A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A probabilistic Hough transform
Pattern Recognition
Scale- and orientation-invariant generalized Hough transform—a new approach
Pattern Recognition
CVGIP: Image Understanding
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Detection of piecewise-linear signals by the randomized Hough transform
Pattern Recognition Letters
Randomized Hough transform: improved ellipse detection with comparison
Pattern Recognition Letters
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Computer Vision
An efficient randomized algorithm for detecting circles
Computer Vision and Image Understanding
Efficient Technique for Ellipse Detection Using Restricted Randomized Hough Transform
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Extended Hough transform for linear feature detection
Pattern Recognition
Applying the Hough transform pseudo-linearity property to improve computing speed
Pattern Recognition Letters
The randomized-Hough-transform-based method for great-circle detection on sphere
Pattern Recognition Letters
Real-time line detection using accelerated high-resolution Hough transform
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
Train service timetabling in railway open markets by particle swarm optimisation
Expert Systems with Applications: An International Journal
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Hough transform (HT) is a well established method for curve detection and recognition due to its robustness and parallel processing capability. However, HT is quite time-consuming. In this paper, an eliminating particle swarm optimization (EPSO) algorithm is employed to improve the speed of a HT. The parameters of the solution after Hough transformation are considered as the particle positions, and the EPSO algorithm searches the optimum solution by eliminating the ''weakest'' particles to speed up the computation. An accumulation array in Hough transformation is utilized as a fitness function of the EPSO algorithm. The experiments on numerous images show that the proposed approach can detect curves or contours of both noise-free and noisy images with much better performance. Especially, for noisy images, it can archive much better results than that obtained by using the existing HT algorithms.