Classification and reconstruction algorithms for the archaeological fragments

Nada A. Rasheed, Md. Jan Nordin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

The ancient pottery is often found in archaeological sites in a broken state, especially when those pieces of unknown organisms and irregular fragments, may take years of hard work, especially in the case of loss of some pieces or require hard work and experienced archaeologists. So this problem is divided into two major tasks the first of which is the Classification of Archaeological Fragments into similar groups (CAF) and the second one is the Reconstruction of each group into the original Archaeological Objects (RAO). To solve this problem, a method has been proposed, which exploits the color and texture properties of the surfaces of the fragments. Furthermore, the reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition. Therefore, this research has implemented the algorithms to reconstruct real datasets using Neural Networks. The challenge of this work is to reconstruct the objects without previous knowledge about the part that should start the assembly; this greatly helps to avoid the presence of gaps created due to missing artifact fragments. The study utilizes the geometric features of the fragments as important features to reconstruct the objects by classifying their fragments using a Neural Network model.

Original languageEnglish
JournalJournal of King Saud University - Computer and Information Sciences
DOIs
Publication statusAccepted/In press - 1 Jan 2018

Fingerprint

Neural networks
Pattern recognition
Textures
Color
Geometry

Keywords

  • Archaeological
  • Artificial intelligence
  • Computer graphics
  • Neural Networks
  • Reconstruction

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

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abstract = "The ancient pottery is often found in archaeological sites in a broken state, especially when those pieces of unknown organisms and irregular fragments, may take years of hard work, especially in the case of loss of some pieces or require hard work and experienced archaeologists. So this problem is divided into two major tasks the first of which is the Classification of Archaeological Fragments into similar groups (CAF) and the second one is the Reconstruction of each group into the original Archaeological Objects (RAO). To solve this problem, a method has been proposed, which exploits the color and texture properties of the surfaces of the fragments. Furthermore, the reconstruction of archaeological fragments in 3D geometry is an important problem in pattern recognition. Therefore, this research has implemented the algorithms to reconstruct real datasets using Neural Networks. The challenge of this work is to reconstruct the objects without previous knowledge about the part that should start the assembly; this greatly helps to avoid the presence of gaps created due to missing artifact fragments. The study utilizes the geometric features of the fragments as important features to reconstruct the objects by classifying their fragments using a Neural Network model.",
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