Meaningful Thickness Detection On Polygonal Curve

Abstract

The notion of meaningful scale was recently introduced to detect the amount of noise present along a digital contour (Kerautret and Lachaud, 2009b). It relies on the asymptotic properties of the maximal digital straight segment primitive. Even though very useful, the method is restricted to digital contour data and is not able to process other types of geometric data like disconnected set of points. In this work, we propose a solution to outcome this limitation. It exploits another primitive called the Blurred Segment (Debled-Rennesson et al., 2006) which controls the straight segment recognition precision of disconnected sets of points. The resulting noise detection provides precise results and is also more simple to implement. A first application of contour smoothing demonstrates the efficiency of the proposed method. The algorithms can also be tested online (Kerautret et al., 2011).

Jacques-Olivier Lachaud

Professor of Computer Science

My research interests include digital geometry, geometry processing, image analysis, variational models and discrete calculus.