{"id":4057,"date":"2020-09-25T20:35:45","date_gmt":"2020-09-26T00:35:45","guid":{"rendered":"https:\/\/portfolios.cs.earlham.edu\/?p=4057"},"modified":"2025-04-15T14:12:47","modified_gmt":"2025-04-15T18:12:47","slug":"yujeongs-research-proposal-on-optimal-edge-detection-for-fatcross-production","status":"publish","type":"post","link":"https:\/\/portfolios.cs.earlham.edu\/index.php\/2020\/09\/25\/yujeongs-research-proposal-on-optimal-edge-detection-for-fatcross-production\/","title":{"rendered":"Yujeong’s Research Proposal, on optimal edge detection for fatcross production"},"content":{"rendered":"\n
The file can be found here: https:\/\/drive.google.com\/file\/d\/1oljfEJN0xPCyrRnoc160miq2ZJ8VWOWX\/view?usp=sharing. <\/p>\n\n\n\n
Cuneiform tablets of the ancient Near East document one of the earliest writing systems known. Millions of such cuneiform tablets are archived and are beginning to be digitized by many collections around the world. The six-sided tablet is conventionally digitized by scanning each sides on a flat-bed scanner and stitching the processed scans in the form of a `fatcross’. With the increasing demand for digitization, an effective means of fatcross automation is in demand to tackle the time-consuming process of manual digitization. A challenge in implementing an automated fatcross production is identifying appropriate image segmentation methods. Edge detection is a viable method given its sensitivity to pixel intensity change, such as that of the rapid transition between an illuminated tablet and its dark background. This research aims to identify the most appropriate edge detection method and its implementation parameters to detect the edges from a cuneiform tablet scan.<\/p>\n","protected":false},"excerpt":{"rendered":"