{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T22:48:08Z","timestamp":1785797288317,"version":"3.56.0"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,1,12]],"date-time":"2018-01-12T00:00:00Z","timestamp":1515715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s18010209","type":"journal-article","created":{"date-parts":[[2018,1,15]],"date-time":"2018-01-15T04:01:55Z","timestamp":1515988915000},"page":"209","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":247,"title":["Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9112-0574","authenticated-orcid":false,"given":"Paolo","family":"Napoletano","sequence":"first","affiliation":[{"name":"Department of Computer Science, Systems and Communications, University of Milano-Bicocca, Milan 20126, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7432-4284","authenticated-orcid":false,"given":"Flavio","family":"Piccoli","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Systems and Communications, University of Milano-Bicocca, Milan 20126, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raimondo","family":"Schettini","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Systems and Communications, University of Milano-Bicocca, Milan 20126, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1109\/JSYST.2014.2322503","article-title":"Design techniques and applications of cyberphysical systems: A survey","volume":"9","author":"Khaitan","year":"2015","journal-title":"IEEE Syst. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MIE.2017.2649104","article-title":"The future of industrial communication: Automation networks in the era of the internet of things and industry 4.0","volume":"11","author":"Wollschlaeger","year":"2017","journal-title":"IEEE Ind. Electron. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1016\/j.future.2015.09.021","article-title":"Integration of cloud computing and internet of things: A survey","volume":"56","author":"Botta","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Banavar, G.S. (2016, January 5\u20138). Cognitive computing: From breakthroughs in the lab to applications on the field. Proceedings of the 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, USA.","DOI":"10.1109\/BigData.2016.7840579"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gilchrist, A. (2016). Introducing Industry 4.0. Industry 4.0, Springer.","DOI":"10.1007\/978-1-4842-2047-4_13"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s12599-014-0334-4","article-title":"Industry 4.0","volume":"6","author":"Lasi","year":"2014","journal-title":"Bus. Inf. Syst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TIE.1930.896476","article-title":"Computer-vision-based fabric defect detection: A survey","volume":"55","author":"Kumar","year":"2008","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1109\/28.993164","article-title":"Defect detection in textured materials using Gabor filters","volume":"38","author":"Kumar","year":"2002","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1267","DOI":"10.1109\/28.871274","article-title":"Fabric defect detection by Fourier analysis","volume":"36","author":"Chan","year":"2000","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_10","unstructured":"Wheeler, D.A., Brykczynski, B., and Meeson, R.N. (1996). Software Inspection: An Industry Best Practice for Defect Detection and Removal, IEEE Computer Society Press."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/S1369-7021(06)71389-X","article-title":"Electrospun nanofibers: Solving global issues","volume":"9","author":"Ramakrishna","year":"2006","journal-title":"Mater. Today"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1146\/annurev.matsci.36.011205.123537","article-title":"Nanofibrous materials and their applications","volume":"36","author":"Burger","year":"2006","journal-title":"Annu. Rev. Mater. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1609","DOI":"10.3390\/s90301609","article-title":"Gas Sensors Based on Electrospun Nanofibers","volume":"9","author":"Ding","year":"2009","journal-title":"Sensors"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"934","DOI":"10.1016\/j.snb.2014.11.130","article-title":"Ultrasensitive and ultraselective detection of H2S using electrospun CuO-loaded In2O3 nanofiber sensors assisted by pulse heating","volume":"209","author":"Liang","year":"2015","journal-title":"Sens. Actuators B Chem."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15","DOI":"10.2147\/nano.2006.1.1.15","article-title":"Nanofibers and their applications in tissue engineering","volume":"1","author":"Vasita","year":"2006","journal-title":"Int. J. Nanomed."