Fourier–Bessel rotational invariant eigenimages

Fourier-Bessel rotational invariant eigenimages

We present an efficient and accurate algorithm for principal component analysis (PCA) of a large set of two-dimensional images and, for each image, the set of its uniform rotations in the plane and its reflection. The algorithm starts by expanding each image, originally given on a Cartesian grid, in the Fourier-Bessel basis for the disk. Because the images are essentially band limited in the Fo...

Eigenimages and multivariate analyses

Painting Recognition Using Eigenimages

We propose a method for identifying a painting in an image using eigenimages. We first pre-process the image to remove the background and then correct for skew and scaling. We then apply the method of eigenimages on de-meaned images. The algorithm correctly matched 97% of the images in a test database of 110 images, with an execution time of 3.6 s per image.

Robust Recognition Using Eigenimages

The basic limitations of the standard appearance-based matching methods using eigenimages are nonrobust estimation of coefficients and inability to cope with problems related to outliers, occlusions, and varying background. In this paper we present a new approach which successfully solves these problems. The major novelty of our approach lies in the way the coefficients of the eigenimages are d...

Panoramic Eigenimages for Spatial Localisation

Recent biological evidence suggests that position and orientation can be estimated from an adequately compressed set of environment snapshots and their relationships. In this paper we present a pure appearance-based localisation method using an eigenspace representation of panoramic images. We rst review several types of rotational invariant representation of panoramic images in terms of their ...