IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear feature detection and enhancement in noisy images via the Radon transform
Pattern Recognition Letters
The Radon transform and its application to shape parametrization in machine vision
Image and Vision Computing - Special issue: papers from the second Alvey Vision Conference
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Robust detection of lines using the progressive probabilistic Hough transform
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Shape Detection in Computer Vision Using the Hough Transform
Shape Detection in Computer Vision Using the Hough Transform
Extended Hough transform for linear feature detection
Pattern Recognition
Old and new straight-line detectors: Description and comparison
Pattern Recognition
An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration
IEEE Transactions on Pattern Analysis and Machine Intelligence
N-Point Hough transform for line detection
Journal of Visual Communication and Image Representation
Advanced hough transform using a multilayer fractional fourier method
IEEE Transactions on Image Processing
Hough Transform from the Radon Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Line detection in images through regularized hough transform
IEEE Transactions on Image Processing
Short Communication: A rectilinear Gaussian model for estimating straight-line parameters
Journal of Visual Communication and Image Representation
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Straight line detection is common in computer vision. The Radon transform has received much attention for its efficiency and accuracy compared to the Hough transform. In this paper, a generalized interpolated Fourier transform, hereafter called GIFT, is proposed to speed up the Radon transform. Based on the GIFT, a methodology that can detect straight lines from a gray scale image without any pre-processing is implemented. Two contributions can be claimed. First, the recent work by Pan et al. is reinterpreted and implemented in a clearer way so the traditional Fourier transform can be interpolated to achieve an accurate sampling in the frequency domain. Second, the interpolated Fourier transform is further generalized with flexible parameter determination in two dimensions when applied to 2-D images. The experiments demonstrate that our proposed methodology outperforms the standard Radon transform with lower computational load and higher accuracy. The experiments also show that the GIFT line detector can compete against the random sample consensus, which is a robust estimator popularly used in the field of computer vision.