Image Identification Based on the Compound Model of Wavelet Transform and RBF Neural Networks

Rui Kun Gong; Ping Ting Liu; Yu Han Gong; Chong Hao Wang

doi:10.4028/www.scientific.net/AMM.513-517.4152

Paper Titles

An Analysis on the Effect of External Shock Loads on Building Structure Vibration
p.4135

An Accurate Prediction Method for Budgets of Large Construction Project
p.4139

A Fabric Simulation Optimization Method Based on Intelligent 3D Vision Technology
p.4143

On Piston Rod System Operation Analysis and Piston Structure Optimization Research
p.4147

Image Identification Based on the Compound Model of Wavelet Transform and RBF Neural Networks
p.4152

Designing Parameters of Ion-Implanted Channel Waveguide Using Beam Propagation Method
p.4156

Multi-Valued Decision Diagram Based Reliability Analysis of Demand-Based Warm Standby Systems with Imperfect Fault Coverage
p.4161

Propagation and Responses of EMP on Transmission-Line Networks
p.4167

Photon Correlator Implemented by Field Programmable Gate Array
p.4171

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 513-517Image Identification Based on the Compound Model...

Image Identification Based on the Compound Model of Wavelet Transform and RBF Neural Networks

Abstract:

The image definition identification method based on the composite model of wavelet transform and neural networks is stronger in image edge character extraction, nonlinear process, self-adapted study and pattern recognition. The paper puts forward an evaluation method of image definition based on the focusing mechanism of simulating persons eyes by neural networks and on the composite model of wavelet transformation and neural networks. The wavelet component statistics obtained by the wavelet transform are taken as the inputs of the 5 layer RBF neural network model. The model identifies the image definition applying the steepest descent method of the additional momentum in a variable step size to adjust the network weights. The compound model is first trained by 75 images from the training set, and then is tested by 102 images from the testing set. The results show that this is a very effective identification method which can obtain a higher recognition rate.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 513-517)

Pages:

4152-4155

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.513-517.4152

Citation:

Cite this paper

Online since:

February 2014

Authors:

Rui Kun Gong*, Ping Ting Liu, Yu Han Gong, Chong Hao Wang

Keywords:

Auto-Focusing, Image Definition, Image Processing, RBF Neural Network, Wavelet Transform (WT)

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Liu Chi. Image representation and Application Research Based on visual perception. Zhengzhou University(2013).

Google Scholar

[2] Zhang Weipeng. Image denoising algorithm of refuge chamber by combining wavelet transform and bilateral filtering[J]. International Journal of Mining Science and Technology, (2013), 02: 228-232.

DOI: 10.1016/j.ijmst.2013.04.016

Google Scholar

[3] Chunhui Zhao, Lijuan Ma, Guofeng Shao, Li Liu. Improved Region Energy Based Image Fusion Rule for Multi focus Image Fusion[J]. Journal of Harbin Institute of Technology, (2013), 04: 109-115.

Google Scholar

[4] Guantong Chan. Research and Application on the algorithm of face detection and recognition [D]. Dalian University of Technology, (2013).

Google Scholar

[5] Zhengxing Cheng, Yongping Lin. Application of wavelet analysis in image processing[J]. Journal of Engineering Mathematics, (2001), S1: 57-86.

Google Scholar