Contour Estimation in Synthetic and Real Weld Defect Images based on Maximum Likelihood
This paper describes a novel method for automatic
estimation of the contours of weld defect in radiography images.
Generally, the contour detection is the first operation which we apply
in the visual recognition system. Our approach can be described as a
region based maximum likelihood formulation of parametric
deformable contours. This formulation provides robustness against
the poor image quality, and allows simultaneous estimation of the
contour parameters together with other parameters of the model.
Implementation is performed by a deterministic iterative algorithm
with minimal user intervention. Results testify for the very good
performance of the approach especially in synthetic weld defect
images.
[1] M. Figueiredo and J. Leitão, ".Bayesian estimation of ventricular
contours in angiographic images",. IEEE Trans. On Medical Imaging,
vol. 11, no. 3, 1992, pp. 416-429.
[2] M. Figueiredo, J. Leitão, and A. K. Jain, "Unsupervised contour
representation and estimation using B-splines and a minimum description
length criterion", IEEE Trans. on Image Processing, vol. 9, 2000, pp.
1075-1087.
[3] S. V. B. Jardim and M. Figueiredo, "automatic contour estimation in
fetal ultrasound images", Proc. ICIP '2003, 2003.
[4] M. Kass, A. Witkin, and D. Terzopolous, .Snakes: Active contour
models,.Proc. First ICCV, 1987, pp. 259.268.
[5] D.Mery, M.A.Berty, "Automatic detection of welding defects using
texture feature", International Symposium on Computed Tomography
and Image Processing for Industrial Radiology, Berlin, June 23-25,
2003.
[6] N.Nacereddine, R.Drai, A.Benchaala, "Weld defect extraction and
identification in radiograms based neural networks", Proceedings of the
IASTED International Conference, Signal Processing, Pattern
Recognition Applications, Crete, Greece, June 25-28, 2002.
[7] N.Nacereddine, M.Zelmat, S.S.Belaifa and M.Tridi, "Weld defect
detection in industrial radiography based on digital image processing",
ICSP 2004, Istanbul, Turkey, December 17-19, 2004, pp.145-148.
[8] N.Nacereddine, M.Tridi, S.S.Belaifa and M.Zelmat, "Quantitative
analysis of weld defect images in industrial radiography based on
invariant attributes", ICSP 2004, Istanbul, Turkey, December 17-19,
2004, pp.313-316.
[1] M. Figueiredo and J. Leitão, ".Bayesian estimation of ventricular
contours in angiographic images",. IEEE Trans. On Medical Imaging,
vol. 11, no. 3, 1992, pp. 416-429.
[2] M. Figueiredo, J. Leitão, and A. K. Jain, "Unsupervised contour
representation and estimation using B-splines and a minimum description
length criterion", IEEE Trans. on Image Processing, vol. 9, 2000, pp.
1075-1087.
[3] S. V. B. Jardim and M. Figueiredo, "automatic contour estimation in
fetal ultrasound images", Proc. ICIP '2003, 2003.
[4] M. Kass, A. Witkin, and D. Terzopolous, .Snakes: Active contour
models,.Proc. First ICCV, 1987, pp. 259.268.
[5] D.Mery, M.A.Berty, "Automatic detection of welding defects using
texture feature", International Symposium on Computed Tomography
and Image Processing for Industrial Radiology, Berlin, June 23-25,
2003.
[6] N.Nacereddine, R.Drai, A.Benchaala, "Weld defect extraction and
identification in radiograms based neural networks", Proceedings of the
IASTED International Conference, Signal Processing, Pattern
Recognition Applications, Crete, Greece, June 25-28, 2002.
[7] N.Nacereddine, M.Zelmat, S.S.Belaifa and M.Tridi, "Weld defect
detection in industrial radiography based on digital image processing",
ICSP 2004, Istanbul, Turkey, December 17-19, 2004, pp.145-148.
[8] N.Nacereddine, M.Tridi, S.S.Belaifa and M.Zelmat, "Quantitative
analysis of weld defect images in industrial radiography based on
invariant attributes", ICSP 2004, Istanbul, Turkey, December 17-19,
2004, pp.313-316.
@article{"International Journal of Information, Control and Computer Sciences:61079", author = "M. Tridi and N. Nacereddine and N. Oucief", title = "Contour Estimation in Synthetic and Real Weld Defect Images based on Maximum Likelihood", abstract = "This paper describes a novel method for automatic
estimation of the contours of weld defect in radiography images.
Generally, the contour detection is the first operation which we apply
in the visual recognition system. Our approach can be described as a
region based maximum likelihood formulation of parametric
deformable contours. This formulation provides robustness against
the poor image quality, and allows simultaneous estimation of the
contour parameters together with other parameters of the model.
Implementation is performed by a deterministic iterative algorithm
with minimal user intervention. Results testify for the very good
performance of the approach especially in synthetic weld defect
images.", keywords = "Contour, gaussian, likelihood, rayleigh.", volume = "1", number = "9", pages = "2836-4", }