Fast automatic microstructural segmentation of ferrous alloy samples using optimum-path forest

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Papa, João Paulo [UNESP]
De Albuquerque, Victor Hugo C.
Falcão, Alexandre Xavier

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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.



Cast irons, Image segmentation, Materials science, Microstructural evaluation, Supervised classification, Ferrous alloys, Forest classifiers, Kernel mapping, Malleable cast iron, Micro-structural, Microscopic image, Radial basis functions, Segmented images, Damping, Digital image storage, Iron, Malleable iron castings, Radial basis function networks, Support vector machines, Cast iron

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 6026 LNCS, p. 210-220.