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1385\/ABAB:125:3:147","article-title":"Applications of polymer nanofibers in biomedicine and biotechnology","volume":"125","author":"Venugopal","year":"2005","journal-title":"Appl. Biochem. Biotechnol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/j.desal.2009.06.064","article-title":"Performance assessment of electrospun nanofibers for filter applications","volume":"249","author":"Bjorge","year":"2009","journal-title":"Desalination"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2223","DOI":"10.1016\/S0266-3538(03)00178-7","article-title":"A review on polymer nanofibers by electrospinning and their applications in nanocomposites","volume":"63","author":"Huang","year":"2003","journal-title":"Compos. Sci. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1021\/bm501834m","article-title":"Alginate nanofibrous mats with adjustable degradation rate for regenerative medicine","volume":"16","author":"Hajiali","year":"2015","journal-title":"Biomacromolecules"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1021\/acsbiomaterials.5b00500","article-title":"Low-cost and effective fabrication of biocompatible nanofibers from silk and cellulose-rich materials","volume":"2","author":"Ceseracciu","year":"2016","journal-title":"ACS Biomater. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.ejps.2017.03.044","article-title":"Transparent ciprofloxacin-povidone antibiotic films and nanofiber mats as potential skin and wound care dressings","volume":"104","author":"Contardi","year":"2017","journal-title":"Eur. J. Pharm. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"041001","DOI":"10.1088\/1748-6041\/11\/4\/041001","article-title":"Fumarate-loaded electrospun nanofibers with anti-inflammatory activity for fast recovery of mild skin burns","volume":"11","author":"Romano","year":"2016","journal-title":"Biomed. Mater."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wei, K., Kim, H.R., Kim, B.S., and Kim, I.S. (2011). Electrospun metallic nanofibers fabricated by electrospinning and metallization. Nanofibers-Production, Properties and Functional Applications, InTech.","DOI":"10.5772\/24594"},{"key":"ref_24","first-page":"63","article-title":"The history of the science and technology of electrospinning from 1600 to 1995","volume":"7","author":"Tucker","year":"2012","journal-title":"J. Eng. Fibers Fabr."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1109\/TII.2016.2641472","article-title":"Defect Detection in SEM Images of Nanofibrous Materials","volume":"13","author":"Carrera","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_26","unstructured":"Carrera, D., Manganini, F., Boracchi, G., and Lanzarone, E. (2016). Defect Detection in Nanostructures. CNR IMATI REPORT Series, IMATI CNR."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"16024","DOI":"10.1038\/micronano.2016.24","article-title":"Recent advances in nanorobotic manipulation inside scanning electron microscopes","volume":"2","author":"Shi","year":"2016","journal-title":"Microsyst. Nanoeng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4751","DOI":"10.1016\/j.ces.2007.06.007","article-title":"Nanoparticle filtration by electrospun polymer fibers","volume":"62","author":"Yun","year":"2007","journal-title":"Chem. Eng. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","article-title":"Anomaly detection: A survey","volume":"41","author":"Chandola","year":"2009","journal-title":"ACM Comput. Surv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Navarro, P.J., Fern\u00e1ndez-Isla, C., Alcover, P.M., and Suard\u00edaz, J. (2016). Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level. Sensors, 16.","DOI":"10.3390\/s16081178"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s12652-015-0337-0","article-title":"Falls as anomalies? An experimental evaluation using smartphone accelerometer data","volume":"8","author":"Micucci","year":"2017","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Berry, M.W., and Castellanos, M. (2008). Survey of Text Mining II, Springer.","DOI":"10.1007\/978-1-84800-046-9"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Rajasegarar, S., Leckie, C., Palaniswami, M., and Bezdek, J.C. (November, January 30). Distributed anomaly detection in wireless sensor networks. Proceedings of the 10th IEEE Singapore International Conference on Communication Systems (ICCS 2006), Singapore.","DOI":"10.1109\/ICCS.2006.301508"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.sigpro.2013.12.026","article-title":"A review of novelty detection","volume":"99","author":"Pimentel","year":"2014","journal-title":"Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Boracchi, G., Carrera, D., and