Locally orderless registration

Research output: Contribution to journalJournal articleResearchpeer-review

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Locally orderless registration. / Darkner, Sune; Sporring, Jon.

In: I E E E Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, No. 6, 2013, p. 1437-1450.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Darkner, S & Sporring, J 2013, 'Locally orderless registration', I E E E Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 6, pp. 1437-1450. https://doi.org/10.1109/TPAMI.2012.238

APA

Darkner, S., & Sporring, J. (2013). Locally orderless registration. I E E E Transactions on Pattern Analysis and Machine Intelligence, 35(6), 1437-1450. https://doi.org/10.1109/TPAMI.2012.238

Vancouver

Darkner S, Sporring J. Locally orderless registration. I E E E Transactions on Pattern Analysis and Machine Intelligence. 2013;35(6):1437-1450. https://doi.org/10.1109/TPAMI.2012.238

Author

Darkner, Sune ; Sporring, Jon. / Locally orderless registration. In: I E E E Transactions on Pattern Analysis and Machine Intelligence. 2013 ; Vol. 35, No. 6. pp. 1437-1450.

Bibtex

@article{e0ba62c32470497a9682001cfda30b06,
title = "Locally orderless registration",
abstract = "This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. Histograms by Locally Orderless Images are well posed and offer explicit control over the 3 inherent and unavoidable scales: the spatial resolution, intensity levels, and spatial extent of local histograms. Through Locally Orderless Images, we offer new insight into the relations between these scales. We demonstrate our unification by developing a Locally Orderless Registration algorithm for two quite different similarity measures, namely, Normalized Mutual Information and Sum of Squared Differences, and we compare these variations both theoretically, and empirically. Finally, using our algorithm, we explain the empirically observed differences between two popular joint density estimation techniques used in registration: Parzen Windows and Generalized Partial Volume.",
keywords = "Faculty of Science",
author = "Sune Darkner and Jon Sporring",
year = "2013",
doi = "10.1109/TPAMI.2012.238",
language = "English",
volume = "35",
pages = "1437--1450",
journal = "I E E E Transactions on Pattern Analysis and Machine Intelligence",
issn = "0162-8828",
publisher = "Institute of Electrical and Electronics Engineers",
number = "6",

}

RIS

TY - JOUR

T1 - Locally orderless registration

AU - Darkner, Sune

AU - Sporring, Jon

PY - 2013

Y1 - 2013

N2 - This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. Histograms by Locally Orderless Images are well posed and offer explicit control over the 3 inherent and unavoidable scales: the spatial resolution, intensity levels, and spatial extent of local histograms. Through Locally Orderless Images, we offer new insight into the relations between these scales. We demonstrate our unification by developing a Locally Orderless Registration algorithm for two quite different similarity measures, namely, Normalized Mutual Information and Sum of Squared Differences, and we compare these variations both theoretically, and empirically. Finally, using our algorithm, we explain the empirically observed differences between two popular joint density estimation techniques used in registration: Parzen Windows and Generalized Partial Volume.

AB - This paper presents a unifying approach for calculating a wide range of popular, but seemingly very different, similarity measures. Our domain is the registration of n-dimensional images sampled on a regular grid, and our approach is well suited for gradient-based optimization algorithms. Our approach is based on local intensity histograms and built upon the technique of Locally Orderless Images. Histograms by Locally Orderless Images are well posed and offer explicit control over the 3 inherent and unavoidable scales: the spatial resolution, intensity levels, and spatial extent of local histograms. Through Locally Orderless Images, we offer new insight into the relations between these scales. We demonstrate our unification by developing a Locally Orderless Registration algorithm for two quite different similarity measures, namely, Normalized Mutual Information and Sum of Squared Differences, and we compare these variations both theoretically, and empirically. Finally, using our algorithm, we explain the empirically observed differences between two popular joint density estimation techniques used in registration: Parzen Windows and Generalized Partial Volume.

KW - Faculty of Science

U2 - 10.1109/TPAMI.2012.238

DO - 10.1109/TPAMI.2012.238

M3 - Journal article

C2 - 23599057

VL - 35

SP - 1437

EP - 1450

JO - I E E E Transactions on Pattern Analysis and Machine Intelligence

JF - I E E E Transactions on Pattern Analysis and Machine Intelligence

SN - 0162-8828

IS - 6

ER -

ID: 41814266