{"id":{"repo_id":"heid-diss","oai_identifier":"oai:archiv.ub.uni-heidelberg.de:3106"},"canonical_url":"https://search.dev.ndltd.org/etd/heid-diss/oai:archiv.ub.uni-heidelberg.de:3106","repository":{"repo_id":"heid-diss","name":"Universität Heidelberg","base_url":"http://archiv.ub.uni-heidelberg.de/volltextserver/cgi/oai2"},"display":{"title":"Automated defect detection and evaluation in X-ray CT images","abstract":"X-ray computed tomography gains increasing popularity for industrial quality inspection tasks. It is a challenge however to use it in a fully automated assembly line, which requires robust algorithms for volumetric image analysis on noisy data. This work provides methods for the automatic detection of defects and their geometric description. The approach will be based on spatial statistical analysis of the voxel structure to determine the presence of a defect. Once detected, defect voxels will be clustered to determine shape and size of the associated material faults. The effectiveness of the method will be shown on pores and cracks in steel parts.","abstract_html":"X-ray computed tomography gains increasing popularity for industrial quality inspection tasks. It is a challenge however to use it in a fully automated assembly line, which requires robust algorithms for volumetric image analysis on noisy data. This work provides methods for the automatic detection of defects and their geometric description. The approach will be based on spatial statistical analysis of the voxel structure to determine the presence of a defect. Once detected, defect voxels will be clustered to determine shape and size of the associated material faults. The effectiveness of the method will be shown on pores and cracks in steel parts.","abstract_has_math":false,"creators":["Eisele, Heiko"],"institution":"Universität Heidelberg","degree_name":null,"degree_level":"thesis.doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Hamprecht, Fred"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2002,"date_issued":"2002-12-18","date_published":"2002-12-18","updated_at":"2026-07-24T02:29:25Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://www.ub.uni-heidelberg.de/archiv/3106","outbound_label":"Repository record","outbound_source":"source_url"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Hamprecht, Fred"]},{"key":"dc:creator","label":"Author","values":["Eisele, Heiko"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:publisher","label":"Institution","values":["Universitätsbibliothek Heidelberg"]},{"key":"dc:type","label":"Dc Type","values":["doctoralThesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["thesis.doctoral"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Universität Heidelberg"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["X-ray computed tomography gains increasing popularity for industrial quality inspection tasks. It is a challenge however to use it in a fully automated assembly line, which requires robust algorithms for volumetric image analysis on noisy data. This work provides methods for the automatic detection of defects and their geometric description. The approach will be based on spatial statistical analysis of the voxel structure to determine the presence of a defect. Once detected, defect voxels will be clustered to determine shape and size of the associated material faults. The effectiveness of the method will be shown on pores and cracks in steel parts.","Die Röntgencomputertomographie findet zunehmend Einsatz in der industriellen Qualitätskontrolle. Es ist jedoch eine grosse Herausforderung, sie in der vollautomatisierten Fertigung zu verwenden, da dies u.a. robuste Volumenbildverarbeitung auf verrauschten Bildern erfordert. Die vorliegende Arbeit präsentiert Methoden zur automatischen Detektion und geometrischen Beschreibung von Defekten. Die Erkennung der Defekte beruht auf einer statistischen Grauwertanalyse. Im Anschluss an die Detektion werden defekte Voxel gruppiert um Form und Größe der zugehörigen Materialfehler zu bestimmen. Es werden Ergebnisse in der Poren- und Rissdetektion von Stahlteilen gezeigt"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Automated defect detection and evaluation in X-ray CT images","Automatisierte Defekterkennung und Bewertung in Röntgen-CT Bildern"]}]}],"canonical_facts":{"dc:contributor":["Hamprecht, Fred"],"dc:creator":["Eisele, Heiko"],"dc:description.abstract":["X-ray computed tomography gains increasing popularity for industrial quality inspection tasks. It is a challenge however to use it in a fully automated assembly line, which requires robust algorithms for volumetric image analysis on noisy data. This work provides methods for the automatic detection of defects and their geometric description. The approach will be based on spatial statistical analysis of the voxel structure to determine the presence of a defect. Once detected, defect voxels will be clustered to determine shape and size of the associated material faults. The effectiveness of the method will be shown on pores and cracks in steel parts.","Die Röntgencomputertomographie findet zunehmend Einsatz in der industriellen Qualitätskontrolle. Es ist jedoch eine grosse Herausforderung, sie in der vollautomatisierten Fertigung zu verwenden, da dies u.a. robuste Volumenbildverarbeitung auf verrauschten Bildern erfordert. Die vorliegende Arbeit präsentiert Methoden zur automatischen Detektion und geometrischen Beschreibung von Defekten. Die Erkennung der Defekte beruht auf einer statistischen Grauwertanalyse. Im Anschluss an die Detektion werden defekte Voxel gruppiert um Form und Größe der zugehörigen Materialfehler zu bestimmen. Es werden Ergebnisse in der Poren- und Rissdetektion von Stahlteilen gezeigt"],"dc:format.medium":["application/pdf"],"dc:publisher":["Universitätsbibliothek Heidelberg"],"dc:title":["Automated defect detection and evaluation in X-ray CT images","Automatisierte Defekterkennung und Bewertung in Röntgen-CT Bildern"],"dc:type":["doctoralThesis"],"thesis:degree_level":["thesis.doctoral"],"thesis:institution_name":["Universität Heidelberg"]},"updated_at":"2026-07-24T02:29:25Z"}