灰色关联度与改进的八方向Sobel算子边缘检测

Q3 Computer Science
Yang Yang, Lian Wei
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引用次数: 2

摘要

在图像处理中,边缘检测是提高图像边缘质量的一个重要方面。边缘检测的目的是识别数字图像中亮度变化较大的点。然而,传统的边缘检测方法在边缘提取中准确率较低。对于实际图像来说,灰度边缘有时不是很清晰,图像中还含有噪声。传统Sobel算子的检测结果相对准确,但检测结果粗糙且对噪声敏感。针对上述问题,本文提出了一种改进的基于灰色关联度的八方向Sobel算子,该算子将5 × 5 Sobel算子与灰色关联度相结合,提出了一种新的八方向灰色关联方法。结果表明,该方法能更准确地检测出边缘的有用信息,提高了图像的抗噪性能。然而,缺点是该算法不是自动的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grey Relevancy Degree and Improved Eight-Direction Sobel Operator Edge Detection
Edge detection is an important aspect to improve image edge quality in image processing. The purpose of edge detection is to identify the points in digital images with great brightness variation. However, the accuracy of traditional edge detection methods in edge extraction is low. For the actual image, the grey edge is sometimes not very clear, the image also contains noise. The detection result of the traditional Sobel operator is relatively accurate, but the detection result is rough and sensitive to noise. To solve the above problems, this paper proposes an improved eight-direction Sobel operator based on grey relevancy degree, which combines 5 × 5 Sobel operator with a grey relational degree and a new eight-direction grey relevancy method. The results show that this method can detect the useful information of edge more accurately and improve the anti-noise performance. However, the drawback is that the algorithm is not automatic.
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