PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Region-based active contour with adaptive B-spline. Application in radiographic weld inspection

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This paper describes a probabilistic region-based deformable model using a new adaptive scheme for B-spline representation. The idea is to adapt the number of spline control points which are necessary to describe an object with complex shape. For this purpose, the curve segment length (CSL) is used as criterion. The proposed split and merge strategy on the spline model consists in : adding a new control point when CSL is greater than a certain splitting threshold so that the contour tracks all the concavities and, removing a control point when CSL is less to a certain merging threshold so that the contour aspect maintains its smoothness. Noise on synthetic and real weld radiographic images is assumed following Gaussian or Rayleigh distribution. The experiments carried out confirm the adequacy of this approach, especially in tracking pronounced concavities contained in images.
Twórcy
autor
autor
Bibliografia
  • [1] C. Schwartz. Automatic evaluation of welded joints using image processing on radiographs. Conf. Proceed, of American Inst. of Physics 657:689-694, 2003.
  • [2] M. Kass, A. Witkin, D. Terzopoulos. Snakes: Active contour models. Int. J. Computing and Vision 1(4):321-331, 1988.
  • [3] C. Chesnaud, P. Refregier, W. Boulet. Statistical region snake-based segmentation adapted to different physical noise models. IEEE Trans, on PAMI 21 (11): 1145-1157, 1999.
  • [4] L. D. Cohen. On active contour models and balloons. CVGIF: Image Understanding 53:211-218, 1991.
  • [5] L. D. Cohen, I. Cohen. Finite-element methods for active contour models and balloons for 2-D and 3-D images. IEEE Trans, on PAMI 15(11):1131-1147, 1993.
  • [6] B. Leroy, I. Herlin, L. D. Cohen. Multi-resolution algorithms for active contour models. 12th Int. Conf. Analysis and Optimization of Systems: 58-65, 1996.
  • [7] C. Xu, J. Prince. Snakes, shapes, and gradient vector flow. IEEE Trans. Image Processing 7(3):359-369, 1998.
  • [8] C. Xu, J. Prince. Generalized gradient vector flow: External forces for active contours. Signal Processing 71:131-139, 1998.
  • [9] R. Malladi, J. Sethian, B. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Trans PAMI 17:158-175, 1995.
  • [10] V. Caselles, F. Catte, T. Coll, F. Dibos. A geometric model for active contours in image processing. Num. Math. 66:1-31, 1993.
  • [11] A. B. Goumeidane, M. Khamadja, N. Nacereddine, F. Mekhalfa. Parametric active aontour for weld defect boundary extraction in radiographic testing, in SPIE conf. on Quality Control by Artificial vision, Le Creusot, France, 2007.
  • [12] L. Cohen, E. Bardinet, N. Ayache. Surface reconstruction using active contour models, in SPIE conf. on Geometric Methods in Computer Vision, San Diego, CA, 1993.
  • [13] R. Ronfard. Region-based strategies for active contour models. Int. Journal of Computer Vision 13 (2): 229-251, 1994.
  • [14] S. Zhu, A. Yuille. Region competition: unifying snakes, region growing, and bayes/MDL for multiband image segmentation. IEEE Trans, on PAMI 18:884-900, 1996.
  • [15] O. Germain, P. Refregier. Optimal snake-based segmentation of a random luminance target on a spatially disjoint background. Optics Letters 21 (22): 1845-1848, 1996.
  • [16] A. Yezzi, A. Tsai, A. Willsky. A statistical approach to snakes for bimodal and trimodal imagery, in Int. Conf. on Image Processing:898-903, Kobe, 1999.
  • [17] J. M. B. Dias. Adaptive bayesian contour estimation: A vector space representation approach. LNCS 1654, Springer: 157-172, 1999.
  • [18] M. A. T. Figueiredo, J. M. N. Leitao, A. K. Jain. Unsupervised contour representation and estimation using B-splines and a minimum description length criterion. IEEE Trans, on Image Processing 9(6): 1075-1087, 2000.
  • [19] N. Paragios, R. Deriche. Geodesic active contours and level sets for the detection and tracking of moving objects. IEEE Trans, on PAMI 22:266-280, 2000.
  • [20] T. Chan, L. Vese. Active contours without edges. IEEE Trans, on Image Processing 10(2):266-277, 2001.
  • [21] S. Jehan-Besson, M. Barlaud, G. Aubert. DREAM2S: Deformable regions driven by an Eulerian accurate minimization method for image and video segmentation. Int. Journal of Computer Vision 53(1):45-70, 2003.
  • [22] A. Herbulot, S. Jehan-Besson, S. Duffner, M. Barlaud, G. Aubert. Segmentation of vectorial image features using shape gradients and information measures. Journal of Mathematical Imaging and Vision 25(3):365-386, 2006.
  • [23] N. Nacereddine, L. Hamami, D. Ziou, M. Tridi. Probabilistic deformable model for weld defect contour estimation in radiography. Machine Graphics & Vision 15(3/4):547-556, 2006.
  • [24] N. Nacereddine, L. Hamami, D. Ziou, A. B. Goumeidane, Adaptive B-spline Model based Probabilistic Active Contour for Weld Defect Detection in Radiographic Imaging, In: R. S. Choras (Ed.): Image Processing and Communication Challenges 2, Advances in Soft Computing, 489-497, Springer, 2010.
  • [25] P. Dierchx. Curve and surface fitting with splines. Oxford Univ. Press, 1993.
  • [26] F. Precioso, M. Barlaud, T. Blu, M. Unser. Robust Real-time segmentation of images and videos using a smooth-spline Snake-based algorithm. IEEE Trans, on Image Processing 14(7):910-924, 2005.
  • [27] H. Tagare. Deformable 2-D template matching using orthogonal curves. IEEE Trans, on Medical Imaging 16(1): 108-117, 1997.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BAT5-0057-0006
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.