003 chapter

Geodesic object representation and recognition

In B. Hamza & H. Krim (Eds.), Discrete geometry for computer imagery: 11th International Conference, DGCI 2003, Naples, Italy, November 19-21, 2003 (Vol. 2886, pp. 378–387).

Ed(s): . B. Hamza & H. Krim n

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)
Source: NC State University Libraries
Added: August 6, 2018

2003 chapter

Image registration and segmentation by maximizing the Jensen-Renyi divergence

In M. F. A. Rangarajan & J. Zerubia (Eds.), Energy minimization methods in computer vision and pattern recognition (Vol. 2683, pp. 147–163).

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

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)
Source: NC State University Libraries
Added: August 6, 2018

2002 journal article

Unifying probabilistic and variational estimation


By: A. Hamza*, H. Krim* & G. Unal*

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)
Source: Web Of Science
Added: August 6, 2018

2001 journal article

A variational approach to maximum a posteriori estimation for image denoising

Energy Minimization Methods in Computer Vision and Pattern Recognition: Third International Workshop, EMMCVPR 2001, Sophia Antipolis, France, September 3-5, 2001: Proceedings, 2134, 19–33.

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

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)
Source: NC State University Libraries
Added: August 6, 2018

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