Reconstructions Of Noisy Digital Contours With Maximal Primitives Based On Multi-Scale/Irregular Geometric Representation And Generalized Linear Programming

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.