Application of the Compound Model of BP Neural Networks and Wavelet Transform in Image Definition Identification

Rui Kun Gong; Ya Nan Zhang; Chong Hao Wang; Li Jing Zhao

doi:10.4028/www.scientific.net/AMR.605-607.2265

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 605-607Application of the Compound Model of BP Neural...

Application of the Compound Model of BP Neural Networks and Wavelet Transform in Image Definition Identification

Abstract:

First, the background, significance and general implementation of the image definition identification are introduced. Then, basic theory of wavelet transform and neural network is expounded. An identification method of image definition based on the composite model of wavelet analysis and neural network is suggested．The two—dimensional discrete wavelet transformation is used to filter image signal and extract its brim character which is input into BP neural network for identification. 4 layers of BP neural network are constructed to perform image definition identification. The compound model is first trained by 90 images from the training set, and then is tested by 87 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

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Periodical:

Advanced Materials Research (Volumes 605-607)

Pages:

2265-2269

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.605-607.2265

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Online since:

December 2012

Authors:

Rui Kun Gong, Ya Nan Zhang, Chong Hao Wang, Li Jing Zhao

Keywords:

BP Neural Network (BPNN), Image Definition, Image Processing, Wavelet Transform (WT)

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