Skip to content

humanai-foundation/HealingStones

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

25 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Healing Stones

Reconstructing Digitized Cultural Heritage Artifacts with Artificial Intelligence.

Background

Historically works of art and architecture have been subjected to fragmentation: ancient Maya stelae were cut away from monuments by collectors; medieval sculptures from Notre-Dame in Paris were broken into multiple parts and dispersed in acts of political iconoclasm. Art historians and archaeologists seek to reconstruct these works to more fully understand their cultural meaning and value, however the traditional method of physical refitting is labor intensive and not always possible when fragments are dispersed throughout the world. We use AI in combination with existing digital scan models of fragments to develop a means for reconstructing fragmented cultural heritage artifacts in a virtual space. The project dataset used are the remaining and reconstructed stone fragments of Stela #43 from the archaeological site of Naranjo, in the Petén region of Guatemala. This stela, adorned with carved, high-relief images and hieroglyphs, holds immense historical significance in studying ancient Maya iconography and dating. Its reconstruction becomes particularly crucial in advancing new initiatives in the preservation of historical art and architecture.

Tasks

  • Search for direct fit matches between surfaces (e.g. two parts of something broken).
  • Identify continuity of carved topography (e.g. parts of the same carved feature, but with gaps).
  • Identify continuity of surface designs (e.g. parts of the same carved feature, but with gaps).
  • Identify broader dimensional resemblance (e.g. the shape of the stone blocks used to make that sculptural facade).

Expected results

  • Develop machine learning models that can reconstruct digitized fragments with at least 80% accuracy.
  • Train AI model to search for matches between digitized fragments for which is original orientation is certain.
  • Test and train AI model using fragments for which original orientation is uncertain.

Links

University of Alabama

Human AI Foundation

Notre Dame in Color

Visual Documentation Lab

Mentors

Jennifer Feltman University of Alabama About Link
Alexandre Tokovinine University of Alabama About Link
Emanuele Usai University of Alabama About Link
Sergei Gleyzer University of Alabama About Link
Lizzette Soto University of Alabama About Link

About

No description, website, or topics provided.

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Contributors 3

  •  
  •  
  •  

Languages