It is known that many image enhancement methods have a tradeoff between noise suppression and edge enhancement. In this paper, we propose a new technique for image enhancement filtering and explain it in human visual perception theory. It combines kernel regression and local homogeneity and evaluates the restoration performance of smoothing method. First, image is filtered in kernel regression....

Text categorization plays a crucial role in both academic and commercial platforms due to the growing demand for automatic organization of documents. Kernel-based classification algorithms such as Support Vector Machines (SVM) have become highly popular in the task of text mining. This is mainly due to their relatively high classification accuracy on several application domains as well as their...

This paper develops adaptive nonparametric methods for analyzing seismic data. Kernel smoothing techniques are suitable for space-time point processes; however, they must be adapted to deal with the nonstationarity of earthquakes. By this we mean changes in the spatial and temporal pattern of point occurrences. A class of recursive kernel density and regression estimators are proposed to study ...

We propose a cross-validation method suitable for smoothing of kernel quantile estimators. In particular, our proposed method selects the bandwidth parameter, which is known to play a crucial role in kernel smoothing, based on unbiased estimation of a mean integrated squared error curve of which the minimising value determines an optimal bandwidth. This method is shown to lead to asymptotically...

A lot of groups and individual developers work on improving the Linux kernel. Many innovative new features are developed all the time. The best and smoothest way to distribute and maintain new kernel features is to incorporate them into the standard mainline source tree. This involves a review process and some standard conventions. Unfortunately actually getting innovative new features through ...

Modal regression estimates the local modes of the distribution of Y given X = x, instead of the mean, as in the usual regression sense, and can hence reveal important structure missed by usual regression methods. We study a simple nonparametric method for modal regression, based on a kernel density estimate (KDE) of the joint distribution of Y and X. We derive asymptotic error bounds for this m...

We present a novel surface smoothing framework using the Laplace-Beltrami eigenfunctions. The Green’s function of an isotropic diffusion equation on a manifold is analytically represented using the eigenfunctions of the Laplace-Beltraimi operator. The Green’s function is then used in explicitly constructing heat kernel smoothing as a series expansion of the eigenfunctions. Unlike many previous ...

Introduction: Over the years, various diffusion based cortical surface data smoothing techniques [1,2] have been proposed but without numerical validation. We present a novel validation technique that uses the analytical solution of a diffusion equation as the ground truth. The proposed framework is used in validating and comparing the performance of heat kernel smoothing [2] and the weighted s...

The perspectives and methods of functional data analysis and longitu dinal data analysis for smoothing are contrasted and compared Topics include kernel methods and random e ects models for smoothing basis function methods and examination of correlates of curve shapes Some directions in which method ology might advance are identi ed