Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements
Original language description
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.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2018 24rd International Conference on Pattern Recognition (ICPR)
ISBN
978-1-5386-3788-3
ISSN
—
e-ISSN
1051-4651
Number of pages
6
Pages from-to
3068-3073
Publisher name
IEEE
Place of publication
Piscataway, NJ
Event location
Beijing
Event date
Aug 20, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000455146803013