Curvature estimation along noisy digital contours by approximate global optimization

PATTERN RECOGNITION

卷:42

Article; Proceedings Paper

Kerautret, B.¹  Lachaud, J-O² 

[1] Nancy Univ, CNRS, UMR 7503, LORIA, F-54506 Vandoeuvre Les Nancy, France

[2] Univ Savoie, CNRS, UMR 5127, LAMA, F-73376 Le Bourget Du Lac, France

关键词: Discrete geometry;    Digital contours;    Curvature estimation;    Feature detection;    Robustness to noise;   

DOI  :  10.1016/j.patcog.2008.11.013

来源: Elsevier

【 摘 要 】

In this paper, we introduce a new curvature estimator along digital contours, which we called global min-curvature (GMC) estimator. 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. (c) 2008 Elsevier Ltd. All rights reserved.

【 授权许可】

Free