Image registration by minimization of mapping complexity

Andriy Myronenko, Xubo Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

The criterion for the correct spatial alignment is a key component in image registration. We formulate the registration problem as one that finds the spatial and intensity mappings of minimal complexity that make images exactly equal. We do not assume any parametric forms of these functions, and estimate them within variational calculus. We analytically solve for non-stationary intensity mapping, eliminate it from the objective function and arrive with a new similarity measure. We name it the Mapping Complexity (MC) similarity measure, because it achieves the optimum when intensity and spatial mappings are of minimal complexity. Due to its general formulation, the similarity measure works both for complex intensity relationships (e.g. multimodal registration) and for spatially-varying intensity distortions. Our similarity measure can be interpreted as the one that favors one image to lie mostly within a span of the leading eigenvectors of the kernel matrix, where the kernel matrix is constructed from the second image. We introduce a fast algorithm to compute the similarity measure. In particular, we introduce a fast kernel vector product (FKVP) algorithm, which is of general interest in computer vision. We demonstrate the accuracy of the new similarity measure on several mono- and multi-modal examples with complex intensity non-uniformities.

Original languageEnglish (US)
Title of host publication2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
Pages17-24
Number of pages8
DOIs
StatePublished - Nov 20 2009
Event2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 - Miami, FL, United States
Duration: Jun 20 2009Jun 25 2009

Publication series

Name2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009

Other

Other2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009
CountryUnited States
CityMiami, FL
Period6/20/096/25/09

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

Cite this

Myronenko, A., & Song, X. (2009). Image registration by minimization of mapping complexity. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 (pp. 17-24). [5204345] (2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009). https://doi.org/10.1109/CVPR.2009.5204345