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Randomised hough transform with error propagation for line and circle detection

机译：带有误差传播的随机Hough变换用于直线和圆的检测

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摘要

In this paper, we introduce a new Randomised Hough Transform aimed at improving curve detection accuracy and robustness, as well as computational efficiency. Robustness and accuracy improvement is achieved by analytically propagating the errors with image pixels to the estimated curve parameters. The errors with the curve parameters are then used to determine the contribution of pixels to the accumulator array. The computational efficiency is achieved by mapping a set of points near certain selected seed points to the parameter space at a time. Statistically determined, the seed points are points that are most likely located on the curves and that produce the most accurate curve estimation. Further computational advantage is achieved by performing progressive detection. Examples of detection of lines using the proposed technique are given in the paper. The concept can be extended to non-linear curves such as circles and ellipses.
机译：在本文中，我们引入了一种新的随机霍夫变换，旨在提高曲线检测的准确性和鲁棒性以及计算效率。通过将图像像素的误差解析地传播到估计的曲线参数，可以实现鲁棒性和准确性的提高。然后使用曲线参数的误差来确定像素对累加器阵列的贡献。通过一次将某些选定种子点附近的一组点映射到参数空间来实现计算效率。通过统计确定，种子点是最有可能位于曲线上并产生最准确的曲线估计的点。通过执行渐进式检测可获得进一步的计算优势。本文给出了使用所提出的技术检测线的示例。该概念可以扩展到非线性曲线，例如圆形和椭圆形。

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