ENHANCING IMPACT CRATER CONTOURS TO INCREASE RECOGNITION RATES

Lourenço P. C. Bandeira, José Saraiva, Pedro Pina

2006

Abstract

This paper introduces an enhancement to the edge detection procedures that are part of a general methodology which aims at increasing the robustness of the automatic recognition of impact craters on planetary surfaces. It is demonstrated that the proposed improvement is a major contribution to increase the recognition rates and to simultaneously diminish the rates of false positives. Its performance is evaluated through a comparison with other classic edge detectors, which are applied to a set of images of the surface of Mars acquired by the MOC instrument aboard Mars Global Surveyor, a probe currently orbiting the planet.

References

  1. Barata T., Alves E.I., Saraiva J., Pina P, 2004, Automatic recognition of impact craters on the surface of Mars. In: Campilho A., Kamel M. (eds.), Image Analysis and Recognition, Lecture Notes in Computer Science - LNCS 3212, Springer, Berlin, 489-496.
  2. Brumby, S., Plesko, C., Asphaug, E., 2003, Evolving Automated Feature Extraction Algorithms for Planetary Science. In: Proc. ISPRS WG IV/9: Extraterrestrial Mapping Workshop - Advances in Planetary Mapping 2003, Houston, Texas, 2 pp.
  3. Canny, J., 1986 A computational approach to edge detection, IEEE Trans. on PAMI, 8(6): 679-698.
  4. Costantini, M., Zavagli, M., Di Martino, M., Marchetti, P., Di Stadio, F., 2002, Crater Recognition. Proc. IGARSS'2002 - International Geoscience & Remote Sensing Symposium.
  5. Earl, J., Chicarro A., Koeberl, Ch., Marchetti, P.G., Milnes, M., 2005, Automatic Recognition of Craterlike Structures in Terrestrial and Planetary Images. Lunar and Planetary Science XXXVI, abs #1319.
  6. Flores-Méndez, A., 2003, Crater Marking and Classification Using Computer Vision. In: Sanfeliu, A., Ruiz-Shulcloper (eds.): Progress in Pattern Recognition, Speech and Image Analysis, Lecture Notes in Computer Science LNCS 2905, Springer, Berlin, 79-86.
  7. Hartmann, W., Neukum, G., 2001, Cratering Chronology and the Evolution of Mars. Space Science Reviews, 96: 165-194.
  8. Homma, K., Yamamoto, H., Isobe, T., Matsushima, K., Ohkubo, J., 1997, Parallel Processing for Crater Recognition. Lunar and Planetary Science XXVIII, abs #1073.
  9. Honda, R., Azuma, R., 2000, Crater Extraction and Classification System for Lunar Images. Mem. Fac. Sci. Kochi Univ., 21: 13-22.
  10. Kim, J., Muller, J-P., 2003, Impact Crater Detection on Optical Images and DEMs. In: Proc. ISPRS WG IV/9: Extraterrestrial Mapping Workshop - Advances in Planetary Mapping 2003, Houston, Texas, 2 pp.
  11. Kim, J.R., Muller, J.-P., Morley J.G., 2004, Quantitative assessment of automated crater detection on Mars. In: Proc. of ISPRS'2004, Istanbul, Turkey, 6 pp.
  12. Leroy, B., Medioni, G., Johnson, E., Matthies, L., 2001, Crater Detection for Autonomous Landing on Asteroids. Image and Vision Computing, 19: 787-792.
  13. Levine, M.D., Nazif, A.M., 1985, Dynamic measurement of computer generated image segmentation. IEEE Transactions on PAMI, 7(2): 155-164.
  14. Magee, M., Chapman, C., Dellenback, S., Enke, B., Merline, W., Rigney, M., 2003, Automated Identification of Martian Craters Using Image Processing. Lunar and Planetary Science XXXIV, abs #1756.
  15. Marr, D., Hildreth, E., 1980, Theory of Edge Detection, Proc. Royal Society of London, B207: 187-217.
  16. Matsumoto, N., Asada, N., Demura, H., 2005, Automatic Crater Recognition on Digital terrain Model. Lunar and Planetary Science XXXVI, abs #1995.
  17. Michael, G., 2003, Coordinate Registration by Automated Crater Recognition. Planetary and Space Science, 51: 563-568.
  18. Neukum, G., Jaumann, R., Hoffmann, H., Hauber, E., Head, J.W., Basilevsky, A.T., Ivanov, B.A., Werner S.C., van Gasselt, S., Murray, J.B., McCord, T., 2004, The HRSC Co-Investigator Team. Recent and episodic volcanic and glacial activity on Mars revealed by the High Resolution Stereo Camera. Nature, 432: 971- 979.
  19. Plesko, C., Brumby, S., Asphaug, E., Chamberlain, D., Engel, T., 2004, Automatic Crater Counts on Mars. Lunar and Planetary Science XXXV, abs #1935.
  20. Prewitt, J.S.M., Mendelsohn, M.L., 1966, The analysis of cell images, Ann. N.Y. Acad. Sci., 128: 1035-1053.
  21. Roberts, L.G., 1965, Machine Perception of ThreeDimensional Solids, Optical and Electro-Optical Information Processing, MIT Press, 159-197.
  22. Sobel, I.E., 1970, Camera models and machine perception, PhD Thesis, Stanford University.
  23. Vinogradova, T., Burl, M., Mjolness, E., 2002, Training of a Crater Detection Algorithm for Mars Crater Imagery. Proc. IEEE Aerospace Conference, Vol. 7: 3201-3211.
Download


Paper Citation


in Harvard Style

P. C. Bandeira L., Saraiva J. and Pina P. (2006). ENHANCING IMPACT CRATER CONTOURS TO INCREASE RECOGNITION RATES . In Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, ISBN 972-8865-40-6, pages 407-412. DOI: 10.5220/0001375104070412


in Bibtex Style

@conference{visapp06,
author={Lourenço P. C. Bandeira and José Saraiva and Pedro Pina},
title={ENHANCING IMPACT CRATER CONTOURS TO INCREASE RECOGNITION RATES},
booktitle={Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,},
year={2006},
pages={407-412},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001375104070412},
isbn={972-8865-40-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP,
TI - ENHANCING IMPACT CRATER CONTOURS TO INCREASE RECOGNITION RATES
SN - 972-8865-40-6
AU - P. C. Bandeira L.
AU - Saraiva J.
AU - Pina P.
PY - 2006
SP - 407
EP - 412
DO - 10.5220/0001375104070412