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EN
FIR filters are often applied, as they possess many advantages, including linear-phase response and well elaborated design methods. However, group delay introduced by FIR filters is usually large. The reduction of group delay can be obtained by restriction of the linear phase requirement only to the passband. One of the problems that appear while designing FIR filters with a prescribed value of group delay is the choice of the filter order. In the paper a formula for filter order calculation for the given filter parameters and dedicated for equiripple or quasi-equiripple approximation of the magnitude response has been derived based on experiments. Numerous examples that explain how to use the derived formula have been included.
EN
Known methods for IIR digital filters design require specification of filter order. In case of direct methods applied for filters with the prescribed linear phase response, the order cannot be derived from the specified filter parameters. The authors propose a procedure for filter order estimation, based on design of the FIR filter prototype, with the same design parameters. Two examples demonstrate application of the proposed procedure for filter design tasks. Comparison of the filter order obtained by the discussed approach with the real value has been also presented.
EN
The linear-phase IIR filters are described in many cases, mainly due to distortion-free transmission of signals. One of the major problems of IIR filter design is stability, which can be obtained with suitable value of group delay τ This paper concerns calculation of filter order Ν and group delay τ in case of quasi-equiripple design of IIR filters. We propose a novel procedure for determining N and τ values; the procedure is valid for all types of filters with arbitrary number of zeros and a few non-zero poles. Evaluation of the proposed approach as well as examples illustrating its application are provided in the paper.
4
Content available remote Markov random fields and constrained optimization for textured image segmentation
EN
Classical methods of image segmentation , like discontinuity detection or region growing concepts, are not satisfactory in case in textured images. The alternative is the application of stochastic models like Markov Random Fields (MRF) for image modelling and segmentation. Stochastic model may be described in terms of energy function that should be minimized during a relaxation procedure. Instead of doubly-stochastic model, in which boty the intensity and the label process are modelled by the set of deterministic features. Local texture properties are evaluated using local linear transforms or results from the first order histogram. We measure the disparity between spatial freatures on the basis of the Kolmogorov-Smirnov statistics. Stochastic relaxation algorithms is applied for the minimization of the global energy function. The forbidden label configuration task are given. The examples presented in the paper confirm the usefulness of proposed models and the efficiency of the designed algorithms. Parallel implementation of the constrained optimization can be considered due to the local computation.
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