Mining of Topographic Feature from Heterogeneous Imagery and Its Application to Lunar Craters
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Robust Real-Time Face Detection
International Journal of Computer Vision
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Subkilometer crater discovery with boosting and transfer learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Bernoulli trials based feature selection for crater detection
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
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Using gray-scale texture features has recently become a new trend in supervised machine learning crater detection algorithms. To provide better classification of craters in planetary images, feature subset selection is used to reduce irrelevant and redundant features. Feature selection is known to be NP-hard. To provide an efficient suboptimal solution, three genetic algorithms are proposed to use greedy selection, weighted random selection, and simulated annealing to distinguish discriminate features from indiscriminate features. A significant increase in the classification ability of a Bayesian classifier in crater detection using image texture features.