School of Engineering and Information Sciences, Middlesex University in London, The Burroughs, London NW4 4BT, UK.
IEEE Trans Image Process. 2010 Jun;19(6):1558-66. doi: 10.1109/TIP.2010.2042102. Epub 2010 Feb 8.
The Hough transform (HT) is a commonly used technique for the identification of straight lines in an image. The Hough transform can be equivalently computed using the Radon transform (RT), by performing line detection in the frequency domain through use of central-slice theorem. In this research, an advanced Radon transform is developed using a multilayer fractional Fourier transform, a Cartesian-to-polar mapping, and 1-D inverse Fourier transforms, followed by peak detection in the sinogram. The multilayer fractional Fourier transform achieves a more accurate sampling in the frequency domain, and requires no zero padding at the stage of Cartesian-to-polar coordinate mapping. Our experiments were conducted on mix-shape images, noisy images, mixed-thickness lines and a large data set consisting of 751,000 handwritten Chinese characters. The experimental results have shown that our proposed method outperforms all known representative line detection methods based on the standard Hough transform or the Fourier transform.
霍夫变换（HT）是一种常用于图像中直线识别的技术。霍夫变换可以通过使用中心切片定理在频域中进行线检测，从而等效地使用 Radon 变换（RT）进行计算。在这项研究中，使用多层分数傅里叶变换、笛卡尔到极坐标映射和 1-D 逆傅里叶变换开发了一种先进的 Radon 变换，然后在正弦图中进行峰值检测。多层分数傅里叶变换在频域中实现了更精确的采样，并且在笛卡尔到极坐标映射的阶段不需要零填充。我们的实验是在混合形状图像、噪声图像、混合厚度线以及由 751,000 个手写汉字组成的大型数据集上进行的。实验结果表明，我们提出的方法优于所有基于标准霍夫变换或傅里叶变换的已知代表性线检测方法。
