Linked Life Data

18579940

Source:http://linkedlifedata.com/resource/pubmed/id/18579940

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
pubmed:issue
6
pubmed:dateCreated
2008-6-26
pubmed:abstractText
Often an image g(x,y) is regularized and even restored by minimizing the Mumford-Shah functional. Properties of the regularized image u(x,y) depends critically on the numerical value of the two parameters alpha and gamma controlling smoothness and fidelity. When alpha and gamma are constant over the image, small details are lost when an extensive filtering is used in order to remove noise. In this paper, it is shown how the two parameters alpha and gamma can be made self-adaptive. In fact, alpha and gamma are not constant but automatically adapt to the local scale and contrast of features in the image. In this way, edges at all scales are detected and boundaries are well-localized and preserved. In order to preserve trihedral junctions alpha and gamma become locally small and the regularized image u(x,y) maintains sharp and well-defined trihedral junctions. Images regularized by the proposed procedure are well-suited for further processing, such as image segmentation and object recognition.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
0162-8828
pubmed:author
pubmed:issnType
Print
pubmed:volume
26
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
804-9
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Self-adaptive regularization.
pubmed:affiliation
Neurobiology Sector, SISSA/ISAS, Via Beirut 7, Trieste, Italy. vanzella@sissa.it
pubmed:publicationType
Journal Article