Automating the analysis and cataloging of sky surveys
Advances in knowledge discovery and data mining
Modeling subjective uncertainty in image annotation
Advances in knowledge discovery and data mining
Self-organizing maps
Learning to Recognize Volcanoes on Venus
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Designing and mining multi-terabyte astronomy archives: the Sloan Digital Sky Survey
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Mining of Moving Objects from Time-Series Images and its Application to Satellite Weather Imagery
Journal of Intelligent Information Systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Subkilometer crater discovery with boosting and transfer learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Automatic recognition of landforms on mars using terrain segmentation and classification
DS'06 Proceedings of the 9th international conference on Discovery Science
Genetically enhanced feature selection of discriminative planetary crater image features
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
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In this study, a crater detection system for a large-scale image database is proposed. The original images are grouped according to spatial frequency patterns and both optimized parameter sets and noise reduction techniques used to identify candidate craters. False candidates are excluded using a self-organizing map (SOM) approach. The results show that despite the fact that a accurate classification is achievable using the proposed technique, future improvements in detection process of the system are needed.