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Naji, M. |
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Motta, Antonella |
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Aletan, Dirar |
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Mohamed, Tarek |
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Ertürk, Emre |
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Taccardi, Nicola |
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Kononenko, Denys |
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Petrov, R. H. | Madrid |
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Alshaaer, Mazen | Brussels |
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Bih, L. |
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Casati, R. |
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Muller, Hermance |
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Kočí, Jan | Prague |
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Šuljagić, Marija |
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Kalteremidou, Kalliopi-Artemi | Brussels |
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Azam, Siraj |
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Ospanova, Alyiya |
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Blanpain, Bart |
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Ali, M. A. |
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Popa, V. |
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Rančić, M. |
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Ollier, Nadège |
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Azevedo, Nuno Monteiro |
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Landes, Michael |
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Rignanese, Gian-Marco |
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Donskoi, Eugene
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Topics
Publications (12/12 displayed)
- 2021Characterisation of SFCA phases in iron ore sinter by combined optical microscopy and electron probe microanalysis (EPMA)
- 2021Characterisation of SFCA phases in iron ore sinter by combined optical microscopy and electron probe microanalysis (EPMA)
- 2021Transformation of Automated Optical Image Analysis Software Mineral4/Recognition4 to Mineral5/Recognition5
- 2021Automated Optical Image Analysis of Iron Ore Sintercitations
- 2020Advances in Optical Image Analysis Textural Segmentation in Ironmakingcitations
- 2018Importance of textural information in mathematical modelling of iron ore fines sintering performancecitations
- 2017Mineral 4/Recognition 4: A Universal Optical Image Analysis Package for Iron Ore, Sinter and Coke Characterizationcitations
- 2016Mineralogical quantification of iron ore sintercitations
- 2015Mineralogical quantification of iron ore sinter
- 2015Advances in optical image analysis of iron ore sinter
- 2015Automated optical image analysis of natural and sintered iron orecitations
- 2013Comparative study of iron ore characterisation using a scanning electron microscope and optical image analysiscitations
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article
Advances in Optical Image Analysis Textural Segmentation in Ironmaking
Abstract
Optical image analysis is commonly used to characterize different feedstock material for ironmaking, such as iron ore, iron ore sinter, coal and coke. Information is often needed for phases which have the same reflectivity and chemical composition, but different morphology. Such information is usually obtained by manual point counting, which is quite expensive and may not provide consistent results between different petrologists. To perform accurate segmentation of such phases using automated optical image analysis, the software must be able to identify specific textures. CSIRO’s Carbon Steel Futures group has developed an optical image analysis software package called Mineral4/Recognition4, which incorporates a dedicated textural identification module allowing segmentation of such phases. The article discusses the problems associated with segmentation of similar phases in different ironmaking feedstock material using automated optical image analysis and demonstrates successful algorithms for textural identification. The examples cover segmentation of three different coke phases: two types of Inert Maceral Derived Components (IMDC), non-reacted and partially reacted, and Reacted Maceral Derived Components (RMDC); primary and secondary hematite in iron ore sinter; and minerals difficult to distinguish with traditional thresholding in iron ore.