An edge detection algorithm for mine roadway image
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摘要: 针对传统的Canny算法在处理模糊的矿井巷道图像时存在边缘提取效果较差的问题,提出了一种基于小波变换和Canny算法的矿井巷道图像边缘检测算法。该算法首先对矿井巷道原始图像做小波分解,获得低频图像和高频图像,从而避免模糊图像对边缘检测效果的影响;然后采用Canny算法计算低频图像和高频图像的一阶差分,获得低频图像和高频图像的梯度图,通过计算局部梯度最大值,获得高频图像和低频图像的边缘图;当高频图像的边缘图上出现间断点时,在低频图像的边缘图中检测该点的8点邻域,寻找连接点,即可得到完整的矿井巷道边缘检测图。实验结果表明,与传统的Canny算法相比,该算法能够检测到较多的图像边缘点,具有较好的边缘连接效果。Abstract: In view of problem of bad edge extraction effect when traditional Canny algorithm processes fuzzy mine roadway image, the paper proposed an edge detection algorithm for mine roadway image based on wavelet transform and Canny algorithm. The algorithm firstly makes wavelet decomposition for original mine roadway image to obtain low-frequency image and high-frequency image, so as to avoid influence of fuzzy image on edge detection effect. Then it uses Canny algorithm to calculate first order differences of low-frequency image and high-frequency image to obtain gradient images of low-frequency image and high-frequency image and obtains edge images of low-frequency image and high-frequency image through calculating the maximum value of local gradient. When edge image of high-frequency image has discontinuity point, it detects eight-point neighborhood of the point in edge image of low-frequency image to find connection point to obtain complete edge detection image of mine roadway. The experiment result showed that the algorithm can detect more edge points of image with better edge connection effect comparing with traditional Canny algorithm.
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Key words:
- mine roadway image /
- fuzzy image /
- edge detection /
- wavelet transform /
- Canny algorithm /
- low-frequency image /
- high-frequency image
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