Fast registration by boundary sampling and linear programming

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327728" target="_blank" >RIV/68407700:21230/18:00327728 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Kybic-MICCAI2018.pdf" target="_blank" >http://cmp.felk.cvut.cz/pub/cmp/articles/kybic/Kybic-MICCAI2018.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-00928-1_88" target="_blank" >10.1007/978-3-030-00928-1_88</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast registration by boundary sampling and linear programming

Popis výsledku v původním jazyce

We address the problem of image registration when speed is more important than accuracy. We present a series of simplification and approximations applicable to almost any pixel-based image similarity criterion. We first sample the image at a set of sparse keypoints in a direction normal to image edges and then create a piecewise linear convex approximation of the individual contributions. We obtain a linear program for which a global optimum can be found very quickly by standard algorithms. The linear program formulation also allows for an easy addition of regularization and trust-region bounds. We have tested the approach for affine and B-spline transformation representation but any linear model can be used. Larger deformations can be handled by multiresolution. We show that our method is much faster than pixel- based registration, with only a small loss of accuracy. In comparison to standard keypoint based registration, our method is applicable even if individual keypoints cannot be reliably identiffied and matched.

Název v anglickém jazyce

Fast registration by boundary sampling and linear programming

Popis výsledku anglicky

We address the problem of image registration when speed is more important than accuracy. We present a series of simplification and approximations applicable to almost any pixel-based image similarity criterion. We first sample the image at a set of sparse keypoints in a direction normal to image edges and then create a piecewise linear convex approximation of the individual contributions. We obtain a linear program for which a global optimum can be found very quickly by standard algorithms. The linear program formulation also allows for an easy addition of regularization and trust-region bounds. We have tested the approach for affine and B-spline transformation representation but any linear model can be used. Larger deformations can be handled by multiresolution. We show that our method is much faster than pixel- based registration, with only a small loss of accuracy. In comparison to standard keypoint based registration, our method is applicable even if individual keypoints cannot be reliably identiffied and matched.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/GA17-15361S" target="_blank" >GA17-15361S: Učení lokálních konceptů z globálních trénovacích dat pro klasifikaci a segmentaci biomedicínských obrazů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Medical Image Computing and Computer Assisted Intervention, Part I

ISBN

978-3-030-00927-4

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

783-791

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Granada

Datum konání akce

16. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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