What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise

An Intelligent Decision Support System for the Detection of Meat Spoilage using Multispectral Images

In food industry, quality and safety are considered important issues worldwide that are directly related to health and social progress. The use of vision technology for quality testing of food production has the obvious advantage of being able to continuously monitor a production using non-destructive methods, thus increasing the quality and minimizing cost. The performance of an intelligent decision support system has been evaluated in monitoring the spoilage of minced beef stored either aerobically or under modified atmosphere packaging, at different storage temperatures (0, 5, 10, and 15 °C) utilising multispectral imaging information. This paper utilises a neuro-fuzzy model which incorporates a clustering pre-processing stage for the definition of fuzzy rules, while its final fuzzy rule base is determined by competitive learning. Initially, meat samples are classified according to their storage conditions, while identification models are then utilised for the prediction of the Total Viable Counts of bacteria. The innovation of the proposed approach is further extended to the identification of the temperature used for storage, utilizing only imaging spectral information. Results indicated that spectral information in combination with the proposed modelling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage