Crater Detection using CGC - A New Circle Detection Method

Vinciane Lacroix, Sabine Vanhuysse

2015

Abstract

”Constrained Gradient for Circle” (CGC) is a new circle detection algorithm based on the gradient of the intensity image. The method relies on two conditions. The “gradient angle compatibility condition” constrains the gradient of a given percentage of the pixels belonging to some digital circles having a radius in the range of radii to detect to point towards the centre of the circle or in the opposite direction. The “curvature compatibility condition” constrains the variation of the gradient angle of the same pixels in a range depending on the radius of the circle. These two conditions are sufficient to detect the core of circular shapes. The best-fitting circle is then identified. The method is applied to artificial and reference images and compared to state-of-the-art methods. It is also applied to water-filled crater detection in Cambodia: these craters that might indicate the presence of Unexploded Ordnance (UXO) dating from the US bombing produce dark circles on satellite panchromatic images.

References

  1. Akinlar, C. and Topal, C. (2013). Edcircles: A real-time circle detector with a false detection control. Pattern Recognition, 46.
  2. Atherton, T. J. and Kerbyson, D. J. (1999). Size invariant circle detection. Image and Vision Computing, 17:795803.
  3. Blinn, J. (1996). Jim Blinn's Corner: A Trip Down the Graphics Pipeline. Morgan Kaufmann series in computer graphics and geometric modeling. Morgan Kaufmann Publishers.
  4. Canny, J. (1986). A computational approach to edge detection. IEEE Trans. on Pattern Anal. and Machine Intelligence, pages 679-697.
  5. Chung, K.-L., Chen, P.-Z., and Pan, Y.-L. (2009). Speed up of the edge-based inverse halftoning algorithm using a finite state machine model approach. Computers & Mathematics with Applications, 58(3):484 - 497.
  6. Chung, K.-L., Huang, Y.-H., Shen, S.-M., Krylov, A. S., Yurin, D. V., and Semeikina, E. V. (2012). Efficient sampling strategy and refinement strategy for randomized circle detection. Pattern Recognition, pages 252- 263.
  7. Hatfield-Consultants (2014). Uxo predictive modeling in mmg lxml sepon mine development area. Technical Report 1791.D6.1, Hatfield consultants, East Lansing, Michigan.
  8. Marco, T. D., Cazzato, D., Leo, M., and Distante, C. (2014). Randomized circle detection with isophotes curvature analysis. Pattern Recognition.
  9. Xu, L., Oja, E., and Kultanen, P. (1990). A new curve detection method: Randomized hough transform (rht). Pattern Recognition Letters, 11:331338.
  10. Yip, R. K., Tam, P. K., and Leung, D. N. (1992). Modification of hough transform for circles and ellipses detection using a 2-dimensional array. Pattern Recognition, 25:10071022.
  11. Zelniker, E. E. (2006). Maximum-likelihood estimation of circle parameters via convolution. IEEE Transaction on Image Processing, 16:865-876.
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Paper Citation


in Harvard Style

Lacroix V. and Vanhuysse S. (2015). Crater Detection using CGC - A New Circle Detection Method . In Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-076-5, pages 320-327. DOI: 10.5220/0005222503200327


in Bibtex Style

@conference{icpram15,
author={Vinciane Lacroix and Sabine Vanhuysse},
title={Crater Detection using CGC - A New Circle Detection Method},
booktitle={Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2015},
pages={320-327},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005222503200327},
isbn={978-989-758-076-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Crater Detection using CGC - A New Circle Detection Method
SN - 978-989-758-076-5
AU - Lacroix V.
AU - Vanhuysse S.
PY - 2015
SP - 320
EP - 327
DO - 10.5220/0005222503200327