A modified Hough scheme for general circle location
Pattern Recognition Letters
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding circles by an array of accumulators
Communications of the ACM
An elementary algorithm for digital arc segmentation
Discrete Applied Mathematics - The 2001 international workshop on combinatorial image analysis (IWCIA 2001)
Optimal blurred segments decomposition of noisy shapes in linear time
Computers and Graphics
Multi-scale Analysis of Discrete Contours for Unsupervised Noise Detection
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
On three constrained versions of the digital circular arc recognition problem
DGCI'09 Proceedings of the 15th IAPR international conference on Discrete geometry for computer imagery
DGCI'13 Proceedings of the 17th IAPR international conference on Discrete Geometry for Computer Imagery
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In image processing and pattern recognition, the accuracy of most algorithms is dependent on a good parameterization, generally a computation scale or an estimation of the amount of noise, which may be global or variable within the input image. Recently, a simple and linear time algorithm for arc detection in images was proposed [1]. Its accuracy is dependent on the correct evaluation of the amount of noise, which was set by the user in this former version. In the present work we integrate a promising unsupervised noise detection method [2] in this arc recognition method, in order to process images with or without noise, uniformly distributed or variable within the picture. We evaluate the performance of this algorithm and we compare it with standard arc and circle detection methods based on extensions of the Hough transform.