The primary goal of a forensic archaeologist is to reconstruct the context of scenes involving skeletal remains using recording and mapping methods. However, the outdoor locations of most forensic archaeology scenes can result in difficulties when recording and mapping scenes. While close-range photogrammetry (CRP) has been considered for documenting context of forensic sciences, this method lacks a sufficient procedural basis to guide data recording when encountering problematic environmental conditions. The purpose of this research is to test how light correction tools, a sheet and artificial lights, could improve harsh lighting conditions. Photographs were taken of controlled scenes with skeletal remains in open, dappled, and shaded lighting environments, and the models were processed using Agisoft® Metashape® Professional. Phase 1 tested three different scenarios with four different iterations while varying the light correction tools: (1) no artificial lighting tool; (2) only a sheet over the scene; (3) artificial lights placed around the scene; and (4) a combination of lights and sheet. The accuracy was assessed quantitatively, using the root-mean square (RMS) reprojection error and total scale bar error, and qualitatively. The results indicated that no significant quantitative accuracy of the model changed between iterations. However, the visual accuracy of the scene did improve with the sheet by decreasing shadows across the scene. Phase 2 tested two larger scenarios using the same four iterations. While the models were all highly accurate quantitatively, the iterations that included the sheet appeared to have fewer qualitative errors. Guidelines are provided to successfully use light correction tools to improve harsh lighting conditions of outdoor scenes.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd2020-2140 |
| Date | 01 January 2021 |
| Creators | Jasiak, Caroline |
| Publisher | STARS |
| Source Sets | University of Central Florida |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Electronic Theses and Dissertations, 2020- |
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