Evidence map›Paper›PMID 39330448›Full record

ArticleJournal of imaging2024

A Hybrid Approach for Image Acquisition Methods Based on Feature-Based Image Registration.

Anchal Kumawat, Sucheta Panda, Vassilis C Gerogiannis, Andreas Kanavos, Biswaranjan Acharya, Stella Manika

Abstract read
In one paragraph

Article in Journal of imaging, 2024. 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

6 authors.

Anchal KumawatDepartment of Computer Science Engineering and Application, Sambalpur University Institute of Information Technology (SUIIT), Burla, Sambalpur 768018, India.
Sucheta PandaSchool of Computer Sciences, Veer Surendra Sai University of Technology (VSSUT), Burla, Sambalpur 768018, India.
Vassilis C GerogiannisDepartment of Digital Systems, University of Thessaly, 41500 Larissa, Greece.ORCID 0000-0002-9895-7606
Andreas KanavosDepartment of Informatics, Ionian University, 49100 Corfu, Greece.ORCID 0000-0002-9964-4134
Biswaranjan AcharyaDepartment of Computer Engineering-AI, Marwadi University, Rajkot 360003, India.ORCID 0000-0002-6506-9207
Stella ManikaDepartment of Planning and Regional Development, University of Thessaly, 38334 Volos, Greece.ORCID 0000-0001-8790-9409

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This paper presents a novel hybrid approach to feature detection designed specifically for enhancing Feature-Based Image Registration (FBIR). Through an extensive evaluation involving state-of-the-art feature detectors such as BRISK, FAST, ORB, Harris, MinEigen, and MSER, the proposed hybrid detector demonstrates superior performance in terms of keypoint detection accuracy and computational efficiency. Three image acquisition methods (i.e., rotation, scene-to-model, and scaling transformations) are considered in the comparison. Applied across a diverse set of remote-sensing images, the proposed hybrid approach has shown marked improvements in match points and match rates, proving its effectiveness in handling varied and complex imaging conditions typical in satellite and aerial imagery. The experimental results have consistently indicated that the hybrid detector outperforms conventional methods, establishing it as a valuable tool for advanced image registration tasks.

Indexed as

binary robust invariant scalable keypoints (BRISK)feature detectionfeatures from accelerated segment test (FAST)hybrid feature detectorimage registrationmaximally stable extremal regions (MSER)oriented FAST and rotated BRIEF (ORB)rotation invariancescale invariance

Identifiers

PMID39330448
PMCPMC11433287

What OpenQuestion holds

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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.