A PDE Solution of Brownian Warping

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Standard

A PDE Solution of Brownian Warping. / Nielsen, Mads; Johansen, Peter.

Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV. <Forlag uden navn>, 2004. s. 180-191 (Lecture notes in computer science, Bind 3024/2004).

Publikation: Bidrag til bog/antologi/rapportKonferencebidrag i proceedingsForskningfagfællebedømt

Harvard

Nielsen, M & Johansen, P 2004, A PDE Solution of Brownian Warping. i Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV. <Forlag uden navn>, Lecture notes in computer science, bind 3024/2004, s. 180-191, European Conference on Computer Vision, Prague, Tjekkiet, 29/11/2010. https://doi.org/10.1007/b97873

APA

Nielsen, M., & Johansen, P. (2004). A PDE Solution of Brownian Warping. I Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV (s. 180-191). <Forlag uden navn>. Lecture notes in computer science Bind 3024/2004 https://doi.org/10.1007/b97873

Vancouver

Nielsen M, Johansen P. A PDE Solution of Brownian Warping. I Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV. <Forlag uden navn>. 2004. s. 180-191. (Lecture notes in computer science, Bind 3024/2004). https://doi.org/10.1007/b97873

Author

Nielsen, Mads ; Johansen, Peter. / A PDE Solution of Brownian Warping. Computer Vision - ECCV 2004: 8th European Conference on Computer Vision, Prague, Czech Republic, May 11-14, 2004. Proceedings, Part IV. <Forlag uden navn>, 2004. s. 180-191 (Lecture notes in computer science, Bind 3024/2004).

Bibtex

@inproceedings{4b0408806ac511dd8d9f000ea68e967b,
title = "A PDE Solution of Brownian Warping",
abstract = "A Brownian motion model in the group of diffeomorphisms has been introduced as creating a least committed prior on warps. This prior is source destination symmetric, fulfills a natural semi-group property for warps, and with probability 1 create invertible warps. In this paper, we formulate a Partial Differential Equation for obtaining the maximum likelihood warp given matching constraints derived from the images. We solve for the free boundary conditions, and the bias toward smaller areas in the finite domain setting. Furthermore, we demonstrate the technique on 2D images, and show that the obtained warps are also in practice source-destination symmetric.",
author = "Mads Nielsen and Peter Johansen",
note = "Poster presentation; null ; Conference date: 29-11-2010",
year = "2004",
doi = "10.1007/b97873",
language = "English",
isbn = "978-3-540-21981-1",
series = "Lecture notes in computer science",
publisher = "<Forlag uden navn>",
pages = "180--191",
booktitle = "Computer Vision - ECCV 2004",

}

RIS

TY - GEN

T1 - A PDE Solution of Brownian Warping

AU - Nielsen, Mads

AU - Johansen, Peter

N1 - Conference code: 8

PY - 2004

Y1 - 2004

N2 - A Brownian motion model in the group of diffeomorphisms has been introduced as creating a least committed prior on warps. This prior is source destination symmetric, fulfills a natural semi-group property for warps, and with probability 1 create invertible warps. In this paper, we formulate a Partial Differential Equation for obtaining the maximum likelihood warp given matching constraints derived from the images. We solve for the free boundary conditions, and the bias toward smaller areas in the finite domain setting. Furthermore, we demonstrate the technique on 2D images, and show that the obtained warps are also in practice source-destination symmetric.

AB - A Brownian motion model in the group of diffeomorphisms has been introduced as creating a least committed prior on warps. This prior is source destination symmetric, fulfills a natural semi-group property for warps, and with probability 1 create invertible warps. In this paper, we formulate a Partial Differential Equation for obtaining the maximum likelihood warp given matching constraints derived from the images. We solve for the free boundary conditions, and the bias toward smaller areas in the finite domain setting. Furthermore, we demonstrate the technique on 2D images, and show that the obtained warps are also in practice source-destination symmetric.

U2 - 10.1007/b97873

DO - 10.1007/b97873

M3 - Article in proceedings

SN - 978-3-540-21981-1

T3 - Lecture notes in computer science

SP - 180

EP - 191

BT - Computer Vision - ECCV 2004

PB - <Forlag uden navn>

Y2 - 29 November 2010

ER -

ID: 5521062