Kernel adaptive filter

About: Kernel adaptive filter is a research topic. Over the lifetime, 8771 publications have been published within this topic receiving 142711 citations.

A systolic array realization of an LMS adaptive filter and the effects of delayed adaptation

Abstract: A systolic array design for an adaptive filter is presented. The filter is based on the least-mean-square algorithm, but due to the problems in implementation of the systolic array, a modified algorithm, a special case of the delayed LMS (DLMS), is used. The DLMS algorithm introduces a delay in the updating of the filter coefficients. The convergence and steady-state behavior of the systolic array are analyzed. It is shown that the performance of the systolic array is similar to that of a conventional LMS implementation for a wide range of practical conditions. >

Adaptive Rao–Blackwellized Particle Filter and Its Evaluation for Tracking in Surveillance

Abstract: Particle filters can become quite inefficient when being applied to a high-dimensional state space since a prohibitively large number of samples may be required to approximate the underlying density functions with desired accuracy. In this paper, by proposing an adaptive Rao-Blackwellized particle filter for tracking in surveillance, we show how to exploit the analytical relationship among state variables to improve the efficiency and accuracy of a regular particle filter. Essentially, the distributions of the linear variables are updated analytically using a Kalman filter which is associated with each particle in a particle filtering framework. Experiments and detailed performance analysis using both simulated data and real video sequences reveal that the proposed method results in more accurate tracking than a regular particle filter

Generalized selection weighted vector filters

Abstract: This paper introduces a class of nonlinear multichannel filters capable of removing impulsive noise in color images. The here-proposed generalized selection weighted vector filter class constitutes a powerful filtering framework for multichannel signal processing. Previously defined multichannel filters such as vector median filter, basic vector directional filter, directional-distance filter, weighted vector median filters, and weighted vector directional filters are treated from a global viewpoint using the proposed framework. Robust order-statistic concepts and increased degree of freedom in filter design make the proposed method attractive for a variety of applications. Introduced multichannel sigmoidal adaptation of the filter parameters and its modifications allow to accommodate the filter parameters to varying signal and noise statistics. Simulation studies reported in this paper indicate that the proposed filter class is computationally attractive, yields excellent performance, and is able to preserve fine details and color information while efficiently suppressing impulsive noise. This paper is an extended version of the paper by Lukac et al. presented at the 2003 IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (NSIP '03) in Grado, Italy.

Adaptive filter for dual energy radiographic imaging

Abstract: A radiographic scanner (A) generates a high energy image representation which is stored in a high energy image matrix (V) and a low energy image representation which is stored in a low energy image memory (U). A pair of filter functions selecting circuits (C) select a first or soft tissue specific filter function and second or bone specific filter function, respectively. The soft tissue filter function selecting circuit selects and adjusts the soft tissue filter function in accordance with the pixel value of the low energy image representation for each corresponding pair of pixel values. Convolvers (44, 46) convolve pixel values from the high and low energy image representations with the selected and adjusted filter functions. A soft tissue transform function (48) transforms the filtered high and low energy image representations into a soft tissue or other material specific image representation (42). The other filter selecting and adjusting circuit selects and adjusts the bone specific filter functions which are convolved with the high and low energy image representations by convolvers (54, 56). A bone specific transform function (58) transforms the filtered high and low energy image representations into a bone basis image.

Adaptive nonlinear digital filter with canonical piecewise-linear structure

Abstract: A novel adaptive nonlinear filter with the least-mean-square (LMS) error criterion is presented. It is based on the so-called canonical piecewise-linear structure. As an alternative to approaches based on the Wiener-Volterra series which have so far been widely employed for adaptive nonlinear filtering, the proposed approach can exhibit adaptive performance, especially in strongly nonlinear cases, while saving computation and implementation cost. The performance of this adaptive nonlinear filter is illustrated by computer simulation results. >