Iterative gradient-based shift estimation: To multiscale or not to multiscale?

Martin Rais; Jean-Michel Morel; Gabriele Facciolo

doi:10.1007/978-3-319-25751-8_50

Iterative gradient-based shift estimation: To multiscale or not to multiscale?

Martin Rais^*
, Jean-Michel Morel
, Gabriele Facciolo

^*Corresponding author for this work

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Fast global shift estimation is a critical preprocessing step on many high level tasks such as remote sensing or medical imaging. In this work we deal with a simple question: should we use an iterative technique to perform shift estimation or should we use a multiscale approach. Based on the obtained results, both methodologies proved to lose accuracy as the noise increases, however this accuracy loss increases with the shift magnitude. The conclusion is that a multiscale strategy should be used when the shift magnitude is higher than approximately a fifth of a pixel. © Springer International Publishing Switzerland 2015.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher	Springer Verlag
Pages	416-423
Volume	9423
ISBN (Print)	9783319257501
DOIs	https://doi.org/10.1007/978-3-319-25751-8_50
Publication status	Published - 2015
Externally published	Yes
Event	20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015 - Montevideo, Uruguay Duration: 9 Nov 2015 → 12 Nov 2015

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9423
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015
Place	Uruguay
City	Montevideo
Period	9/11/15 → 12/11/15

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Funding

This work was also partially supported by the Centre National d’Etudes Spatiales (MISS Project), the European Research Council (Advanced Grant Twelve Labours), the Office of Naval Research (under Grant N00014-97-1-0839), Direction G´en´erale de l’Armement, Fondation Math´ematique Jacques Hadamard and Agence Nationale de la Recherche (Stereo project).

Research Keywords

Iterative
Multiscale
Shift estimation

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10.1007/978-3-319-25751-8_50

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Rais, M., Morel, J.-M., & Facciolo, G. (2015). Iterative gradient-based shift estimation: To multiscale or not to multiscale? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9423, pp. 416-423). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9423). Springer Verlag. https://doi.org/10.1007/978-3-319-25751-8_50

Rais, Martin ; Morel, Jean-Michel ; Facciolo, Gabriele. / Iterative gradient-based shift estimation : To multiscale or not to multiscale?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9423 Springer Verlag, 2015. pp. 416-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Iterative gradient-based shift estimation: To multiscale or not to multiscale?",

abstract = "Fast global shift estimation is a critical preprocessing step on many high level tasks such as remote sensing or medical imaging. In this work we deal with a simple question: should we use an iterative technique to perform shift estimation or should we use a multiscale approach. Based on the obtained results, both methodologies proved to lose accuracy as the noise increases, however this accuracy loss increases with the shift magnitude. The conclusion is that a multiscale strategy should be used when the shift magnitude is higher than approximately a fifth of a pixel. {\textcopyright} Springer International Publishing Switzerland 2015.",

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Rais, M, Morel, J-M & Facciolo, G 2015, Iterative gradient-based shift estimation: To multiscale or not to multiscale? in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9423, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9423, Springer Verlag, pp. 416-423, 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015, Montevideo, Uruguay, 9/11/15. https://doi.org/10.1007/978-3-319-25751-8_50

Iterative gradient-based shift estimation: To multiscale or not to multiscale? / Rais, Martin; Morel, Jean-Michel; Facciolo, Gabriele.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9423 Springer Verlag, 2015. p. 416-423 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9423).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Iterative gradient-based shift estimation

T2 - 20th Iberoamerican Congress on on Pattern Recognition, CIARP 2015

AU - Rais, Martin

AU - Morel, Jean-Michel

AU - Facciolo, Gabriele

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2015

Y1 - 2015

N2 - Fast global shift estimation is a critical preprocessing step on many high level tasks such as remote sensing or medical imaging. In this work we deal with a simple question: should we use an iterative technique to perform shift estimation or should we use a multiscale approach. Based on the obtained results, both methodologies proved to lose accuracy as the noise increases, however this accuracy loss increases with the shift magnitude. The conclusion is that a multiscale strategy should be used when the shift magnitude is higher than approximately a fifth of a pixel. © Springer International Publishing Switzerland 2015.

AB - Fast global shift estimation is a critical preprocessing step on many high level tasks such as remote sensing or medical imaging. In this work we deal with a simple question: should we use an iterative technique to perform shift estimation or should we use a multiscale approach. Based on the obtained results, both methodologies proved to lose accuracy as the noise increases, however this accuracy loss increases with the shift magnitude. The conclusion is that a multiscale strategy should be used when the shift magnitude is higher than approximately a fifth of a pixel. © Springer International Publishing Switzerland 2015.

KW - Iterative

KW - Multiscale

KW - Shift estimation

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DO - 10.1007/978-3-319-25751-8_50

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SN - 9783319257501

VL - 9423

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 416

EP - 423

BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

PB - Springer Verlag

Y2 - 9 November 2015 through 12 November 2015

ER -

Rais M, Morel JM, Facciolo G. Iterative gradient-based shift estimation: To multiscale or not to multiscale? In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9423. Springer Verlag. 2015. p. 416-423. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-25751-8_50

Iterative gradient-based shift estimation: To multiscale or not to multiscale?

Abstract

Publication series

Conference

Bibliographical note

Funding

Research Keywords

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