Computing the success factors in consistent acquisition and recognition of objects in color digital images by explicit preconditioning

Maglogiannis, IG; Zafiropoulos, EP; Platis, AN; Gravvanis, GA

dc.contributor.author	Maglogiannis, IG	en
dc.contributor.author	Zafiropoulos, EP	en
dc.contributor.author	Platis, AN	en
dc.contributor.author	Gravvanis, GA	en
dc.date.accessioned	2014-03-01T02:42:33Z
dc.date.available	2014-03-01T02:42:33Z
dc.date.issued	2004	en
dc.identifier.issn	0920-8542	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31043
dc.subject	Approximate inverses	en
dc.subject	Bayesian networks	en
dc.subject	Camera calibration	en
dc.subject	Color measurement	en
dc.subject	Computer vision	en
dc.subject	Digital image acquisition	en
dc.subject	Markov modeling	en
dc.subject	Markov Reward Models	en
dc.subject	Parallel computations	en
dc.subject	Preconditioning	en
dc.subject	Reproducibility	en
dc.subject.classification	Computer Science, Hardware & Architecture	en
dc.subject.classification	Computer Science, Theory & Methods	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Color	en
dc.subject.other	Computer vision	en
dc.subject.other	Diseases	en
dc.subject.other	Image processing	en
dc.subject.other	Linear systems	en
dc.subject.other	Markov processes	en
dc.subject.other	Mathematical models	en
dc.subject.other	Parallel processing systems	en
dc.subject.other	Probability	en
dc.subject.other	Skin	en
dc.subject.other	Approximate inverses	en
dc.subject.other	Bayesian networks	en
dc.subject.other	Camera calibration	en
dc.subject.other	Color measurement	en
dc.subject.other	Digital image acquisition	en
dc.subject.other	Markov modeling	en
dc.subject.other	Markov reward models	en
dc.subject.other	Parallel computations	en
dc.subject.other	Precondioning	en
dc.subject.other	Reproducibility	en
dc.subject.other	Object recognition	en
dc.title	Computing the success factors in consistent acquisition and recognition of objects in color digital images by explicit preconditioning	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1023/B:SUPE.0000040614.03197.e2	en
heal.identifier.secondary	http://dx.doi.org/10.1023/B:SUPE.0000040614.03197.e2	en
heal.language	English	en
heal.publicationDate	2004	en
heal.abstract	The paper studies the factors influencing the consistent acquisition and recognition of object's color and border features in digital imaging. The proposed image acquisition process is utilized by a computer supported imaging system implementing the acquisition and analysis of skin lesion images supporting medical diagnosis. In addition the same approach may be used for several problems requiring reliable color measurement and object identification. Two methodologies are adopted: The Bayesian Networks, which provide an efficient way of reasoning under uncertainty and are used to incorporate the expert judgement into the estimation of the probability of successful operation, and a Markov chain approach, which is generally used for the dynamic modeling of the system behavior. The Markov chain model requires asymptotically the solution of sparse linear systems. Explicit preconditioned methods are used for the efficient solution of the derived sparse linear system, and the parallel implementation of the dominant computational part is exploited. © 2004 Kluwer Academic Publishers.	en
heal.publisher	KLUWER ACADEMIC PUBL	en
heal.journalName	Journal of Supercomputing	en
dc.identifier.doi	10.1023/B:SUPE.0000040614.03197.e2	en
dc.identifier.isi	ISI:000224675900007	en
dc.identifier.volume	30	en
dc.identifier.issue	2	en
dc.identifier.spage	179	en
dc.identifier.epage	198	en