Multi-contour registration based on feature points correspondence and two-stage gene expression programming

Image registration is a fundamental task in 3D reconstruction from an image sequence. Although this topic has been studied for decades, a general, robust, and automatic image registration method is rare, and most existing image registration methods are designed for a particular application. In this paper, image registration is treated as a formula discovery problem. A novel contour registration pipeline was proposed based on a foot-point-based feature point correspondence algorithm and a two-stage evolutionary algorithm. Our proposal has three objectives. First, we introduce a novel feature point extraction method that uses estimation of the curvature and the support region for every contour in the floating image. Second, we approximate the reference contour using a Gaussian mixture model (GMM) continuous optimization algorithm followed by an order-preserved foot-point detection method used to extract the feature points that correspond to the feature points of the floating contours. Third, we propose a hybrid evolutionary algorithm used to identify the registration formula for the reference image and the floating image. The hybrid evolutionary algorithm is a two-stage algorithm based on gene expression programming (GEP) and the improved cooperative particle swarm optimizer (CPSO). The optimal or near-optimal structure is accomplished using the GEP algorithm, and the parameters embedded in the structure are optimized by an opposition based learning (OBL)-based cooperative particle swarm optimizer (CPSO). Compared with other non-rigid registration methods, the developed registration pipeline produces competitive results with high accuracy.