Top-Points as Interest Points for Image Matching
Abstract
We consider the use of top-points for object retrieval. These points are based on scale-space and catastrophe theory, and are invariant under gray value scaling and offset as well as scale-Euclidean transformations. The differential properties and noise characteristics of these points are mathematically well understood. It is possible to retrieve the exact location of a top-point from any coarse estimation through a closed-form vector equation which only depends on local derivatives in the estimated point. All these properties make top-points highly suitable as anchor points for invariant matching schemes. By means of a set of repeatability experiments and receiver-operator-curves we demonstrate the performance of top-points and differential invariant features as image descriptors.
Cite
Text
Platel et al. "Top-Points as Interest Points for Image Matching." European Conference on Computer Vision, 2006. doi:10.1007/11744023_33Markdown
[Platel et al. "Top-Points as Interest Points for Image Matching." European Conference on Computer Vision, 2006.](https://mlanthology.org/eccv/2006/platel2006eccv-top/) doi:10.1007/11744023_33BibTeX
@inproceedings{platel2006eccv-top,
title = {{Top-Points as Interest Points for Image Matching}},
author = {Platel, Bram and Balmachnova, Evguenia and Florack, Luc and ter Haar Romeny, Bart M.},
booktitle = {European Conference on Computer Vision},
year = {2006},
pages = {418-429},
doi = {10.1007/11744023_33},
url = {https://mlanthology.org/eccv/2006/platel2006eccv-top/}
}