Digital image processing techniques are useful in abnormality detection in mammogram images. Recently, texture based image segmentation of mammogram images has become popular due to its better precision and accuracy. Local Binary Pattern has been a recently proposed texture descriptor which attracted the research community rigorously towards texture based analysis of digital images. Many textur...

For real-time disparity estimation from stereo images the coordinates of feature points are evaluated. This paper analyses the influence of camera noise on the accuracy of feature point coordinates of a feature point detector similar to the Harris Detector, modified for disparity estimation. As a result the error variance of the horizontal coordinate of each feature point and the variance of ea...

Four types of global error for initial value problems are considered in a common framework. They include classical forward error analysis and shadowing error analysis together with extensions of both to rescaling of time. To determine the amplification of the local error that bounds the global error we present a linear analysis similar in spirit to condition number estimation for linear systems...

This paper presents a numerical study of a posteriori error estimators for convection±di€usion equations. The study involves the gradient indicator, an a posteriori error estimator which is based on gradient recovery, three residual-based error estimators for di€erent norms, and two error estimators which are de®ned by solutions of local Neumann problems. They are compared with respect to the r...

The a posteriori error estimation of finite element approximations of elliptic boundary value problems has reached some state of maturity, as it is documented by a variety of monographs on this subject (cf., e.g., [1, 2, 3, 4, 5]). There are different concepts such as • residual type a posteriori error estimators, • hierarchical type a posteriori error estimators, • error estimators based on lo...

In this paper, we study adaptive nonparametric regression estimation in the presence of conditional heteroskedastic error terms. We demonstrate that both the conditional mean and conditional variance functions in a nonparametric regression model can be estimated adaptively based on the local profile likelihood principle. Both the one-step Newton-Raphson estimator and the local profile likelihoo...

Introduction: Noise level estimation in DTI plays a crucial role in determining outliers, fitting likelihood-based tensor models, and estimating the reliability of measured quantities. However, noise level estimation methods developed for other magnetic resonance techniques are often inappropriate for DTI acquisitions. For example, noise level cannot be directly computed from background regions...