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    <id>7395</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <pageLast/>
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    <issue/>
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    <title language="eng">Possibilities and Limitations of Automatic Feature Extraction shown by the Example of Crack Detection in 3D-CT Images of Concrete Specimen</title>
    <abstract language="eng">To assess the influence of the alkali-silica reaction (ASR) on pavement concrete 3D-CT imaging has been applied to concrete samples. Prior to imaging these samples have been drilled out of a concrete beam pre-damaged by fatigue loading. The resulting high resolution 3D-CT images consist of several gigabytes of voxels. Current desktop computers can visualize such big datasets without problems but a visual inspection or manual segmentation of features such as cracks by experts can only be carried out on a few slices. A quantitative analysis of cracks requires a segmentation of the whole specimen which could only be done by an automatic feature detection. This arises the question of the reliability of an automatic crack detection algorithm, its certainty and limitations. Does the algorithm find all cracks? Does it find too many cracks? Can parameters of that algorithm, once identified as good, be applied to other samples as well? Can ensemble computing with many crack parameters overcome the difficulties with parameter finding? By means of a crack detection algorithm based on shape recognition (template matching) these questions will be discussed. Since the author has no access to reliable ground truth data of cracks the assessment of the certainty of the automatic crack is restricted to visual inspection by experts. Therefore, an artificial dataset based on a combination of manually segmented cracks processed together with simple image processing algorithms is used to quantify the accuracy of the crack detection algorithm. Part of the evaluation of cracks in concrete samples is the knowledge of the surrounding material. The surrounding material can be used to assess the detected cracks, e.g. micro-cracks within the aggregate-matrix interface may be starting points for cracks on a macro scale. Furthermore, the knowledge of the surrounding material can help to find better parameter sets for the crack detection itself because crack characteristics may vary depending on their surrounding material. Therefore, in addition to a crack detection a complete segmentation of the sample into the components of concrete, such as aggregates, cement matrix and pores is needed. Since such a segmentation task cannot be done manually due to the amount of data, an approach utilizing convolutional neuronal networks stemming from a medical application has been applied. The learning phase requires a ground truth i.e. a segmentation of the components. This has to be created manually in a time-consuming task. However, this segmentation can be used for a quantitative evaluation of the automatic segmentation afterwards. Even though that work has been performed as a short term subtask of a bigger project funded by the German Research Foundation (DFG) this paper discusses problems which may arise in similar projects, too.&#13;
[1.2MB | id=23664 ]       &#13;
	iCT 2019&#13;
Session: Short talks&#13;
Thu 13:50 Auditorium	2019-03&#13;
Möglichkeiten und Grenzen automatischer Merkmalserkennung am Beispiel von Risserkennungen in 3D-CT-Aufnahmen von Betonproben&#13;
O. Paetsch11&#13;
Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
Abstract &#13;
[1MB | id=23104 ]  DE       &#13;
	DGZfP 2018&#13;
Session: Bauwesen	2018-09&#13;
Quantitative Rissanalyse im Fahrbahndeckenbeton mit der 3D-Computertomographie&#13;
D. Meinel125, K. Ehrig128, F. Weise16, O. Paetsch211&#13;
1Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
2Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
concrete, ROI tomography, in-situ-CT, 3D-CT, Beton, AKR, Feuchtetransport, automatic crack detection&#13;
Abstract &#13;
[0.7MB | id=18980 ]  DE       &#13;
	DGZfP 2015&#13;
Session: CT Algorithmen	2016-04&#13;
3D Corrosion Detection in Time-dependent CT Images of Concrete&#13;
O. Paetsch111, D. Baum15, S. Prohaska17, K. Ehrig228, D. Meinel225, G. Ebell24&#13;
1Visualisation and Data Analysis; Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
2Division 8.5; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
CT, multi-angle radiography, defect detection, Feature Extraction, image processing, concrete, corrosion&#13;
