文本性的盛宴：美国汉学现当代文学研究的亮点和拐点

由于传统焊缝区域检测算法难以准确提取模糊和对比度低的厚钢管焊缝区域，提出一种新的基于鲁棒PCA模型的焊缝区域检测算法，该算法能克服传统方法的不足，并能准确提取焊缝区域.首先，收集一序列X射线图像，并对其进行空域对齐及亮度归一化预处理.其次，计算得到系列图像的背景图像，并将背景图像与待测试X射线图像张成一个观测矩阵.最后，使用鲁棒PCA算法对观测矩阵进行低秩与稀疏分解，测试图像中的不均匀强度及噪声被消除，焊缝区域被凸显出来，通过全局阈值可将焊缝区域较好地分割出来.实验结果表明，该算法能较大地消除厚钢管X射线图像中噪声及不均匀强度分布带来的干扰、同时增强模糊的焊缝边缘及对比度低的区域，相比传统焊缝区域检测算法，具有更高的检测灵敏度(0.952)和精度(0.989)，能更好地将模糊和对比度低的焊缝区域完整检测出来.

Detection of Weld Regions in X-ray Images of Thick Steel Pipes

Since traditional detection algorithms of welding seam area have difficulties in accurately extracting the fuzzy and low-contrast welding areas in the X-ray images of thick steel pipes, this paper proposed a novel robust detection method of weld seam region based on the robust PCA model. The proposed technique can overcome the shortcomings of the traditional methods, and can accurately extract the weld regions. Firstly, a sequence of X-ray images were collected, and their spatial alignment and brightness normalization were carried out. Then, a series of background images were obtained, and these preprocessed images and a test X-ray image were combined to form an observation matrix. The robust PCA was then used to decompose the observation matrix into a low-rank and sparse image. As the uneven intensity and noise are greatly eliminated in the test images, the weld region of the test image is highlighted in the corresponding sparse image, and can be well segmented by a global threshold. The test results show that the uneven brightness distribution and noise from X-ray images of thick steel pipes are largely eliminated, and the weld seam edges and low contrast areas are also enhanced. Compared with the traditional welding area detection methods, the proposed algorithm can better detect the fuzzy and low-contrast welding regions with a higher detection sensitivity (0.952) and accuracy (0.989).