Automated relief-based discrimination of non-opaque minerals in optical image analysis |
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| Affiliation: | 1. Department of Physics and Earth Sciences, University of Parma, Parco Area delle Scienze 157/A, 43124 Parma, Italy;2. Department of Neurosciences, University of Parma, Via Volturno 39, 43125 Parma, Italy;3. Inaf-IAPS, Via Fosso del Cavaliere 100, 00133 Rome, Italy;4. Department of Psychologic, Umanistic and Earth Sciences, University of Chieti-Pescara, Via Vestini 31, 66013 Chieti, Italy;5. CNR-ISMAR, Via Gobetti 101, 40129 Bologna, Italy;1. Research Center in Semiconductors Technologies for Energetic, 2 Bd Frantz Fanon, Alger 7 Merveilles, Algeria;2. Ecole Normale Supérieure (ENS), Laboratoire de Géologie, 46, Allée d''Italie 69364 Lyon Cedex, France;1. Ulsan Ship and Ocean College, Ludong University, Yantai 264025, China;2. Key Laboratory for Liquid-Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061, China;3. Department of Nanotechnology and Advanced Materials Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul 143-747, South Korea;1. State Key Laboratory of Pollution Control and Resources Reuse, College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China;2. Research Group of Sustainable Energy, Air and Water Technology, Department of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerpen, Belgium;3. Centre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW, 2007, Australia;4. College of Environmental Science and Engineering, Hunan University, Changsha, 410082, China;5. Key Laboratory of Environmental Biology and Pollution Control, Hunan University, Ministry of Education, Changsha, 410082, China;1. State Key Laboratory Breeding Base of Nuclear Resources and Environment, East China Institute of Technology, Nanchang, Jiangxi, PR China;2. School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, Heilongjiang, PR China;3. Department of Water Resources and Environmental Engineering, East China Institute of Technology, Nanchang, Jiangxi, PR China |
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| Abstract: | Ore characterisation is important in order to understand the quality of ores and their behaviour during downstream processing. Many significant ore characteristics can only be determined through the use of various imaging techniques. Optical Image Analysis (OIA) is one such technique and is particularly attractive for many applications due to its low cost and high resolution. However OIA also has some limitations, one of which is the difficulty with discriminating non-opaque minerals. Some non-opaque minerals, such as quartz, are typical gangue minerals in certain types of iron ores. Even though in many cases quartz particles can be easily seen and attributed by mineralogists in polished sections, their automated discrimination has always been an issue, the reasons for which are discussed in this article. The ability to automatically discriminate quartz and other non-opaque minerals would significantly increase the value of OIA for the mineral industry.This paper describes a novel method of discriminating non-opaque minerals in the sample by their optical relief, which results in visible borders between the mineral and the epoxy resin mounting medium. An algorithm for such discrimination that has been developed for the CSIRO Mineral4/Recognition4 OIA software package is described. The algorithm is based on dynamic thresholding of the image with subsequent cleanup and enhancement to reliably determine borders between non-opaque particles and epoxy and on subsequent attribution of image areas created by these borders to either the non-opaque mineral or the epoxy resin. Further, this article discusses difficulties that may arise when applying this algorithm due to sample peculiarities and describes algorithm enhancements incorporated in Mineral4 in order to overcome these issues. The resulting software is capable of reliably discriminating non-opaque minerals in a variety of samples, including iron and manganese ores. |
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| Keywords: | Optical image analysis OIA Non-opaque Relief Characterisation Automated mineralogy |
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