A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A probabilistic Hough transform
Pattern Recognition
A probabilistic algorithm for computing Hough transforms
Journal of Algorithms
Extracting geometric primitives
CVGIP: Image Understanding
A new method for quadratic curve detection using K-RANSAC with acceleration techniques
A new method for quadratic curve detection using K-RANSAC with acceleration techniques
Machine vision
Constrained Hough transforms for curve detection
Computer Vision and Image Understanding
Use of the Hough transformation to detect lines and curves in pictures
Communications of the ACM
Adaptive Termination of Voting in the Probabilistic Circular Hough Transform
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust and efficient automated detection of tooling defects in polished stone
Computers in Industry - Special issue: Machine vision
Analysis of the inclination of elongated biological objects: microtubules
Machine Graphics & Vision International Journal
Robust and efficient automated detection of tooling defects in polished stone
Computers in Industry
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A new random sampling strategy, designed for retrieving subsets consisting of two edge pixels from an input image, is proposed as the sampling process for RANSAC circle detection using coaxal transform. The proposed strategy is shown to have the following advantages over the conventional random sampling strategy. First, a poll size can be planned in a principled manner. Second, once a poll size is set, the probability that a circle is missed by the sampling process is kept relatively constant regardless of noise. Third, the actual number of subsets taken is automatically adjusted for different image complexities. Experimental results in agreement with the claimed advantages are presented.