Affine invariant shape representation and recognition using Gaussian kernels and multi-dimensional indexing

This paper presents a new approach for object recognition using affine-invariant recognition of image patches that correspond to object surfaces that are roughly planar. A novel set of affine-invariant spectral signatures (AISSs) are used to recognize each surface separately invariant to its 3D pose. These local spectral signatures are extracted by convolving the image with a novel configuration of Gaussian kernels. The spectral signature of each image patch is then matched against a set of iconic models using multi-dimensional indexing (MDI) in the frequency domain. Affine-invariance of the signatures is achieved by a new configuration of Gaussian kernels with modulation in two orthogonal axes. The proposed configuration of kernels is Cartesian with varying aspect ratios in two orthogonal directions. The kernels are organized in subsets where each subset has a distinct orientation. Each subset spans the entire frequency domain and provides invariance to slant, scale and limited translation. The complete set of orientations is utilized to achieve invariance to rotation and tilt. Hence, the proposed set of kernels achieve complete affine-invariance