Image Processing for Granulometry Analysis via Neural Networks

The analysis of granulometry of substances is relevant in a great variety of the research and industrial applications as such as the pharmaceutical sector, the food sector, the basic materials production and in the concrete and wood panel industries. This analysis is important since many relevant properties of the materials can depend on the distribution of the particles sizes/shapes during the production. In this work we present an innovative method capable to estimate the particles size distribution in an image without the use of segmentation techniques by using neural networks. The paper contribution is twofold. The proposed method presents a set of techniques based on wavelet analysis and image processing techniques suitable to extract relevant features for the granulometry analysis. Then, the extracted set of features is used as input to neural networks in order to achieve the classification of each single pixel accordingly to the probability to belong to a specific class of particles size (a single band in the histogram of the distribution of the particles size). The produced outputs have been used to perform the estimation of the particle granulometry contained in the image. Results are encouraging and show the effectiveness of the proposed method.