article

A study on skeletonization of complex petroglyph shapes

Authors:

Matthias ZeppelzauerAuthors Info & Claims

Multimedia Tools and Applications, Volume 76, Issue 6

Pages 8285 - 8303

https://doi.org/10.1007/s11042-016-3395-1

Published: 01 March 2017 Publication History

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 skeleton-based. 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.

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Cited By

Li ZZhang TMiao YZhang JTorbaghan MHe YDai J(2024)Automated quantification of crack length and width in asphalt pavementsComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1334439:21(3317-3336)Online publication date: 22-Oct-2024
https://dl.acm.org/doi/10.1111/mice.13344
Zhang FChen XZhang X(2020)Parallel thinning and skeletonization algorithm based on cellular automatonMultimedia Tools and Applications10.1007/s11042-020-09660-579:43-44(33215-33232)Online publication date: 31-Aug-2020
https://dl.acm.org/doi/10.1007/s11042-020-09660-5
Pinilla-Buitrago LCarrasco-Ochoa JMartínez-Trinidad JRomán-Rangel E(2019)Improved Hieroglyph Representation for Image RetrievalJournal on Computing and Cultural Heritage 10.1145/328438812:2(1-15)Online publication date: 30-Apr-2019
https://dl.acm.org/doi/10.1145/3284388

A study on skeletonization of complex petroglyph shapes
1. Computing methodologies
  1. Computer graphics
  2. Machine learning
    1. Machine learning algorithms

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 76, Issue 6

March 2017

1495 pages

ISSN:1380-7501

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Copyright © Copyright © 2017 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 March 2017

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Cited By

Li ZZhang TMiao YZhang JTorbaghan MHe YDai J(2024)Automated quantification of crack length and width in asphalt pavementsComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1334439:21(3317-3336)Online publication date: 22-Oct-2024
https://dl.acm.org/doi/10.1111/mice.13344
Zhang FChen XZhang X(2020)Parallel thinning and skeletonization algorithm based on cellular automatonMultimedia Tools and Applications10.1007/s11042-020-09660-579:43-44(33215-33232)Online publication date: 31-Aug-2020
https://dl.acm.org/doi/10.1007/s11042-020-09660-5
Pinilla-Buitrago LCarrasco-Ochoa JMartínez-Trinidad JRomán-Rangel E(2019)Improved Hieroglyph Representation for Image RetrievalJournal on Computing and Cultural Heritage 10.1145/328438812:2(1-15)Online publication date: 30-Apr-2019
https://dl.acm.org/doi/10.1145/3284388

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