Inverting RANSAC: Global Model Detection via Inlier Rate Estimation

  • Simon Korman | Tel-Aviv University

This work presents a novel approach for detecting inliers in a given set of correspondences (matches). It does so without explicitly identifying any consensus set, based on a method for inlier rate estimation (IRE). Given such an estimator for the inlier rate, we also present an algorithm that detects a globally optimal transformation. We provide a theoretical analysis of the IRE method using a stochastic generative model on the continuous spaces of matches and transformations. This model allows rigorous investigation of the limits of our IRE method for the case of 2D translation, further giving bounds and insights for the more general case. Our theoretical analysis is validated empirically and is shown to hold in practice for the more general case of 2D affinities. In addition, we show that the combined framework works on challenging cases of 2D homography estimation, with very few and possibly noisy inliers, where RANSAC generally fails.

(Joint work with Roee Litman, Alex Bronstein and Shai Avidan, to appear at CVPR 2015)

Speaker Details

Simon Korman is a Ph.D. student at the Department of Electrical Engineering at Tel-Aviv university, where he is advised by Professor Shai Avidan. Simon holds a B.Sc. in Mathematics and Computer Science from the Hebrew University of Jerusalem and an M.Sc. in Mathematics and Computer Science from the Weizmann Institute of Science. His research addresses a variety of tasks in image matching and understanding, both in 2D and 3D.

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