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.
Grey wolf optimizer (GWO) is a nature inspired optimization algorithm. It can be used to solve both minimization and maximization problems. The binary version of GWO (BGWO) uses binary values for wolves' positions rather than probabilistic values in the original GWO. Integrating BGWO with quantum inspired operations produce a novel enhanced quantum inspired binary grey wolf algorithm (EQI-BGWO). In this paper we used feature selection as an optimization problem to evaluate the performance of our proposed algorithm EQI-BGWO. Our method was evaluated against BGWO method by comparing the fitness value, number of eliminated features and global optima iteration number. it showed a better accuracy and eliminates higher number of features with good performance. Results show that the average error rate enhanced from 0.09 to 0.06 and from 0.53 to 0.52 and from 0.26 to 0.23 for zoo, Lymphography and diabetes dataset respectively using EQI-BGWO, Where the average number of eliminated features was reduced from 6.6 to 6.7 for zoo dataset and from 7.3 to 7.1 for Lymphography dataset and from 2.9 to 3.2 for diabetes dataset.
Abstract. Improving the performance of feature matching plays a key role in computers vision and photogrammetry applications, such as fast image recognition, Structure from Motion (SFM), aerial triangulation, Visual Simultaneous Localization and Mapping (VSLAM), etc., where the RANSAC algorithm is frequently used for outlier detection; note that RANSAC is the most widely used robust approach in photogrammetry and computer vision for outlier detection. It is known that the outlier ratio used in RANSAC primarily determines the number of trial runs needed, which eventually, determines the computation time. Over time, different methods have been proposed to reject the false-positive correspondences and improve RANSAC, such as GR_RANSAC, SuperGlue, and LPRANSAC. The specific objective of this study is to propose a filtering algorithm based on Graph Neural Networks (GNN), as a pre-processing step before RANSAC, which can result in improvements for rejecting the outliers. The research is based on the idea that descriptors of corresponding points, as well as their spatial relationship, should be similar in image sequences. In graph representation, built by the adjacency matrix of data (nodes features), there should be similarity for corresponding points that are close to each other in the image domain. From the many GNNs techniques, Graph Attention Networks (GATs) were selected for this study as they assign different importance to each neighbour’s contribution as anisotropic operations, so the features of neighbour nodes are not considered in the same way, unlike other GNNs techniques. In our approach, we build a graph in each image, because the similarity of the two-dimensional spatial relationships between points in the image domain of consecutive images should be similar. Then during processing, points with any significantly different neighbours are considered as outliers. Next, the points can be updated in the GNN layer. GNN-RANSAC is tested experimentally on real image pairs. Clearly, the proposed pre-filtering increases the inlier ratio and results in faster convergence compared to ordinary RANSAC, making it attractive for real-time applications. Furthermore, there is no need to learn the features.
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