Modular linear iconic matching using higher order graphs

Vivien Fecamp, Aristeidis Sotiras, Nikos Paragios

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

We introduce a novel approach to tackle iconic linear mapping between two images. We adopt a grid-based parametrization of the deformation field that is encoded by a higher order graphical model. In the proposed formulation, latent variables correspond to local grid displacement vectors and unary potentials locally quantify the level of alignment between the two images. Higher order constraints that involve third and forth order potentials, enforce the linearity of the resulting transformation. The resulting formulation is modular with respect to the image metric used to evaluate the correctness of mapping as well as with respect to the nature of the linear transformation (rigid, similarity, or affine). Inference on this graph is performed through dual decomposition. Comparison with classic algorithms demonstrates the potential of our approach.

Original languageEnglish
Title of host publication2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
PublisherIEEE Computer Society
Pages1097-1101
Number of pages5
ISBN (Electronic)9781479923748
DOIs
StatePublished - Jul 21 2015
Event12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States
Duration: Apr 16 2015Apr 19 2015

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2015-July
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/TerritoryUnited States
CityBrooklyn
Period04/16/1504/19/15

Fingerprint

Dive into the research topics of 'Modular linear iconic matching using higher order graphs'. Together they form a unique fingerprint.

Cite this