9F Machine learning and Pattern Recognition for Archaeological Research

Daniel Gatica-Perez, Salvador Ruiz-Correa, Diego Jimenez-Badillo, Edgar Roman-Rangel 

Recent advances in machine learning, computer vision and pattern recognition have brought about new opportunities to tackle several challenges in archeological data analysis. This session aims to bring  together a group of multidisciplinary researchers to share experiences in the application of state-of-the-art methodologies, techniques and/or algorithms produced in the fields of machine learning, computer vision and pattern recognition.

You are invited to submit original, unpublished work that highlights the benefits of the techniques and methodologies in the context of archaeological or eHeritage research using both a formal presentation of the mathematical principles involved and an intuitive description in simple terms, so that non-experts can assess the benefits of the proposals.

The topics of interest include  (but are not limited to) the following:

  • Feature selection
  • Clustering
  • Classification
  • Neural networks and deep learning
  • Computer vision applications
  • Automated retrieval
  • Object recognition systems
  • 3-D matching algorithms
  • Content-based image retrieval
  • Automatic translation of ancient documents

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