Topic
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.
Papers published on a yearly basis
Papers
More filters
Patent•
18 Sep 1991TL;DR: In this article, a method for processing a field of image data samples to provide for one or more of the functions of decimation, interpolation, and sharpening is accomplished by use of an array transform processor such as that employed in a JPEG compression system.
Abstract: A method for processing a field of image data samples to provide for one or more of the functions of decimation, interpolation, and sharpening is accomplished by use of an array transform processor such as that employed in a JPEG compression system. Blocks of data samples are transformed by the discrete even cosine transform (DECT) in both the decimation and interpolation processes, after which the number of frequency terms is altered. In the case of decimation, the number of frequency terms is reduced, this being followed by inverse transformation to produce a reduced-size matrix of sample points representing the original block of data. In the case of interpolation, additional frequency components of zero value are inserted into the array of frequency components after which inverse transformation produces an enlarged data sampling set without an increase in spectral bandwidth. In the case of sharpening, accomplished by a convolution or filtering operation involving multiplication of transforms of data and filter kernel in the frequency domain, there is provided an inverse transformation resulting in a set of blocks of processed data samples. The blocks are overlapped followed by a savings of designated samples, and a discarding of excess samples from regions of overlap. The spatial representation of the kernel is modified by reduction of the number of components, for a linear-phase filter, and zero-padded to equal the number of samples of a data block, this being followed by forming the discrete odd cosine transform (DOCT) of the padded kernel matrix.
156 citations
Book•
01 Jan 2003
TL;DR: This chapter discusses the design and implementation of Filter Design and Implementation for Multivariate Signal Processing, as well as some of the techniques used in Image Processing Fundamentals.
Abstract: Chapter 1. Fundamental Concepts.Chapter 2. Fourier Analysis.Chapter 3. Z-Transform and Digital Filters.Chapter 4. Filter Design and Implementation.Chapter 5. Multivariate Signal Processing.Chapter 6. Finite-Wordlength Effects.Chapter 7. Adaptive Signal Processing.Chapter 8. Least-Squares Adaptive Algorithms.Chapter 9. Linear Prediction.Chapter 10. Image Processing Fundamentals.Chapter 11. Image Compression and Coding.Appendix. Concepts of Linear Algebra.Index.
156 citations
TL;DR: The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter, computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise.
Abstract: The authors outline the design of an optimal, computationally efficient, infinite impulse response edge detection filter. The optimal filter is computed based on Canny's high signal to noise ratio, good localization criteria, and a criterion on the spurious response of the filter to noise. An expression for the width of the filter, which is appropriate for infinite-length filters, is incorporated directly in the expression for spurious responses. The three criteria are maximized using the variational method and nonlinear constrained optimization. The optimal filter parameters are tabulated for various values of the filter performance criteria. A complete methodology for implementing the optimal filter using approximating recursive digital filtering is presented. The approximating recursive digital filter is separable into two linear filters, operating in two orthogonal directions. The implementation is very simple and computationally efficient. has a constant time of execution for different sizes of the operator, and is readily amenable to real-time hardware implementation. >
156 citations
TL;DR: With the LS-SVMAF, the least squares support vector machines adaptive filter, this paper can model and predict the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery.
Abstract: One of the main problems for effective control of a minimally invasive surgery (MIS) is the imprecision that caused by hand tremor. In this paper, a novel adaptive filter, the least squares support vector machines adaptive filter (LS-SVMAF), is proposed to overcome this problem. Compared with traditional methods like multi layer perceptron (MLP), LS-SVM shows a superior performance of nonlinear modeling with small scale of data set or high dimensional input space. With the LS-SVMAF, we can model and predict the hand tremor more effectively and improve the precision and reliability in the master–slave robotic system for microsurgery. Simulation results demonstrate the effectiveness of the proposed filter and its superior performance over its competing rivals.
155 citations
TL;DR: A new class of nonlinear adaptive filters, consisting of a linear combiner followed by a flexible memory-less function, is presented, based on a spline function that can be modified during learning.
155 citations