Meaningful Thickness Detection on Polygonal Curve

Bertrand Kerautret (1) , Jacques-Olivier Lachaud (2) , Mouhammad Said (3)

1 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery
2 LAMA - Laboratoire de Mathématiques
3 LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

The notion of meaningful scale was recently introduced to detect the amount of noise present along a digital contour. 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 overcome this limitation. It exploits another primitive called the Blurred Segment which controls the straight segment recognition precision of disconnected sets of points. The resulting noise detection provides precise results and is also simpler to implement. A first application of contour smoothing demonstrates the efficiency of the proposed method. The algorithms can also be tested online.

Mots clés: Noise detection; meaningful scale detection; polygonalisation; smoothing polygon

Domaines: Traitement des images [eess.IV]

Bertrand Kerautret, Jacques-Olivier Lachaud, Mouhammad Said. Meaningful Thickness Detection on Polygonal Curve. ICPRAM - International Conference on Pattern Recognition Applications and Methods - 2012, Feb 2012, Vilamoura, Portugal. pp.372--379, ⟨10.5220/0003760903720379⟩. ⟨hal-00780710⟩