Impact of geometrical restrictions in RANSAC sampling on the ID document classification

Natalya Skoryukina; Igor Faradjev; Konstantin Bulatov; Vladimir V. Arlazarov

doi:10.1117/12.2559306

31 January 2020 Impact of geometrical restrictions in RANSAC sampling on the ID document classification

Natalya Skoryukina, Igor Faradjev, Konstantin Bulatov, Vladimir V. Arlazarov

Proceedings Volume 11433, Twelfth International Conference on Machine Vision (ICMV 2019); 1143306 (2020) https://doi.org/10.1117/12.2559306
Event: Twelfth International Conference on Machine Vision, 2019, Amsterdam, Netherlands

Abstract

In this paper we explore the impact of geometrical restrictions in RANSAC sampling on the ID document type recognition accuracy in images, as well as on the accuracy of the projective distortion parameters estimation. The studied method is based on representing images as constellations of keypoints and their descriptors. The distortion parameters are estimated by applying RANSAC on the matched keypoints. Cases are studied where the base algorithm can yield erroneous or insufficiently accurate solution. A RANSAC scheme is presented with geometrical restrictors and several restriction are proposed, limiting the samples and the computed transform parameters. An experiment was conducted on the open dataset MIDV-500 and the data is presented of the dependence of classification and localization accuracy on the considered restrictors. It was shown that the introduction of restrictors allows to achieve a accuracy improvement and significant speed up.

Citation Download Citation

Natalya Skoryukina, Igor Faradjev, Konstantin Bulatov, and Vladimir V. Arlazarov "Impact of geometrical restrictions in RANSAC sampling on the ID document classification", Proc. SPIE 11433, Twelfth International Conference on Machine Vision (ICMV 2019), 1143306 (31 January 2020); https://doi.org/10.1117/12.2559306

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