Synthesis and applications of lattice image operators based on fuzzy norms

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	2014-03-01T02:42:02Z
dc.date.available	2014-03-01T02:42:02Z
dc.date.issued	2001	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/30728
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.other	Convolution	en
dc.subject.other	Edge detection	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Gradient methods	en
dc.subject.other	Mathematical morphology	en
dc.subject.other	Mathematical operators	en
dc.subject.other	Nonlinear filtering	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Fuzzy edge gradients	en
dc.subject.other	Fuzzy operators	en
dc.subject.other	Image operators	en
dc.subject.other	Nonlinear convolution	en
dc.subject.other	Parametric fuzzy norms	en
dc.subject.other	Image analysis	en
dc.title	Synthesis and applications of lattice image operators based on fuzzy norms	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/ICIP.2001.959068	en
heal.identifier.secondary	http://dx.doi.org/10.1109/ICIP.2001.959068	en
heal.publicationDate	2001	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 can be expressed as nonlinear convolutions that are suprema or infima of fuzzy intersection or union norms. Our emphasis (and differences with previous works) is the construction of pairs of fuzzy dilation and erosion operators that form lattice adjunctions. This guarantees that their composition will be a valid algebraic opening or closing. The power but also the difficulty in applying these fuzzy operators to image analysis is the large variety of fuzzy norms and the absence of systematic ways in selecting them. Towards this goal, we have performed extensive experiments in applying these fuzzy operators to various nonlinear filtering and image analysis tasks, attempting first to understand the effect that the type of fuzzy norm and the shape-size of structuring function have on the resulting new image operators. Further, we have developed some new fuzzy edge gradients and optimized their usage for edge detection on test problems via a parametric fuzzy norm.	en
heal.journalName	IEEE International Conference on Image Processing	en
dc.identifier.doi	10.1109/ICIP.2001.959068	en
dc.identifier.volume	1	en
dc.identifier.spage	521	en
dc.identifier.epage	524	en