Abstract
Tripitaka Koreana is a collection of over 80,000 Buddhist texts carved on wooden blocks. In this study, we investigated whether six hardwood species used as blocks could be recognized by image recognition. An image data set comprising stereograms in transverse section was acquired at 10× magnification. After auto-rotation, cropping, and filtering processes, the data set was analyzed by an image recognition system, which comprised a gray-level co-occurrence matrix method for feature extraction and a weighted neighbor distance algorithm for classification. The estimated accuracy obtained by leave-one-out cross-validation was up to 100% after optimizing the pretreatments and parameters, thereby indicating that the proposed system may be useful for the non-destructive analysis of all wooden carvings. We also examined the specific anatomical features represented by textures in the images. Many of the texture features were apparently related to the density of vessels, and others were associated with the ray intervals. However, some anatomical features that are helpful for visual inspection were ignored by the proposed system despite its perfect accuracy. In addition to the high analytical accuracy of this system, a deeper understanding of the relationships between the calculated and actual features is essential for the further development of automated recognition.
Highlights
Each wood species has its own anatomical features, such as cell types, shapes, and arrangements as well as the pitting among them, which allow the identification of wood species [1, 2]
We analyzed stereograms of six diffuseporous hardwoods in transverse section to facilitate the non-destructive identification of wood species used in the Tripitaka Koreana
The results indicated the possibility of recognition using a lower resolution data set, such as computed tomography (CT) data
Summary
Each wood species has its own anatomical features, such as cell types, shapes, and arrangements as well as the pitting among them, which allow the identification of wood species [1, 2]. The micron-order structure is observed by optical or electron microscopy after preparing thin slices or small pieces from wood block samples. This is the most reliable method for wood identification, but the sample preparation process involves many steps, which can only be conducted by specialists with sufficient knowledge and experience. In industry and trade, where it is important to check whether the correct wood species are used or in circulation, a novel method should be developed that can be employed readily and quickly. Due to the increasing demand to protect and understand culturally important properties, establishing a non-destructive method is an important issue
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