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.
© 2013 IEEE. Graph alignment refers to the problem of finding a bijective mapping across vertices of...
Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors...
International audienceMatching articulated shapes represented by voxelsets reduces to maximal sub-gr...
The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigen...
We present an efficient spectral method for finding consistent correspondences between two sets of f...
We present an efficient spectral method for finding consistent correspondences between two sets of f...
Finding correspondences between two related feature point sets is a basic task in computer vision an...
With the rise and advent of graph learning techniques, graph data has become ubiquitous. However, wh...
Spectral clustering uses eigenvectors of the Laplacian of the similarity matrix. They are most con...
This paper proposes to use compression-based similarity measures to cluster spectral signatures on t...
This project presents an innovative way of solving the inexact graph matching problem of weighted gr...
Abstract. We propose preprocessing spectral clustering with b-matching to remove spurious edges in t...
Besides stating the problem of image registration this chapter is built on the following parts : 1) ...
This paper addresses the problem of establishing point correspondences between two object instances ...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
© 2013 IEEE. Graph alignment refers to the problem of finding a bijective mapping across vertices of...
Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors...
International audienceMatching articulated shapes represented by voxelsets reduces to maximal sub-gr...
The modal correspondence method of Shapiro and Brady aims to match point-sets by comparing the eigen...
We present an efficient spectral method for finding consistent correspondences between two sets of f...
We present an efficient spectral method for finding consistent correspondences between two sets of f...
Finding correspondences between two related feature point sets is a basic task in computer vision an...
With the rise and advent of graph learning techniques, graph data has become ubiquitous. However, wh...
Spectral clustering uses eigenvectors of the Laplacian of the similarity matrix. They are most con...
This paper proposes to use compression-based similarity measures to cluster spectral signatures on t...
This project presents an innovative way of solving the inexact graph matching problem of weighted gr...
Abstract. We propose preprocessing spectral clustering with b-matching to remove spurious edges in t...
Besides stating the problem of image registration this chapter is built on the following parts : 1) ...
This paper addresses the problem of establishing point correspondences between two object instances ...
High dimensional data is usually produced by the source that only enjoys a limited number of degrees...
© 2013 IEEE. Graph alignment refers to the problem of finding a bijective mapping across vertices of...
Spectral clustering is a powerful method for finding structure in a dataset through the eigenvectors...
International audienceMatching articulated shapes represented by voxelsets reduces to maximal sub-gr...