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Grey Relevancy Degree and Improved Eight-Direction Sobel Operator Edge Detection

机译:灰色相关程度和改进的八方向Sobel操作员边缘检测

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摘要

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 style="font-family:Verdana;">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.
机译:边缘检测是提高图像处理中的图像边缘质量的重要方面。边缘检测的目的是识别具有良好亮度变化的数字图像中的点。然而,边缘提取中传统边缘检测方法的准确性低。对于实际图像,灰色边缘有时不太清晰,图像也包含噪声。传统Sobel操作员的检测结果相对准确,但检测结果对噪声粗略而且敏感。为了解决上述问题,本文提出了一种基于灰色相关程度的改进的八方向Sobel运算符,将5×5 Sobel运算符与灰色关联度和新的八方向灰色相关法。结果表明,该方法可以更准确地检测边缘的有用信息,提高抗噪声性能。然而,缺点是算法不是自动的。

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