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JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Aug 2024
Next issue: Nov 2024
Avg review time: 55 days
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APC: 300 EUR


PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  2/2022 - 7

Digital Video Stabilization Verification Based on Genetic Algorithm Template Matching

PAVLOVIC, M. See more information about PAVLOVIC, M. on SCOPUS See more information about PAVLOVIC, M. on IEEExplore See more information about PAVLOVIC, M. on Web of Science, BANJAC, Z. See more information about  BANJAC, Z. on SCOPUS See more information about  BANJAC, Z. on SCOPUS See more information about BANJAC, Z. on Web of Science, KOVACEVIC, B. See more information about KOVACEVIC, B. on SCOPUS See more information about KOVACEVIC, B. on SCOPUS See more information about KOVACEVIC, B. on Web of Science
 
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Download PDF pdficon (3,432 KB) | Citation | Downloads: 705 | Views: 1,440

Author keywords
formal verification, genetic algorithms, image motion analysis, multispectral imaging, pattern matching

References keywords
video(15), stabilization(11), systems(9), algorithm(8), genetic(7), algorithms(6), sensors(5), image(5), optical(4), matching(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2022-05-31
Volume 22, Issue 2, Year 2022, On page(s): 53 - 60
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2022.02007
Web of Science Accession Number: 000810486800007
SCOPUS ID: 85131721772

Abstract
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Having high precision ground-truth data is a very important factor for the development and evaluation of computer vision algorithms such as digital video stabilization. However, generating this data is time consuming and cost intensive work, requiring a lot of manual effort. In this paper we both propose a way to automatically generate a large amount of accurate data for digital video stabilization verification and provide a comprehensive dataset of video sequences taken from multi-sensor imaging system with different types of disturbances. A novel method for generating verification data is based on genetic algorithm template matching. Paper provides quantitative analysis together with the visual assessment of digital video stabilization performance.


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