Detection of false feature correspondences in feature based object detection systems

In this paper we present a method for the detection of wrong feature correspondences in a local feature based object detection system. Common visual objects in different images share not only similar local features but also a similar spatial layout of their features. We will utilize this fact in order to distinguish between correct and wrong feature correspondences. The spatial feature layout will be modeled through a Delaunay triangulation. This triangulation is used to find clusters of feature correspondences that follow the same affine transformation. The decision whether a correspondence is correct or wrong can than be made based on this clustering. Our method is independent from the number of common objects in the images and produces reliable results even in difficult scenarios. It can also be used if the number of wrong correspondences is much higher than the number of correct correspondences. Experiments on real and synthetically generated images demonstrate the good performance of our approach.