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.…


| 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 |

