Author:
Nasri Adel,Huang XianFeng
Abstract
AbstractAncient 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.
Publisher
Springer Science and Business Media LLC
Reference40 articles.
1. Garg, S. & Sahoo, G. A comparative study of classification methods for cracks in old digital paintings. In: Int. Conf. on Emerging Trends in Engineering and Technology (2013).
2. Australia, I. The Burra Charter: The Australia ICOMOS Charter for Places of Cultural Significance (Deakin University, Burwood, 2013).
3. Hassani, F. Documentation of cultural heritage techniques, potentials and constraints. Int. Arch. Photogramm. Remote Sensi. Spatial Inf. Sci. 40, 207–214 (2015).
4. Witharana, C., Civco, D. L. & Meyer, T. H. Evaluation of data fusion and image segmentation in earth observation based rapid mapping workflows. ISPRS J. Photogramm. Remote Sens. 87, 1–18 (2014).
5. Núñez, J. & Llacer, J. Astronomical image segmentation by self-organizing neural networks and wavelets. Neural Netw. 16, 411–417 (2003).
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