Non-destructive method for wood identification using conventional X-ray computed tomography data
Bibliographic Data
| ID | 4661228 |
|---|---|
| Authors | Kayoko Kobayashi (0000-0001-9321-0522, Kyoto University, corresponding author), Sung-Wook Hwang (0000-0002-1276-1101), Sung‐Wook Hwang (0000-0001-8265-8700, Kyoto University), Takayuki Okochi (Nara National Research Institute for Cultural Properties), Won-Hee Lee, Wonhee Lee (0000-0003-0119-4372, Kyungpook National University), Junji Sugiyama (0000-0002-5388-4925, Nanjing Forestry University, corresponding author) |
| Year | 2019 |
| Volume | 38 |
| Pages | 88-93 |
| Publication date | 2019-07-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Journal of Cultural Heritage (JOURNAL) |
| Journal identifiers | ISSN: 1296-2074 • E-ISSN: 1778-3674 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.culher.2019.02.001 |
| OpenAlex | W2916714928 |
| Language | EN |
| Citations received | 4 |
| References cited | 13 |
We establish an efficient and reliable method of wood identification that combines a non-destructive and non-invasive laboratory-scale tool, X-ray computed tomography (CT), with machine learning for image recognition. We selected six hardwood species used to create the Tripitaka Koreana and obtained the X-ray CT data of its woodblocks. Image recognition systems using the gray-level co-occurrence matrix (GLCM) or local binary patterns (LBP) were applied to the CT images and the prediction accuracies were evaluated. Because the gray level of the CT data is linearly related with the density, the CT images were preprocessed to calibrate the density. Although the resolution of the images is too low for the anatomical microstructures required for wood identification to be easily recognized visually, the predicted accuracies are quite high in both systems. However, the LBP system has slight advantages over the GLCM system. The results moreover show that the calibration of gray level to density improves the accuracies of the results. If the candidates for the wood species are selected properly and sufficient data for training is available, this technique will provide novel information about the properties of wooden historical objects
Computed tomography · Computer vision · Geography · Gray level · Grayscale · High resolution · Low resolution · Pixel · Radiology · Remote sensing · Tomography · Computer Science · Cultural Heritage Materials Analysis · Image Processing and 3D Reconstruction · Medicine · Wood and Agarwood Research · Artificial Intelligence
| Citations per year | 1.33 |
|---|---|
| Citation span | 2023 - 2026 (4) |
| Highly cited | No |