Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00327981" target="_blank" >RIV/68407700:21230/18:00327981 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/ICPR.2018.8546041" target="_blank" >http://dx.doi.org/10.1109/ICPR.2018.8546041</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICPR.2018.8546041" target="_blank" >10.1109/ICPR.2018.8546041</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

Popis výsledku v původním jazyce

Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.

Název v anglickém jazyce

Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

Popis výsledku anglicky

Many computer vision and image processing applications rely on local features. It is well-known that motion blur decreases the performance of traditional feature detectors and descriptors. We propose an inertial-based deblurring method for improving the robustness of existing feature detectors and descriptors against the motion blur. Unlike most deblurring algorithms, the method can handle spatially-variant blur and rolling shutter distortion. Furthermore, it is capable of running in real-time contrary to state-of-the-art algorithms. The limitations of inertial-based blur estimation are taken into account by validating the blur estimates using image data. The evaluation shows that when the method is used with traditional feature detector and descriptor, it increases the number of detected keypoints, provides higher repeatability and improves the localization accuracy. We also demonstrate that such features will lead to more accurate and complete reconstructions when used in the application of 3D visual reconstruction.

Druh

D - Stať ve sborníku

CEP obor

—

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/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

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

2018 24rd International Conference on Pattern Recognition (ICPR)

ISBN

978-1-5386-3788-3

ISSN

—

e-ISSN

1051-4651

Počet stran výsledku

6

Strana od-do

3068-3073

Název nakladatele

IEEE

Místo vydání

Piscataway, NJ

Místo konání akce

Beijing

Datum konání akce

20. 8. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000455146803013