Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity
AbstractAutomatic 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. View Full-Text
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Napoletano, P.; Piccoli, F.; Schettini, R. Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity. Sensors 2018, 18, 209.
Napoletano P, Piccoli F, Schettini R. Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity. Sensors. 2018; 18(1):209.Chicago/Turabian Style
Napoletano, Paolo; Piccoli, Flavio; Schettini, Raimondo. 2018. "Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity." Sensors 18, no. 1: 209.
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