TY - JOUR
T1 - Classifiers performance evaluation in quantitative metallography,Avaliação do desempenho de classificadores em metalografia quantitativa
AU - Jordao Moreira Sarruf, Bernardo
AU - Cidade, R.A.
AU - Braga, V.P.
AU - Santana, G.J.
PY - 2015
Y1 - 2015
N2 - As the need for increasing speed takes place at industrial processes in general, the use of digital techniques such as image processing and automatic classification have been playing an important role at materials characterization and quantitative metallography fields. The aim of this work was to develop and evaluate computational vision techniques on solving ordinary problems as the area fraction of phases determination of AISI 1020 steels. Three techniques were implemented and evaluated: k-Nearest Neighbors (KNN) which consists in classifying pixels based on their neighborhood information, Artificial Neural Networks and Support Vectors Machine, these last two centered on supervised machine learning processes. Indexes that denote classification quality were then evaluated. Concerning classification time and relative accuracy, the SVM results have shown superiority. Nevertheless, in all cases, the classification values have agreed with the area fraction values expected for this type of steel based on theoretical metallurgical analysis.
AB - As the need for increasing speed takes place at industrial processes in general, the use of digital techniques such as image processing and automatic classification have been playing an important role at materials characterization and quantitative metallography fields. The aim of this work was to develop and evaluate computational vision techniques on solving ordinary problems as the area fraction of phases determination of AISI 1020 steels. Three techniques were implemented and evaluated: k-Nearest Neighbors (KNN) which consists in classifying pixels based on their neighborhood information, Artificial Neural Networks and Support Vectors Machine, these last two centered on supervised machine learning processes. Indexes that denote classification quality were then evaluated. Concerning classification time and relative accuracy, the SVM results have shown superiority. Nevertheless, in all cases, the classification values have agreed with the area fraction values expected for this type of steel based on theoretical metallurgical analysis.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-84936769407&partnerID=MN8TOARS
U2 - 10.1590/S1517-707620150002.0039
DO - 10.1590/S1517-707620150002.0039
M3 - Article
SN - 1517-7076
VL - 20
JO - Revista Materia
JF - Revista Materia
ER -