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Automated Crater Detection and Counting Using the Hough Transform

Automated Crater Detection and Counting Using the Hough Transform (Decoding the Geology of Mars). Monty Galloway University of Western Australia. Supervisors: G.K. Benedix 1 , P.A. Bland 1 , J. Paxman 2 , M.C. Towner 1 , T. Tan 2. 1 Department of Applied Geology, Curtin University

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Automated Crater Detection and Counting Using the Hough Transform

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  1. Automated Crater Detection and Counting Using the Hough Transform (Decoding the Geology of Mars) Monty Galloway University of Western Australia Supervisors: G.K. Benedix1, P.A. Bland1, J. Paxman2, M.C. Towner1, T. Tan2 1Department of Applied Geology, Curtin University 2Department of Mechanical Engineering, Curtin University

  2. Overview • Why count craters? Why automate? • Method • Edge Detection • Hough Transform • Performance Analysis • Results and Future Work 6 km HiRISE Crater Image: https://hirise.lpl.arizona.edu/ESP_034809_1655

  3. Martian Meteorites Crater: http://static.uahirise.org/images/wallpaper/800/ESP_034285_1835.jpg Earth: http://eoimages.gsfc.nasa.gov/images/imagerecords/57000/57723/globe_east_540.jpg MartianMeteorite LA001: http://www2.jpl.nasa.gov/snc/la001.jpg

  4. Surface Age-Dating Older Surface Younger Surface Mars: http://hubblesite.org/newscenter/archive/releases/2001/24/image/a/ HRSC Surface Images: http://pds-geosciences.wustl.edu/mex/mex-m-hrsc-5-refdr-mapprojected-v2/mexhrsc_1001/data/ Superposed Craters

  5. Method Image Canny Edge Detection OpenCV Edge Pixels Hough Transform Circles Performance Analysis

  6. Canny Edge Detection High Threshold Gradient Low

  7. Hough Transform Image Detection Edges Voting

  8. Performance Analysis 1 True Positive False True Negative False

  9. Performance Analysis 2 (Windows) Positive Negative

  10. Performance Analysis 3 (Results) ROC for three Canny threshold settings. TPR TPR = TP / (FN + TP) FPR = FP / (TN + FP) FPR

  11. Summary and Future Work • Tested influence of edge detection on Hough Transform. • Results suggest refinement of edge detection will improve. • Sun direction edge data. • Integrate with Urbach& Stepinski1 crater candidate method. • Parallelization. 1E.R. Urbach and T.F. Stepinski, “Automatic detection of sub-km craters in high resolution planetary images,” Planetary and Space Science, vol. 57, pp. 880–887, 2009.

  12. Acknowledgements Thank you to iVEC for supporting this work through the 2013-2014 Summer Internship program. Thank you to Tele, Jonathan, Gretchen, Phil and Martin for their support and technical advice.

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