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The Application of VR and AR in the Digital Preservation of Old City Cultural Elements

8 pagesPublished: June 9, 2021

Abstract

High-throughput phenotyping of seeds is the assessment of seed morphometry to aid in the prediction of yield, tolerance, resistance, and development of seeds in various environmental conditions. The paper focuses on the application of 3D graphics to image processing as a means to conduct seed phenotyping better. The paper proposes two algorithms - similar in the outcome, but different in implementation. The algorithms perform image processing on a variety of seeds such as wheat, soy, sorghum, rough rice, white rice, and canola to arrive at their morphometric estimations. In the area of static image processing, addressed are at least three common yet significant problems of seed clusters on images, skewed images, and poor image quality. As a means to address the problems, we propose the use of low-cost physical components. The algorithms provide the estimated count, area, perimeter, length, and width of seeds within an image.

Keyphrases: AR, cultural heritage, Photogrammetry, VR

In: Yan Shi, Gongzhu Hu, Takaaki Goto and Quan Yuan (editors). CAINE 2020. The 33rd International Conference on Computer Applications in Industry and Engineering, vol 75, pages 90--97

Links:
BibTeX entry
@inproceedings{CAINE2020:Application_of_VR_and,
  author    = {Naai-Jung Shih and Hui-Xu Chen},
  title     = {The Application of VR and AR in the Digital Preservation of Old City Cultural Elements},
  booktitle = {CAINE 2020. The 33rd International Conference on Computer Applications  in Industry and Engineering},
  editor    = {Yan Shi and Gongzhu Hu and Takaaki Goto and Quan Yuan},
  series    = {EPiC Series in Computing},
  volume    = {75},
  pages     = {90--97},
  year      = {2021},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/Hmw2},
  doi       = {10.29007/cmn7}}
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