Harita Üzerinde Sembol Tanima Için Test Otomasyonu

Fatmanur Turhan; Levent Çarkacioǧlu; Behçet Uǧur TöreyIn

doi:10.1109/SIU59756.2023.10223831

Harita Üzerinde Sembol Tanima Için Test Otomasyonu

Fatmanur Turhan, Levent Çarkacioǧlu, Behçet Uǧur TöreyIn

Bilişim Enstitüsü

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

Özet

In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

Tercüme edilen katkı başlığı	Test Automation for Symbol Recognition on the Map
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9798350343557
DOI'lar	https://doi.org/10.1109/SIU59756.2023.10223831
Yayın durumu	Yayınlandı - 2023
Etkinlik	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 - Istanbul, Turkey Süre: 5 Tem 2023 → 8 Tem 2023

Yayın serisi

Adı	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	5/07/23 → 8/07/23

Bibliyografik not

Publisher Copyright:
© 2023 IEEE.

Keywords

Convolutional Neural Network
Feature Extraction
Object Detection
Software Testing
Support Vector Machines
Symbol Recognition
Template Matching

Belgeye Erişim

10.1109/SIU59756.2023.10223831

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Turhan, F., Çarkacioǧlu, L., & TöreyIn, B. U. (2023). Harita Üzerinde Sembol Tanima Için Test Otomasyonu. In 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU59756.2023.10223831

@inproceedings{3d29a68107e4438597566e5a8fa364d1,

title = "Harita {\"U}zerinde Sembol Tanima I{\c c}in Test Otomasyonu",

abstract = "In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.",

keywords = "Convolutional Neural Network, Feature Extraction, Object Detection, Software Testing, Support Vector Machines, Symbol Recognition, Template Matching",

author = "Fatmanur Turhan and Levent {\c C}arkacioǧlu and T{\"o}reyIn, {Beh{\c c}et Uǧur}",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023 ; Conference date: 05-07-2023 Through 08-07-2023",

year = "2023",

doi = "10.1109/SIU59756.2023.10223831",

language = "T{\"u}rk{\c c}e",

series = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023",

address = "United States",

}

Turhan, F, Çarkacioǧlu, L & TöreyIn, BU 2023, Harita Üzerinde Sembol Tanima Için Test Otomasyonu. in 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Institute of Electrical and Electronics Engineers Inc., 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023, Istanbul, Turkey, 5/07/23. https://doi.org/10.1109/SIU59756.2023.10223831

Harita Üzerinde Sembol Tanima Için Test Otomasyonu. / Turhan, Fatmanur; Çarkacioǧlu, Levent; TöreyIn, Behçet Uǧur.
31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Harita Üzerinde Sembol Tanima Için Test Otomasyonu

AU - Turhan, Fatmanur

AU - Çarkacioǧlu, Levent

AU - TöreyIn, Behçet Uǧur

PY - 2023

Y1 - 2023

N2 - In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

AB - In this study, various machine learning and image analysis approaches such as Template Matching, HOG, SVM, Faster RCNN and YOLO are examined and compared for the symbol recognition problem in color maps. Some difficulties were identified regarding the forms of the symbols, the complexity of the maps or the placement of the symbols on the map. Observations about the success or failure of the methods against the difficulties defined according to the experiments are presented. It has been observed that methods involving artificial neural networks are more successful when performing symbol recognition on color maps. The highest result was obtained with Faster RCNN as 91%.

KW - Convolutional Neural Network

KW - Feature Extraction

KW - Object Detection

KW - Software Testing

KW - Support Vector Machines

KW - Symbol Recognition

KW - Template Matching

UR - http://www.scopus.com/inward/record.url?scp=85173432800&partnerID=8YFLogxK

U2 - 10.1109/SIU59756.2023.10223831

DO - 10.1109/SIU59756.2023.10223831

M3 - Konferans katkısı

AN - SCOPUS:85173432800

T3 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

BT - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 31st IEEE Conference on Signal Processing and Communications Applications, SIU 2023

Y2 - 5 July 2023 through 8 July 2023

ER -

Harita Üzerinde Sembol Tanima Için Test Otomasyonu

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

Keywords

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap