A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
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
A General Framework for Object Detection
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Robust Real-Time Face Detection
International Journal of Computer Vision
Learning to Detect Small Impact Craters
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning
Subkilometer crater discovery with boosting and transfer learning
ACM Transactions on Intelligent Systems and Technology (TIST)
Dynamic Landmarking for Surface Feature Identification and Change Detection
ACM Transactions on Intelligent Systems and Technology (TIST)
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Identifying impact craters on planetary surfaces is one fundamental task in planetary science. In this paper, we present an embedded framework on auto-detection of craters, using feature selection and boosting strategies. The paradigm aims at building a universal and practical crater detector. This methodology addresses three issues that such a tool must possess: (i) it utilizes mathematical morphology to efficiently identify the regions of an image that can potentially contain craters; only those regions, defined as crater candidates, are the subjects of further processing; (ii) it selects Haar-like image texture features in combination with boosting ensemble supervised learning algorithms to accurately classify candidates into craters and non-craters; (iii) it uses transfer learning, at a minimum additional cost, to enable maintaining an accurate auto-detection of craters on new images, having morphology different from what has been captured by the original training set. All three aforementioned components of the detection methodology are discussed, and the entire framework is evaluated on a large test image of 37,500 x 56,250$ m2 on Mars, showing heavily cratered Martian terrain characterized by nonuniform surface morphology. Our study demonstrates that this methodology provides a robust and practical tool for planetary science, in terms of both detection accuracy and efficiency.