Precipitates segmentation from scanning electron microscope images through machine learning techniques
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The presence of precipitates in metallic materials affects its durability, resistance and mechanical properties. Hence, its automatic identification by image processing and machine learning techniques may lead to reliable and efficient assessments on the materials. In this paper, we introduce four widely used supervised pattern recognition techniques to accomplish metallic precipitates segmentation in scanning electron microscope images from dissimilar welding on a Hastelloy C-276 alloy: Support Vector Machines, Optimum-Path Forest, Self Organizing Maps and a Bayesian classifier. Experimental results demonstrated that all classifiers achieved similar recognition rates with good results validated by an expert in metallographic image analysis. © 2011 Springer-Verlag Berlin Heidelberg.
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Hastelloy C-276, Metallic Precipitates Segmentation, Optimum-Path Forest, Scanning Electron Microscope, Support Vector Machines, Automatic identification, Bayesian classifier, Dissimilar welding, Machine learning techniques, Metallic material, Metallographic images, Recognition rates, Supervised pattern recognition, Automation, Durability, Electron microscopes, Image analysis, Learning algorithms, Pattern recognition, Scanning, Scanning electron microscopy, Self organizing maps, Support vector machines, Image segmentation
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6636 LNCS, p. 456-468.
