Thresholding is a popular image segmentation method that often requires as a preliminary and indis- pensable stage in the computer aided image process, particularly in the analysis of X-ray welding images. In this pap...Thresholding is a popular image segmentation method that often requires as a preliminary and indis- pensable stage in the computer aided image process, particularly in the analysis of X-ray welding images. In this paper, a modified gray level difference-based transition region extraction and thresholding algorithm is presented for segmentation of the images that have been corrupted by intensity inhomogeneities or noise. Classical gray level difference algorithm is improved by selective output of the result of the maximum or the minimum of the gray level with the pixels in the surrounding, and multi-structuring of neighborhood window is used to represent the essence of transition region. The proposed algorithm could robustly measure the gray level changes, and accurately extract transition region of an image. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. K展开更多
文摘Thresholding is a popular image segmentation method that often requires as a preliminary and indis- pensable stage in the computer aided image process, particularly in the analysis of X-ray welding images. In this paper, a modified gray level difference-based transition region extraction and thresholding algorithm is presented for segmentation of the images that have been corrupted by intensity inhomogeneities or noise. Classical gray level difference algorithm is improved by selective output of the result of the maximum or the minimum of the gray level with the pixels in the surrounding, and multi-structuring of neighborhood window is used to represent the essence of transition region. The proposed algorithm could robustly measure the gray level changes, and accurately extract transition region of an image. Comparisons with other approaches demonstrate the superior performance of the proposed algorithm. K
