This paper presents a framework for shape matching and classification through scalespace decomposition of 3D models. The algorithm is based on recent developments in efficient hierarchical decomposition of a point distribution in metric space ~p,d! using its spectral properties. Through spectral decomposition, we reduce the problem of matching to that of computing a mapping and distance measure...

Principal component analysis (PCA) is possibly one of the most widely used statistical tools to recover a low rank structure of the data. In the high-dimensional settings, the leading eigenvector of the sample covariance can be nearly orthogonal to the true eigenvector. A sparse structure is then commonly assumed along with a low rank structure. Recently, minimax estimation rates of sparse PCA ...

background: the second processing cycle for adding the artificial teeth to heat-polymerized acrylic resin denture bases may result in dimensional changes of the denture bases. the aim of thisstudy was to evaluate the dimensional changes of the heat-polymerized acrylic resin denture bases with one and two-cycle processing methods. materials and methods: a metal edentulous maxillary arch was used...

Clustering in high-dimensional spaces is a difficult problem which is recurrent in many domains, for example in image analysis. The difficulty is due to the fact that highdimensional data usually live in different low-dimensional subspaces hidden in the original space. This paper presents a family of Gaussian mixture models designed for highdimensional data which combine the ideas of subspace c...

This paper considers the subspace clustering problem where the data contains irrelevant or corrupted features. We propose a method termed “robust Dantzig selector” which can successfully identify the clustering structure even with the presence of irrelevant features. The idea is simple yet powerful: we replace the inner product by its robust counterpart, which is insensitive to the irrelevant f...

In this paper, we present a robust normal estimation algorithm based on the low-rank subspace clustering technique. The main idea is based on the observation that compared with the points around sharp features, it is relatively easier to obtain accurate normals for the points within smooth regions. The points around sharp features and smooth regions are identified by covariance analysis of thei...

We present a linear three-dimensional modeling paradigm for lips and face, that captures the audiovisual speech activity of a given speaker by only six parameters. Our articulatory models are constructed from real data (front and profile images), using a linear component analysis of about 200 3D coordinates of fleshpoints on the subject's face and lips. Compared to a raw component analysis, our...

In this work an acoustic application is studied. The goal is to estimate the complex-valued admittance from given point measurements of the sound pressure. This parameter identification problem is formulated in terms of an infinite-dimensional optimization problem. First-and second-order optimality conditions are discussed. For the numerical realization a reduced-order model based on proper ort...