Reliable image matching via photometric and geometric constraints structured by Delaunay triangulation

Image matching is a basic task in the field of photogrammetry and remote sensing. By using the advantages of the Delaunay triangulation, this paper proposes a novel image matching method. First, neighboring structures of randomly distributed feature points are formed with the assistance of the Delaunay triangulation and its corresponding graph, and the image planes are simultaneously divided into patches of near-regular triangles. Second, two constraints, a photometric constraint and a geometric constraint, are implemented based on the constructed neighboring structures, which incorporate the hierarchical elimination and left-right checking strategies to deliver the influences of outliers on the decision of inliers and ensure the high precision of the final matches. The former utilizes a line descriptor as a second-order photometric constraint, and the latter adopts the spatial angular order (SAO) to achieve a geometric constraint for the calculation of dissimilarity scores between correspondences. In addition, with the constraints between triangles of the refined Delaunay triangulation and its corresponding graph, a match expansion is designed to exploit as many inliers as possible. Finally, a reliable image matching algorithm is proposed by sequentially executing the three constraints for outlier elimination and match expansion. Under comprehensive analysis and comparison with five state-of-the-art algorithms, the performance of the proposed method is verified by using both rigid and non-rigid datasets. The experimental results demonstrate that the Delaunay triangulation is sufficient to construct neighboring structures for the implementation of local photometric and geometric constraints, and the proposed method can achieve good performance in terms of the precision, recall and number of inliers, and provide reliable matches for stereo image pairs with both rigid and non-rigid transformations.