Curvature estimation along noisy digital contours by approximate global optimization

[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.

[发布日期] 2009-10-01

[效力级别]  Proceedings Paper

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