Boosting Chamfer Matching by Learning Chamfer Distance Normalization
Abstract
We propose a novel technique that significantly improves the performance of oriented chamfer matching on images with cluttered background. Different to other matching methods, which only measures how well a template fits to an edge map, we evaluate the score of the template in comparison to auxiliary contours, which we call normalizers. We utilize AdaBoost to learn a Normalized Oriented Chamfer Distance (NOCD). Our experimental results demonstrate that it boosts the detection rate of the oriented chamfer distance. The simplicity and ease of training of NOCD on a small number of training samples promise that it can replace chamfer distance and oriented chamfer distance in any template matching application.
Cite
Text
Ma et al. "Boosting Chamfer Matching by Learning Chamfer Distance Normalization." European Conference on Computer Vision, 2010. doi:10.1007/978-3-642-15555-0_33Markdown
[Ma et al. "Boosting Chamfer Matching by Learning Chamfer Distance Normalization." European Conference on Computer Vision, 2010.](https://mlanthology.org/eccv/2010/ma2010eccv-boosting/) doi:10.1007/978-3-642-15555-0_33BibTeX
@inproceedings{ma2010eccv-boosting,
title = {{Boosting Chamfer Matching by Learning Chamfer Distance Normalization}},
author = {Ma, Tianyang and Yang, Xingwei and Latecki, Longin Jan},
booktitle = {European Conference on Computer Vision},
year = {2010},
pages = {450-463},
doi = {10.1007/978-3-642-15555-0_33},
url = {https://mlanthology.org/eccv/2010/ma2010eccv-boosting/}
}