Fast Motion Deblurring for Feature Detection and Matching Using Inertial Measurements

Janne Mustaniemi, Juho Kannala, Simo Särkkä, Jiri Matas, Janne Heikkilä

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

11 Sitaatiot (Scopus)


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.
Otsikko2018 24th International Conference on Pattern Recognition (ICPR)
Sivut3068 -3073
ISBN (elektroninen)978-1-5386-3788-3
ISBN (painettu)978-1-5386-3787-6
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Pattern Recognition - Beijing, Kiina
Kesto: 20 elok. 201824 elok. 2018
Konferenssinumero: 24


NimiInternational Conference on Pattern Recognition (ICPR)
ISSN (painettu)1051-4651


ConferenceInternational Conference on Pattern Recognition


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