Goumeidane Aicha Baya, Nacereddine Nafaa, Khamadja Mohammed
Centre de Recherche en Soudage et Contrôle, Algiers, Algeria.
Lab. Math. et leurs Interactions, Centre Univ. de Mila, Algeria.
J Xray Sci Technol. 2015;23(3):289-310. doi: 10.3233/XST-150488.
A perfect knowledge of a defect shape is determinant for the analysis step in automatic radiographic inspection. Image segmentation is carried out on radiographic images and extract defects indications. This paper deals with weld defect delineation in radiographic images. The proposed method is based on a new statistics-based explicit active contour. An association of local and global modeling of the image pixels intensities is used to push the model to the desired boundaries. Furthermore, other strategies are proposed to accelerate its evolution and make the convergence speed depending only on the defect size as selecting a band around the active contour curve. The experimental results are very promising, since experiments on synthetic and radiographic images show the ability of the proposed model to extract a piece-wise homogenous object from very inhomogeneous background, even in a bad quality image.
对于自动射线检测中的分析步骤而言，对缺陷形状的精确了解至关重要。在射线图像上进行图像分割并提取缺陷指示。本文探讨射线图像中的焊缝缺陷描绘。所提出的方法基于一种新的基于统计的显式活动轮廓。利用图像像素强度的局部和全局建模相结合的方式，将模型推向期望的边界。此外，还提出了其他策略来加速其演化，并使收敛速度仅取决于缺陷大小，比如在活动轮廓曲线周围选择一个带。实验结果非常可观，因为在合成图像和射线图像上进行的实验表明，所提出的模型能够从非常不均匀的背景中提取出分段均匀的物体，即使是在质量较差的图像中。
