Local Feature Extraction from Salient Regions by Feature Map Transformation
Yerim Jung (Inha Univ.), NUR SURIZA SYAZWANY BINTI AHMAD NIZAM (Inha University), Sang-Chul Lee (Inha univ)*
The 33rd British Machine Vision Conference
Abstract
Local feature matching is essential for many applications, such as localization and 3D reconstruction. However, it is challenging to match feature points accurately in various camera viewpoints and illumination conditions. In this paper, we propose a framework that robustly extracts and describes salient local features regardless of changing light and viewpoints. The framework suppresses illumination variations and encourages structural information to ignore the noise from light and to focus on edges. We classify the elements in the feature covariance matrix, an implicit feature map information, into two components. Our model extracts feature points from salient regions leading to reduced incorrect matches. In our experiments, the proposed method achieved higher accuracy than the state-of-the-art methods in the public dataset, such as HPatches, Aachen Day-Night, and ETH, which especially show highly variant viewpoints and illumination.Video
Citation
@inproceedings{Jung_2022_BMVC,
author = {Yerim Jung and NUR SURIZA SYAZWANY BINTI AHMAD NIZAM and Sang-Chul Lee},
title = {Local Feature Extraction from Salient Regions by Feature Map Transformation },
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0552.pdf}
}
Copyright © 2022
The British Machine Vision Conference is organised by