Minimum description length shape model based on elliptic fourier descriptors

Authors:
Shaoyu Wang;Feihu Qi;Huaqing Li
Affiliations:
Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China;Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, P.R. China
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

Citing 1
Cited 1

3D Statistical Shape Models Using Direct Optimisation of Description Length

ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III

Recursive computation of moments of 2D objects represented by elliptic Fourier descriptors

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper provides the construction of statistical shape model based on elliptic Fourier transformation and minimum description length (MDL). The method does not require manual identification of landmarks on training shapes. Each training shapes can be decomposed into a set of ellipse by elliptic Fourier transformation at a different frequency level. The MDL objective function is based on elliptic Fourier descriptors and principal component analysis (EF-PCA). Experiments show that our method can get better models.