Recent developments for longitudinal crack detection using a Continuous Density Hidden Markov Model (CDHMM)

  • Recent developments for automatic detection of crack indications in welding seams are presented addressing the task from a probabilistic point of view. This work improves the algorithm developed several years ago. The distinctive feature of these algorithms is a translation of the task of detection into an optimisation task in such a way, that the the maximum of a scoring function in the space of all possible positions, lengths and shapes can be found exactly and computationally effective. The progress of the new algorithm presented here consists in the replacement of the discrete (binary) model of crack indications (i.e. defect present vs. defect absent) previously used with a continuous grey value model and the development of a new scoring function. The restriction of the noise model to additive Gaussian allowed the utilisation of conjugate priors for the posterior probability estimation in a computationally effective way too. Results of experimental evaluation of the developedRecent developments for automatic detection of crack indications in welding seams are presented addressing the task from a probabilistic point of view. This work improves the algorithm developed several years ago. The distinctive feature of these algorithms is a translation of the task of detection into an optimisation task in such a way, that the the maximum of a scoring function in the space of all possible positions, lengths and shapes can be found exactly and computationally effective. The progress of the new algorithm presented here consists in the replacement of the discrete (binary) model of crack indications (i.e. defect present vs. defect absent) previously used with a continuous grey value model and the development of a new scoring function. The restriction of the noise model to additive Gaussian allowed the utilisation of conjugate priors for the posterior probability estimation in a computationally effective way too. Results of experimental evaluation of the developed algorithm are presented.zeige mehrzeige weniger

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Metadaten
Autor*innen:Oleksandr Alekseychuk, Uwe ZscherpelORCiD
Dokumenttyp:Beitrag zu einem Tagungsband
Veröffentlichungsform:Graue Literatur
Sprache:Englisch
Titel des übergeordneten Werkes (Englisch):DIR 2007 - International Symposium on Digital Industrial Radiology and Computed Tomography (Proceedings)
Jahr der Erstveröffentlichung:2007
Verlagsort:Lyon, France
Ausgabe/Heft:(Poster P1)
Erste Seite:1
Letzte Seite:16
Freie Schlagwörter:Bayesian network; CDHMM; Crack detection; Curve detection; Detection in noisy images; Dynamic programming; HMM; Industrial radiographic testing; Kalman filter
Veranstaltung:DIR 2007 - International Symposium on Digital industrial Radiology and Computed Tomography
Veranstaltungsort:Lyon, France
Beginndatum der Veranstaltung:2007-06-25
Enddatum der Veranstaltung:2007-06-27
URL:http://www.ndt.net/article/dir2007/papers/p1.pdf
Verfügbarkeit des Dokuments:Physisches Exemplar in der Bibliothek der BAM vorhanden ("Hardcopy Access")
Bibliotheksstandort:Sonderstandort: Publica-Schrank
Datum der Freischaltung:19.02.2016
Referierte Publikation:Nein
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