The purpose of this study was to investigate the effect of different preparation processes (used for the addition of ground glass) on the morphology and mechanical properties of the red lake glaze reconstructions. Our aim was to study these properties with a microscopic resolution and find quantitative expressions. Therefore, optical coherence tomography (OCT) was used to non-invasively visualize the glass particles in the reconstructions. In semi-transparent layers, OCT can be a powerful imaging tool to visualize structures and inclusions below the surface, such as glass particles in our case. A custom written data-analysis pipe-line was used to extract size and spatial distributions of the ground glass particles from the 3D OCT images. In recent years, OCT has been more widely applied to visualize paint layers [16,17,18] including paint layers containing smalt [19]. Here, we extend our analysis to obtain quantitative parameters from OCT images. Furthermore, we have investigated the viscoelastic behaviour of red glaze paints using a nanoindentation protocol [20], to quantify the influence of the addition of glass and of the grinding process on the elastic and viscous moduli of the dried glaze, as alluded to in the recipes.
contemporary optical image processing with matlab pdf download
A semi-automated data analysis pipeline was developed in order to process and analyse 3D OCT images. Initially, the collected images were averaged and pre-processed using ImageJ, to minimize speckle and maximize contrast. The images were averaged to 714 by 286 by 225 pixels (x,y,z), despeckled and sharpened with the existing Image J functionalities. Next, the layers were classified using an off the shelf random forest classifier called Ilastik (Interactive Learning and Segmentation Toolkit) [22]. This classification is probabilistic, and the program outputs a series of 3D images whose pixel values are the probability of being glass, the oil/pigment medium, or the boundary of the glaze layer. These probability maps were then read into Matlab 2020a and then a maximum likelihood approach is used to find the approximate boundary locations. Due to the presence of speckle, noise, and various imaging artifacts the boundary segmentation can contain errors such as discontinuities. To account for this, the locations of the boundaries were smoothed, and physical ordering of the layers is enforced where needed. After this processing, the index of refraction of the layers is accounted for and the specific layer geometry is measured. The layer shapes can be clearly seen in Fig. 7. 2ff7e9595c
Comments