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Meaningful Scales Detection: an Unsupervised Noise Detection Algorithm for Digital Contours

Bertrand Kerautret 1 Jacques-Olivier Lachaud 2 
1 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery
LORIA - ALGO - Department of Algorithms, Computation, Image and Geometry
Abstract : This work presents an algorithm which permits to detect locally on digital contours what is the amount of noise estimated from a given maximal scale. The method is based on the asymptotic properties of the length of the maximal segment primitive.
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https://hal.inria.fr/hal-01112936
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Submitted on : Tuesday, February 3, 2015 - 11:08:05 PM
Last modification on : Saturday, October 16, 2021 - 11:26:08 AM
Long-term archiving on: : Wednesday, May 27, 2015 - 4:35:56 PM

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Bertrand Kerautret, Jacques-Olivier Lachaud. Meaningful Scales Detection: an Unsupervised Noise Detection Algorithm for Digital Contours. Image Processing On Line, 2014, Special Issue on Discrete Geometry (DGCI 2011), 4, pp.18. ⟨10.5201/ipol.2014.75⟩. ⟨hal-01112936⟩

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