Fast Hough transform: A hierarchical approach
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
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A Real-Time Processor for the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
Computer Vision, Graphics, and Image Processing
On improving the accuracy of the Hough transform
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Hough Transform Modified by Line Connectivity and Line Thickness
IEEE Transactions on Pattern Analysis and Machine Intelligence
A simple and robust line detection algorithm based on small eigenvalue analysis
Pattern Recognition Letters
Detection of linear objects in GPR data
Signal Processing
An effective voting method for circle detection
Pattern Recognition Letters
Accuracy of the straight line Hough transform: the non-voting approach
Computer Vision and Image Understanding
An approach to beacons detection for a mobile robot using a neural network model
MOAS'07 Proceedings of the 18th conference on Proceedings of the 18th IASTED International Conference: modelling and simulation
A CAD System for Long-Bone Segmentation and Fracture Detection
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
An approach to beacons detection for a mobile robot using a neural network model
MS '07 The 18th IASTED International Conference on Modelling and Simulation
Robust edge extraction for Swissranger SR-3000 range images
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
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ADCONS'11 Proceedings of the 2011 international conference on Advanced Computing, Networking and Security
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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.