A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contribution to the Prediction of Performances of the Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Performance of the Hough transform and its relationship to statistical signal detection theory
Computer Vision, Graphics, and Image Processing
A survey of the Hough transform
Computer Vision, Graphics, and Image Processing
On improving the accuracy of the Hough transform
Machine Vision and Applications
A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A hierarchical approach to line extraction based on the Hough transform
Computer Vision, Graphics, and Image Processing
A probabilistic Hough transform
Pattern Recognition
Antialiasing the Hough transform
CVGIP: Graphical Models and Image Processing
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Hough transform algorithm with a 2D hypothesis testing kernel
CVGIP: Image Understanding
CVGIP: Image Understanding
Deriving stopping rules for the probabilistic Hough transform by sequential analysis
Computer Vision and Image Understanding
An optimizing line finder using a Hough transform algorithm
Computer Vision and Image Understanding
Hough Transform Modified by Line Connectivity and Line Thickness
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Hough Transform Versus the UpWrite
IEEE Transactions on Pattern Analysis and Machine Intelligence
Guaranteed convergence of the Hough transform
Computer Vision and Image Understanding
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in 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
Hypothesis Testing: A Framework for Analyzing and Optimizing Hough Transform Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive Termination of Voting in the Probabilistic Circular Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Polygon Detection Algorithm for Robot Visual Servoing
ICIRA '08 Proceedings of the First International Conference on Intelligent Robotics and Applications: Part I
An improved Hough transform voting scheme utilizing surround suppression
Pattern Recognition Letters
Robust line detection using two-orthogonal direction image scanning
Computer Vision and Image Understanding
A novel Hough transform method for line detection by enhancing accumulator array
Pattern Recognition Letters
An efficient method of road extraction in SAR image
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Line segments and dominate points detection based on hough transform
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part II
Modified hough transform for images containing many textured regions
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Hough transform (HT) is a well-known technique for extracting lines. However, it is difficult for most existing HT methods to extract line segments robustly from complicated images, mainly because the influence from various objects other than line segments are not taken into account. This paper proposes an accurate and robust evaluator that dynamically removes contributions of backgrounds and analyzes voting patterns around peaks in the accumulator space. In the experiments, four peak detection algorithms are tested against seven images completely automatically. Results show that our method is superior to existing methods in terms of accuracy and robustness while there are no clear differences in execution time. The proposed evaluator detects peaks after the HT and hence it can be applied to any HT that keeps the basic characteristics of the voting process.