基于Pade逼近的gabor型滤波器CNN模板设计

Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on Pub Date : 2003-07-10 DOI:10.1109/SCS.2003.1226982

E. David, P. Ungureanu, M. Ansorge, L. Goras

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引用次数: 2

摘要

Gabor滤波器广泛应用于各种图像处理和计算机视觉应用中。由于计算密集，使用细胞神经网络(CNN)的模拟实现可能是一个有吸引力的解决方案。本文提出了一种基于高斯滤波器的Pade逼近的类Gabor滤波器CNN模板设计方法。对不同邻域半径下的近似误差进行了计算。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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On the CNN template design for Gabor-type filters based on Pade approximation

Gabor filters are widely used in various image processing and computer-vision applications. Being computationally intensive, analog implementation using Cellular Neural Networks (CNN) can be an attractive solution. In this communication is presented a method for CNN template design of Gabor like filters, based on Pade approximation of Gaussian filters. The errors of approximation are evaluated for various neighborhood radii.

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Signals, Circuits and Systems, 2003. SCS 2003. International Symposium on

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