模糊霍夫变换中有意义特征的滤波

E. Pugin, A. Zhiznyakov
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引用次数: 2

摘要

霍夫变换和它的修正是用来寻找直线或简单的几何图形的图像。但这并不能完全阻止我们探测到不有趣的物体。本文介绍了一种对直线进行模糊霍夫变换后滤波或融合的新方法。对图像上线条的相互排列进行了分析。描述了线之间可能的距离。提出了一种基于图像的直线与边界相交的距离算法。给出了基于标准选择的线段分组和融合方法。对管道的真实图像和测试图像进行了测试。所开发的方法具有良好的鲁棒性(误差小于5%)和性能(每幅图像0.2-0.4秒)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Filtering of meaningful features of fuzzy hough transform
Hough Transform and its modifications are used to find straight lines or simple geometric figures on images. But this cannot prevent us from detecting not interesting objects completely. The paper introduces a novel method of filtering or fusion of straight lines after performing Fuzzy Hough Transform. The analysis of mutual arrangements of lines on the image is given. Possible distances between lines are described. A novel distance based on intersection of lines and borders of an image is introduced. Methods of line grouping and fusion based on some criteria selection are shown. Testing on real and test images of pipes was performed. Developed methods shows good robustness (less than 5% of errors) and performance (0.2–0.4 s. per image).
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