From Bayes to PDEs in image warping

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskning

Standard

From Bayes to PDEs in image warping. / Nielsen, Mads; Markussen, Bo.

Handbook of Mathematical Models in Computer Vision. USA : Springer, 2006. s. 259-272.

Publikation: Bidrag til bog/antologi/rapportBidrag til bog/antologiForskning

Harvard

Nielsen, M & Markussen, B 2006, From Bayes to PDEs in image warping. i Handbook of Mathematical Models in Computer Vision. Springer, USA, s. 259-272. https://doi.org/10.1007/0-387-28831-7_16

APA

Nielsen, M., & Markussen, B. (2006). From Bayes to PDEs in image warping. I Handbook of Mathematical Models in Computer Vision (s. 259-272). Springer. https://doi.org/10.1007/0-387-28831-7_16

Vancouver

Nielsen M, Markussen B. From Bayes to PDEs in image warping. I Handbook of Mathematical Models in Computer Vision. USA: Springer. 2006. s. 259-272 https://doi.org/10.1007/0-387-28831-7_16

Author

Nielsen, Mads ; Markussen, Bo. / From Bayes to PDEs in image warping. Handbook of Mathematical Models in Computer Vision. USA : Springer, 2006. s. 259-272

Bibtex

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title = "From Bayes to PDEs in image warping",
abstract = "In many disciplines of computer vision, such as stereo vision, flow computation, medical image registration, the essential computational problem is the geometrical alignment of images. In this chapter we describe how such an alignment may be obtained as statistical optimal through solving a partial differential equation (PDE) in the matching function. We treat different choices of matching criteria such as minimal square difference, maximal correlation, maximal mutual information, and several smoothness criteria. All are treated from a Bayes point of view leading to a functional minimization problem solved through an Euler-Lagrange formulation as the solution to a PDE. We try in this chapter to collect the most used methodologies and draw conclusions on their properties and similarities.",
author = "Mads Nielsen and Bo Markussen",
year = "2006",
doi = "10.1007/0-387-28831-7_16",
language = "English",
isbn = "978-0-387-26371-7",
pages = "259--272",
booktitle = "Handbook of Mathematical Models in Computer Vision",
publisher = "Springer",
address = "Switzerland",

}

RIS

TY - CHAP

T1 - From Bayes to PDEs in image warping

AU - Nielsen, Mads

AU - Markussen, Bo

PY - 2006

Y1 - 2006

N2 - In many disciplines of computer vision, such as stereo vision, flow computation, medical image registration, the essential computational problem is the geometrical alignment of images. In this chapter we describe how such an alignment may be obtained as statistical optimal through solving a partial differential equation (PDE) in the matching function. We treat different choices of matching criteria such as minimal square difference, maximal correlation, maximal mutual information, and several smoothness criteria. All are treated from a Bayes point of view leading to a functional minimization problem solved through an Euler-Lagrange formulation as the solution to a PDE. We try in this chapter to collect the most used methodologies and draw conclusions on their properties and similarities.

AB - In many disciplines of computer vision, such as stereo vision, flow computation, medical image registration, the essential computational problem is the geometrical alignment of images. In this chapter we describe how such an alignment may be obtained as statistical optimal through solving a partial differential equation (PDE) in the matching function. We treat different choices of matching criteria such as minimal square difference, maximal correlation, maximal mutual information, and several smoothness criteria. All are treated from a Bayes point of view leading to a functional minimization problem solved through an Euler-Lagrange formulation as the solution to a PDE. We try in this chapter to collect the most used methodologies and draw conclusions on their properties and similarities.

U2 - 10.1007/0-387-28831-7_16

DO - 10.1007/0-387-28831-7_16

M3 - Book chapter

SN - 978-0-387-26371-7

SP - 259

EP - 272

BT - Handbook of Mathematical Models in Computer Vision

PB - Springer

CY - USA

ER -

ID: 60597