Performance of the Hough transform and its relationship to statistical signal detection theory
Computer Vision, Graphics, and Image Processing
IEEE Transactions on Pattern Analysis and Machine Intelligence
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 probabilistic Hough transform
Pattern Recognition
Antialiasing the Hough transform
CVGIP: Graphical Models and Image Processing
CVGIP: Image Understanding
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
A comparison between the standard Hough transform and the Mahalanobis distance Hough transform
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Deriving stopping rules for the probabilistic Hough transform by sequential analysis
Computer Vision and Image Understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
VMV '01 Proceedings of the Vision Modeling and Visualization Conference 2001
Comparisons of Probabilistic and Non-probabilistic Hough Transforms
ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II
Condensation Tracking through a Hough Space
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
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The conventional implementation of the Hough Transform is inadequate in many cases due to its integrative effects of the discrete spaces. The design of an algorithm to extract optimal parameters of curves passing through image points requires a measure of statistical fitness. A strategy for image feature extraction called Tracking Hough Transform (THT) is presented that combines Extended Kalman Filtering with a Hough voting scheme that incorporates a formal noise model. The minimum mean-squares filtering process leads to high accuracy. Computing cost for real-time applications is addressed by introducing a converging sampling scheme. Extensive performance tests show that the algorithm can achieve faster speed, lower storage requirement and higher accuracy than the Standard Hough Transform.