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Author(s): João P. Papa
Clayton R. Pereira
Victor H.C. de Albuquerque
Cleiton C. Silva
Alexandre X. Falcão
João Manuel R. S.Tavares
Title: Precipitates Segmentation from Scanning Electron Microscope Images through Machine Learning Techniques
Issue Date: 2011
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.
Subject: Ciências Tecnológicas, Outras ciências da engenharia e tecnologias
Technological sciences, Other engineering and technologies
Scientific areas: Ciências da engenharia e tecnologias::Outras ciências da engenharia e tecnologias
Engineering and technology::Other engineering and technologies
Source: Combinatorial Image Analysis
Document Type: Capítulo ou Parte de Livro
Rights: openAccess
Appears in Collections:FEUP - Capítulo ou Parte de Livro

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