# Robust reconstructions by multi-scale/irregular tangential covering

arXiv: https://arxiv.org/abs/2111.05688 · HTML: https://mlsanity.com/paper/2111.05688/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 38 references that are in the map.
- First posted on arXiv 10 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Deep Vectorization of Technical Drawings](https://mlsanity.com/paper/2003.05471/) (2020)
- [Vectorization of Line Drawings via PolyVector Fields](https://mlsanity.com/paper/1801.01922/) (2018)
- [The Saturated Subpaths Decomposition in Z 2 : a short note on generalized Tangential Cover](https://mlsanity.com/paper/1806.03053/) (2018)
- [Polyline defined NC trajectories parametrization. A compact analysis and solution focused on 3D Printing](https://mlsanity.com/paper/1808.01831/) (2018)

## Abstract (arXiv)

In this paper, we propose an original manner to employ a tangential cover algorithm - minDSS - in order to geometrically reconstruct noisy digital contours. To do so, we exploit the representation of graphical objects by maximal primitives we have introduced in previous works. By calculating multi-scale and irregular isothetic representations of the contour, we obtained 1-D (one-dimensional) intervals, and achieved afterwards a decomposition into maximal line segments or circular arcs. By adapting minDSS to this sparse and irregular data of 1-D intervals supporting the maximal primitives, we are now able to reconstruct the input noisy objects into cyclic contours made of lines or arcs with a minimal number of primitives. In this work, we explain our novel complete pipeline, and present its experimental evaluation by considering both synthetic and real image data. We also show that this is a robust approach, with respect to selected references from state-of-the-art, and by considering a multi-scale noise evaluation process.