Object detection in gray scale images based on invariant polynomial features

In this paper we present an effective method for object detection in digital images. Our approach is fast, stable and easy to implement. It is motivated by a strong physical and mathematical basis, which ensures an invariance of the recognition with respect to illumination, rotations, scaling and translations. In addition, there are no assumptions on the geometry of the object which has to be recognized. Our method extracts distinctive points of the image and approximates a small pixel neighborhood by polynomials. The corresponding polynomial coefficients are used to compute invariant feature vectors for solving point correspondences in order to calculate an optimal prototype fitting.