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.
Highlights
A key aspect of all vision applications, such as image registration and alignment, structure from motion applications, Visual Simultaneous Localization and Mapping, and vision-based localization and navigation is how to find correct correspondences between the images, feature matching plays a pivotal role in these applications
The Geometric relationship (GR)-RANSAC method utilizes the 2D distance and angle relation in the image domain. This allows for more accurate outlier detection and subsequent removal from the list of match pairs from feature detection prior running RANSAC
The proposed method could be beneficial to computer vision and photogrammetry applications that heavily depend on time dependent RANSAC operations or applications that utilize large datasets
Summary
A key aspect of all vision applications, such as image registration and alignment, structure from motion applications, Visual Simultaneous Localization and Mapping, and vision-based localization and navigation is how to find correct correspondences between the images, feature matching plays a pivotal role in these applications. A primary concern of the feature matching is the correctness of the matched point pairs, so one of the biggest challenges is how to refine the correspondences by rejecting the mismatched point pairs. The performance of RANSAC (Yang and Li., 2013.) depends primarily on the features which are obtained from different feature detection and extraction methods. The main challenge for these features is to be invariant in both scale and rotation changes so that the same features can be detected for the same object under different projections. The most popular ones are brute force matching, approximate nearest neighbor, and local sensitivity hashing (Li et al, 2015)
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