Wohlberg, B. (2014, January 9\u201312). Novelty detection in images by sparse representations. Proceedings of the 2014 IEEE Symposium on Intelligent Embedded Systems (IES), Orlando, FL, USA.","DOI":"10.1109\/INTELES.2014.7008985"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2545","DOI":"10.1109\/TIP.2013.2251645","article-title":"Structural texture similarity metrics for image analysis and retrieval","volume":"22","author":"Zujovic","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11265-014-0913-0","article-title":"Sparse coding with anomaly detection","volume":"79","author":"Adler","year":"2015","journal-title":"J. Signal Process. Syst."},{"key":"ref_38","first-page":"866113","article-title":"Intensity and color descriptors for texture classification","volume":"Volume 8661","author":"Cusano","year":"2013","journal-title":"Image Processing: Machine Vision Applications VI"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Napoletano, P. (2017, January 29\u201331). Hand-Crafted vs Learned Descriptors for Color Texture Classification. Proceedings of the International Workshop on Computational Color Imaging, Milan, Italy.","DOI":"10.1007\/978-3-319-56010-6_22"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","article-title":"Deep learning in neural networks: An overview","volume":"61","author":"Schmidhuber","year":"2015","journal-title":"Neural Netw."},{"key":"ref_41","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, Proceedings of the 2012 Annual Conference on Neural Information Processing Systems (NIPS), Stateline, NV, USA, 3\u20138 December 2012, MIT Press."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Li, F. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Razavian, A.S., Azizpour, H., Sullivan, J., and Carlsson, S. (2014, January 24\u201327). CNN features off-the-shelf: An astounding baseline for recognition. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Columbus, OH, USA.","DOI":"10.1109\/CVPRW.2014.131"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., and Lenc, K. (arXiv, 2014). MatConvNet\u2014Convolutional Neural Networks for MATLAB, arXiv.","DOI":"10.1145\/2733373.2807412"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01431161.2017.1399472","article-title":"Visual descriptors for content-based retrieval of remote-sensing images","volume":"39","author":"Napoletano","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bianco, S., Celona, L., Napoletano, P., and Schettini, R. (arXiv, 2017). On the Use of Deep Learning for Blind Image Quality Assessment, arXiv.","DOI":"10.1007\/s11760-017-1166-8"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"061410","DOI":"10.1117\/1.JEI.25.6.061410","article-title":"Combining multiple features for color texture classification","volume":"25","author":"Cusano","year":"2016","journal-title":"J. Electron. Imaging"},{"key":"ref_48","unstructured":"Simonyan, K., and Zisserman, A. (arXiv, 2014). Very deep convolutional networks for large-scale image recognition, arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Stanford, CA, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1364\/JOSAA.33.000017","article-title":"Evaluating color texture descriptors under large variations of controlled lighting conditions","volume":"33","author":"Cusano","year":"2016","journal-title":"J. Opt. Soc. Am. A"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","article-title":"Principal component analysis","volume":"2","author":"Wold","year":"1987","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_53","unstructured":"(2018, January 12). NanoTWICE: NANOcomposite NANOfibres for Treatment of Air and Water by an Industrial Conception of Electrospinning. Available online: http:\/\/www.mi.imati.cnr.it\/ettore\/NanoTWICE\/."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_55","unstructured":"Arthur, D., and Vassilvitskii, S. (2007, January 7\u20139). k-means++: The advantages of careful seeding. Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, New Orleans, LA, USA."},{"key":"ref_56","unstructured":"(2018, January 12). PyTorch. Available online: http:\/\/pytorch.org\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/209\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T14:51:06Z","timestamp":1760194266000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/1\/209"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1,12]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2018,1]]}},"alternative-id":["s18010209"],"URL":"https:\/\/doi.org\/10.3390\/s18010209","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,1,12]]}}}