Industrial applications of computer image processing
Computers and Industrial Engineering
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
The Image Foresting Transform: Theory, Algorithms, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Classification of weld flaws with imbalanced class data
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Computers and Industrial Engineering
Supervised pattern classification based on optimum-path forest
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Expert Systems with Applications: An International Journal
Multi-objective optimization of a welding process by the estimation of the Pareto optimal set
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An overview of statistical learning theory
IEEE Transactions on Neural Networks
ECG arrhythmia classification based on optimum-path forest
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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The automatic characterization of particles in metallographic images has been paramount, mainly because of the importance of quantifying such microstructures in order to assess the mechanical properties of materials common used in industry. This automated characterization may avoid problems related with fatigue and possible measurement errors. In this paper, computer techniques are used and assessed towards the accomplishment of this crucial industrial goal in an efficient and robust manner. Hence, the use of the most actively pursued machine learning classification techniques. In particularity, Support Vector Machine, Bayesian and Optimum-Path Forest based classifiers, and also the Otsu's method, which is commonly used in computer imaging to binarize automatically simply images and used here to demonstrated the need for more complex methods, are evaluated in the characterization of graphite particles in metallographic images. The statistical based analysis performed confirmed that these computer techniques are efficient solutions to accomplish the aimed characterization. Additionally, the Optimum-Path Forest based classifier demonstrated an overall superior performance, both in terms of accuracy and speed.