Please use this identifier to cite or link to this item:
https://hdl.handle.net/10216/56015
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 |
URI: | https://hdl.handle.net/10216/56015 |
Source: | Combinatorial Image Analysis |
Document Type: | Capítulo ou Parte de Livro |
Rights: | openAccess |
License: | https://creativecommons.org/licenses/by-nc/4.0/ |
Appears in Collections: | FEUP - Capítulo ou Parte de Livro |
This item is licensed under a Creative Commons License