Local Feature Extraction from Salient Regions by Feature Map Transformation

Yerim Jung, Nur Suriza Syazwany, Sang Chul Lee

Research output: Contribution to conferencePaperpeer-review

1 Scopus citations

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.

Original languageEnglish
StatePublished - 2022
Event33rd British Machine Vision Conference Proceedings, BMVC 2022 - London, United Kingdom
Duration: 21 Nov 202224 Nov 2022

Conference

Conference33rd British Machine Vision Conference Proceedings, BMVC 2022
Country/TerritoryUnited Kingdom
CityLondon
Period21/11/2224/11/22

Bibliographical note

Publisher Copyright:
© 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

Fingerprint

Dive into the research topics of 'Local Feature Extraction from Salient Regions by Feature Map Transformation'. Together they form a unique fingerprint.

Cite this