Introduction to algorithms
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
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
A New Variant of the Optimum-Path Forest Classifier
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing
Supervised pattern classification based on optimum-path forest
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Data clustering as an optimum-path forest problem with applications in image analysis
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
A Learning Algorithm for the Optimum-Path Forest Classifier
GbRPR '09 Proceedings of the 7th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition
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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.