Evidence map›Paper›PMID 36662103›Full record

ArticleJournal of imaging2022

A Parallax Image Mosaic Method for Low Altitude Aerial Photography with Artifact and Distortion Suppression.

Jing Xu, Dandan Zhao, Zhengwei Ren, Feiran Fu, Yuxin Sun, Ming Fang

Abstract read
In one paragraph

Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

6 authors.

Jing XuSchool of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
Dandan ZhaoSchool of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.
Zhengwei RenSchool of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China.
Feiran FuSchool of Artificial Intelligence, Changchun University of Science and Technology, Changchun 130022, China.
Yuxin SunScience and Technology on Electro-Optical Information Security Control Laboratory, Tianjin 300308, China.
Ming FangSchool of Computer Science and Technology, Changchun University of Science and Technology, Changchun 130022, China.

Funding

Key Laboratory Fund 2021JCJQLB055011
6 · The paper itself

Abstract

In this paper, we propose an aerial images stitching method based on an as-projective-as-possible (APAP) algorithm, aiming at the problem artifacts, distortions, or stitching failure due to fewer feature points for multispectral aerial image with certain parallax. Our method incorporates accelerated nonlinear diffusion algorithm (AKAZE) into APAP algorithm. First, we use the fast and stable AKAZE to extract the feature points of aerial images, and then, based on the registration model of the APAP algorithm, we add line protection constraints, global similarity constraints, and local similarity constraints to protect the image structure information, to produce a panorama. Experimental results on several datasets demonstrate that proposed method is effective when dealing with multispectral aerial images. Our method can suppress artifacts, distortions, and reduce incomplete splicing. Compared with state-of-the-art image stitching methods, including APAP and adaptive as-natural-as-possible image stitching (AANAP), and two of the most popular UAV image stitching tools, Pix4D and OpenDroneMap (ODM), our method achieves them both quantitatively and qualitatively.

Indexed as

aerial imagefeature point extractionimage mosaicmultispectralstitching model

Identifiers

PMID36662103
PMCPMC9863664

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.