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Year 2022, Volume: 6 Issue: 2, 259 - 278, 31.12.2022

Ceren Akman , Ergun Gumus

https://doi.org/10.38088/jise.1061166

Abstract

References

  • [1] Azuma, R. T. (1997). A Survey Of Augmented Reality. Presence: Teleoperators & Virtual Environments, 6(4), 355-385.
  • [2] Milgram, P., & Kishino, F. (1994). A Taxonomy Of Mixed Reality Visual Displays.Ieice Transactions On Information And Systems, 77(12), 1321-1329.
  • [3] Ufkes, A., & Fiala, M. (2013, May). A Markerless Augmented Reality System For Mobile Devices. In 2013 International Conference On Computer And Robot Vision (pp. 226-233). IEEE.
  • [4] Gonzato, J. C., Arcila, T., & Crespin, B. (2008). Virtual Objects On Real Oceans. In Graphicon'2008 (Pp. 49-54).
  • [5] Keskin, N. Ö., & Kilinç, A. G. H. (2015). Mobil Öğrenme Uygulamalarına Yönelik Geliştirme Platformlarının Karşılaştırılması ve Örnek Uygulamalar. Açıköğretim Uygulamaları Ve Araştırmaları Dergisi, 1(3), 68-90.
  • [6] Korucu, A. T., & Biçer, H. (2019). Mobil Öğrenme: 2010-2017 Çalışmalarına Yönelik Bir İçerik Analizi. Trakya Eğitim Dergisi, 9(1), 32-43.
  • [7] Avcı, A. F., & Taşdemir, Ş. (2019). Artırılmış Ve Sanal Gerçeklik ile Periyodik Cetvel Öğretimi. Selcuk University Journal Of Engineering Sciences, 18(2), 68-83.
  • [8] Uzun, Y., Bilban, M., & Kalaç, M. Ö. (2018). Artırılmış Gerçeklik Kullanılarak Engelli Çocukların Öğrenme Yeteneklerinin Geliştirilmesi. Uluslararası Engelsiz Bilişim Kongresi.
  • [9] Schall, G., Grabner, H., Grabner, M., Wohlhart, P., Schmalstieg, D., & Bischof, H. (2008, June). 3d Tracking In Unknown Environments Using On-Line Keypoint Learning For Mobile Augmented Reality. In 2008 IEEE Computer Society Conference On Computer Vision And Pattern Recognition Workshops (Pp. 1-8). IEEE.
  • [10] Kağan, G. Ü. L., & Şahin, S. (2017). Bilgisayar Donanım Öğretimi Için Artırılmış Gerçeklik Materyalinin Geliştirilmesi ve Etkililiğinin Incelenmesi. Bilişim Teknolojileri Dergisi, 10(4), 353-362.
  • [11] Içten, T., & Güngör, B. A. L. (2017). Artırılmış Gerçeklik Üzerine Son Gelişmelerin ve Uygulamaların Incelenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 111-136.
  • [12] Kaleci, D., Demirel, T., & Akkuş, I. (2016). Örnek Bir Artırılmış Gerçeklik Uygulaması Tasarımı. xviii. Akademik Bilişim Konferansı, Aydın, Türkiye.
  • [13] Lowe, D. G. (2004). Distinctive Image Features From Scale-Invariant Keypoints. International Journal Of Computer Vision, 60(2), 91-110.
  • [14] https://docs.opencv.org/3.4/da/df5/tutorial_py_sift_intro.html [Accessed: April 4th, 2021].
  • [15] Bay, H., & Tuytelaars, T. (2006). Luc Van Gool. Surf: Speeded Up Robust Features, 14.
  • [16] Evans, C. (2009). Notes On The Opensurf Library. University Of Bristol. Tech. Rep.
  • [17] Li, A., Jiang, W., Yuan, W., Dai, D., Zhang, S., & Wei, Z. (2017). An Improved Fast+ Surf Fast Matching Algorithm. Procedia Computer Science, 107, 306-312.
  • [18] Viswanathan, D. G. (2011). Features From Accelerated Segment Test (Fast) Deepak Geetha Viswanathan 1.
  • [19] Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010, September). Brief: Binary Robust Independent Elementary Features. In European Conference On Computer Vision (Pp. 778-792). Springer, Berlin, Heidelberg.
  • [20] Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011, November). Orb: An Efficient Alternative To Sift Or Surf. In 2011 International Conference On Computer Vision (Pp. 2564-2571). IEEE.
  • [21] Rosin, P. L. (1999). Measuring Corner Properties. Computer Vision And Image Understanding, 73(2), 291-307.
  • [22] Muja, M. (2009). Approximate Nearest Neighbors With Automatic Algorithm Configuration. In Proc. Int. Conf. On Computer Vision Theory And Applications, Lisbon, 2009.
  • [23] https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html [Accessed: Augustth 29, 2021].
  • [24] Fischler, M. A., & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm For Model Fitting With Applications To Image Analysis And Automated Cartography. Communications Of The Acm, 24(6), 381-395.
  • [25] Dihkan, M. (2019). Uzaktan Algılanmış Görüntülerin Surf Özellik Verileri Ve Ransac Algoritması Ile Otomatik Çakıştırılması. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(3), 425-432.
  • [26] Erdöl, E. S. (2019). Anahtar Noktası Ve Yama Eşleşme Yöntemleri Ile Doku Bazlı Görüntü Sahteciliği Tespiti (Doctoral Dissertation, Karadeniz Teknik Üniversitesi).
  • [27] Çömert, R., & Avdan, U. Yersel Lazer Tarayici Verilerinden Basit Geometrik Yüzeylerin Otomatik Olarak Çıkarılması. [28] A. Jakubović and J. Velagić, "Image Feature Matching and Object Detection Using Brute-Force Matchers," 2018 International Symposium ELMAR, 2018, pp. 83-86, doi: 10.23919/ELMAR.2018.8534641.
  • [29] Li, H., Qin, J., Xiang, X., Pan, L., Ma, W., & Xiong, N. N. (2018). An efficient image matching algorithm based on adaptive threshold and RANSAC. IEEE Access, 6, 66963-66971.
  • [30] S. A. K. Tareen and Z. Saleem, "A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK," 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018, pp. 1-10, doi: 10.1109/ICOMET.2018.8346440.
  • [31] Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726.

