Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 56 days
Avg accept to publ: 60 days
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


TRAFFIC STATS

3,030,153 unique visits
1,178,320 downloads
Since November 1, 2009



Robots online now
SemanticScholar
bingbot
DotBot
Googlebot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 4 / 2024
 
     »   Issue 3 / 2024
 
     »   Issue 2 / 2024
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

A Proposed Signal Reconstruction Algorithm over Bandlimited Channels for Wireless Communications, ASHOUR, A., KHALAF, A., HUSSEIN, A., HAMED, H., RAMADAN, A.
Issue 1/2023

AbstractPlus






LATEST NEWS

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.

Read More »


    
 

  2/2017 - 12

 HIGH-IMPACT PAPER 

Cogent Confabulation based Expert System for Segmentation and Classification of Natural Landscape Images

BRAOVIC, M. See more information about BRAOVIC, M. on SCOPUS See more information about BRAOVIC, M. on IEEExplore See more information about BRAOVIC, M. on Web of Science, STIPANICEV, D. See more information about  STIPANICEV, D. on SCOPUS See more information about  STIPANICEV, D. on SCOPUS See more information about STIPANICEV, D. on Web of Science, KRSTINIC, D. See more information about KRSTINIC, D. on SCOPUS See more information about KRSTINIC, D. on SCOPUS See more information about KRSTINIC, D. on Web of Science
 
Extra paper information in View the paper record and citations in Google Scholar View the paper record and similar papers in Microsoft Bing View the paper record and similar papers in Semantic Scholar the AI-powered research tool
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (6,154 KB) | Citation | Downloads: 971 | Views: 3,830

Author keywords
expert systems, image classification, image color analysis, image segmentation, knowledge engineering

References keywords
image(12), processing(9), vision(7), detection(7), stipanicev(6), classification(6), smoke(5), segmentation(5), jakovcevic(5), fire(5)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 85 - 94
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02012
Web of Science Accession Number: 000405378100012
SCOPUS ID: 85020089483

Abstract
Quick view
Full text preview
Ever since there has been an increase in the number of automatic wildfire monitoring and surveillance systems in the last few years, natural landscape images have been of great importance. In this paper we propose an expert system for fast segmentation and classification of regions on natural landscape images that is suitable for real-time applications. We focus primarily on Mediterranean landscape images since the Mediterranean area and areas with similar climate are the ones most associated with high wildfire risk. The proposed expert system is based on cogent confabulation theory and knowledge bases that contain information about local and global features, optimal color spaces suitable for classification of certain regions, and context of each class. The obtained results indicate that the proposed expert system significantly outperforms well-known classifiers that it was compared against in both accuracy and speed, and that it is effective and efficient for real-time applications. Additionally, we present a FESB MLID dataset on which we conducted our research and that we made publicly available.


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

[1] M. Bugaric, T. Jakovcevic, D. Stipanicev, "Adaptive estimation of visual smoke detection parameters based on spatial data and fire risk index", Computer Vision and Image Understanding, vol. 118, pp. 184-196, 2014.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 21]


[2] D. Krstinic, T. Jakovcevic, D. Stipanicev, "Histogram-based smoke segmentation in forest fire detection system", Information Technology and Control, vol. 38, no. 3, pp. 237-244, 2009.

[3] T. Jakovcevic, D. Stipanicev, D. Krstinic, "Visual spatial-context based wildfire smoke sensor", Machine Vision and Applications, vol. 24, issue 4, pp. 707-719, May 2013.
[CrossRef] [Web of Science Times Cited 21] [SCOPUS Times Cited 22]


[4] T. Jakovcevic, "Wildfire-smoke detection based on visible-spectrum image analysis" (In Croatian: "Detekcija dima pozara raslinja analizom slika dobivenih u vidljivom dijelu spektra"), doctoral dissertation, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Croatia, 2011.

