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Pattern Analysis and Applications

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01.05.2007 | Short Paper

Optimal binarization of images by neural networks for morphological analysis of ductile cast iron

verfasst von: A. De Santis, O. Di Bartolomeo, D. Iacoviello, F. Iacoviello

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2007

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Abstract

This work aims to characterize different objects on a scene by means of some of their morphological properties. The leading application consists in the analysis of ductile cast iron specimen pictures, in order to provide a quantitative evaluation of the graphite nodules shape; to this aim the material specimen pictures are binarized. Such a binarization process can be formulated as an optimal segmentation problem. The search for the optimal solution is solved efficiently by training a neural network on a suitable set of binary templates. A robust procedure is obtained, amenable for parallel or hardware implementation, so that real-time applications can be effectively dealt with. The method was developed as the core of an expert system aimed at the unsupervised analysis of ductile cast iron mechanical properties that are influenced by the microstructure and the peculiar morphology of graphite elements.

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Titel: Optimal binarization of images by neural networks for morphological analysis of ductile cast iron
verfasst von: A. De Santis
O. Di Bartolomeo
D. Iacoviello
F. Iacoviello
Publikationsdatum: 01.05.2007
Verlag: Springer-Verlag
Erschienen in: Pattern Analysis and Applications / Ausgabe 2/2007
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-006-0052-8

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