Marco Filax, Tim Gonschorek, Frank Ortmeier: QuadSIFT: Unwrapping Planar Quadrilaterals to Enhance Feature Matching. In: Proceedings of the 25rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2017 - Short Papers Proceedings, 2017.

Abstract

Feature matching is one of the fundamental issues in computer vision. The established methods, however, do not provide reliable results, especially for extreme viewpoint changes. Different approaches have been proposed to lower this hurdle, e. g., by randomly sampling different viewpoints to obtain better results. However, these methods are computationally intensive. In this paper, we propose an algorithm to enhance image matching under the assumption that an image, taken in man-made environments, typically contains planar, rectangular objects. We use line segments to identify image patches and compute a homography which unwraps the perspective distortion for each patch. The unwrapped image patches are used to detect, describe and match SIFT features.
We evaluate our results on a series of slanted views of a magazine and augmented reality markers. Our results demonstrate, that the proposed algorithm performs well for strong perspective distortions.

BibTeX (Download)

@inproceedings{filax17-wscg,
title = {QuadSIFT: Unwrapping Planar Quadrilaterals to Enhance Feature Matching},
author = {Marco Filax and Tim Gonschorek and Frank Ortmeier},
url = {http://wscg.zcu.cz/wscg2017/short/I07-full.PDF
https://cse.cs.ovgu.de/cse-wordpress/wp-content/uploads/2017/10/wscg2017_FilaxEtAl_QuadSIFT.pdf},
year  = {2017},
date = {2017-01-01},
booktitle = {Proceedings of the 25rd International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, WSCG 2017 - Short Papers Proceedings},
volume = {25},
abstract = {Feature matching is one of the fundamental issues in computer vision. The established methods, however, do not provide reliable results, especially for extreme viewpoint changes. Different approaches have been proposed to lower this hurdle, e. g., by randomly sampling different viewpoints to obtain better results. However, these methods are computationally intensive. In this paper, we propose an algorithm to enhance image matching under the assumption that an image, taken in man-made environments, typically contains planar, rectangular objects. We use line segments to identify image patches and compute a homography which unwraps the perspective distortion for each patch. The unwrapped image patches are used to detect, describe and match SIFT features.
We evaluate our results on a series of slanted views of a magazine and augmented reality markers. Our results demonstrate, that the proposed algorithm performs well for strong perspective distortions.},
keywords = {Feature Detection, Perspective Distortion, Projective Transformation, SIFT},
pubstate = {published},
tppubtype = {inproceedings}
}