文章基本信息

标题：A missing color area extraction approach from high-resolution statue images for cultural heritage documentation
本地全文：下载
作者：Adel Nasri ; XianFeng Huang
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2020
卷号：10
期号：1
页码：1-14
DOI：10.1038/s41598-020-78254-w
出版社：Springer Nature
摘要：Ancient statues are usually fragile and have a tendency to deteriorate over time, developing cracks, corrosion, and losing color. Before any intervention on the object of art, a conservator must map degradation and take measurements. Deterioration mapping is an extremely long process, as the conservator or restorer must locate and digitize the damages manually and collect physical measurements from the artwork. Extracting and measuring the deterioration automatically from images is less expensive and aids the digital documentation process, thus reducing the time cost of manual deterioration mapping. In this paper, we propose an effective approach named Missing Color Area Extraction in order to extract and measure missing color areas from high-resolution imagery statues, using a thresholding technique. The conversion from RGB color space to HSV color space is applied, in addition to morphological operations to remove the dust and small objects.
其他摘要：Abstract Ancient statues are usually fragile and have a tendency to deteriorate over time, developing cracks, corrosion, and losing color. Before any intervention on the object of art, a conservator must map degradation and take measurements. Deterioration mapping is an extremely long process, as the conservator or restorer must locate and digitize the damages manually and collect physical measurements from the artwork. Extracting and measuring the deterioration automatically from images is less expensive and aids the digital documentation process, thus reducing the time cost of manual deterioration mapping. In this paper, we propose an effective approach named Missing Color Area Extraction in order to extract and measure missing color areas from high-resolution imagery statues, using a thresholding technique. The conversion from RGB color space to HSV color space is applied, in addition to morphological operations to remove the dust and small objects.