Geometric Image Correspondence Verification by Dense Pixel Matching

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

This paper addresses the problem of determining dense pixel correspondences between two images and its application to geometric correspondence verification in image retrieval. The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level. We determine a set of cyclically consistent dense pixel matches between the pair of images and evaluate local similarity of matched pixels using neural network based image descriptors. Final re-ranking is based on a novel similarity function, which fuses the local similarity metric with a global similarity metric and a geometric consistency measure computed for the matched pixels. For dense matching our approach utilizes a modified version of a recently proposed dense geometric correspondence network (DGC-Net), which we also improve by optimizing the architecture. The proposed model and similarity metric compare favourably to the state-of-the-art image retrieval methods. In addition, we apply our method to the problem of longterm visual localization demonstrating promising results and generalization across datasets.

AlkuperäiskieliEnglanti
OtsikkoIEEE Winter Conference on Applications of Computer Vision
KustantajaIEEE
Sivut2510-2519
Sivumäärä10
ISBN (elektroninen)978-1-7281-6553-0
DOI - pysyväislinkit
TilaJulkaistu - maaliskuuta 2020
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE Winter Conference on Applications of Computer Vision - Snowmass Village, Yhdysvallat
Kesto: 1 maaliskuuta 20205 maaliskuuta 2020

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
LyhennettäWACV
Maa/AlueYhdysvallat
KaupunkiSnowmass Village
Ajanjakso01/03/202005/03/2020

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