A welded structure must be inspected to guarantee that the weld quality meets the design requirements to assure safety and reliability. However, X-ray image analyses and defect recognition with the computer vision techniques are very complex. Most difficulties lie in finding the small, irregular defects in poor contrast images which requires pre processing to image, extract, and classify features from strong background noise. This paper addresses the issue of designing methodology to extract defect from noisy background radiograph with image processing. Based on the use of actives contours this methodology seems to give good results
[1] Nockemann, C., Heidt, H., & Thomsen, N. (1991). Reliability in ndt: Roc study of radiographic weld inspections. NDT and E International, 24(5), 235-245.
[2] N. Nacereddine, L. Hamami, D. Ziou, N. Tridi "Statistical tools for weld
defect evaluation in radiographic testing" in Proceeding of 9th European
Conference on Non-Destructive Testing 2006, Berlin, Germany, Sept.
25-29, 2006.
[3] Nacereddine, N., Tridi, M.: Computer-Analysis and Classification of Welded industrial Radiography based Invariant Attributes Neural
Networks. Proceeding of the 4th Symposium on Image and Signal Processing Analysis, Zagreb, Croatia, (2005) 88-93.
[4] Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models.
Int-l J. Computer Vision, 321-331 (1988)
[5] Caselles V, Catte F, Coll T, Dibos F. A geometric model for active
contours in image processing. Numer Math 1993;66(1):1
[6] Xu C, Prince J. Snakes, shapes, and gradient vector flow. IEEE Trans
Image Process 1998;7(3):359-69.
[7] C. Xu and J. Prince, "Generalized Gradient Vector Flow: External
Forces For active Contours", Signal Processing, 1998, vol. 71, pp.
131-139.
[8] Li C, Liu J, Fox MD. Segmentation of edge preserving gradient vector
flow: an approach toward automatically initializing and splitting of
snakes. In: Proceedings of IEEE conference on computer vision and
pattern recognition (CVPR), vol. 1. 2005. p. 162-7.
[9] Ronfard R. Region-based strategies for active contour models. Int J
Comput Vis 1994;13(2):229-51.
[10] Ivins, J., Porrill, J.: Active region models for segmenting medical
images. In: Proceedings of the IEEE Internation Conference on Image
Processing (1994)
[11] Abd-Almageed, W., Smith, C.E.: Mixture models for dynamic statistical
pressure snakes. In: IEEE International Conference on Pattern
Recognition, Quebec City, Canada (2002)
[12] Abd-Almageed, W., Ramadan, S., Smith, C.E.: Kernel Snakes: Nonparametric
Active Contour Models. In: IEEE international conference on
systems, man and cybernetics, Washington (2003)
[13] Chan T, Vese L. Active contours without edges. IEEE Trans Image
Process 2001;10(2):266-77.
[14] Paragios N, Deriche R. Geodesic active regions and level set methods
for supervised texture segmentation. Int J Comput Vis 2002;46(3):223-47.
[15] Jardim, S.M.G.V.B., Figuerido, M.A.T.: Segmentation of Fetal
Ultrasound Images. Ultrasound in Med. & Biol. 31(2), 243–250 (2005)
[16] A. B. Goumeidane, M. Khamadja, N. Nacereddine: Maximum
Likelihood Curves for Multiple Objects Extraction:
Application to Radiographic Inspection for Weld Defects Detection.
International Journal on New Computer Architectures and Their
Applications (IJNCAA) 1(4): 210-222
[17] TIAN Yuan, DU Dong, CAI Guorui, WANG Li, ZHANG Hua:
Automatic Defect Detection in X-Ray Images Using Image Data Fusion
[18] P. Perona and J. Malik. “Scale-space and edge detection using
anisotropic diffusion”, IEEE trans. on Pattern Analysis and Machine
Intelligence, 12(7), July 1990.
[19] F. Preteux, Description et interprétation des images par la morphologie
mathématique. Application à l’imagerie médicale. Thèse d’état d
l’université P. et M. Curie Paris VI. (1987) .
[20] T. Stojiè, I. Reljin, B. Rejlin, “Local Contrast Enhancement In Digital
Mammography by using Mathematical Morphology” IEEE Confrence
on 2005.
[21] I. El Feghi, S. Huang, M.A. Sid-Ahmed and M. Ahmadi , “Contrast
enhancement of radiograph image based on local heterogeneity
measures,”ICIP 2004.
[22] Z. Tu and C. Bajai, “A Fast Adaptive Method for Image Contrast
Enhancement”, ICIP 2004.
[23] J. Kim, L. Kim, and S. Hwang, “An Advanced Contrast Enhancement
Using Partially Overlapped Sub-Block Histogram Equalization,” IEEE
Trans. Circuit and systems VOL. 11, NO. 4, APRIL 2001.
[24] L. D. Cohen, “On active contour models and balloons,” CVGIF: Image
Understand., vol. 53, pp. 211 218, Mar. 1991.
[25] L. D. Cohen and I. Cohen, “Finite-element methods for active contour
models and balloons for 2-D and 3-D images,” IEEE Trans. Pattern
Anal. Machine Intell,
[1] Nockemann, C., Heidt, H., & Thomsen, N. (1991). Reliability in ndt: Roc study of radiographic weld inspections. NDT and E International, 24(5), 235-245.
