Fast registration by boundary sampling and linear programming

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

Fast registration by boundary sampling and linear programming

Original language description

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.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

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

Project

<a href="/en/project/GA17-15361S" target="_blank" >GA17-15361S: Learning local concepts from global training data for biomedical image segmentation and classification</a><br>

Continuities

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

Publication year

2018

Confidentiality

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

Article name in the collection

Medical Image Computing and Computer Assisted Intervention, Part I

ISBN

978-3-030-00927-4

ISSN

0302-9743

e-ISSN

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Number of pages

9

Pages from-to

783-791

Publisher name

Springer, Cham

Place of publication

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Event location

Granada

Event date

Sep 16, 2018

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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