Robust reconstructions by multi-scale/irregular tangential covering

TL;DR

This paper introduces a robust method for reconstructing noisy digital contours using a multi-scale tangential covering approach, effectively decomposing contours into simple primitives like lines and arcs.

Contribution

The work presents a novel pipeline that combines multi-scale irregular representations with the minDSS tangential cover algorithm for accurate, minimal primitive reconstruction of noisy contours.

Findings

Robust reconstruction of noisy contours demonstrated on synthetic and real data.

Method outperforms state-of-the-art approaches in noise robustness.

Effective multi-scale noise evaluation confirms stability of the approach.

Abstract

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.