Crater Marking and Classification Using Computer Vision

  • Alejandro Flores-Méndez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2905)


In the last three years NASA and some other Space Agencies have draw some interest to date Mars surface, mainly because the relationship between its geological age and the probable presence of water beneath it. One way to do this is by classifying craters on the surface attending to their degree of erosion. The naïve way to solve this problem would let a group of experts analyze the images of the surface and let them mark and classify the craters. Unfortunately, this solution is unfeasible because the number of images is huge in comparison with the human resources any group can afford. Different solutions have been tried [1], [2] over this period of time. This paper offers an autonomous Computer Vision System to detect the craters, and classify them.


Fuzzy System False Detection False Recognition Hough Transform Computer Vision System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Kanefsky, B., Barlow, N.G., Gulick, V.C.: Can distributed volunteers accomplish massive data analysis task? Lunar and Planetary Science XXXII, paper 1272 (2001)Google Scholar
  2. 2.
    Negrete, V.: Crater Image Classification using Classical Methods and Ontologies, M. Sc. Thesis, University of Houston (2002)Google Scholar
  3. 3.
    Parker, J.R.: Algorithms for Image Processing and Computer Vision. John Wiley & Sons, Chichester (1996)Google Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison-Wesley Pub Co., Reading (2002)Google Scholar
  5. 5.
    Zadeh, L.A.: A theory of approximate reasoning. In: Hayes, J., Michie, D., Mikulich, L.I. (eds.) Machine Intelligence 9, pp. 149–194. Halstead Press, New York (1979)Google Scholar
  6. 6.
    Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis and Machine Vision. Chapman and Hall Computing, London (1993)Google Scholar
  7. 7.
    Cormen, T.H. (ed.): Introduction to Algorithms, 2nd edn. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  8. 8.
    Belacel, N., Boulassel, M.R.: Multicriteria Fuzzy Classification Procedure PROCFTN: Methodology and Medical Application. Fuzzy Sets and Systems (2003) (in press)Google Scholar
  9. 9.
    McBratney, A.B., deGruijter, J.J.: A continuum approach to soil classification by modified fuzzy k-means with extragrades. Journal of Soil Science 43, 159–175 (1992)CrossRefGoogle Scholar
  10. 10.
    Hu, B.G., Gosine, R.G., Cao, L.X., de Silva, C.W.: IEEE Trans. on Fuzzy Systems 6(1) (February 1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Alejandro Flores-Méndez
    • 1
  1. 1.LIDETEAUniversidad la SalleMéxico, D.F.México

Personalised recommendations