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Curvature Estimation along Noisy Digital Contours by Approximate Global Optimization

Abstract : In this paper we introduce a new curvature estimator along digital contours, that we called Global Min-Curvature estimator (GMC). As opposed to previous curvature estimators, it considers all the possible shapes that are digitized as this contour, and selects the most probable one with a global optimization approach. The GMC estimator exploits the geometric properties of digital contours by using local bounds on tangent directions defined by the maximal digital straight segments. The estimator is then adapted to noisy contours by replacing maximal segments with maximal blurred digital straight segments. Experiments on perfect and damaged digital contours are performed and in both cases, comparisons with other existing methods are presented.
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https://hal.inria.fr/inria-00345783
Contributor : Bertrand Kerautret <>
Submitted on : Tuesday, December 9, 2008 - 11:43:46 PM
Last modification on : Saturday, July 20, 2019 - 11:44:02 AM
Document(s) archivé(s) le : Monday, June 7, 2010 - 9:29:24 PM

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  • HAL Id : inria-00345783, version 1
  • DOI : 10.1016

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Bertrand Kerautret, Jacques-Olivier Lachaud. Curvature Estimation along Noisy Digital Contours by Approximate Global Optimization. Pattern Recognition, Elsevier, 2008, j.patcog.2008.11.013, 42 (10), pp.2265--2278. ⟨10.1016⟩. ⟨inria-00345783⟩

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