The classiication of planar shapes using spectral features is presented in this paper. The contour of a planar shape is represented by magnitude and phase of radial vector drawn from a centroid, and they are modeled by an autoregressive process. The spectral features are extracted from the least squares estimators of model parameters , and planar shapes are classiied by an artiicial neural netw...

Face recognition is a challenging task in computer vision and pattern recognition. It is well-known that obtaining a low-dimensional feature representation with enhanced discriminatory power is of paramount importance to face recognition. Moreover, recent research has shown that the face images reside on a possibly nonlinear manifold. Thus, how to effectively exploit the hidden structure is a k...

In this paper we explore how to embed symbolic relational graphs with unweighted edges in a pattern-space. We adopt a graph-spectral approach. We use the leading eigenvectors of the graph adjacency matrix to de0ne eigenmodes of the adjacency matrix. For each eigenmode, we compute vectors of spectral properties. These include the eigenmode perimeter, eigenmode volume, Cheeger number, inter-mode ...