Cascading Crater Detection with Active Learning

Details Cascading Crater Detection with Active Learning Cascading Crater Detection with Active Learning

Mar 2011

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011lpi....42.1469m&link_type=abstract

42nd Lunar and Planetary Science Conference, held March 7–11, 2011 at The Woodlands, Texas. LPI Contribution No. 1608, p.1469

Computer Science

Learning

Scientific paper

Our strategy for automatic crater detection consists of employing a
cascading AdaBoost classifier for identification of craters in images,
and using the SOM as an active learning tool to minimize the number of
image examples that need to be labeled by an analyst.

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