Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
Identifikátory výsledku
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>
Alternativní jazyky
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.
Klasifikace
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)
Návaznosti výsledku
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)
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
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