Spectral Correspondence Using the TPS Deformation Model

Authors:
Jun Tang;Nian Wang;Dong Liang;Yi-Zheng Fan;Zhao-Hong Jia
Affiliations:
Key Lab of Intelligent Computing & Signal Processing Ministry of Education, Anhui University, Hefei 230039, China;Key Lab of Intelligent Computing & Signal Processing Ministry of Education, Anhui University, Hefei 230039, China;Key Lab of Intelligent Computing & Signal Processing Ministry of Education, Anhui University, Hefei 230039, China;Key Lab of Intelligent Computing & Signal Processing Ministry of Education, Anhui University, Hefei 230039, China and Department of Mathematics, Anhui University, Hefei 230039, China;Key Lab of Intelligent Computing & Signal Processing Ministry of Education, Anhui University, Hefei 230039, China
Venue:
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Year:
2007

Citing 7
Cited 0

Principal Warps: Thin-Plate Splines and the Decomposition of Deformations

IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature-based correspondence: an eigenvector approach

Image and Vision Computing - Special issue: BMVC 1991
Regularization theory and neural networks architectures

Neural Computation
Shape Matching and Object Recognition Using Shape Contexts

IEEE Transactions on Pattern Analysis and Machine Intelligence
A new point matching algorithm for non-rigid registration

Computer Vision and Image Understanding - Special issue on nonrigid image registration
Correspondence Matching with Modal Clusters

IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Point Matching for Nonrigid Shapes by Preserving Local Neighborhood Structures

IEEE Transactions on Pattern Analysis and Machine Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a novel algorithm for point correspondences using spectral graph analysis. Firstly, the correspondence probabilities are computed by using the modes of proximity matrix and the method of doubly stochastic matrix. Secondly, the TPS deformation model is introduced into the field of spectral correspondence to estimate the transformation parameters between two matched point-sets. The accuracy of correspondences is improved by bringing one point-set closer to the other in each iteration with transformation parameters estimated from the current correspondences. Experiments on both real-world and synthetic data show that our method possesses comparatively high accuracy.