ORB yöntemi ile oy tespiti ve sayımını gerçekleştiren sistemin tasarımı

Öz

Bu çalışmada, oy tespitini ve sayımını gerçekleştiren görüntü işleme tabanlı bir sistem geliştirilmiştir. Sistem, donanımsal ve yazılımsal olmak üzere iki bölümden oluşmaktadır. Görüntü almak için kullanılan kamera ve görüntü işlemek için kullanılan Raspberry Pi3 donanım kısmını oluşturmaktadır. Yazılım kısmında ise görüntü işleme yöntemlerinden olan Oriented FAST and Rotated BRIEF (ORB) metodu ile Brute-Force Eşleştirmesi kullanılarak görüntülerdeki öznitelikler eşleştirilmektedir. Yazılımın kodları Python programlama dilinde yazılmış olup ve OpenCV kütüphanesinden faydalanılmıştır. Çalışmada oy tespiti ve sayımı için 72 punto büyüklüğünde, Calibri yazı tipi ile yazılmış EVET ve HAYIR oy pusulaları kullanılmıştır. Sistemde yüksek çözünürlüklü kamera sayesinde oy pusulasının görüntüsü alınmakta ve görüntü işleme yazılımına aktarılmaktadır. Yazılımın çalışma mantığı, kayıtlı görüntüdeki köşe ve dönüm noktaları gibi ayırt edici özelliklerin belirlenmesi ve belirlenen özelliklerin, kamera tarafından çekilen görüntüler ile eşleştirilmesi prensibine dayanmaktadır. Oyun kime verildiğinin tespiti için ise kullanılan oyun yatay düzlemindeki konumuna bakılmaktadır. Sistem eşleşen görüntüye göre evet ve hayır oy sayılarını arttırmaktadır. Yapılan çalışmada %100 başarı oranı ile oy tespiti ve sayımı gerçekleşmiştir.

Anahtar Kelimeler

• Oy tespiti ve sayımı
• Görüntü işleme
• ORB
• Brute-Force Eşleştirme

Design of the system that performs vote detection and counting by ORB method

Öz

In this study, an image processing-based system was developed that performs vote detection and counting. The system consists of two parts, hardware and software. The camera used for image capturing and Raspberry Pi3 used for image processing constitute the hardware part. In the software part of the system, the features in the images are matched using Oriented FAST and Rotated BRIEF (ORB) method and Brute-Force Matching method which are the image processing methods. The software's codes are written in the Python programming language and the OpenCV library is used. In the study, YES and NO ballots written in Calibri font, 72 points in size, were used for vote detection and counting. Thanks to the high-resolution camera in the system, the image of the ballot is taken and transferred to the image processing software. The operating logic of the software works according to the principle of determining the corners and milestones, which are distinctive features in the recorded image, and matching them with the images taken. In order to determine who the game was given to, the position of the game on the horizontal plane used is looked at. The system increases the number of yes and no votes according to the matching image. In the study, it is performed that vote determination and counting with a 100% success rate.

Anahtar Kelimeler

• Ballot detection and counting
• Image processing
• ORB
• Brute-Force Matching

Kaynakça

1. [1] Smith, E.H.B., Lopresti, D., Nagy, G. Ballot mark detection. 19th International Conference on Pattern Recognition, Florida, United State, 8-11 December 2008.
2. [2] Smith, E.H.B., Goyal, S., Scott, R., Lopresti, D. Evaluation of voting with form dropouttechniques for ballot vote counting. International Conference on Document Analysis and Recognition, Beijing, China, 18-21 September 2011.
3. [3] Nagy, G., Lopresti, D., Smith, E.H.B., Wu, Z. Characterizing challenged Minnesota ballots. Document Recognition and Retrieval XVIII, California, United State, 23-27 January 2011.
4. [4] Barney-Smith, E.H., Nagy, G., Lopresti, D. Mark detection from scanned ballots. Document Recognition and Retrieval XVI, California, United State, 20-22 January 2009.
5. [5] Nagy, G., Clifford, B., Berg, A., Saunders, G., Lopresti, D., Smith, E.B. Camera-based ballot counter. 10th International Conference on Document Analysis and Recognition, Barcelona, Spain, 26-29 July 2009.
6. [6] Wang, K., Rescorla, E., Shacham, H., Belongie, S.J. OpenScan: A Fully Transparent Optical Scan Voting System. EVT/WOTE, 10, 1-13, 2010.
7. [7] Calonder, M., Lepetit, V., Strecha, C., Fua, P. Brief: Binary robust independent elementary features. European conference on computer vision, 778-792, Springer, Berlin, Heidelberg, 2010.
8. [8] Zhang, H.Z., Lu, Y.F., Kang, T.K., Lim, M.T. B-HMAX: A fast binary biologically inspired model for object recognition. Neurocomputing, 218, 242-250, 2016.

9. [9] Lowe, D.G. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2), 91-110, 2004.
10. [10] Rublee, E., Rabaud, V., Konolige, K., Bradski, G. ORB: An efficient alternative to SIFT or SURF. International conference on computer vision, Barcelona, Spain, 6-13 November 2011.
11. [11] Se, S., Lowe, D., Little, J. Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. The International Journal of Robotics Research, 21(8), 735-758, 2002.
12. [12] Snavely, N., Seitz, S.M., Szeliski, R. Skeletal sets for efficient structure from motion. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, Alaska, 23-28 June 2008.
13. [13] Bay, H., Ess, A., Tuytelaars, T., Van Gool, L. Speeded-up robust features (SURF). Computer vision and image understanding, 110(3), 346-359, 2008.
14. [14] Aglave, P., Kolkure, V.S. Implementation Of High Performance Feature Extraction Method Using Oriented Fast And Rotated Brief Algorithm. International Journal of Research in Engineering and Technology, 4(2), 394-397, 2015.
15. [15] Xu, J., Chang, H.W., Yang, S., Wang, M. Fast feature-based video stabilization without accumulative global motion estimation. IEEE Transactions on Consumer Electronics, 58(3), 993-999, 2012.
16. [16] Qin, Y., Xu, H., Chen, H. Image feature points matching via improved ORB. IEEE International Conference on Progress in Informatics and Computing, Shanghai, China, 2014.
17. [17] Wang, M., Niu, S., Yang X. A novel panoramic image stitching algorithm based on ORB. International Conference on Applied System Innovation (ICASI), Sapporo, Japan, 2017.
18. [18] Yeh C.C., Chang, Y.L., Hsu, P.H., Hsien, C.H. GPU Acceleration of UAV Image Splicing Using Oriented Fast and Rotated Brief Combined with PCA. IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22-27 July 2018.
19. [19] Awaludin, M., Yasin, V. Application of Oriented Fast and Rotated Brief (ORB) and Brute force Hamming in Library Open CV for Classification of Plants. Journal of Information System, Applied, Management, Accounting and Research, 4(3), 51-59, 2020.
20. [20] Kolomenkin, M., Pollak, S., Shimshoni, I., Lindenbaum, M. Geometric voting algorithm for star trackers. IEEE Transactions on Aerospace and Electronic Systems, 44(2), 441-456, 2008.
21. [21] Antony, N., Devassy, B.R. Copy Move Image Forgery Detection Using Adaptive Over-Segmentation and Brute-Force Matching. Image, 5(4), 2018.
22. [22] Khan, N., McCane, B., Mills, S. Better than SIFT. Machine Vision and Applications, 26(6), 819-836, 2015.
23. [23] Muja, M., Lowe, D.G. Scalable nearest neighbor algorithms for high dimensional data. IEEE transactions on pattern analysis and machine intelligence, 36(11), 2227-2240, 2014.