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Updated: Oct 22, 2025

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

A Methodology for Capturing Joint Visual Attention Using Mobile Eye-Trackers

Published on: January 18, 2020

Improved Visual Localization via Graph Filtering.

Carlos Lassance¹, Yasir Latif², Ravi Garg²

¹Electronics Department, IMT Atlantique, 29280 Brest, France.

Journal of Imaging

|August 30, 2021

Summary

This summary is machine-generated.

This study enhances vision-based localization by using a graph-based framework that incorporates GPS and temporal data. The method improves camera pose accuracy and image retrieval performance.

Keywords:

deep learning graph signal processing image retrieval transfer learning visual localization

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Area of Science:

Computer Vision
Robotics
Geospatial Analysis

Background:

Vision-based localization estimates camera pose from images.
Image retrieval methods compare query images to a database for localization.
Existing methods struggle with variations in capture conditions.

Purpose of the Study:

To enhance retrieval-based vision-based localization performance.
To improve camera pose accuracy and reliability.
To boost mean average precision in image retrieval.

Main Methods:

Developed a framework integrating GPS coordinates and temporal proximity.
Constructed a graph using this additional information.
Filtered feature representations of images via the graph to enhance retrieval.

Main Results:

Significantly improved localization accuracy on two large-scale datasets.
Demonstrated enhanced mean average precision in image retrieval.
Showcased the framework's effectiveness in improving retrieval reliability.

Conclusions:

The proposed graph-based framework effectively enhances vision-based localization.
Integrating auxiliary data like GPS and temporal information improves robustness.
The method offers a significant advancement for retrieval-based localization systems.