Reconstructions of Noisy Digital Contours with Maximal Primitives Based on Multi-Scale/Irregular Geometric Representation and Generalized Linear Programming

Antoine Vacavant (1) , Bertrand Kerautret (2) , Tristan Roussillon (3) , Fabien Feschet (4)

1 ISIT - Image Science for Interventional Techniques
2 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery
3 M2DisCo - Geometry Processing and Constrained Optimization
4 LLAIC1 - Laboratoire de Logique, Algorithmique et Informatique

The reconstruction of noisy digital shapes is a complex question and a lot of contributions have been proposed to address this problem , including blurred segment decomposition or adaptive tangential covering for instance. In this article, we propose a novel approach combining multi-scale and irregular isothetic representations of the input contour, as an extension of a previous work [Vacavant et al., A Combined Multi-Scale/Irregular Algorithm for the Vectorization of Noisy Digital Contours , CVIU 2013]. Our new algorithm improves the representation of the contour by 1-D intervals, and achieves afterwards the decomposition of the contour into maximal arcs or segments. Our experiments with synthetic and real images show that our contribution can be employed as a relevant option for noisy shape reconstruction.

Mots clés: Digital shape analysis; Multi-scale; analysis; Decomposition into maximal arcs; Decomposition into maximal; segments; Irregular isothetic grids; Multi-scale analysis; Decomposition into maximal segments

Domaines: Informatique [cs]

Antoine Vacavant, Bertrand Kerautret, Tristan Roussillon, Fabien Feschet. Reconstructions of Noisy Digital Contours with Maximal Primitives Based on Multi-Scale/Irregular Geometric Representation and Generalized Linear Programming. 20th IAPR International Conference on Discrete Geometry for Computer Imagery, Sep 2017, Vienna, Austria. pp.291-303, ⟨10.1007/BFb0038202⟩. ⟨hal-01621504⟩