A review of hough transform and line segment detection approaches

In a wide range of image processing and computer vision problems, line segment detection is one of the most critical challenges. For more than three decades researchers have contributed to build more robust and accurate algorithms with faster performance. In this paper we review the main approaches and in particular the Hough transform and its extensions, which are among the most well-known techniques for the detection of straight lines in a digital image. This paper is based on extensive practical research and is organised into two main parts. In the first part, the HT and its major research directions and limitations are discussed. In the second part of the paper, state-of-the-art line segmentation techniques are reviewed and categorized into three main groups with fundamentally distinctive characteristics. Their relative advantages and disadvantages are compared and summarised in a table

UND: unite-and-divide method in Fourier and Radon domains for line segment detection

In this paper, we extend our previously proposed line detection method to line segmentation using a so-called unite-and-divide (UND) approach. The methodology includes two phases, namely the union of spectra in the frequency domain, and the division of the sinogram in Radon space. In the union phase, given an image, its sinogram is obtained by parallel 2D multilayer Fourier transforms, Cartesian-to-polar mapping and 1D inverse Fourier transform. In the division phase, the edges of butterfly wings in the neighborhood of every sinogram peak are firstly specified, with each neighborhood area corresponding to a window in image space. By applying the separated sinogram of each such windowed image, we can extract the line segments. The division Phase identifies the edges of butterfly wings in the neighborhood of every sinogram peak such that each neighborhood area corresponds to a window in image space. Line segments are extracted by applying the separated sinogram of each windowed image. Our experiments are conducted on benchmark images and the results reveal that the UND method yields higher accuracy, has lower computational cost and is more robust to noise, compared to existing state-of-the-art methods