Rank Minimization Across Appearance and Shape for AAM Ensemble Fitting
Abstract
Active Appearance Models (AAMs) employ a paradigm of inverting a synthesis model of how an object can vary in terms of shape and appearance. As a result, the ability of AAMs to register an unseen object image is intrinsically linked to two factors. First, how well the synthesis model can reconstruct the object image. Second, the degrees of freedom in the model. Fewer degrees of freedom yield a higher likelihood of good fitting performance. In this paper we look at how these seemingly contrasting factors can complement one another for the problem of AAM fitting of an ensemble of images stemming from a constrained set (e.g. an ensemble of face images of the same person).
Cite
Text
Cheng et al. "Rank Minimization Across Appearance and Shape for AAM Ensemble Fitting." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.77Markdown
[Cheng et al. "Rank Minimization Across Appearance and Shape for AAM Ensemble Fitting." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/cheng2013iccv-rank/) doi:10.1109/ICCV.2013.77BibTeX
@inproceedings{cheng2013iccv-rank,
title = {{Rank Minimization Across Appearance and Shape for AAM Ensemble Fitting}},
author = {Cheng, Xin and Sridharan, Sridha and Saragih, Jason and Lucey, Simon},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.77},
url = {https://mlanthology.org/iccv/2013/cheng2013iccv-rank/}
}