Many problems in early vision are ill posed 1. Edge detection is a typical example. This paper applies regular-ization techniques to the problem of edge detection. We derive an optimal filter for edge detection with a size controlled by the regularization parameter and compare it to the Gaussian filter. A formula relating the signal-to-noise ratio to the parameter is derived from regularization...

Seismic data are often inadequately or irregularly sampled along spatial axes. Irregular sampling can produce artifacts in seismic imaging results. We present a new approach to interpolate aliased seismic data based on adaptive predictionerror filtering (PEF) and regularized nonstationary autoregression. Instead of cutting data into overlapping windows (patching), a popular method for handling ...

rotating machines in particular induction electrical machines are important industry instruments. in manufacturing, electrical motors are exposed to many damages, and this causes stators and rotors not to work correctly. in this paper we addressed modal analysis and an intelligent method to detect motor load condition and also the stator faults such as turn-to-turn and coil-to-coil faults using...

this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman's filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman's filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients...

We report on the successful application of feature selection methods to a classification problem in molecular biology involving only 72 data points in a 7130 dimensional space. Our approach is a hybrid of filter and wrapper approaches to feature selection. We make use of a sequence of simple filters, culminating in Koller and Sahami’s (1996) Markov Blanket filter, to decide on particular featur...

Image deconvolution is still to be a challenging illposed problem for recovering a clear image from a given blurry image, when the point spread function is known. Although competitive deconvolution methods are numerically impressive and approach theoretical limits, they are becoming more complex, making analysis, and implementation difficult. Furthermore, accurate estimation of the regularizati...

In this paper we show that a large class of regularization methods designed for solving ill-posed inverse problems gives rise to novel learning algorithms. All these algorithms are consistent kernel methods which can be easily implemented. The intuition behind our approach is that, by looking at regularization from a filter function perspective, filtering out undesired components of the target ...

Collinearity and near-collinearity of predictors cause difficulties when doing regression. In these cases, variable selection becomes untenable because of mathematical issues concerning the existence and numerical stability of the regression coefficients, and interpretation of the coefficients is ambiguous because gradients are not defined. Using a differential geometric interpretation, in whic...