Automated rapid artefact surface area measurement from imagery with computer vision
Abstract
Automated surface area measurements have been of interest to archaeologists since digital imagery began allowing researchers to remotely collect artefact metrics. We present a method of automatically measuring 2D surface area from artefact planform images employing computer vision in Python. The Python script, provided as a .py file in supplementary data, creates boundaries around regions of relatively homogeneous pixels (artefacts) in the image. These bounded regions are called contours. A count of the number of pixels within each contour provides a surface area in pixels. A circular reference object provides a conversion factor for the contours, as well as a point of reference for geometric accuracy of outputs.
Measurements of 2D artefact surface area can be used in combination with measurements of length, width, thickness, and mass, or in some cases, replace such measurements. As presented, this technique provides utility to archaeology with applications to new documentation of artefacts, archived artefact images containing a scale, as well as landscape geoarchaeology and sedimentary contexts. Limitations of this type of surface area measurement include the requirement of the image background being of a solid colour heavily contrasting the artefacts being measured. Effectively, the background requirement limits deployment supporting collection of rapid field measurements from in-situ surface scatters without modification to the script or manipulation of the artefacts. Analytical applications utilizing this technique include studies of relative artefact abundance, shape and size class characterizations in artefact scatters, and redistribution of artefacts by geomorphological processes.
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