Evidence map›Paper›PMID 32260068›Full record

ArticleSensors (Basel, Switzerland)2020

Fast Anchor Point Matching for Emergency UAV Image Stitching Using Position and Pose Information.

Ruizhe Shao, Chun Du, Hao Chen, Jun Li

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Ruizhe ShaoDepartment of Cognitive Communication, College of Electronic Science and Technology, National University of Defense Technology, Changsha, Hunan 410000, China.
Chun DuDepartment of Cognitive Communication, College of Electronic Science and Technology, National University of Defense Technology, Changsha, Hunan 410000, China.
Hao ChenDepartment of Cognitive Communication, College of Electronic Science and Technology, National University of Defense Technology, Changsha, Hunan 410000, China.
Jun LiDepartment of Cognitive Communication, College of Electronic Science and Technology, National University of Defense Technology, Changsha, Hunan 410000, China.

Funding

National Natural Science Foundation of China 61806211
6 · The paper itself

Abstract

With the development of unmanned aerial vehicle (UAV) techniques, UAV images are becoming more widely used. However, as an essential step of UAV image application, the computation of stitching remains time intensive, especially for emergency applications. Addressing this issue, we propose a novel approach to use the position and pose information of UAV images to speed up the process of image stitching, called FUIS (fast UAV image stitching). This stitches images by feature points. However, unlike traditional approaches, our approach rapidly finds several anchor-matches instead of a lot of feature matches to stitch the image. Firstly, from a large number of feature points, we design a method to select a small number of them that are more helpful for stitching as anchor points. Then, a method is proposed to more quickly and accurately match these anchor points, using position and pose information. Experiments show that our method significantly reduces the time consumption compared with the-state-of-art approaches with accuracy guaranteed.

Indexed as

anchor pointsfeature pointsimage stitchingUAV

Identifiers

PMID32260068
PMCPMC7181040

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.