Kullback Leibler divergence based curve matching method

Authors:
Pengwen Chen;Yunmei Chen;Murali Rao
Affiliations:
Department of Mathematics, University of Florida, Gainesville, FL;Department of Mathematics, University of Florida, Gainesville, FL;Department of Mathematics, University of Florida, Gainesville, FL
Venue:
SSVM'07 Proceedings of the 1st international conference on Scale space and variational methods in computer vision
Year:
2007

Citing 13
Cited 1

Using Dynamic Programming for Solving Variational Problems in Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Elements of information theory

Elements of information theory
Computable elastic distances between shapes

SIAM Journal on Applied Mathematics
Data structures and algorithms for nearest neighbor search in general metric spaces

SODA '93 Proceedings of the fourth annual ACM-SIAM Symposium on Discrete algorithms
Relative Loss Bounds for On-Line Density Estimation with the Exponential Family of Distributions

Machine Learning
On Aligning Curves

IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking Points on Deformable Objects Using Curvature Information

ECCV '92 Proceedings of the Second European Conference on Computer Vision
Analysis of Planar Shapes Using Geodesic Paths on Shape Spaces

IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Mathematical Models in Computer Vision

Handbook of Mathematical Models in Computer Vision
Clustering with Bregman Divergences

The Journal of Machine Learning Research
Cursive script recognition by elastic matching

IBM Journal of Research and Development
Shape matching by variational computation of geodesics on a manifold

DAGM'06 Proceedings of the 28th conference on Pattern Recognition
A new metric for probability distributions

IEEE Transactions on Information Theory

A Novel Kernel Correlation Model with the Correspondence Estimation

Journal of Mathematical Imaging and Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a variational model for curve matching based on Kullback-Leibler (KL) divergence. This framework accomplishes the difficult task of finding correspondences for a group of curves simultaneously in a symmetric and transitive fashion. Moreover the distance in the energy functional has the metric property. We also introduce a location weighted model to handle noise, distortion and occlusion. Numerical results indicate the effective of this framework. The existence of this model is also provided.