Home

About

Advanced Search

Browse by Discipline

Scientific Societies

E-print Alerts

Add E-prints

E-print Network
FAQHELPSITE MAPCONTACT US


  Advanced Search  

 
3D Point Correspondence by Minimum Description Length with 2DPCA Jiun-Hung Chen and Linda G. Shapiro
 

Summary: 3D Point Correspondence by Minimum Description Length with 2DPCA
Jiun-Hung Chen and Linda G. Shapiro
Abstract-- Finding point correspondences plays an important
role in automatically building statistical shape models from a
training set of 3D surfaces. Davies et al. assumed the projected
coefficients have a multivariate Gaussian distributions and
derived an objective function for the point correspondence
problem that uses minimum description length to balance the
training errors and generalization ability.
Recently, two-dimensional principal component analysis has
been shown to achieve better performance than PCA in face
recognition. Motivated by the better performance of 2DPCA,
we generalize the MDL-based objective function to 2DPCA in
this paper. We propose a gradient descent approach to minimize
the objective function. We evaluate the generalization abilities
of the proposed and original methods in terms of reconstruction
errors. From our experimental results on different sets of 3D
shapes of different human body organs, the proposed method
performs significantly better than the original method.
I. INTRODUCTION

  

Source: Anderson, Richard - Department of Computer Science and Engineering, University of Washington at Seattle

 

Collections: Computer Technologies and Information Sciences