Abstract &#13;
[0.5MB | id=18043 ]       &#13;
	DIR 2015&#13;
Session: Quantitative imaging and image processing	2015-08&#13;
Korrosionsverfolgung in 3D-computertomographischen Aufnahmen von Stahlbetonproben&#13;
O. Paetsch111, D. Baum15, G. Ebell24, K. Ehrig228, A. Heyn2, D. Meinel225, S. Prohaska17&#13;
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
Computertomographie [0.4MB | id=17375 ]  DE       &#13;
	DGZfP 2014&#13;
Session: Bauwesen	2015-03&#13;
Examination of Damage Processes in Concrete with CT&#13;
D. Meinel125, K. Ehrig128, V. L’Hostis2, B. Muzeau2, O. Paetsch311&#13;
1BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
2Laboratoire d’Etude du Comportement des Bétons et des Argiles; Commissariat Energie Atomique (CEA)287, Gif-Sur-Yvette, France&#13;
3Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
X-ray computed tomography, concrete, corrosion, crack detection, 3D visualization&#13;
Abstract &#13;
[4.9MB | id=15692 ]       &#13;
	iCT 2014&#13;
Session: Non-destructive Testing and 3D Materials Characterisation of...	2014-06&#13;
3-D-Visualisierung und statistische Analyse von Rissen in mit Computer-Tomographie untersuchten Betonproben&#13;
O. Paetsch111, D. Baum15, D. Breßler1, K. Ehrig228, D. Meinel225, S. Prohaska1,17&#13;
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
Radiographic Testing (RT), statistical analysis, 3D Computed Tomography, visualization, concrete structural damage, automated crack detection [1MB | id=15343 ]  DE       &#13;
	DGZfP 2013&#13;
Session: Computertomographie	2014-03&#13;
Vergleich automatischer 3D-Risserkennungsmethoden für die quantitative Analyse der Schadensentwicklung in Betonproben mit Computertomographie&#13;
O. Paetsch111, K. Ehrig228, D. Meinel225, D. Baum15, S. Prohaska1,1,17&#13;
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
Radiographic Testing (RT), visualization, crack detection, Visualisierung, computer tomography, template matching, Hessian eigenvalues, ZIBAmira, automated crack detection, percolation [0.9MB | id=14269 ]  DE       &#13;
	DGZfP 2012&#13;
Session: Computertomographie	2013-05&#13;
Automated 3D Crack Detection for Analyzing Damage Processes in Concrete with Computed Tomography&#13;
O. Paetsch111, D. Baum15, K. Ehrig228, D. Meinel225, S. Prohaska1,1,17&#13;
1Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
2Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
computed tomography, template matching, Hessian eigenvalues, crack statistics, visualization, crack surface, ZIBAmira [0.6MB | id=13736 ]       &#13;
	iCT 2012&#13;
Session: Poster - Analysis and Algorithms	2012-12&#13;
3-D-Visualisierung von Radar- und Ultraschallecho-Daten mit ZIBAmira&#13;
D. Streicher112, O. Paetsch211, R. Seiler2, S. Prohaska27, M. Krause360 [Profile of Krause] , C. Boller178&#13;
1Saarland University74, Saarbrücken, Germany&#13;
2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany&#13;
3BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany [0.4MB | id=12284 ]  DE       &#13;
	DGZfP 2011&#13;
Session: Bauwesen	2012-05&#13;
Comparison of Crack Detection Methods for Analyzing Damage Processes in Concrete with Computed Tomography&#13;
K. Ehrig128, J. Goebbels153, D. Meinel125, O. Paetsch211, S. Prohaska27, V. Zobel2&#13;
1Division VIII.3; BAM Federal Institute for Materials Research and Testing1277, Berlin, Germany&#13;
2Konrad-Zuse-Institut Berlin (ZIB)18, Berlin, Germany [0.7MB | id=11150 ]       &#13;
	DIR 2011&#13;
Session: Poster	2011-11&#13;
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© NDT.net - Where expertise comes together. The Largest Open Access Portal of Nondestructive Testing (NDT)- since 1996</abstract>
    <parentTitle language="eng">iCT 2019</parentTitle>
    <enrichment key="Series">iCT 2019 Conference Proceedings</enrichment>
    <enrichment key="PeerReviewed">no</enrichment>
    <enrichment key="SubmissionStatus">in press</enrichment>
    <enrichment key="AcceptedDate">Sept. 2018</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>Olaf Paetsch</author>
    <submitter>Olaf Paetsch</submitter>
    <collection role="institutes" number="vis">Visual Data Analysis</collection>
    <collection role="persons" number="paetsch">Paetsch, Olaf</collection>
    <collection role="projects" number="BAM-RISSBETON">BAM-RISSBETON</collection>
    <collection role="institutes" number="VDcC">Visual and Data-centric Computing</collection>
  </doc>
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