[5] R. Hecht-Nielsen, "Cogent Confabulation", Neural Networks, vol. 18, no. 2, pp. 111-115, March 2005.
[CrossRef] [Web of Science Times Cited 39] [SCOPUS Times Cited 43]


[6] R. Hecht-Nielsen, "The Mechanism of Thought", International Joint Conference on Neural Networks, Vancouver, Canada, pp. 419-426, July, 16-21 2006.
[CrossRef] [SCOPUS Times Cited 21]


[7] E. A. Khan, E. Reinhard, "Evaluation of color spaces for edge classification in outdoor scenes", IEEE International Conference on Image Processing, vol. 3, pp. 952-5, 2005.
[CrossRef] [SCOPUS Times Cited 23]


[8] J. M. Chaves-Gonzalez, M. A. Vega-Rodriguez, J. A. Gomez-Pulido, J. M. Sanchez-Perez, "Detecting skin in face recognition systems: A colour spaces study", Digital Signal Processing, vol. 20, issue 3, pp. 806-823, 2010.
[CrossRef] [Web of Science Times Cited 128] [SCOPUS Times Cited 183]


[9] Y.-C. Wang, C.-C. Han, C.-T. Hsieh, K.-C. Fan, "Vehicle color classification using manifold learning methods from urban surveillance videos", EURASIP Journal on Image and Video Processing, 2014.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 2]


[10] H. Stokman, T. Gevers, "Selection and fusion of color models for image feature detection", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, pp. 371-381, 2007.
[CrossRef] [Web of Science Times Cited 80] [SCOPUS Times Cited 101]


[11] A. Bosch, X. Munoz, J. Freixenet, "Segmentation and description of natural outdoor scenes", Image and Vision Computing, vol. 25, issue 5, pp. 727-740, 2007.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 51]


[12] J. Marti, J. Freixenet, J. Batlle, A. Casals, "A new approach to outdoor scene description based on learning and top-down segmentation", Image and Vision Computing, vol. 19, issue 14, pp. 1041-1055, 2001.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 14]


[13] V. Burak Celen, M. Fatih Demirci, "Fire detection in different color models", Proceedings of the 2012 International Conference on Image Processing, Computer Vision, & Pattern Recognition, 2012.

[14] T. Çelik, H. Ozkaramanli, H. Demirel, "Fire and smoke detection without sensors: image processing based approach", 15th European Signal Processing Conference (EUSIPCO 2007), pp. 1794-1798, 2007.

[15] J. J. de Dios, N. Garcia, "Face detection based on a new color space YCgCr", International Conference on Image Processing, pp. III-909-III-912, 2003.
[CrossRef]


[16] Y.-I. Ohta, T. Kanade, T. Sakai, "Color information for region segmentation", Computer Graphics and Image Processing, vol. 13, pp. 222-241, 1980.

[17] D. Stipanicev, Lj. Seric, M. Braovic, D. Krstinic, T. Jakovcevic, M. Štula, M. Bugaric, J. Maras, "Vision based wildfire and natural risk observers", 2012 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 37-42, 15-18 October 2012.
[CrossRef] [SCOPUS Times Cited 10]


[18] M. Sokolova, N. Japkowicz, S. Szpakowicz, "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation", AI 2006: Advances in Artificial Intelligence: 19th Australian Joint Conference on Artificial Intelligence. Lecture Notes in Computer Science, vol. 4304, pp. 1015-1021, 2006.
[CrossRef]


[19] D. Stipanicev, "Intelligent forest fire monitoring system - from idea to realization", Annual 2010/2011 of the Croatian Academy of Engineering, pp. 58-73, 2012.

[20] T. Roncevic, M. Braovic, D. Stipanicev, "Non-parametric context-based object classification in images", Information Technology and Control. vol. 46, no. 1, pp. 86-99, 2017.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]


[21] M. Braovic, "Segmentation and classification of non-transparent and semi-transparent regions on natural landscape images" (In Croatian: Segmentacija i klasifikacija neprozirnih i poluprozirnih regija na slikama prirodnog krajolika), doctoral dissertation, Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Croatia, 2015.

[22] M. Braovic, "Color based region classification in Mediterranean landscape images", Abstract Book - Fourth Croatian Computer Vision Workshop / Editors: Sven Loncaric and Josip Krapac, Zagreb, 2015.

[23] M. Braovic, "Color-based region classification in Mediterranean landscape images", The 2nd ACROSS Workshop on Advanced Cooperative Systems, Poster Session, Zagreb, Croatia, 2016.



References Weight

Web of Science® Citations for all references: 337 TCR
SCOPUS® Citations for all references: 494 TCR

Web of Science® Average Citations per reference: 14 ACR
SCOPUS® Average Citations per reference: 21 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 2024-12-02 11:10 in 102 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy