A comparison of different window formulations for two-dimensional FIR filter design

Speake, T.; Mersereau, R.M.

doi:10.1109/icassp.1979.1170812

Cited by 10 publications

(4 citation statements)

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“…Among the evolutionary approaches, four non-genetic approaches are used in their initial versions; the parameters are listed in Table 2. Many studies presenting conventional and alternative approaches are available in the literature [39][40][41][42][43][44][45][46][47][48][49][50][51][52]. Among the conventional approaches, two of them based on Remez and least squares were tested.…”

Section: Resultsmentioning

confidence: 99%

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Boudjelaba

Ros

Chikouche

2014

IET signal process.

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International audienceThe design of finite impulse response (FIR) filters can be formulated as a non-linear optimization problem reputed to be difficult for conventional approaches. The constraints are high and a large number of parameters have to be estimated, especially when dealing with 2-D FIR filters. In order to improve the performance of conventional approaches, we explore several stochastic methodologies capable of handling large spaces. We specifically propose a new genetic algorithm in which some innovative concepts are introduced to improve the convergence and make its use easier for practitioners. The algorithm is globally improved by adapting the mutation and crossover and selection operators with the genetic advances. A dynamic ranking selection scheme is introduced to limit the promotion of extraordinary chromosomes. A refreshing mechanism is investigated to manage the trade-off between diversity and elitism. The key point of our approach stems from the capacity of the genetic algorithm (GA) to adapt the genetic operators during the genetic life while remaining simple and easy to implement. Most of parameters and operators are changed by the GA itself. From an initial calibration, the GA performs the design problem while calibrating and repeatedly re-calibrating itself for solving it. We demonstrate on various cases of filter design a significant improvement in performance

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Section: Resultsmentioning

confidence: 99%

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Boudjelaba

Ros

Chikouche

2014

IET signal process.

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“…There are two related types of window functions that can satisfy this requirement for the 2D extension. The first is the Cartesian product window proposed by Speake and Mersereau [70], which exploits the separation property of each filter and is defined as

w_{normalS} false(x, y false) = w_{1} false(x false) w_{2} false(y false),

where

w_{1} false(x false)

and

w_{2} false(y false)

must both be good 1D windows.…”

Section: Methodsmentioning

confidence: 99%

Section: Two-dimensional Casementioning

confidence: 99%

Edge feature enhancement approach using hilbert transform of Cauchy distribution and its applications

Wang

Rohde

Xiao

2021

IET Image Processing

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Building on past work showing that the Hilbert transform can be used for edge feature enhancement, a new edge feature enhancement method is developed using a twodimensional (2D) isotropic Hilbert transform of the Cauchy distribution. First, both the shape of the Hilbert kernel and the Hilbert transform of edge feature models (step and delta) and various simulated signals result in edge feature enhancement, the properties of which are derived and confirmed. Second, Cauchy distribution as low-pass filter is introduced in Hilbert transform to get new edge feature enhancement operator, and its efficiency under various criteria is comprehensively discussed. Third, a 2D isotropic extension is presented using a circularly symmetric window function. Finally, two experiments, including edge detection and image segmentation, are performed to validate the proposed edge feature enhancement method. The experimental results of the method applied to the Berkeley Segmentation Dataset and remote sensing images demonstrate that the new method is effective for edge detection and image segmentation. INTRODUCTIONEdges are important features in images and provide prominent information about objects in the natural scenes, while edge strength is essential to computer vision, image processing, and pattern recognition, especially directly edge detection, contour detection, and image segmentation. Its applications benefit many fields, including image segmentation [1-3], object recognition[4], remote sensing [5,6], medical imaging [7-9], 3D reconstruction [10], face recognition [11,12], and image retrieval [11,13]. In the past several decades, various edge feature enhancement approaches have been proposed, including gradient-based methods [14][15][16], morphology gradient methods [17,18], logicbased methods [19], machine learning methods [2,20,21], local energy [22,23], and phase congruency [24,25]. Though considerable progress has been made regarding the efficiency of edge feature enhancement, there is still a challenge, particularly obtaining more efficient edge strength for further application.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In some filter specifications the desired responses are characterized by ideal frequency responses in which passbands and stopbands are separated by straight-line boundaries that are not necessarily parallel to the frequency axes. Examples of these are the various kinds of fan filters [4,15,17,27] and diamondshaped filters [6,48]. Other shapes with straight-line boundaries are also approximated [8,9,13,29,28,50].…”

Section: Examplementioning

confidence: 99%

Two-Dimensional FIR Filters

Çetin¹,

Ansari²

2001

Knowledge Management

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A comparison of different window formulations for two-dimensional FIR filter design

Cited by 10 publications

References 1 publication

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Adaptive genetic algorithm‐based approach to improve the synthesis of two‐dimensional finite impulse response filters

Edge feature enhancement approach using hilbert transform of Cauchy distribution and its applications

Two-Dimensional FIR Filters

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