A computer technology for determining radiographic testing sensitivity by the image of a groove standard
Source abstract
Abstract
In determining the sensitivity of radiographic testing of welded joints by groove standard, computer analysis of brightness is usually carried out along a longitudinal line drawn in the middle of an X-ray image of the standard. It is suggested that a brightness curve of the line outlined along the standard should not be analyzed but the brightness should be determined by averaging the gray levels of the line segments parallel to the smaller side of the standard. To determine points belonging to the line segments, methods for indicating the boundaries of an object in an image and Hough transforms are used. The radiographic testing sensitivity is determined taking into account the Weber-Fechner law as well as values of the grayscale of grooves and their background calculated by the proposed technique.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Image and Object Detection Techniques · Welding Techniques and Residual Stresses · Image Processing and 3D Reconstruction
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (19)
Show source-provided references
1. GOST (State Standard) 7512-82.DS Nondestructing Testing, Welded Joints, Radiographic Method, Introduction, Moscow: Izd. Standartov, 1988.
2. “MARS” Hardware-Software Complex for Automated Processing and Decoding of Radiographic Pictures Produced as a Result of Nondestructive Testing of Welded Joints, http://www.hank.ru/strmars.htm.
3. Kapustin, A.E., Bardusova, I.I., and Dechko, M., Estimate of the Quality of X-ray Images of Welded Joints, Tekhn. Diagnost. Nerazr. Kontr., 2001, no. 3, pp. 48–49.
4. Alekhnovich, E.A., Van’kova, N.E., Kapustin, V.I., et al., Development of the Main Requirements for Contemporary Systems of Radiographic Testing Including Digital Image Processing Taking into Account Specific Features of Inspection of Welded Joints of Oil and Gas Pipelines (a Review), Defektoskopiya, 2008, no. 3, pp. 75–85 [Rus. J. Nondestr. Test. (Engl. Transl.), 2008, vol. 44., no. 3, pp. 215–222].10.1134/S106183090803008X ↗
5. Rumyantsev, S.V., Radiatsionnaya defektoskopiya (Radiation Flaw Detection), Moscow: Atomizdat, 1974.
6. Kapustin, A.E., Bardusova, I.I., and Kolosov, V.V., Computer Technologies and X-ray Flaw Detection of Welded Joints, Tekhn. Diagnost. Nerazr. Kontr., 2001, no. 4, pp. 14–20.
7. Mezhuev, V., Kuznetsov, A., Akhmetov, A., and Kruteev, A., Digital X-ray TV Systems in Serial Production Inspection of Welds, Proc. Intern. Conf. WCNDT.XX, Roma, 2000, pp. 108–109.
8. Pratt, W. K., Digital Image Processing, New York: Wiley, 1978.
9. Canny, J.F., A Computational Approach to Edge Detection, IEEE Trans. Pattern Analysis and Machine Intelligence, 1986, vol. 8(6), pp. 679–698.10.1109/TPAMI.1986.4767851 ↗
10. Duda, R.O. and Hart, P.E., Use of the Hough Transform to Detect Lines and Curves in Pictures, Comm. ACM, 1972, vol. 15, pp. 11–15.10.1145/361237.361242 ↗
11. Han, J.H., Koczy, L.T., and Poston, T., Fuzzy Hough Transform, Pattern Recognition Letters, 1994, vol. 15, pp. 649–658.10.1016/0167-8655(94)90068-X ↗
12. Chatzis, V. and Pitas, I., Fuzzy Cell Hough Transform for Curve Detection, Pattern Recognition, 1997, vol. 30, no. 12, pp. 2031–2042.10.1016/S0031-3203(97)00025-3 ↗
13. Berehulyak, O.R., Fuzzy Hough Transform for Detection of Lines in a Multigradation Image, XX Open Research Conference of Young Scientists and Specialists of Karpenka PhMI NANU “KMN-2007,” Sb. Trudov (Collection of Papers), L’vov, 2007, pp. 218–221.
14. Bavda, G.I., Berehulyak, O.R., Vorobel, R.A., et. al., Determination of the Sensitivity of X-ray Films and Calibration of Brightness in Terms of Images of a Groove Standard in Testing of Welds, Fizicheckie metody i sredstva kontrolya sred, materialov i izdelii, Sb. nauchnykh trudov (Physical Metods and Facilities for Testing Media, Materials and Articles, Collection of Scientific Papers), L’vov, 2008, no. 13, pp. 209–215.
15. Pavlidis, T., Algorithms for Graphics and Image Processing, Rockvill, Maryland: Computer Science Press, 1982.10.1007/978-3-642-93208-3 ↗
16. Hecht, S., The Visual Discrimination of Intensity and Weber-Fechner Law, J. General Physiology, 1924, no. 7, p. 241.10.1085/jgp.7.2.235 ↗
17. Ma, K.K. and Huang, L., Perceptually Based Subband AMBTC Image Coder, in Proc. of Int. Conf. on Information, Communication, and Signal Processing, IEEE, 1997, vol. 1, pp. 505–509.10.1109/ICICS.1997.647149 ↗
18. Wang Chao, Ye Zhong-Fu, Perceptual Contrast-Based Image Fusion: A Variational Approach, Acta Automatica Sinica, 2007, vol. 33, no. 2, pp. 132–137.10.1360/aas-007-0132 ↗
19. Shen, D. and Wang, S., Measurements of JND Property of HVS and Application to Image Segmentation, Coding, and Requantization, Proc. SPIE Digital Compression Technologies and Systems for Video Communications, 1996, no. 2952, pp. 113–121.10.1117/12.251269 ↗