Local mean multiphase segmentation with HMMF models

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

This paper presents two similar multiphase segmentation methods for recovery of segments in complex weakly structured images, with local and global bias fields, because they can occur in some X-ray CT imaging modalities. Derived from the Mumford-Shah functional, the proposed methods assume a fixed number of classes. They use local image average as discriminative features. Region labels are modelled by Hidden Markov Measure Field Models. The resulting problems are solved by straightforward alternate minimisation methods, particularly simple in the case of quadratic regularisation of the labels. We demonstrate the proposed methods’ capabilities on synthetic data using classical segmentation criteria as well as criteria specific to geoscience. We also present a few examples using real data.

OriginalsprogEngelsk
TitelScale Space and Variational Methods in Computer Vision : 6th International Conference, SSVM 2017, Kolding, Denmark, June 4-8, 2017, Proceedings
RedaktørerFrançois Lauze, Yiqui Dong, Anders Bjorholm Dahl
Antal sider12
ForlagSpringer
Publikationsdato2017
Sider396-407
ISBN (Trykt)978-3-319-58770-7
ISBN (Elektronisk)978-3-319-58771-4
DOI
StatusUdgivet - 2017
Begivenhed6th International Conference on Scale Space and Variational Methods in Computer Vision - Kolding, Danmark
Varighed: 4 jun. 20178 jun. 2017
Konferencens nummer: 6

Konference

Konference6th International Conference on Scale Space and Variational Methods in Computer Vision
Nummer6
LandDanmark
ByKolding
Periode04/06/201708/06/2017
NavnLecture notes in computer science
Vol/bind10302
ISSN0302-9743

ID: 179559150