Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting
Abstract
A widely used approach for locating points on deformable objects is to generate feature response images for each point, then to fit a shape model to the response images. We demonstrate that Random Forest regression can be used to generate high quality response images quickly. Rather than using a generative or a discriminative model to evaluate each pixel, a regressor is used to cast votes for the optimal position. We show this leads to fast and accurate matching when combined with a statistical shape model. We evaluate the technique in detail, and compare with a range of commonly used alternatives on several different datasets. We show that the random forest regression method is significantly faster and more accurate than equivalent discriminative, or boosted regression based methods trained on the same data.
Cite
Text
Cootes et al. "Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33786-4_21Markdown
[Cootes et al. "Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/cootes2012eccv-robust/) doi:10.1007/978-3-642-33786-4_21BibTeX
@inproceedings{cootes2012eccv-robust,
title = {{Robust and Accurate Shape Model Fitting Using Random Forest Regression Voting}},
author = {Cootes, Timothy F. and Ionita, Mircea C. and Lindner, Claudia and Sauer, Patrick},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {278-291},
doi = {10.1007/978-3-642-33786-4_21},
url = {https://mlanthology.org/eccv/2012/cootes2012eccv-robust/}
}