A new curve detection method: randomized Hough transform (RHT)
Pattern Recognition Letters
A probabilistic Hough transform
Pattern Recognition
Extracting geometric primitives
CVGIP: Image Understanding
CVGIP: Image Understanding
Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
CVGIP: Image Understanding
Circular arc detection based on Hough transform
Pattern Recognition Letters
Deriving stopping rules for the probabilistic Hough transform by sequential analysis
Computer Vision and Image Understanding
The Hough Transform Versus the UpWrite
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constrained Hough transforms for curve detection
Computer Vision and 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
Randomized or probabilistic Hough transform: unified performance evaluation
Pattern Recognition Letters - Selected papers from the 11th scandinavian conference on image analysis
A two-step circle detection algorithm from the intersecting chords
Pattern Recognition Letters
An efficient randomized algorithm for detecting circles
Computer Vision and Image Understanding
Polling an Image for Circles by Random Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Decomposition of the Hough Transform: Curve Detection with Efficient Error Propagation
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
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The automated detection of process-induced defects such as tooling marks is a common and important problem in machine vision. Such defects are often distinguishable from natural flaws and other features by their geometric form, for example their circularity or linearity. This paper discusses the automated inspection of polished stone, where process-induced defects present as circular arcs. This is a particularly demanding circle detection problem due to the large radii and disrupted form of the arcs, the complex nature of the stone surface, the presence of other natural flaws and the fact that each circle is represented by a relatively small proportion of its total boundary. Once detected and characterized, data relating to the defects may be used to adaptively control the polishing process. We discuss the hardware requirements of imaging such a surface and present a novel implementation of a randomised circle detection algorithm that is able to reliably detect these defects. The algorithm minimizes the number of iterations required, based on a failure probability specified by the user, thus providing optimum efficiency for a specified confidence whilst requiring no prior knowledge of the image. The probabilities of spurious results are also analyzed, and an optimization routine introduced to address the inaccuracies often associated with randomized techniques. Experimental results demonstrate the validity of this approach.