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ARCA (Automatic Recognition of Color for Archaeology): A Desktop Application for Munsell Estimation

  • Filippo L. M. Milotta
  • Filippo Stanco
  • Davide Tanasi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10485)

Abstract

Archaeologists are used to employing the Munsell Soil Charts on cultural heritage sites to identify colors of soils and retrieved artifacts. The standard practice of Munsell estimation exploits the Soil Charts by visual means. This procedure is error prone, time consuming and very subjective. To obtain an accurate estimation the process should be repeated multiple times and possibly by other users, since colors might not be perceived uniformly by different people. Hence, a method for objective and automatic Munsell estimation would be a valuable asset to the field of archaeology. In this work we present ARCA: Automatic Recognition of Color for Archaeology, a desktop application for Munsell estimation. The following pipeline for Munsell estimation aimed towards archaeologists has been proposed: image acquisition of specimens, manual sampling of the image in the ARCA desktop application, automatic Munsell estimation of the sampled points and creation of a sampling report. A dataset, called ARCA108, consisting of 22, 848 samples has been gathered, in an unconstrained environment, and evaluated with respect to the Munsell Soil Charts. Experimental results are reported to define the best configuration that should be used in the acquisition phase. Color tolerance values of the proposed framework are also reported.

Keywords

Color standardization Munsell Color space conversion Digital archaelogy Color specification 

Notes

Acknowledgments

The method described in this work was filed with the United States Patent and Trademark Office on April 19, 2017, as “Automatic Digital Method for Classification of Colors in Munsell Color System”, and assigned Serial No. 62/487,178.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Filippo L. M. Milotta
    • 1
    • 2
  • Filippo Stanco
    • 1
  • Davide Tanasi
    • 2
  1. 1.Image Processing Laboratory (IPLab), Department of Mathematics and Computer ScienceUniversity of CataniaCataniaItaly
  2. 2.Center for Virtualization and Applied Spatial Technologies (CVAST), Department of HistoryUniversity of South FloridaTampaUSA

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