A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions

Year 2022, Volume: 6 Issue: 2, 259 - 278, 31.12.2022

Ceren Akman , Ergun Gumus

https://doi.org/10.38088/jise.1061166

Abstract

Until today, various approaches have been proposed in order to create Augmented Reality (AR) environment where virtual-real integration takes place. One of these approaches is vision-based model, and it is divided into two branches as Marker-Based Augmented Reality (MBAR) and Markerless Augmented Reality (MAR). In the use of MBAR model, a reference image is introduced to the system before, and when this image enters the camera view, an AR environment is created. However, in MAR model, no image is introduced to the system before. Instead, it uses natural  characteristics present in the image, such as edges, corners, and geometrical shapes to create an AR environment. In order to use MAR model, it is necessary to use algorithms which require high processing power and memory capacity. Within the scope of this study, MAR model was chosen as reference and an evaluation on combinations of descriptive extractors (such as ORB, SIFT, and SURF) and matchers (such as Bruteforce, Bruteforce-Hamming, and Flannbase) was presented. In this context, it was aimed that we could obtain knowledge about i) the number of key points and detection time with the use of different descriptor extractors, and ii) the number of matching key points and the amount of positional deviation of a virtual object placed on a real world scene with the use of different matchers. In line with this goal, analyses were made, using different image scales and brightness levels on both PC and mobile platforms. Results showed that, for both platforms, combinations using ORB method could work faster with less deviation than the combinations using other methods in all conditions. In addition, RANSAC algorithm was also used to reduce the total mean deviation ratio, and it was seen that the rate could be reduced from 70% to 4.5%..

Keywords

Augmented Reality Markerless Augmented Reality Performance Analysis ORB SIFT SURF.

