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  • 1.
    Landström, Anders
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nellros, Frida
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Thurley, Matthew
    Image reconstruction by prioritized incremental normalized convolution2011In: Image analysis: 17th Scandinavian conference, SCIA 2011, Ystad, Sweden, May 2011 ; proceedings / [ed] Anders Heyden; Fredrik Kahl, Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2011, p. 176-185Conference paper (Refereed)
    Abstract [en]

    A priority-based method for pixel reconstruction and incrementalhole filling in incomplete images and 3D surface data is presented.The method is primarily intended for reconstruction of occluded areasin 3D surfaces and makes use of a novel prioritizing scheme, based on apixelwise defined confidence measure, that determines the order in whichpixels are iteratively reconstructed. The actual reconstruction of individualpixels is performed by interpolation using normalized convolution.The presented approach has been applied to the problem of reconstructing3D surface data of a rock pile as well as randomly sampled imagedata. It is concluded that the method is not optimal in the latter case,but the results show an improvement to ordinary normalized convolutionwhen applied to the rock data and are in this case comparable to thoseobtained from normalized convolution using adaptive neighborhood sizes.

  • 2.
    Nellros, Frida
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Quantitative image analysis: a focus on automated characterization of structures in optical microscopy of iron ore pellets2013Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Sintering occurs in many types of material such as iron, ceramics and snow, typically during thermal treatment, and aects the material properties, particularly the strength, by the bonding of particles into a coherent structure. In order to improve the mechanical strength in magnetite iron ore pellets it is important to be able to characterize and quantitatively measure the degree of sintering and features that impact the process of sintering.The aim for this licentiate thesis has been to create tools for sintering characterization through automated image analysis of optical microscopy images. Such tools are of interest since they provide a comparable quantication of pellet properties that can be related to other parameters, giving a historical record that is digital, objective and not dependent on the eyes of a trained expert. In this work, two dierent studies of the microstructure in indurated (heat hardened) pellets have been performed. The methods presented in these studies have been shown suitable for characterizing sintering properties in iron ore pellets, and possibly also other materials that experience sintering phenomena.The first study presents research to automate image capture and analysis of entire crosssections of indurated iron ore pellets to characterize proportions of magnetite, hematite, and other components. Spatial distributions of the mentioned phases are produced for each pellet, graphing proportions in relation to the distance to the pellet surface. The results are not directly comparable to a chemical analysis but comparisons with manual segmentation of images validates the method. Dierent types of pellets have been tested and the system has produced robust results for varying cases.The second study focuses on the analysis of the particle joins and structure. The joins between particles have been identied with a method based mainly on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and been determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional physical dilation measurements on the heated samples.A note of caution for quantitative studies of iron ore pellet has been identied in this thesis. Especially for green pellets, the microscopy sample preparation prohibit any statistical inference studies due to particle rip-out during polishing. Researchers performing qualitative microscopy studies are generally aware of the phenomenon of rip-outs, but the extent of how even seemingly good samples are aected has not been unveiled until attempting extensive quantitative analysis of features such as green pellet porosity during the course of this work.

  • 3.
    Nellros, Frida
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Thurley, Matthew
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Automated image analysis of iron-ore pellet structure using optical microscopy2011In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 24, no 14, p. 1525-1531Article in journal (Refereed)
    Abstract [en]

    Knowledge about pellet microstructure such as porosity and oxidation degree is essential in improving the pellet macro-behavior such as structural integrity and reduction properties. Manual optical microscopy is commonly used to find such information but is both highly time consuming and operator dependent. This paper presents research to automate image capture and analysis of entire cross-sections of baked iron ore pellets to characterize proportions of magnetite, hematite, and other components.The presented results cover: semi-automated image acquisition of entire pellets, separation of pellet and epoxy and calculation of total percentages of magnetite, hematite and pores. Using the Leica Qwin microscope software and a segmentation method based on Otsu thresholding these three objectives have been achieved with the phases labeled as magnetite, hematite and pores and additives. Furthermore, spatial distributions of magnetite, hematite and pores and additives are produced for each pellet, graphing proportions in relation to the distance to the pellet surface. The results are not directly comparable to a chemical analysis but comparisons with manual segmentation of images validates the method. Different types of pellets have been tested and the system has produced robust results for varying cases.

  • 4.
    Nellros, Frida
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Thurley, Matthew
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Charlotte
    LKAB.
    Forsmo, Seija
    LKAB.
    Automated measurement of sintering degree in optical microscopy through image analysis of particle joins2015In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 11, p. 3451-3465Article in journal (Refereed)
    Abstract [en]

    In general terms, sintering describes the bonding of particles into a more coherent structure, where joins form between packed particles, usually as a result of heating. Characterization of sintering is an important topic in the fields of metallurgy, steel, iron ore pellets, ceramics, and snow for understanding material properties and material strength. Characterization using image analysis has been applied in a number of these fields but is either semi-automatic, requiring human interaction in the analysis, or based on statistical sampling and stereology to characterize the sample. This paper presents a novel fully automatic image analysis algorithm to analyze and determine the degree of sintering based on analysis of the particle joins and structure. Quantitative image analysis of the sintering degree is demonstrated for samples of iron ore pellets but could be readily applied to other packed particle materials. Microscope images of polished cross-sections of iron ore pellets have been imaged in their entirety and automated analysis of hundreds of images has been performed. Joins between particles have been identified based on morphological image processing and features have been calculated based on the geometric properties and curvature of these joins. The features have been analyzed and determined to hold discriminative power by displaying properties consistent with sintering theory and results from traditional pellet diameter measurements on the heated samples, and a statistical evaluation using the Welch t-test.

  • 5.
    Thurley, Matthew
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Nellros, Frida
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
    Jonsson, Håkan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Project: HLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures2012Other (Other (popular science, discussion, etc.))
1 - 5 of 5
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