Improving Ransac Feature Matching Based on Geometric Relation

Abstract

Abstract. Feature Matching between images is an essential task for many computer vision and photogrammetry applications, such as Structure from Motion (SFM), Surface Extraction, Visual Simultaneous Localization and Mapping (VSLAM), and vision-based localization and navigation. Among the matched point pairs, there are typically false positive matches. Therefore, outlier detection and rejection are important steps in any vision application. RANSAC has been a well-established approach for outlier detection. The outlier ratio and the number of required correspondences used in RANSAC determine the number of iterations needed, which ultimately, determines the computation time. We propose a simple algorithm (GR_RANSAC) based on the two-dimensional spatial relationships between points in the image domain. The assumption is that the distances and bearing angles between the 2D feature points should be similar in images with small disparity, such as the case for video image sequences. In the proposed approach, the distances and angles are measured from a reference point in the first image and its correspondence in the other image, and the points with any significant differences are considered as outliers. This process can pre-filter the matched points, and thus increase the inliers’ ratio. As a result, GR_RANSAC can converge to the correct hypothesis in fewer trial runs than ordinary RANSAC.

Feature Matching Enhancement Using the Graph Neural Network (Gnn-Ransac)

“…Over time, different methods have been proposed to reject the false-positive correspondences and improve the RANSAC, such as GR_RANSAC (Elashry et al, 2021) which depends on the geometric relation between the features but requires adjustable thresholds based on the images' relative orientation, SuperGlue (Sarlin et al, 2020) which learns feature matching based on Graph Neural Networks but needs to learn each feature based on all the features in the same image and the other image, and thus, it consumes more time, and LP-RANSAC (Wang et al, 2020) which uses RANSAC with locality preserving constraint. The specific objective of this study is to propose a filtering algorithm based on the Graph Networks, as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers and needs no variable threshold or to learn features, etc.…”

“…Our former work (Elashry et al, 2021) used the geometric relation between points based on their spatial relationship that should be similar in image sequences in the image domain. For example, the distances and the angles between the points should be similar in the image sequences where the difference between images is not big (high overlap).…”

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

“…Feature matching was performed by Random Sample Consensus (RANSAC) algorithm which was widely used in the state-of-the-art [17][18][19]. Starting from the first frame, consecutive images were stitched together by identifying the rigid transformation that minimized the sum of absolute distance between corresponding features.…”