References

  • [1] Azuma, R. T. (1997). A Survey Of Augmented Reality. Presence: Teleoperators & Virtual Environments, 6(4), 355-385.
  • [2] Milgram, P., & Kishino, F. (1994). A Taxonomy Of Mixed Reality Visual Displays.Ieice Transactions On Information And Systems, 77(12), 1321-1329.
  • [3] Ufkes, A., & Fiala, M. (2013, May). A Markerless Augmented Reality System For Mobile Devices. In 2013 International Conference On Computer And Robot Vision (pp. 226-233). IEEE.
  • [4] Gonzato, J. C., Arcila, T., & Crespin, B. (2008). Virtual Objects On Real Oceans. In Graphicon'2008 (Pp. 49-54).
  • [5] Keskin, N. Ö., & Kilinç, A. G. H. (2015). Mobil Öğrenme Uygulamalarına Yönelik Geliştirme Platformlarının Karşılaştırılması ve Örnek Uygulamalar. Açıköğretim Uygulamaları Ve Araştırmaları Dergisi, 1(3), 68-90.
  • [6] Korucu, A. T., & Biçer, H. (2019). Mobil Öğrenme: 2010-2017 Çalışmalarına Yönelik Bir İçerik Analizi. Trakya Eğitim Dergisi, 9(1), 32-43.
  • [7] Avcı, A. F., & Taşdemir, Ş. (2019). Artırılmış Ve Sanal Gerçeklik ile Periyodik Cetvel Öğretimi. Selcuk University Journal Of Engineering Sciences, 18(2), 68-83.
  • [8] Uzun, Y., Bilban, M., & Kalaç, M. Ö. (2018). Artırılmış Gerçeklik Kullanılarak Engelli Çocukların Öğrenme Yeteneklerinin Geliştirilmesi. Uluslararası Engelsiz Bilişim Kongresi.
  • [9] Schall, G., Grabner, H., Grabner, M., Wohlhart, P., Schmalstieg, D., & Bischof, H. (2008, June). 3d Tracking In Unknown Environments Using On-Line Keypoint Learning For Mobile Augmented Reality. In 2008 IEEE Computer Society Conference On Computer Vision And Pattern Recognition Workshops (Pp. 1-8). IEEE.
  • [10] Kağan, G. Ü. L., & Şahin, S. (2017). Bilgisayar Donanım Öğretimi Için Artırılmış Gerçeklik Materyalinin Geliştirilmesi ve Etkililiğinin Incelenmesi. Bilişim Teknolojileri Dergisi, 10(4), 353-362.
  • [11] Içten, T., & Güngör, B. A. L. (2017). Artırılmış Gerçeklik Üzerine Son Gelişmelerin ve Uygulamaların Incelenmesi. Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5(2), 111-136.
  • [12] Kaleci, D., Demirel, T., & Akkuş, I. (2016). Örnek Bir Artırılmış Gerçeklik Uygulaması Tasarımı. xviii. Akademik Bilişim Konferansı, Aydın, Türkiye.
  • [13] Lowe, D. G. (2004). Distinctive Image Features From Scale-Invariant Keypoints. International Journal Of Computer Vision, 60(2), 91-110.
  • [14] https://docs.opencv.org/3.4/da/df5/tutorial_py_sift_intro.html [Accessed: April 4th, 2021].
  • [15] Bay, H., & Tuytelaars, T. (2006). Luc Van Gool. Surf: Speeded Up Robust Features, 14.
  • [16] Evans, C. (2009). Notes On The Opensurf Library. University Of Bristol. Tech. Rep.
  • [17] Li, A., Jiang, W., Yuan, W., Dai, D., Zhang, S., & Wei, Z. (2017). An Improved Fast+ Surf Fast Matching Algorithm. Procedia Computer Science, 107, 306-312.
  • [18] Viswanathan, D. G. (2011). Features From Accelerated Segment Test (Fast) Deepak Geetha Viswanathan 1.
  • [19] Calonder, M., Lepetit, V., Strecha, C., & Fua, P. (2010, September). Brief: Binary Robust Independent Elementary Features. In European Conference On Computer Vision (Pp. 778-792). Springer, Berlin, Heidelberg.
  • [20] Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011, November). Orb: An Efficient Alternative To Sift Or Surf. In 2011 International Conference On Computer Vision (Pp. 2564-2571). IEEE.
  • [21] Rosin, P. L. (1999). Measuring Corner Properties. Computer Vision And Image Understanding, 73(2), 291-307.
  • [22] Muja, M. (2009). Approximate Nearest Neighbors With Automatic Algorithm Configuration. In Proc. Int. Conf. On Computer Vision Theory And Applications, Lisbon, 2009.
  • [23] https://docs.opencv.org/4.x/dc/dc3/tutorial_py_matcher.html [Accessed: Augustth 29, 2021].
  • [24] Fischler, M. A., & Bolles, R. C. (1981). Random Sample Consensus: A Paradigm For Model Fitting With Applications To Image Analysis And Automated Cartography. Communications Of The Acm, 24(6), 381-395.
  • [25] Dihkan, M. (2019). Uzaktan Algılanmış Görüntülerin Surf Özellik Verileri Ve Ransac Algoritması Ile Otomatik Çakıştırılması. Gümüşhane Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 9(3), 425-432.
  • [26] Erdöl, E. S. (2019). Anahtar Noktası Ve Yama Eşleşme Yöntemleri Ile Doku Bazlı Görüntü Sahteciliği Tespiti (Doctoral Dissertation, Karadeniz Teknik Üniversitesi).
  • [27] Çömert, R., & Avdan, U. Yersel Lazer Tarayici Verilerinden Basit Geometrik Yüzeylerin Otomatik Olarak Çıkarılması. [28] A. Jakubović and J. Velagić, "Image Feature Matching and Object Detection Using Brute-Force Matchers," 2018 International Symposium ELMAR, 2018, pp. 83-86, doi: 10.23919/ELMAR.2018.8534641.
  • [29] Li, H., Qin, J., Xiang, X., Pan, L., Ma, W., & Xiong, N. N. (2018). An efficient image matching algorithm based on adaptive threshold and RANSAC. IEEE Access, 6, 66963-66971.
  • [30] S. A. K. Tareen and Z. Saleem, "A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK," 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), 2018, pp. 1-10, doi: 10.1109/ICOMET.2018.8346440.
  • [31] Karami, E., Prasad, S., & Shehata, M. (2017). Image matching using SIFT, SURF, BRIEF and ORB: performance comparison for distorted images. arXiv preprint arXiv:1710.02726.
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Ceren Akman 0000-0002-1769-5545

