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Fast Pattern Detection Using Normalized Neural Networks and Cross-Correlation in the Frequency Domain

Abstract

Neural networks have shown good results for detection of a certain pattern in a given image. In our previous work, a fast algorithm for object/face detection was presented. Such algorithm was designed based on cross-correlation in the frequency domain between the input image and the weights of neural networks. Our previous work also solved the problem of local subimage normalization in the frequency domain. In this paper, the effect of image normalization on the speedup ratio of pattern detection is presented. Simulation results show that local subimage normalization through weight normalization is faster than subimage normalization in the spatial domain. Moreover, the overall speedup ratio of the detection process is increased as the normalization of weights is done offline.

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Correspondence to Hazem M. El-Bakry.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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El-Bakry, H.M., Zhao, Q. Fast Pattern Detection Using Normalized Neural Networks and Cross-Correlation in the Frequency Domain. EURASIP J. Adv. Signal Process. 2005, 404897 (2005). https://doi.org/10.1155/ASP.2005.2054

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  • DOI: https://doi.org/10.1155/ASP.2005.2054

Keywords and phrases

  • fast pattern detection
  • neural networks
  • cross-correlation
  • image normalization