Robust reconstructions by multi-scale/irregular tangential covering
This work addresses the problem of geometric reconstruction for noisy digital contours in image processing, offering a robust method that is incremental based on existing tangential cover algorithms.
The authors tackled the problem of reconstructing noisy digital contours by proposing a pipeline that uses multi-scale and irregular isothetic representations to decompose contours into maximal line segments or circular arcs, achieving reconstruction with a minimal number of primitives and demonstrating robustness in experiments with synthetic and real image data.
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.