TY - GEN
T1 - Circle Detection System Using Image Moments
AU - Fachruddin, Rifqi
AU - Buliali, Joko Lianto
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The growth of system detection has significant development. The circle detection system is widely used to help people based on their needs, and it also could be used as learning media in educational fields. Especially for students with special needs, applying a circle detection system in Augmented Reality (AR) media would help them a lot. In order to make the study activity more effective and suit the learning material purpose, a circle detection system that only detects perfect full circles is needed to minimalize misconceptions in circle learning material. From the previous method such as Circle Hough Transform (CHT), circle detection faces the complex transition from cartesian coordinate into Hough coordinate. The use of image moments would give a coordinate of centroid that could use to find the radius by using the circle equation. Two groups of datasets would test the newly proposed method of detecting circles. Based on the experiment, the accuracy of the new method was 96.7%. The average time consumption is 0. 405 s which is faster than the CHT method with 1.024 s. Circle detection using image moments is also more robust towards noise than the previous CHT method.
AB - The growth of system detection has significant development. The circle detection system is widely used to help people based on their needs, and it also could be used as learning media in educational fields. Especially for students with special needs, applying a circle detection system in Augmented Reality (AR) media would help them a lot. In order to make the study activity more effective and suit the learning material purpose, a circle detection system that only detects perfect full circles is needed to minimalize misconceptions in circle learning material. From the previous method such as Circle Hough Transform (CHT), circle detection faces the complex transition from cartesian coordinate into Hough coordinate. The use of image moments would give a coordinate of centroid that could use to find the radius by using the circle equation. Two groups of datasets would test the newly proposed method of detecting circles. Based on the experiment, the accuracy of the new method was 96.7%. The average time consumption is 0. 405 s which is faster than the CHT method with 1.024 s. Circle detection using image moments is also more robust towards noise than the previous CHT method.
KW - circle detection
KW - circle hough transform
KW - image moments
UR - http://www.scopus.com/inward/record.url?scp=85138363529&partnerID=8YFLogxK
U2 - 10.1109/CyberneticsCom55287.2022.9865355
DO - 10.1109/CyberneticsCom55287.2022.9865355
M3 - Conference contribution
AN - SCOPUS:85138363529
T3 - Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
SP - 265
EP - 269
BT - Proceedings - 2022 IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE International Conference on Cybernetics and Computational Intelligence, CyberneticsCom 2022
Y2 - 16 June 2022 through 18 June 2022
ER -