Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching
Abstract
We present a Mean Field Theory method for locating two(cid:173) dimensional objects that have undergone rigid transformations. The resulting algorithm is a form of coarse-to-fine correlation matching. We first consider problems of matching synthetic point data, and derive a point matching objective function. A tractable line segment matching objective function is derived by considering each line segment as a dense collection of points, and approximat(cid:173) ing it by a sum of Gaussians. The algorithm is tested on real images from which line segments are extracted and matched.
Cite
Text
Lu and Mjolsness. "Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching." Neural Information Processing Systems, 1993.Markdown
[Lu and Mjolsness. "Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching." Neural Information Processing Systems, 1993.](https://mlanthology.org/neurips/1993/lu1993neurips-twodimensional/)BibTeX
@inproceedings{lu1993neurips-twodimensional,
title = {{Two-Dimensional Object Localization by Coarse-to-Fine Correlation Matching}},
author = {Lu, Chien-Ping and Mjolsness, Eric},
booktitle = {Neural Information Processing Systems},
year = {1993},
pages = {985-992},
url = {https://mlanthology.org/neurips/1993/lu1993neurips-twodimensional/}
}