Modeling the pattern spectrum as a markov process and its use for efficient shape classification

dc.contributor.author	Ζώης, Ηλίας Ν.	el
dc.contributor.author	Αναστασόπουλος, Βασίλειος	el
dc.date.accessioned	2015-01-10T11:25:33Z
dc.date.issued	2015-01-10
dc.identifier.uri	http://hdl.handle.net/11400/3691
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.subject	Shape classification
dc.subject	Image classification
dc.subject	Markov processes
dc.subject	Σχήμα ταξινόμησης
dc.subject	Ταξινόμηση εικόνας
dc.title	Modeling the pattern spectrum as a markov process and its use for efficient shape classification	en
heal.type	conferenceItem
heal.generalDescription	Proceedings	en
heal.classification	Electrical engineering
heal.classification	Electronics
heal.classification	Ηλεκτρολογική μηχανική
heal.classification	Ηλεκτρονική
heal.classificationURI	http://skos.um.es/unescothes/C01311
heal.classificationURI	http://zbw.eu/stw/descriptor/10455-2
heal.classificationURI	N/A-Ηλεκτρολογική μηχανική
heal.classificationURI	N/A-Ηλεκτρονική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85081369
heal.identifier.secondary	DOI: 10.1109/ICIP.2009.5414424
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε.	el
heal.publicationDate	2009
heal.bibliographicCitation	Zois, E. and Anastassopoulos, V. (2009). Modeling the pattern spectrum as a markov process and its use for efficient shape classification. In the 16th IEEE international conference on Image processing. Cairo, 7th-10th November 2009. pp. 429 - 432. IEEE.	en
heal.abstract	n this work the most important morphological granulometry, i.e. the pattern spectrum, is modeled, for the first time in the literature, as a first order Markov process. In addition, each of the terms of the process is shown to be normally distributed. The classification procedure followed for this specific application is based on modeling each separate class as a Markov process and making extensive use of the chain rule. Experimental results support the proposed classification procedure as quite promising, especially when compared to conventional classification techniques.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	true
heal.conferenceName	IEEE international conference on Image processing	en
heal.conferenceItemType	full paper