GPU Accelerated Real Time Rotation, Scale and Translation Invariant Image Registration Method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F12%3A43921647" target="_blank" >RIV/49777513:23520/12:43921647 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.springerlink.com/content/985306314uq284g1/fulltext.pdf" target="_blank" >http://www.springerlink.com/content/985306314uq284g1/fulltext.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-31295-3_27" target="_blank" >10.1007/978-3-642-31295-3_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

GPU Accelerated Real Time Rotation, Scale and Translation Invariant Image Registration Method

Popis výsledku v původním jazyce

This paper presents highly optimized implementation of image registration method that is invariant to rotation scale and translation. Image registration method using FFT works with comparable accuracy as similar methods proposed in the literature, but practical applications seldom use this technique because of high computational requirement. However, this method is highly parallelizable and offloading it to the commodity graphics cards increases its performance drastically. We are proposing the parallelimplementation of FFT based registration method on CUDA and OpenCL. Performance analysis of this implementation suggests that the parallel version can be used for real time image registration even for image size up to 2k x 2k. We have achieved significant speed up of up to 345x on NVIDIA GTX 580 using CUDA and up to 116x on AMD HD 6950 using OpenCL. Comparison of our implementation with other GPU based registration method reveals that our implementation performs better compared to other

Název v anglickém jazyce

GPU Accelerated Real Time Rotation, Scale and Translation Invariant Image Registration Method

Popis výsledku anglicky

This paper presents highly optimized implementation of image registration method that is invariant to rotation scale and translation. Image registration method using FFT works with comparable accuracy as similar methods proposed in the literature, but practical applications seldom use this technique because of high computational requirement. However, this method is highly parallelizable and offloading it to the commodity graphics cards increases its performance drastically. We are proposing the parallelimplementation of FFT based registration method on CUDA and OpenCL. Performance analysis of this implementation suggests that the parallel version can be used for real time image registration even for image size up to 2k x 2k. We have achieved significant speed up of up to 345x on NVIDIA GTX 580 using CUDA and up to 116x on AMD HD 6950 using OpenCL. Comparison of our implementation with other GPU based registration method reveals that our implementation performs better compared to other

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science

ISBN

978-3-642-31294-6

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

224-233

Název nakladatele

Springer

Místo vydání

Berlin Heidelberg

Místo konání akce

Portugalsko

Datum konání akce

25. 6. 2012

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—