Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images

Abstract : 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 \cite{NKDL-Unsupervised_Nguyen10a}. 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 \cite{NKDL-Unsupervised_KerautretL09} 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.
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Communication dans un congrès
International Conference on Computer Vision and Graphics, Sep 2010, Varsovie, Poland. pp.59-68, 2010
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Contributeur : Thanh Phuong Nguyen <>
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Dernière modification le : mardi 24 avril 2018 - 13:51:57
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Thanh Phuong Nguyen, Bertrand Kerautret, Isabelle Debled-Rennesson, Jaques Oliver Lachaud. Unsupervised, Fast and Precise Recognition of Digital Arcs in Noisy Images. International Conference on Computer Vision and Graphics, Sep 2010, Varsovie, Poland. pp.59-68, 2010. 〈inria-00544737〉

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