The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigenvectors of a pairwise point proximity matrix. Although elegant by means of its matrix representation, the method is notoriously susceptible to differences in the relational structure of the point-sets under consideration. In this paper, we demonstrate how the method can be rendered robust to structural differences by adopting a hierarchical approach. To do this, we place the modal matching problem in a probabilistic setting in which the correspondences between pairwise clusters can be used to constrain the individual point correspondences. We demonstrate the utility of the method on a number of synthetic and real-world point-pattern matching problems.

This paper describes a hierarchical spectral method for the correspondence matching of point-sets. Conventional spectral methods for correspondence matching are notoriously susceptible to differences in the relational structure of the point-sets under consideration. In this paper we demonstrate how the method can be rendered robust to structural differences by adopting a hier-archical approach. We show how the point-clusters associated with the most significant spectral modes can be used to locate correspondences when sig-nificant contamination is present. 1

In this paper we investigate how texture recognition can be achieved through the modal analysis of the pattern of peaks in the spectral density function. We commence from a texture characterisation which is based on the positions of peaks in the power spectrum. Our aim is to use the modal structure of the pattern of peaks to perform texture retrieval from an image data-base. We explore two different approaches to the problem. First, we use a variant of the Shapiro and Brady method to perform recognition by comparing the modal structure of the proximity matrix for peak cluster centres. Second, we perform latent semantic indexing on vectors representing the polar distribution of frequency peaks. We provide and experimental evaluation of these two methods on a data-base of fabric and wrapping paper patterns.

This paper investigates the correspondence matching of point-sets using spectral graph analysis. In particular we are interested in the problem of how the modal analysis of point-sets can be rendered robust to contamination and drop-out. We make three contributions. First, we show how the modal structure of point-sets can be embedded within the framework of the EM algorithm. Second, we present several methods for computing the probabilities of point correspondences using the point proximity matrix. Third, we consider alternatives to the Gaussian proximity matrix. We evaluate the new method on both synthetic and real-world data. Here we show that the method can be used to compute useful correspondences even when the level of point contamination is as large as 50%.

This paper investigates the correspondence matching of point-sets using spectral graph analysis. In particular we deal with the problem of how the modal analysis of point-sets can be rendered robust to point movement. To this end we consider alternatives to the Gaussian proximity matrix. We evaluate the new method on both synthetic and real-world data. Here we show that the method can be used to compute useful correspondences even when the level of point movement is large.

This paper describes how 3D facial pose may be estimated by fitting a template to 2D feature locations. The fitting process is realised as projecting the control points of the 3D template onto the 2D feature locations under orthographic projection. The parameters of the orthographic projection are iteratively estimated using the EM algorithm. The method is evaluated on both contrived data with known ground-truth together with some more naturalistic imagery. These experiments reveal that under favourable conditions the algorithm can estimate facial pitch to within 3 degrees.