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Keywords = convolutional neural networks, nanofibrous materials

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15 pages, 4914 KiB  
Article
Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
by Paolo Napoletano, Flavio Piccoli and Raimondo Schettini
Sensors 2018, 18(1), 209; https://doi.org/10.3390/s18010209 - 12 Jan 2018
Cited by 247 | Viewed by 10691
Abstract
Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. [...] Read more.
Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM) imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs) and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art. Full article
(This article belongs to the Section Intelligent Sensors)
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