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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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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.

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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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  4/2016 - 6

 HIGHLY CITED PAPER 

A New Autofocusing Method Based on Brightness and Contrast for Color Cameras

SELEK, M. See more information about SELEK, M. on SCOPUS See more information about SELEK, M. on IEEExplore See more information about SELEK, M. on Web of Science
 
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Download PDF pdficon (1,516 KB) | Citation | Downloads: 1,069 | Views: 374

Author keywords
CCD image sensors, digital images, focusing, image color analysis, image processing

References keywords
focus(25), image(14), measure(10), auto(8), pattern(7), autofocus(7), algorithm(7), systems(6), recognition(6), optics(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2016-11-30
Volume 16, Issue 4, Year 2016, On page(s): 39 - 44
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2016.04006
Web of Science Accession Number: 000390675900006
SCOPUS ID: 85007578320

Abstract
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The autofocusing is one of the most important features of imaging devices. This feature directly affects the quality of the image taken by the imaging device. Currently, many studies are being performed to improve the feature of autofocusing. In this study, we propose a method for passive autofocusing of the color cameras. This method suggested is called as the Passive Autofocusing Based-Brightness and Contrast (PA Based-BC). According to this method, autofocusing is performed by identifying the brightness of the R, G and B color components of the RGB image and by focusing of the camera on the brightest color component. To this end, in this study, many experiments have been conducted. The analyses of these experiments show that the contrast-based focusing made depending on the brightness gives much better results. The use of this method upgrades the focusing accuracy of the color camera up to 95%.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef]


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[15] W. Huang, Z. Jing, "Evaluation of Focus Measures in Multi-Focus Image Fusion," Pattern Recognition Letters, vol. 28, no. 4, pp. 493-500, 2007.
[CrossRef] [Web of Science Times Cited 395] [SCOPUS Times Cited 541]


[16] L. Fan, F. Song, S. Jutamulia, "Edge Detection with Large Depth of Focus Using Differential Haar-Gaussian Wavelet Transform," Optics Communications, vol. 270, no. 2, pp. 169-175, 2007.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 12]


[17] D. M. Tsai, C. C. Chou, "A Fast Focus Measure for Video Display Inspection," Machine Vision and Applications, vol. 14, no. 3, pp. 192-196, 2003.
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 11]


[18] H. C. Chang, T. M. Shih, N. Z. Chen, N. W. Pu, "A Microscope System Based on Bevel-Axial Method Auto-Focus," Optics and Lasers in Engineering, vol. 47, no. 5, pp. 547-551, 2009.
[CrossRef] [Web of Science Times Cited 25] [SCOPUS Times Cited 32]


[19] M. Selek, "An Adaptive Squared Gradient Algorithm for Autofocusing of Thermal Cameras," International Journal of Innovative Computing Information and Control, vol. 9, no. 2, pp. 841-849, 2013.

[20] I. Lee, M. T. Mahmood, T. S. Choi, "Adaptive Window Selection for 3D Shape Recovery from Image Focus," Optics and Laser Technology, vol.45, pp. 21-31, 2013.
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[22] O. Lossona, L. Macairea, Y. Yanga, "Comparison of Color Demosaicing Methods," Advances in Imaging and Electron Physics, vol. 162, pp. 173-265, 2010.
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[27] S. Podlech, "Autofocus by Bayes Spectral Entropy Applied to Optical Microscopy," Microscopy and Microanalysis, pp. 1-9, 2016.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]


[28] R. O. Panicker, B. Soman, G. Saini, J. Rajan, "A Review of Automatic Methods Based on Image Processing Techniques for Tuberculosis Detection from Microscopic Sputum Smear Images," Journal of Medical Systems, vol. 40, pp. 1-13, 2016.
[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 51]


[29] X. Zhang, H. Wu, Y. Ma, "A New Auto-Focus Measure Based on Medium Frequency Discrete Cosine Transform Filtering and Discrete Cosine Transform," Applied and Computational Harmonic Analysis, vol. 40, pp. 430-437, 2016.
[CrossRef] [Web of Science Times Cited 27] [SCOPUS Times Cited 30]


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References Weight

Web of Science® Citations for all references: 3,718 TCR
SCOPUS® Citations for all references: 4,941 TCR

Web of Science® Average Citations per reference: 89 ACR
SCOPUS® Average Citations per reference: 118 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2024-03-28 11:31 in 242 seconds.




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