Lattice fuzzy signal operators and generalized image gradients

Maragos, P; Tzouvaras, V; Stamou, G

dc.contributor.author	Maragos, P	en
dc.contributor.author	Tzouvaras, V	en
dc.contributor.author	Stamou, G	en
dc.date.accessioned	2014-03-01T02:42:17Z
dc.date.available	2014-03-01T02:42:17Z
dc.date.issued	2003	en
dc.identifier.issn	0302-9743	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30907
dc.subject	Edge Detection	en
dc.subject	Fuzzy Set	en
dc.subject	Image Analysis	en
dc.subject	Morphological Operation	en
dc.subject	Nonlinear Filter	en
dc.subject	Structure Function	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.other	Convolution	en
dc.subject.other	Fuzzy control	en
dc.subject.other	Image analysis	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Signal processing	en
dc.subject.other	Spurious signal noise	en
dc.subject.other	Fuzzy intersection	en
dc.subject.other	Image gradients	en
dc.subject.other	Image operators	en
dc.subject.other	Lattice fuzzy signal operators	en
dc.subject.other	Fuzzy sets	en
dc.title	Lattice fuzzy signal operators and generalized image gradients	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1007/3-540-44967-1_50	en
heal.identifier.secondary	http://dx.doi.org/10.1007/3-540-44967-1_50	en
heal.language	English	en
heal.publicationDate	2003	en
heal.abstract	In this paper we use concepts from the lattice-based theory of morphological operators and fuzzy sets to develop generalized lattice image operators that are nonlinear convolutions that can be expressed as supremum (resp. infimum) of fuzzy intersection (resp. union) norms. Our emphasis and differences with many previous works is the construction of pairs of fuzzy dilation (sup of fuzzy intersection) and erosion (inf of fuzzy implication) operators that form lattice adjunctions. This guarantees that their composition will be a valid algebraic opening or closing. We have experimented with applying these fuzzy operators to various nonlinear filtering and image analysis tasks, attempting to understand the effect that the type of fuzzy norm and the shape-size of structuring function have on the resulting new image operators. We also present some theoretical and experimental results on using the lattice fuzzy operators, in combination with morphological systems or by themselves, to develop some new edge detection gradients which show improved performance in noise.	en
heal.publisher	SPRINGER-VERLAG BERLIN	en
heal.journalName	Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)	en
heal.bookName	LECTURE NOTES IN ARTIFICIAL INTELLIGENCE	en
dc.identifier.doi	10.1007/3-540-44967-1_50	en
dc.identifier.isi	ISI:000185510700050	en
dc.identifier.volume	2715	en
dc.identifier.spage	420	en
dc.identifier.epage	427	en