A Comparative Study on the Center-based Iterative Hough Transform

Authors

  • Joshua Park Yongsan International School of Seoul
  • Young-Woo Lee Mokwon University

DOI:

https://doi.org/10.47611/jsrhs.v9i2.1206

Keywords:

Computer Vision, Circle Detection, Voting Process, Gradient Information, Hough Transform, OpenCV, Image Processing

Abstract

Circle detection is one of the most critical aspects of computer vision and has been widely studied and developed in a variety of ways. The Center-based Iterative Hough Transform (CBIHT) is a method for unassisted multiple circle detection, based upon iterative uses of a center-based voting process to determine the circle’s center coordinate. This paper gives a thorough analysis of the CBIHT as well as a comparison with the Standard Hough Transform (SHT) and its well-known variants including the Generalized Hough Transform (GHT) and the Adaptive Hough Transform (AHT). When applied to synthetic and real-life circular images, our accuracy and performance comparison studies show that (i) the CBIHT is more computationally efficient than the SHT’s brute-force algorithm; (ii) the CBIHT’s center-based voting method has greater resilience to noise than the GHT and AHT’s gradient information method; and (iii) the CBIHT’s iterative process provides an adaptability and speed in unassisted multiple circle detection similar to that of the AHT; (iv) yet, the CBIHT requires no parameters for circle detection unlike the GHT and the AHT. All in all, a comparison with other methods highlights the aforementioned merit of the CBIHT, proving the CBIHT to be an excellent choice in detecting the circles with noise in real-life images. 

Downloads

Download data is not yet available.

Author Biography

Young-Woo Lee, Mokwon University

Department of Electrical Engineering

References or Bibliography

Antolovic, D. (2018). Review of the Hough Transform Method, With an Implementation of the Fast Hough Variant for Line Detection, Technical Report TR663, [Online]. Available: https://www.cs.indiana.edu/cgi-bin/techreports/TRNNN.cgi?trnum=TR663. [Accessed 30 11 2018].

Ballard, D. H. (1981). Generalizing the Hough Transform to Detect Arbitrary Shapes, Pattern Recognition, 13(2), pp. 111-122.

Duda, R. and Hart, P. (1972). Use of the Hough Transform to Detect Lines and Curves in Pictures, In Comm. ACM. 15(1), pp. 11-15.

Illingworth, J. and Kittler, J. (1987). The Adaptive Hough Transform, IEEE Trans. On Pattern Analysis and Machine Intelligence, PAMI-9(5), pp. 690-699.

Kim, H-S., Kim, J-H. (2001). Two-step circle detection algorithm from the intersecting chords, Pattern Recognition Letters, 22, pp. 787-798.

Kimme, C., Ballard, B., and Sklansky, J. (1975). Finding Circles by an Array of Accumulators, Comm. Of the ACM, 18(2), pp. 120-122.

O. Team, OpenCV, Intel. (2018). [Online]. Available: https://opencv.org/. [Accessed 30 9 2018].

Park, J. (2019). A Center-based Iterative Hough Transformation for Unassisted Multiple Circle Detection, Journal of the International STEAM, 1, 1, 1-6.

Ünver, H. M., Kökver, Y., Duman, E., Erdem, O. A. (2019). Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization, Applied Sciences, 9, 350; doi:10.3390/app9020350.

Wohlfart, M. (2003). Hough transform applications in Computer Graphics (with focus on medical visualization), unpublished, Spring 2003, [Online]. Available: https://www.cg.tuwien.ac.at/courses/Seminar/SS2003/Ergebnisse/WohlfartMichael_HoughTransform.pdf. [Accessed 30 11 2018].

Wolfram, S. (1988, June 23). Wolfram Mathematica, Wolfram, [Online]. Available: https://www.wolfram.com/mathematica. [Accessed 20 October 2017].

Published

11-20-2020

How to Cite

Park, J., & Lee, Y.-W. (2020). A Comparative Study on the Center-based Iterative Hough Transform. Journal of Student Research, 9(2). https://doi.org/10.47611/jsrhs.v9i2.1206

Issue

Section

HS Research Articles