Affine Invariant Shape Descriptors

Abstract

This paper presents affine-invariant shape descriptor which could be applied to both binary and gray-level images. The proposed algorithm utilizes gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters ([1]) to obtain gradient information at different orientations and scales. We aggregate the gradients into a shape signature. The signature derived from the rotated object is circularly shifted version of the signature derived from the original object. This property is called the circular-shifting rule ([2]). The shape descriptor is defined as the Fourier transform of the signature. We also provide a distance definition for the proposed descriptor taking the circular-shifting rule into account. The performance of the proposed descriptor is evaluated over the databases containing digits taken from vehicle license plates. The experiments show that the devised method outperforms other well-known Fourier-based shape descriptors such as centroid distance and boundary curvature.