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首页> 外文期刊>IEEE Transactions on Pattern Analysis and Machine Intelligence >Multiresolution Hough transform-an efficient method of detecting patterns in images
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Multiresolution Hough transform-an efficient method of detecting patterns in images

机译:多分辨率霍夫变换-一种检测图像中图案的有效方法

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摘要

A new multiresolution coarse-to-fine search algorithm for efficient computation of the Hough transform is proposed. The algorithm uses multiresolution images and parameter arrays. Logarithmic range reduction is proposed to achieve faster convergence. Discretization errors are taken into consideration when accumulating the parameter array. This permits the use of a very simple peak detection algorithm. Comparative results using three peak detection methods are presented. Tests on synthetic and real-world images show that the parameters converge rapidly toward the true value. The errors in rho and theta , as well as the computation time, are much lower than those obtained by other methods. Since the multiresolution Hough transform (MHT) uses a simple peak detection algorithm, the computation time will be significantly lower than other algorithms if the time for peak detection is also taken into account. The algorithm can be generalized for patterns with any number of parameters.
机译:提出了一种新的有效解析霍夫变换的多分辨率粗到细搜索算法。该算法使用多分辨率图像和参数数组。提出对数范围缩小以实现更快的收敛。累积参数数组时要考虑离散化误差。这允许使用非常简单的峰值检测算法。给出了使用三种峰检测方法的比较结果。对合成图像和真实图像的测试表明,这些参数迅速收敛到真实值。 rho和theta的误差以及计算时间比其他方法所获得的误差要低得多。由于多分辨率霍夫变换(MHT)使用简单的峰值检测算法,因此,如果还考虑峰值检测时间,则计算时间将明显低于其他算法。该算法可以推广到具有任意数量参数的模式。

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