图像处理的粒度分析通过神经网络

S. Ferrari, V. Piuri, F. Scotti
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引用次数: 13

摘要

物质粒度分析与各种研究和工业应用相关,如制药部门,食品部门,基本材料生产以及混凝土和木板行业。这种分析很重要,因为材料的许多相关性能可能取决于生产过程中颗粒大小/形状的分布。在这项工作中,我们提出了一种创新的方法,能够在不使用神经网络分割技术的情况下估计图像中的颗粒大小分布。论文贡献是双重的。该方法提出了一套基于小波分析和图像处理技术的技术,适用于提取粒度分析的相关特征。然后,将提取的特征集作为神经网络的输入,根据属于特定粒径类的概率(粒径分布直方图中的单个波段)实现对每个单个像素的分类。所产生的输出已用于执行图像中包含的颗粒粒度的估计。结果令人鼓舞,表明了所提方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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