A study on skeletonization of complex petroglyph shapes

Abstract

In this paper, we present a study on skeletonization of real-world shape data. The data stem from the cultural heritage domain and represent contact tracings of prehistoric petroglyphs. Automated analysis can support the work of archeologists on the investigation and categorization of petroglyphs. One strategy to describe petroglyph shapes is skeletonbased. The skeletonization of petroglyphs is challenging since their shapes are complex, contain numerous holes and are often incomplete or disconnected. Thus they pose an interesting testbed for skeletonization. We present a large real-world dataset consisting of more than 1100 petroglyph shapes. We investigate their properties and requirements for the purpose of skeletonization, and evaluate the applicability of state-of-the-art skeletonization and skeleton pruning algorithms on this type of data. Experiments show that pre-processing of the shapes is crucial to obtain robust skeletons. We propose an adaptive pre-processing method for petroglyph shapes and improve several state-of-the-art skeletonization algorithms to make them suitable for the complex material. Evaluations on our dataset show that 79.8 % of all shapes can be improved by the proposed pre-processing techniques and are thus better suited for subsequent skeletonization. Furthermore we observe that a thinning of the shapes produces robust skeletons for 83.5 % of our shapes and outperforms more sophisticated skeletonization techniques.

Skeletal representations of shape in human vision: Evidence for a pruned medial axis model

“…Our findings not only replicate previous work suggesting medial axis extraction using the tap-the-shape task (Firestone & Scholl, 2014; Psotka, 1978), but they also build upon this research by differentiating between computations that accommodate every available edge and those that are more robust to noisy edges (Shaked & Bruckstein, 1998). By demonstrating how individuals extract the medial axis under conditions of perturbations and illusory contours, our findings help to bridge the gap between the many mathematical formulations of shape skeletons in computer vision (Wieser et al, 2017) and their potential biological implementation in human perception (Kimia, 2003).…”

“…Because of the diversity of algorithms in the literature, our pruning models were created to exemplify two general classes of models (cf. Attali et al, 2009; Wieser, Seidl, & Zeppelzauer, 2017). More specifically, we tested a lenient pruning model that included branches describing the local geometry and a stringent model without these branches.…”

Skeletal descriptions of shape provide unique perceptual information for object recognition

“…More specifically, skeletons describe an object’s shape structure by specifying the spatial configuration of contours and component parts. Modern skeletal algorithms (i.e., pruned medial axis models 22,23 ) are particularly good descriptors of an object’s global shape because their structure remains relatively stable across contour variations typical of natural contexts (e.g., perturbations, bending) 24–26 . Importantly, research in computer vision has formalized many methods by which to compare skeletons (e.g., distance metrics 27 ), thereby providing a quantitative metric by which to compare shapes.…”

Skeletal descriptions of shape provide unique perceptual information for object recognition

“…More specifically, skeletons describe an object's shape structure by specifying the spatial configuration of contours and component parts. Modern skeletal algorithms (i.e., pruned medial axis models 22,23 ) are particularly good descriptors of an object's global shape because their structure remains relatively stable across contour variations typical of natural contexts (e.g., perturbations, bending) [24][25][26] . Importantly, research in computer vision has formalized many methods by which to compare skeletons (e.g., distance metrics 27 ), thereby providing a quantitative metric by which to compare shapes.…”