Automatic lineament detection from geophysical grid data using efficient clustering
and weighted hough transform algorithms

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Περαντώνης, Σταύρος	el
dc.contributor.author	Χάρου, Ελένη	el
dc.contributor.author	Σερέτης, Κ.	el
dc.contributor.author	Τσενόγλου, Θεοχάρης	el
dc.date.accessioned	2015-05-14T19:49:15Z
dc.date.available	2015-05-14T19:49:15Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10430
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://users.iit.demokritos.gr/~exarou/PAPERS/lineaments_acai.pdf	en
dc.subject	Αυτόματη ανίχνευση χαρακτηριστικών
dc.subject	Γεωφυσικό πλέγμα δεδομένων
dc.subject	Σταθμισμένος μετασχηματισμός
dc.subject	αλγόριθμοι
dc.subject	αυτο-οργανούμενοι χάρτες
dc.subject	Automatic Lineament Detection
dc.subject	Geophysical Grid Data
dc.subject	Weighted Hough
dc.subject	Algorithms
dc.subject	Self-organizing maps
dc.title	Automatic lineament detection from geophysical grid data using efficient clustering and weighted hough transform algorithms	en
heal.type	conferenceItem
heal.generalDescription	σε έντυπη μορφή στο γραφείο μου	el
heal.classification	Πληροφορική
heal.classification	Περιβαλλοντική μηχανική
heal.classification	Computer science
heal.classification	Environmental engineering
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Περιβαλλοντική μηχανική
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.classificationURI	http://skos.um.es/unescothes/C01386
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh99004370
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1999-07-05
heal.bibliographicCitation	Vassilas, N., Perantonis, S., Charou, E., Seretis, K. and Tsenoglou, T. (1999) Automatic Lineament Detection from Geophysical Grid Data Using Efficient Clustering and Weighted Hough Transform Algorithms. Proc. Advanced Course in Artificial Intelligence (ACAI’99). Workshοp: Intelligent Techniques for Spatiotemporal Data Analysis in Environmental Applications. pp.16-25. Chania.	en
heal.abstract	An automated lineament detection method based on a weighted Hough transform is presented. The method utilizes the result of unsupervised classification based on Kohonen’s self-organizing maps, for vector quantizing the input data space, followed by neuron clustering. It then post-processes the classification result with classical image processing techniques and finally applies the modified Hough transform in order to identify lineaments. The capabilities of the method are described using geophysical (airborne magnetic and electromagnetic) data from the Vammala area in Finland. The results of the automated analysis show major geological faults in the selected area. Finally, comparisons with the classical Hough transform algorithm show the advantages of our proposed modifications.	en
heal.fullTextAvailability	true
heal.conferenceName	Advanced Course in Artificial Intelligence (ACAI’99)	en
heal.conferenceItemType	full paper