Abstract
With the advances in computer science and artificial intelligence techniques, the opportunity to develop computer aided technique for radiographic inspection in Non Destructive Testing arose. This paper presents an adaptive probabilistic region-based deformable model using an explicit representation that aims to extract automatically defects from a radiographic film. To deal with the height computation cost of such model, an adaptive polygonal representation is used and the search space for the greedy-based model evolution is reduced. Furthermore, we adapt this explicit model to handle topological changes in presence of multiple defects.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Halmshaw, R.: The Grid: Introduction to the Non-Destructive Testing in Welded Joints. Woodhead Publishing, Cambridge (1996)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision, 321–331 (1988)
Xu, C., Prince, J.: Snakes, Shapes, and gradient vector flow. IEEE Transactions on Images Processing 7(3), 359–369 (1998)
Jacob, M., Blu, T., Unser, M.: Efficient energies and algorithms for parametric snakes. IEEE Trans. on Image Proc. 13(9), 1231–1244 (2004)
Tauber, C., Batatia, H., Morin, G., Ayache, A.: Robust b-spline snakes for ultra- sound image segmentation. IEEE Computers in Cardiology 31, 25–28 (2004)
Zimmer, C., Olivo-Marin, J.C.: Coupled parametric active contours. IEEE Trans. Pattern Anal. Mach. Intell. 27(11), 1838–1842 (2005)
Srikrishnan, V., Chaudhuri, S., Roy, S.D., Sevcovic, D.: On Stabilisation of Parametric Active Contours. In: CVPR 2007, pp. 1–6 (2007)
Li, B., Acton, S.T.: Active Contour External Force Using Vector Field Convolution for Image Segmentation. IEEE Trans. on Image Processing 16(8), 2096–2106 (2007)
Li, B., Acton, S.T.: Automatic Active Model Initialization via Poisson Inverse Gradient. IEEE Trans. on Image Processing 17(8), 1406–1420 (2008)
Collewet, C.: Polar snakes: A fast and robust parametric active contour model. In: IEEE Int. Conf. on Image Processing, pp. 3013–3016 (2009)
Wang, Y., Liu, L., Zhang, H., Cao, Z., Lu, S.: Image Segmentation Using Active Contours With Normally Biased GVF External Force. IEEE signal Processing 17(10), 875–878 (2010)
Ronfard, R.: Region based strategies for active contour models. IJCV 13(2), 229–251 (1994)
Dias, J.M.B.: Adaptive bayesian contour estimation: A vector space representation approach. In: Hancock, E.R., Pelillo, M. (eds.) EMMCVPR 1999. LNCS, vol. 1654, pp. 157–173. Springer, Heidelberg (1999)
Jardim, S.M.G.V.B., Figuerido, M.A.T.: Segmentation of Fetal Ultrasound Images. Ultrasound in Med. & Biol. 31(2), 243–250 (2005)
Ivins, J., Porrill, J.: Active region models for segmenting medical images. In: Proceedings of the IEEE Internation Conference on Image Processing (1994)
Abd-Almageed, W., Smith, C.E.: Mixture models for dynamic statistical pressure snakes. In: IEEE International Conference on Pattern Recognition (2002)
Abd-Almageed, W., Ramadan, S., Smith, C.E.: Kernel Snakes: Non-parametric Active Contour Models. In: IEEE International Conference on Systems, Man and Cybernetics (2003)
Goumeidane, A.B., Khamadja, M., Naceredine, N.: Bayesian Pressure Snake for Weld Defect Detection. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2009. LNCS, vol. 5807, pp. 309–319. Springer, Heidelberg (2009)
Chesnaud, C., Réfrégier, P., Boulet, V.: Statistical Region Snake-Based Segmentation Adapted to Different Physical Noise Models. IEEE Transaction on PAMI 21(11), 1145–1157 (1999)
Nacereddine, N., Hammami, L., Ziou, D., Goumeidane, A.B.: Region-based active contour with adaptive B-spline. Application in radiographic weld inspection. Image Processing & Communications 15(1), 35–45 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Goumeidane, A.B., Khamadja, M., Nacereddine, N. (2011). Adaptive and Statistical Polygonal Curve for Multiple Weld Defects Detection in Radiographic Images. In: Cherifi, H., Zain, J.M., El-Qawasmeh, E. (eds) Digital Information and Communication Technology and Its Applications. DICTAP 2011. Communications in Computer and Information Science, vol 166. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21984-9_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-21984-9_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21983-2
Online ISBN: 978-3-642-21984-9
eBook Packages: Computer ScienceComputer Science (R0)