A generalised framework for saliency-based point feature detection

Brown, Mark, Windridge, David ORCID logoORCID: https://orcid.org/0000-0001-5507-8516 and Guillemaut, Jean-Yves (2017) A generalised framework for saliency-based point feature detection. Computer Vision and Image Understanding, 157 . pp. 117-137. ISSN 1077-3142 [Article] (doi:10.1016/j.cviu.2016.09.008)

PDF - Published version (with publisher's formatting)
Available under License Creative Commons Attribution 4.0.

Download (9MB) | Preview
[img] PDF - Final accepted version (with author's formatting)
Restricted to Repository staff and depositor only
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0.

Download (31MB)


Here we present a novel, histogram-based salient point feature detector that may naturally be applied to both images and 3D data. Existing point feature detectors are often modality specific, with 2D and 3D feature detectors typically constructed in separate ways. As such, their applicability in a 2D-3D context is very limited, particularly where the 3D data is obtained by a LiDAR scanner. By contrast, our histogram-based approach is highly generalisable and as such, may be meaningfully applied between 2D and 3D data. Using the generalised approach, we propose salient point detectors for images, and both untextured and textured 3D data. The approach naturally allows for the detection of salient 3D points based jointly on both the geometry and texture of the scene, allowing for broader applicability. The repeatability of the feature detectors is evaluated using a range of datasets including image and LiDAR input from indoor and outdoor scenes. Experimental results demonstrate a significant improvement in terms of 2D-2D and 2D-3D repeatability compared to existing multi-modal feature detectors.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 20575
Notes on copyright: Copyright: © 2016 The Authors. Published by Elsevier Inc.
This is an open access article under the CC BY license. (http://creativecommons.org/licenses/by/4.0/)
Useful Links:
Depositing User: David Windridge
Date Deposited: 23 Sep 2016 09:56
Last Modified: 02 Dec 2022 01:06
URI: https://eprints.mdx.ac.uk/id/eprint/20575

Actions (login required)

View Item View Item


Activity Overview
6 month trend
6 month trend

Additional statistics are available via IRStats2.