[2] N. Nacereddine, L. Hamami, D. Ziou, N. Tridi "Statistical tools for weld
defect evaluation in radiographic testing" in Proceeding of 9th European
Conference on Non-Destructive Testing 2006, Berlin, Germany, Sept.
25-29, 2006.
[3] Nacereddine, N., Tridi, M.: Computer-Analysis and Classification of Welded industrial Radiography based Invariant Attributes Neural
Networks. Proceeding of the 4th Symposium on Image and Signal Processing Analysis, Zagreb, Croatia, (2005) 88-93.
[4] Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models.
Int-l J. Computer Vision, 321-331 (1988)
[5] Caselles V, Catte F, Coll T, Dibos F. A geometric model for active
contours in image processing. Numer Math 1993;66(1):1
[6] Xu C, Prince J. Snakes, shapes, and gradient vector flow. IEEE Trans
Image Process 1998;7(3):359-69.
[7] C. Xu and J. Prince, "Generalized Gradient Vector Flow: External
Forces For active Contours", Signal Processing, 1998, vol. 71, pp.
131-139.
[8] Li C, Liu J, Fox MD. Segmentation of edge preserving gradient vector
flow: an approach toward automatically initializing and splitting of
snakes. In: Proceedings of IEEE conference on computer vision and
pattern recognition (CVPR), vol. 1. 2005. p. 162-7.
[9] Ronfard R. Region-based strategies for active contour models. Int J
Comput Vis 1994;13(2):229-51.
[10] Ivins, J., Porrill, J.: Active region models for segmenting medical
images. In: Proceedings of the IEEE Internation Conference on Image
Processing (1994)
[11] Abd-Almageed, W., Smith, C.E.: Mixture models for dynamic statistical
pressure snakes. In: IEEE International Conference on Pattern
Recognition, Quebec City, Canada (2002)
[12] Abd-Almageed, W., Ramadan, S., Smith, C.E.: Kernel Snakes: Nonparametric
Active Contour Models. In: IEEE international conference on
systems, man and cybernetics, Washington (2003)
[13] Chan T, Vese L. Active contours without edges. IEEE Trans Image
Process 2001;10(2):266-77.
[14] Paragios N, Deriche R. Geodesic active regions and level set methods
for supervised texture segmentation. Int J Comput Vis 2002;46(3):223-47.
[15] Jardim, S.M.G.V.B., Figuerido, M.A.T.: Segmentation of Fetal
Ultrasound Images. Ultrasound in Med. & Biol. 31(2), 243–250 (2005)
[16] A. B. Goumeidane, M. Khamadja, N. Nacereddine: Maximum
Likelihood Curves for Multiple Objects Extraction:
Application to Radiographic Inspection for Weld Defects Detection.
International Journal on New Computer Architectures and Their
Applications (IJNCAA) 1(4): 210-222
[17] TIAN Yuan, DU Dong, CAI Guorui, WANG Li, ZHANG Hua:
Automatic Defect Detection in X-Ray Images Using Image Data Fusion
[18] P. Perona and J. Malik. “Scale-space and edge detection using
anisotropic diffusion”, IEEE trans. on Pattern Analysis and Machine
Intelligence, 12(7), July 1990.
[19] F. Preteux, Description et interprétation des images par la morphologie
mathématique. Application à l’imagerie médicale. Thèse d’état d
l’université P. et M. Curie Paris VI. (1987) .
[20] T. Stojiè, I. Reljin, B. Rejlin, “Local Contrast Enhancement In Digital
Mammography by using Mathematical Morphology” IEEE Confrence
on 2005.
[21] I. El Feghi, S. Huang, M.A. Sid-Ahmed and M. Ahmadi , “Contrast
enhancement of radiograph image based on local heterogeneity
measures,”ICIP 2004.
[22] Z. Tu and C. Bajai, “A Fast Adaptive Method for Image Contrast
Enhancement”, ICIP 2004.
[23] J. Kim, L. Kim, and S. Hwang, “An Advanced Contrast Enhancement
Using Partially Overlapped Sub-Block Histogram Equalization,” IEEE
Trans. Circuit and systems VOL. 11, NO. 4, APRIL 2001.
[24] L. D. Cohen, “On active contour models and balloons,” CVGIF: Image
Understand., vol. 53, pp. 211 218, Mar. 1991.
[25] L. D. Cohen and I. Cohen, “Finite-element methods for active contour
models and balloons for 2-D and 3-D images,” IEEE Trans. Pattern
Anal. Machine Intell,
@article{"International Journal of Information, Control and Computer Sciences:64115", author = "Aicha Baya Goumeidane and Nafaa. Nacereddine", title = "Forces Association-Based Active Contour", abstract = "A welded structure must be inspected to guarantee that the weld quality meets the design requirements to assure safety and reliability. However, X-ray image analyses and defect recognition with the computer vision techniques are very complex. Most difficulties lie in finding the small, irregular defects in poor contrast images which requires pre processing to image, extract, and classify features from strong background noise. This paper addresses the issue of designing methodology to extract defect from noisy background radiograph with image processing. Based on the use of actives contours this methodology seems to give good results
", keywords = "Welding, Radiography, Computer vision, Active contour.", volume = "6", number = "11", pages = "1527-6", }
