Marble Durability Assessment by Means of Total Optical Porosity and Adjacent Grain Analysis

The presence of pores, cracks and microcracks in marble is one of the main features that govern the processes of decay of this stone material and, although marble is characterised by a modest porosity, there is a clear correlation between the presence and movement of fluids, and the phenomena of alteration. Through the study of porosity, it is possible to better understand the phenomena of alteration and degradation in order to obtain useful information, not only in the field of modern building, but also for the protection and recovery of historical and artistic heritage goods. This study was conducted through the characterisation of parameters directly related with the degree of alteration of the materials: water absorption at atmospheric pressure (EN 13755), open porosity (EN 1936), flexural strength (EN 12372) and bowing (EN 16306 par. 8.2). The physical and mechanical measurements have been compared with the Total Optical Porosity method (TOP) and the Adjacent Grain Analysis (AGA) index (a suggested method to evaluate the marbles’ tendency to bow, in EN 16306 annex C); two different methodologies both based on image analysis. The purpose of this study is to demonstrate the effectiveness, for the assessment of marble durability, of the two techniques of microscopic image analysis, the first correlating to the grain shape and the second to the open porosity index. This was done by comparing the microscopic image analysis results with the physical and mechanical properties, both after artificial ageing and after ten years of natural ageing. The results obtained with the TOP method seem to represent the tendency to decay better than the AGA index. The comparison of image analysis of the thin sections, in different portions of the marble specimens, shows the development of degradation due to atmospheric agents, from the surface to the inside, of naturally aged specimens, confirming recent studies made on different marbles.

Automatic Zebrafish Egg Phenotype Recognition from Bright-Field Microscopic Images Using Deep Convolutional Neural Network

Zebrafish eggs are widely used in biological experiments to study the environmental and genetic influence on embryo development. Due to the high throughput of microscopic imaging, automated analysis of zebrafish egg microscopic images is highly demanded. However, machine learning algorithms for zebrafish egg image analysis suffer from the problems of small imbalanced training dataset and subtle inter-class differences. In this study, we developed an automated zebrafish egg microscopic image analysis algorithm based on deep convolutional neural network (CNN). To tackle the problem of insufficient training data, the strategies of transfer learning and data augmentation were used. We also adopted the global averaged pooling technique to overcome the subtle phenotype differences between the fertilized and unfertilized eggs. Experimental results of a five-fold cross-validation test showed that the proposed method yielded a mean classification accuracy of 95.0% and a maximum accuracy of 98.8%. The network also demonstrated higher classification accuracy and better convergence performance than conventional CNN methods. This study extends the deep learning technique to zebrafish egg phenotype classification and paves the way for automatic bright-field microscopic image analysis.

Color Reproduction Accuracy Promotion of 3D-Printed Surfaces Based on Microscopic Image Analysis

Full-color three-dimensional (3D) printing technology is a powerful process to manufacture intelligent customized colorful objects with improved surface qualities; however, poor surface color optimization methods are the main impeding factors for its commercialization. As such, the paper explored the correlation between microstructure and color reproduction, then an assessment and prediction method of color optimization based on microscopic image analysis was proposed. The experimental models were divided into 24-color plates and 4-color cubes printed by ProJet 860 3D printer, then impregnated according to preset parameters, at last measured by a spectrophotometer and observed using both a digital microscope and a scanning electron microscope. The results revealed that the samples manifested higher saturation and smaller chromatic aberration ([Formula: see text]) after postprocessing. Moreover, the brightness of the same color surface increased with the increasing soaked surface roughness. Further, reduction in surface roughness, impregnation into surface pores, and enhancement of coating transparency effectively improved the accuracy of color reproduction, which could be verified by the measured values. Finally, the chromatic aberration caused by positioning errors on different faces of the samples was optimized, and the value of [Formula: see text] for a black cube was reduced from 8.12 to 0.82, which is undetectable to human eyes.