Ergun Gumus 0000-0002-1327-6845

Early Pub Date October 11, 2022
Publication Date December 31, 2022
Published in Issue Year 2022Volume: 6 Issue: 2

Cite

APA Akman, C., & Gumus, E. (2022). A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions. Journal of Innovative Science and Engineering, 6(2), 259-278. https://doi.org/10.38088/jise.1061166
AMA Akman C, Gumus E. A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions. JISE. December 2022;6(2):259-278. doi:10.38088/jise.1061166
Chicago Akman, Ceren, and Ergun Gumus. “A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions”. Journal of Innovative Science and Engineering 6, no. 2 (December 2022): 259-78. https://doi.org/10.38088/jise.1061166.
EndNote Akman C, Gumus E (December 1, 2022) A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions. Journal of Innovative Science and Engineering 6 2 259–278.
IEEE C. Akman and E. Gumus, “A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions”, JISE, vol. 6, no. 2, pp. 259–278, 2022, doi: 10.38088/jise.1061166.
ISNAD Akman, Ceren - Gumus, Ergun. “A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions”. Journal of Innovative Science and Engineering 6/2 (December 2022), 259-278. https://doi.org/10.38088/jise.1061166.
JAMA Akman C, Gumus E. A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions. JISE. 2022;6:259–278.
MLA Akman, Ceren and Ergun Gumus. “A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions”. Journal of Innovative Science and Engineering, vol. 6, no. 2, 2022, pp. 259-78, doi:10.38088/jise.1061166.
Vancouver Akman C, Gumus E. A Comparative Study on the Performance Analysis of Feature Extractors Used in Augmented Reality Applications under Various Image Conditions. JISE. 2022;6(2):259-78.
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