Efficient Object Recognition Using Sampling of Keypoint Triples and Keygraph Structure

Estephan Dazzi, Teofilo de Campos, Adrian Hilton, Roberto M. Cesar

Abstract: We present an object matching method that employs matches of local graphs of keypoints, called keygraphs, instead of simple keypoint matches. For a keygraph match to be valid, vertex (keypoint) descriptors must be similar and both keygraphs must satisfy structural properties concerning keypoints orientation, scale, relative position and cyclic ordering; as a result, the large majority of initial incorrect keypoint matches is correctly filtered out. We introduce a novel approach to sample keypoint triples (i.e. keygraphs) in a query image, based on complementary Delaunay triangulations; this generates a linear number of triples with relation to the number of keypoints. Query keygraphs are then matched against the indexed model keypoints; each established keygraph match is used to evaluate a candidate pose (an affine transformation). The proposed method has been evaluated for object recognition and pose estimation, achieving a better performance in comparison to state-of-the-art methods.