Semantic matching by weakly supervised 2D point set registration

Zakaria Laskar, Hamed R. Tavakoli, Juho Kannala

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

5 Sitaatiot (Scopus)
233 Lataukset (Pure)

Abstrakti

In this paper we address the problem of establishing correspondences between different instances of the same object. The problem is posed as finding the geometric transformation that aligns a given image pair. We use a convolutional neural network (CNN) to directly regress the parameters of the transformation model. The alignment problem is defined in the setting where an unordered set of semantic key-points per image are available, but, without the correspondence information. To this end we propose a novel loss function based on cyclic consistency that solves this 2D point set registration problem by inferring the optimal geometric transformation model parameters. We train and test our approach on a standard benchmark dataset Proposal-Flow (PF-PASCAL)[8]. The proposed approach achieves state-of-the-art results demonstrating the effectiveness of the method. In addition, we show our approach further benefits from additional training samples in PF-PASCAL generated by using category level information.

AlkuperäiskieliEnglanti
Otsikko2019 IEEE WINTER CONFERENCE ON APPLICATIONS OF COMPUTER VISION (WACV)
KustantajaIEEE
Sivut1061-1069
Sivumäärä9
ISBN (elektroninen)9781728119755
DOI - pysyväislinkit
TilaJulkaistu - 4 maalisk. 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE Winter Conference on Applications of Computer Vision - Waikoloa Village, Yhdysvallat
Kesto: 7 tammik. 201911 tammik. 2019
Konferenssinumero: 19

Conference

ConferenceIEEE Winter Conference on Applications of Computer Vision
LyhennettäWACV
Maa/AlueYhdysvallat
KaupunkiWaikoloa Village
Ajanjakso07/01/201911/01/2019

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