A DIRECT-type global optimization algorithm for image registration

Cuicui Zheng; James Calvin; Craig Gotsman

doi:10.1007/s10898-020-00914-y

A DIRECT-type global optimization algorithm for image registration

Cuicui Zheng, James Calvin, Craig Gotsman

Computer Science

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Image registration is an important component of many image processing problems which often require optimizing over some set of parameters. In the image registration problem, one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. We describe a global optimization algorithm and apply it to the problem of identifying the best transformation for aligning two images.

Original language	English (US)
Pages (from-to)	431-445
Number of pages	15
Journal	Journal of Global Optimization
Volume	79
Issue number	2
DOIs	https://doi.org/10.1007/s10898-020-00914-y
State	Published - Feb 2021

All Science Journal Classification (ASJC) codes

Computer Science Applications
Control and Optimization
Management Science and Operations Research
Applied Mathematics

Keywords

Convergence
Global optimization
Image registration

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10.1007/s10898-020-00914-y

Cite this

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title = "A DIRECT-type global optimization algorithm for image registration",

abstract = "Image registration is an important component of many image processing problems which often require optimizing over some set of parameters. In the image registration problem, one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. We describe a global optimization algorithm and apply it to the problem of identifying the best transformation for aligning two images.",

keywords = "Convergence, Global optimization, Image registration",

author = "Cuicui Zheng and James Calvin and Craig Gotsman",

note = "Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. CMMI-1562466. Publisher Copyright: {\textcopyright} 2020, Springer Science+Business Media, LLC, part of Springer Nature.",

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AU - Calvin, James

AU - Gotsman, Craig

N1 - Funding Information: This material is based upon work supported by the National Science Foundation under Grant No. CMMI-1562466. Publisher Copyright: © 2020, Springer Science+Business Media, LLC, part of Springer Nature.

PY - 2021/2

Y1 - 2021/2

N2 - Image registration is an important component of many image processing problems which often require optimizing over some set of parameters. In the image registration problem, one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. We describe a global optimization algorithm and apply it to the problem of identifying the best transformation for aligning two images.

AB - Image registration is an important component of many image processing problems which often require optimizing over some set of parameters. In the image registration problem, one attempts to determine the best transformation for aligning similar images. Such problems typically require minimizing a dissimilarity measure with multiple local minima. We describe a global optimization algorithm and apply it to the problem of identifying the best transformation for aligning two images.

KW - Convergence

KW - Global optimization

KW - Image registration

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A DIRECT-type global optimization algorithm for image registration

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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