Image matching based local Delaunay triangulation and affine invariant geometric constraint

Abstract Finding correspondences between two sets of visual features of 2D image is a key problem in computer vision tasks. In practice situation, objects often undergo occlusion, background clutter, illumination and 3D viewpoint changes which increase the difficulties of matching, thus a robust matching method is needed to tackle with these problems. In this paper, we propose a robust approach to image matching based on Hessian affine region detector and local Delaunay triangulation and affine invariant geometric constraint. Firstly, Hessian affine keypoints with Scale Invariant Feature Transform(SIFT) descriptors are extracted for model and scene image, then matching the Hessian affine keypoints to get initial matched keypoints based on Euclidean distance of their (SIFT) descriptors. Third, Delaunay triangulation of the initial matched Hessian affine keypoints. Based on unique topological structure of Delaunay triangulation of a set of keypoints, we divide the keypoints into two classes, one class with the same number of neighbor triangles, the other class with different number of neighbor triangles, from the two classes of keypoints, we obtained matched triangles by local Delaunay triangulation. There are may have wrong matched triangles, we adopt local affine invariant geometric constraint to refine the matched triangles. Finally, get the final matched keypoints from the refined matched triangles. By using three pairs of images, the experimental results indicate that the proposed method can get higher correctness of image matching than RANSAC based method.