Motion Consistency Guided Robust Geometric Model Fitting With Severe Outliers

Abstract

Geometric model fitting has been widely applied in the electronic industry. However, it remains as a challenging task when handling the data corrupted by a large number of false matches (i.e., severe outliers) between two-view images. In this article, we propose a novel motion consistency guided fitting method (MCF) to robustly and efficiently estimate the parameters of model instances in data involving severe outliers. Specifically, from input data, we first generate a series of neighborhood sets, in each of which gross outliers that are inconsistent in motions can be effectively filtered, according to motion consistency among true matches (i.e., inliers). Then, we propose an effective sampling algorithm to sample minimal subsets from the generated neighborhood sets. In this way, the model hypotheses computed from the sampled minimal subsets can cover all model instances with a high probability. Furthermore, by taking advantages of the generated hypotheses and neighborhood sets, we propose a novel model selection algorithm to estimate the number and the parameters of model instances. For fitting evaluation, we also build a new dataset, in which the images are collected from a fundus camera. Experiments on a variety of electronic industrial applications show that the proposed MCF achieves higher fitting accuracy at a much lower computational cost than several state-of-the-art fitting methods.