Date of Graduation
12-2020
Document Type
Dissertation
Degree Name
Doctor of Philosophy in Engineering (PhD)
Degree Level
Graduate
Department
Computer Science & Computer Engineering
Advisor/Mentor
John Gauch
Committee Member
Khoa Luu
Second Committee Member
Wing Ning Li
Third Committee Member
David Fredrick
Keywords
Computer Vision, Image Processing, Natural Language Processing, Pompeii, Software Interactive Tool, Transfer Learning
Abstract
In this dissertation, we present and analyze the technology used in the making of PPMExplorer: Search, Find, and Explore Pompeii. PPMExplorer is a software tool made with data extracted from the Pompei: Pitture e Mosaic (PPM) volumes. PPM is a valuable set of volumes containing 20,000 historical annotated images of the archaeological site of Pompeii, Italy accompanied by extensive captions. We transformed the volumes from paper, to digital, to searchable. PPMExplorer enables archaeologist researchers to conduct and check hypotheses on historical findings. We present a theory that such a concept is possible by leveraging computer generated correlations between artifacts using image data, text data, and a combination of both. The acquisition and interconnection of the data are proposed and executed using image processing, natural language processing, data mining, and machine learning methods.
Citation
Roullet, C. (2020). PPMExplorer: Using Information Retrieval, Computer Vision and Transfer Learning Methods to Index and Explore Images of Pompeii. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/3926
Included in
Architectural Technology Commons, Computer and Systems Architecture Commons, Databases and Information Systems Commons, Historic Preservation and Conservation Commons, Software Engineering Commons