Actas de congresos
Precipitates segmentation from scanning electron microscope images through machine learning techniques
Date
2011-06-02Registration in:
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6636 LNCS, p. 456-468.
0302-9743
1611-3349
10.1007/978-3-642-21073-0_40
WOS:000303500200040
2-s2.0-79957648069
9039182932747194
Author
Universidade Estadual Paulista (Unesp)
University of Fortaleza
Federal University of Ceará
Universidade Estadual de Campinas (UNICAMP)
University of Porto
Institutions
Abstract
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.