Automatic Defect Extraction and Segmentation in Welding Seam Based on X-Ray Images

Abstract:

Article Preview

In light of the characteristic of thin-wall weld joint in X-ray image, Flaw-edge extraction algorithm and image enhancement algorithm which is based on mathematical morphology are proposed in the study of flaw extraction technique. Therefore, the area of flaw and background can be removed successfully. On this basis, there are two algorithms to identify different flaw types: one is that spatial domain transform to extract flaw edge for clack, the other one is mathematical morphology which is combined with iteration threshold to extract flaw edge for pore; Experimental results show that both of algorithms can implement flaw extraction and segmentation automatically, which is lay a good foundation for flaw feature parameter extraction and recognition.

Info:

Periodical:

Edited by:

Han Zhao

Pages:

2558-2562

Citation:

M. Q. Wang and Y. Wang, "Automatic Defect Extraction and Segmentation in Welding Seam Based on X-Ray Images", Applied Mechanics and Materials, Vols. 130-134, pp. 2558-2562, 2012

Online since:

October 2011

Export:

Price:

$41.00

[1] Yan Qu, Zhao Yuelong. Study of Binarization Method of Identification Card Scanned Image. Computer Measurement & Control. 2005. 13(6): 595~597.

[2] Zhang Xiao-guang. Research of Defect Recognition and Extraction in Radiographic Inspection Weld Image. Doctor's Degree Thesis. ShangHai. East China University of Science & Technology. 2003. 5. 12.

[3] Ruan Qiu-qing . Digital image processing . BeiJing. Publishing house of electronics industry. (2001).

[4] Dong EQ, Liu GZ, Zhang ZP, Fast implementation technique of adaptive Kalman filtering deconvolution via dyadic wavelet transform, CHINESE J GEOPHYS, 2001, 44, 1, 255~262.

DOI: https://doi.org/10.1002/cjg2.138

[5] T Warren Liao, Yue ming Li, An automated of radiographic NDT system for weld Inspection, Part Ⅱ-Flaw Detection, NDT & E international, 1998, 31, 3, 183~192.

DOI: https://doi.org/10.1016/s0963-8695(97)00042-x

[6] Karmakar G C, Dooley L S. A generic fuzzy rule based image segmentation algorithm [J]. Pattern Recognition Letters, 2002, 23(10): 1215~1227.

DOI: https://doi.org/10.1016/s0167-8655(02)00069-7

[7] Pham D L. Spatial models for fuzzy clustering[J]. Computer Vision and Image Understanding, 2001, 84(2): 285~297.

DOI: https://doi.org/10.1006/cviu.2001.0951

[8] Tolias Y A, Panas S M . On applying spatial constraints in fuzzy image clustering using a fuzzy rule based system[J]. IEEE Siganl Processing Letters, 1998, 5(10): 245~247.

DOI: https://doi.org/10.1109/97.720555