Fast registration of segmented images by normal sampling

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00236540" target="_blank" >RIV/68407700:21230/15:00236540 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/CVPRW.2015.7301311" target="_blank" >http://dx.doi.org/10.1109/CVPRW.2015.7301311</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CVPRW.2015.7301311" target="_blank" >10.1109/CVPRW.2015.7301311</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast registration of segmented images by normal sampling

Popis výsledku v původním jazyce

It is known that image registration is mostly driven by image edges. We have taken this idea to the extreme. In segmented images, we ignore the interior of the components and focus on their boundaries only. Furthermore, by assuming spatial compactness ofthe components, the similarity criterion can be approximated by sampling only a small number of points on the normals passing through a sparse set of keypoints. This leads to an order-of-magnitude speed advantage in comparison with classical registration algorithms. Surprisingly, despite the crude approximation, the accuracy is comparable. By virtue of the segmentation and by using a suitable similarity criterion such as mutual information on labels, the method can handle large appearance differences and large variability in the segmentations. The segmentation does not need not be perfectly coherent between images and over-segmentation is acceptable. We demonstrate the performance of the method on a range of different datasets, includi

Název v anglickém jazyce

Fast registration of segmented images by normal sampling

Popis výsledku anglicky

It is known that image registration is mostly driven by image edges. We have taken this idea to the extreme. In segmented images, we ignore the interior of the components and focus on their boundaries only. Furthermore, by assuming spatial compactness ofthe components, the similarity criterion can be approximated by sampling only a small number of points on the normals passing through a sparse set of keypoints. This leads to an order-of-magnitude speed advantage in comparison with classical registration algorithms. Surprisingly, despite the crude approximation, the accuracy is comparable. By virtue of the segmentation and by using a suitable similarity criterion such as mutual information on labels, the method can handle large appearance differences and large variability in the segmentations. The segmentation does not need not be perfectly coherent between images and over-segmentation is acceptable. We demonstrate the performance of the method on a range of different datasets, includi

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA14-21421S" target="_blank" >GA14-21421S: Automatická analýza prostorových vzorů genové exprese</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

CVPRW2015: IEEE Conference on Computer Vision and Pattern Recognition Workshops

ISBN

978-1-4673-6759-2

ISSN

2160-7508

e-ISSN

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Počet stran výsledku

9

Strana od-do

11-19

Název nakladatele

IEEE

Místo vydání

Piscataway (New Jersey)

Místo konání akce

Boston

Datum konání akce

7. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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