Integration of optimal spatial distributed tie-points in RANSAC-based image registration

Abstract

Feature-based image registration requires the identification of correct tie-points between the image pair. In this paper, an improved outlier method is proposed to find correct matching results of optimal distribution based on RANSAC (RANdom SAmple Consensus) algorithm. The main feature of the proposed method is that an optimal spatial designation of tie-points method using stratified random selection (SRS), is integrated into RANSAC framework to filter out the mismatched features that exist in the massive initial matches generated by SIFT operator in order to estimate mapping function accurately. In this way, the selection of relatively disperse and evenly distributed tie-points based on adaptive stratified partition can make RANSAC efficient. We carried out experiments on the registration of three pairs of satellite images. The proposed SIFT-SRS-RANSAC method leads to higher matching and registration accuracy when comparing with the performance of SIFT-RANSAC and SIFT-bucketing-RANSAC algorithms. ARTICLE HISTORY

Utilizing Deep Learning-Based Fusion of Laser Point Cloud Data and Imagery for Digital Measurement in Steel Truss Member Applications

“…Given the intrinsic traits of structural members, plane fitting for rods was undertaken using RANSAC (Random Sample Consensus). Through RANSAC, parameters for a coherent mathematical model were ascertained via a stochastic sampling approach [17]. This procedure not only aided in capturing planar features of the rods but also paved the way for the extraction of geometric features from the identified planes.…”