The problem of detection of multiple compbex sinusoidals, with uncertain parameters, is addressed in this paper. It is shown that uncertainties in amplitude and small uncertainties in frequency can be handled analytically, while unknown phases must be handled numerically. Robust detectors for some or all of the uncertainties are formulated. Performance in noise, and robustness are evaluated thr...

This paper presents a statistical shape model for automatic skull stripping of MR brain images. A surface model of the brain boundary is hierarchically represented by a set of overlapping surface patches, each of which has elastic properties and deformation range that is learned from a training set. The model’s deformation is hierarchical which adds robustness to local minima. Moreover, the def...

In [13], we introduced and analyzed a recovery-based a posteriori error estimator for conforming linear finite element approximation to interface problems. It was shown theoretically that the estimator is robust with respect to the size of jumps provided that the distribution of coefficients is locally monotone. Numerical examples showed that this condition is unnecessary. This paper extends th...

Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforeme...

Shadows are integral parts of natural scenes and one of the elements contributing to naturalness of synthetic scenes. In many image analysis and interpretation applications, shadows interfere with fundamental tasks such as object extraction and description. For this reason, shadow segmentation is an important step in image analysis. In this paper, we propose a new cast shadow segmentation algor...

Implied volatility expansions allow calibration of sophisticated volatility models. They provide an accurate fit and parametrization of implied volatility surfaces that is consistent with empirical observations. Fine-grained higher order expansions offer a better fit but pose the challenge of finding a robust, stable and computationally tractable calibration procedure due to a large number of m...

This paper introduces quasi-maximum likelihood estimator for discretely observed diffusions when a closed-form transition density is unavailable. Higher order Wagner-Platen strong approximation is used to derive the first two conditional moments and a normal density function is used in estimation. Simulation study shows that the proposed estimator has high numerical precision and good numerical...

System identiication takes a space of possible models and a stream of observational data of a physical system, and attempts to identify the element of the model space that best describes the observed system. In traditional approaches, the model space is speciied by a parameterized diierential equation, and identiication selects numerical parameter values so that simulation of the model best mat...