Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest
Abstract
In this work we propose a novel automatic cast iron segmentation approach based on the Optimum-Path Forest classifier (OPF). Microscopic images from nodular, gray and malleable cast irons are segmented using OPF, and Support Vector Machines (SVM) with Radial Basis Function and SVM without kernel mapping. Results show accurate and fast segmented images, in which OPF outperformed SVMs. Our work is the first into applying OPF for automatic cast iron segmentation. © 2010 Springer-Verlag.
How to cite this document
Papa, João Paulo; De Albuquerque, Victor Hugo C.; Falcão, Alexandre Xavier. Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6026 LNCS, p. 210-220. Available at: <http://hdl.handle.net/11449/71689>.
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English
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