Supplemental information for "Applications of Deep Learning to Decorated Ceramic Typology and Classification: A Case Study Using Tusayan White Ware from Northeast Arizona", Journal of Archaeological Science, June 2021. https://doi.org/10.1016/j.jas.2021.105375 (Open-Access) . CNN-Models-Supplement.zip - Contains digital supplement PDF file, Python code, other data Single_Sherd_8_Type_Analyzer.zip - Contains JuPyteR notebook examples, full image datase
International audienceArchaeological studies involve more and more numerical data analyses. In this ...
In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The...
International audienceData organization is a difficult and essential component in cultural heritage ...
Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps an...
Field archeologists are called upon to identify potsherds, for which they rely on their professiona...
International audienceThe ARCADIA project aims at using pattern recognition and machine learning to ...
Machine Learning for the recognition and analysis of prehistoric rock art and pottery is a promising...
This dataset includes recorded information for five material types: ceramic pottery, antler/bone com...
In this article, we consider a version of the challenging problem of learning from datasets whose si...
Pottery is of fundamental importance for understanding archaeological contexts. However, recognition...
Code in Python 3 provided as Online Supplementary Material for Automatic Gender Recognition in Hist...
This paper describes how feature extraction on ancient pottery can be combined with recent developme...
287: Deep Learning based Attribute Representation in Ancient Vase Paintings This video is a 5 minute...
International audienceArchaeological studies involve more and more numerical data analyses. In this ...
In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The...
International audienceData organization is a difficult and essential component in cultural heritage ...
Classification of ceramic fabrics has long held a major role in archaeological pursuits. It helps an...
Field archeologists are called upon to identify potsherds, for which they rely on their professiona...
International audienceThe ARCADIA project aims at using pattern recognition and machine learning to ...
Machine Learning for the recognition and analysis of prehistoric rock art and pottery is a promising...
This dataset includes recorded information for five material types: ceramic pottery, antler/bone com...
In this article, we consider a version of the challenging problem of learning from datasets whose si...
Pottery is of fundamental importance for understanding archaeological contexts. However, recognition...
Code in Python 3 provided as Online Supplementary Material for Automatic Gender Recognition in Hist...
This paper describes how feature extraction on ancient pottery can be combined with recent developme...
287: Deep Learning based Attribute Representation in Ancient Vase Paintings This video is a 5 minute...
International audienceArchaeological studies involve more and more numerical data analyses. In this ...
In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The...
International audienceData organization is a difficult and essential component in cultural heritage ...