Authors: Rafael Sterzinger, Christian Stippel, Robert Sablatnig
Abstract: Etruscan mirrors constitute a significant category in Etruscan art,
characterized by elaborate figurative illustrations featured on their backside.
A laborious and costly aspect of their analysis and documentation is the task
of manually tracing these illustrations. In previous work, a methodology has
been proposed to automate this process, involving photometric-stereo scanning
in combination with deep neural networks. While achieving quantitative
performance akin to an expert annotator, some results still lack qualitative
precision and, thus, require annotators for inspection and potential
correction, maintaining resource intensity. In response, we propose a deep
neural network trained to interactively refine existing annotations based on
human guidance. Our human-in-the-loop approach streamlines annotation,
achieving equal quality with up to 75% less manual input required. Moreover,
during the refinement process, the relative improvement of our methodology over
pure manual labeling reaches peak values of up to 26%, attaining drastically
better quality quicker. By being tailored to the complex task of segmenting
intricate lines, specifically distinguishing it from previous methods, our
approach offers drastic improvements in efficacy, transferable to a broad
spectrum of applications beyond Etruscan mirrors.
Source: http://arxiv.org/abs/2408.03304v1
