DOWNLOADSSparse Statistical Shape Modelling

 

Sparse Statistical Shape Modelling
SparseSSM

 

 
 
 
Details
Description

The provided code is based on the work by A. Gooya et al. [1] which proposes a Gaussian mixture model based approach to training statistical shape models (SSMs). The novel feature of the proposed approach is the application of a symmetric Dirichlet prior on the mixture coefficients to enforce sparsity and search over a continuous space for the optimal number of Gaussian components, to address the common issue of over or under-fitting. Additionally, we provide code to reconstruct surfaces from the unstructured point sets generated, following SSM training.

References

[1] Gooya, Ali, Christos Davatzikos, and Alejandro F. Frangi. "A bayesian approach to sparse model selection in statistical shape models." SIAM Journal on Imaging Sciences 8.2 (2015): 858-887.

File Format

TAR.GZ File

DOI

http://dx.doi.org/10.6084/m9.figshare.1562325

Platform

Linux

Size of download

3.33 MB

People who contribute to this work

Nishant Ravikumar, Department of Mechanical Engineering, The University of Sheffield
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Ali Gooya Electrical and Electronic Engineering Department, The University of Sheffield
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Alejandro F. Frangi Electrical and Electronic Engineering Department, The University of Sheffield
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