Robust Sparse Matching and Motion Estimation Using Genetic Algorithms

Abstract

ABSTRACT:In this paper, we propose a robust technique using genetic algorithm for detecting inliers and estimating accurate motion parameters from putative correspondences containing any percentage of outliers. The proposed technique aims to increase computational efficiency and modelling accuracy in comparison with the state-of-the-art via the following contributions: i) guided generation of initial populations for both avoiding degenerate solutions and increasing the rate of useful hypotheses, ii) replacing random search with evolutionary search, iii) possibility of evaluating the individuals of every population by parallel computation, iv) being performable on images with unknown internal orientation parameters, iv) estimating the motion model via detecting a minimum, however more than enough, set of inliers, v) ensuring the robustness of the motion model against outliers, degeneracy and poorperspective camera models, vi) making no assumptions about the probability distribution of inliers and/or outliers residuals from the estimated motion model, vii) detecting all the inliers by setting the threshold on their residuals adaptively with regard to the uncertainty of the estimated motion model and the position of the matches. The proposed method was evaluated both on synthetic data and real images. The results were compared with the most popular techniques from the state-of-the-art, including RANSAC, MSAC, MLESAC, Least Trimmed Squares and Least Median of Squares. Experimental results proved that the proposed approach perform better than others in terms of accuracy of motion estimation, accuracy of inlier detection and the computational efficiency.

Uav-Based Point Cloud Generation for Open-Pit Mine Modelling

“…At the end, an adaptive thresholding scheme is applied to detect all the inlier correspondences based on the estimated motion models. The details of this algorithm can be found in our recent publication (Shahbazi et al, 2015).…”

An Adaptive △M-ICCP Geomagnetic Matching Algorithm

“…Liu et al (2014) and Komiya et al (2015) respectively reduced the search scope of the algorithm by setting a sliding window and optimising the search space. In addition, some intelligent search strategies, such as an Ant Colony Optimisation (ACO) algorithm (Wang et al, 2014) and Genetic Algorithm (GA) (Shahbazi et al, 2015), are introduced to improve the performance of the algorithm. However, all the improvements are based on the condition that the Geomagnetic Matching Sequence (GMS) is confirmed and focussed on obtaining an efficient transformation between the matching sequence and the closest-point sequence.…”