DocumentCode :
2289331
Title :
The application of Hopfield neural network in enhancing x ray image of steel pipe welding
Author :
Li Yaping ; Zhang Huade ; Gao Weixin
Author_Institution :
SINOPEC Pipeline Transp. & Storage Co., Xuzhou, China
Volume :
2
fYear :
2010
fDate :
10-12 Aug. 2010
Firstpage :
810
Lastpage :
813
Abstract :
This paper analyses the characters of x-ray image of thick and thin steel pipe. In order to enhance the x-ray image automatically and avoid deciding the image´s degraded type, a gray mapping matrix is constructed to replace traditional gray transformation curves and the maximum dimension of the gray mapping matrix is 256×256. So the calculation time has little relation with the size of the image. The criterion function of image quality is used to evaluate the quality of the transformed image. By this way, the problem of image enhancement is transformed to an optimization problem. The paper presents Hopfield neural network to calculate the gray mapping matrix. The energy function and the calculation method are also given. Some examples show that the presented method is effective.
Keywords :
Hopfield neural nets; X-ray imaging; image enhancement; optimisation; pipes; production engineering computing; steel; welding; Hopfield neural network; X-ray image; criterion function; gray mapping matrix; gray transformation curves; image enhancement; image quality; optimization problem; steel pipe welding; Electron tubes; Hopfield neural networks; Image enhancement; Mathematical model; Steel; Transforms; Welding; Hopfield Neural Network; Image Hencing; Image Processing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Natural Computation (ICNC), 2010 Sixth International Conference on
Conference_Location :
Yantai, Shandong
Print_ISBN :
978-1-4244-5958-2
Type :
conf
DOI :
10.1109/ICNC.2010.5583229
Filename :
5583229
Link To Document :
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