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Nellros, Frida
Publications (5 of 5) Show all publications
Nellros, F., Thurley, M., Jonsson, H., Andersson, C. & Forsmo, S. (2015). Automated measurement of sintering degree in optical microscopy through image analysis of particle joins (ed.). Paper presented at . Pattern Recognition, 48(11), 3451-3465
Open this publication in new window or tab >>Automated measurement of sintering degree in optical microscopy through image analysis of particle joins
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2015 (English)In: Pattern Recognition, ISSN 0031-3203, E-ISSN 1873-5142, Vol. 48, no 11, p. 3451-3465Article in journal (Refereed) Published
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.

National Category
Signal Processing Computer Sciences
Research subject
Signal Processing; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-12912 (URN)10.1016/j.patcog.2015.05.012 (DOI)000359028900015 ()2-s2.0-84937814900 (Scopus ID)c0f7d54d-414d-4f70-9a87-df78138249b5 (Local ID)c0f7d54d-414d-4f70-9a87-df78138249b5 (Archive number)c0f7d54d-414d-4f70-9a87-df78138249b5 (OAI)
Projects
HLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures
Note
Validerad; 2015; Nivå 2; 20130224 (frinel)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Nellros, F. (2013). Quantitative image analysis: a focus on automated characterization of structures in optical microscopy of iron ore pellets (ed.). (Licentiate dissertation). Paper presented at . Luleå: Luleå tekniska universitet
Open this publication in new window or tab >>Quantitative image analysis: a focus on automated characterization of structures in optical microscopy of iron ore pellets
2013 (English)Licentiate 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.

Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2013
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Signal Processing
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-26204 (URN)d2915986-de44-485b-aebf-eb6e6e1c98cc (Local ID)978-91-7439-585-3 (ISBN)978-91-7439-586-0 (ISBN)d2915986-de44-485b-aebf-eb6e6e1c98cc (Archive number)d2915986-de44-485b-aebf-eb6e6e1c98cc (OAI)
Note
Godkänd; 2013; 20130224 (frinel); Tillkännagivande licentiatseminarium 2013-04-25 Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Frida Nellros Ämne: Signalbehandling/Signal Processing Uppsats: Quantitative Image Analysis – A Focus on Automated Characterization of Structures in Optical Microscopy of Iron Ore Pellets Examinator: Associate Professor Matthew Thurley, Institutionen för system- och rymdteknik, Luleå tekniska universitet Diskutant: Associate Professor Carolina Wählby, Centrum for Image Analysis, Uppsala universitet Tid: Fredag den 17 maj 2013 kl 12.30 Plats: A109, Luleå tekniska universitetAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-24Bibliographically approved
Thurley, M., Nellros, F. & Jonsson, H. (2012). Project: HLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures. Paper presented at .
Open this publication in new window or tab >>Project: HLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures
2012 (English)Other (Other (popular science, discussion, etc.))
National Category
Signal Processing Computer Sciences
Research subject
Signal Processing; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-36307 (URN)f0156c4c-2592-4e2b-bf47-88a2cd5bf4df (Local ID)f0156c4c-2592-4e2b-bf47-88a2cd5bf4df (Archive number)f0156c4c-2592-4e2b-bf47-88a2cd5bf4df (OAI)
Note

Publikationer: Automated measurement of sintering degree in optical microscopy through image analysis of particle joins; Automated image analysis of iron-ore pellet structure using optical microscopy; Status: Pågående; Period: 01/02/2011 → 01/06/2015

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-01-14Bibliographically approved
Nellros, F. & Thurley, M. (2011). Automated image analysis of iron-ore pellet structure using optical microscopy (ed.). Paper presented at . Minerals Engineering, 24(14), 1525-1531
Open this publication in new window or tab >>Automated image analysis of iron-ore pellet structure using optical microscopy
2011 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 24, no 14, p. 1525-1531Article in journal (Refereed) Published
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.

National Category
Signal Processing
Research subject
Signal Processing
Identifiers
urn:nbn:se:ltu:diva-6100 (URN)10.1016/j.mineng.2011.08.001 (DOI)000297000700001 ()2-s2.0-80054016363 (Scopus ID)44d39a6e-21e5-475d-b777-bb48717d77f5 (Local ID)44d39a6e-21e5-475d-b777-bb48717d77f5 (Archive number)44d39a6e-21e5-475d-b777-bb48717d77f5 (OAI)
Projects
HLRC PIA - Automated Image Analysis for Quantitative Characterisation of Iron Ore Pellet Structures
Note
Validerad; 2011; 20110630 (frinel)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved
Landström, A., Nellros, F., Jonsson, H. & Thurley, M. (2011). Image reconstruction by prioritized incremental normalized convolution (ed.). In: (Ed.), Anders Heyden; Fredrik Kahl (Ed.), Image analysis: 17th Scandinavian conference, SCIA 2011, Ystad, Sweden, May 2011 ; proceedings. Paper presented at Scandinavian Conference on Image Analysis : 23/05/2011 - 27/05/2011 (pp. 176-185). Berlin: Encyclopedia of Global Archaeology/Springer Verlag
Open this publication in new window or tab >>Image reconstruction by prioritized incremental normalized convolution
2011 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Berlin: Encyclopedia of Global Archaeology/Springer Verlag, 2011
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 6688
Keywords
image reconstruction, hole filling, normalized convolution, Information technology - Signal processing, image reconstruction, hole filling, normalized convolution, Informationsteknik - Signalbehandling
National Category
Signal Processing Computer Sciences
Research subject
Signal Processing; Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-38401 (URN)10.1007/978-3-642-21227-7_17 (DOI)2-s2.0-79957518279 (Scopus ID)cca03651-b913-466b-ad88-77de38d703a4 (Local ID)978-3-642-21226-0 (ISBN)cca03651-b913-466b-ad88-77de38d703a4 (Archive number)cca03651-b913-466b-ad88-77de38d703a4 (OAI)
Conference
Scandinavian Conference on Image Analysis : 23/05/2011 - 27/05/2011
Projects
Vision Systems Research Platform
Note
Validerad; 2011; Bibliografisk uppgift: The original publication is available at www.springerlink.com.; 20110613 (andbra)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-07-10Bibliographically approved
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