C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
Theoretical Improvements in Algorithmic Efficiency for Network Flow Problems
Journal of the ACM (JACM)
Saliency, Scale and Image Description
International Journal of Computer Vision
Illumination-Invariant Change Detection
SSIAI '00 Proceedings of the 4th IEEE Southwest Symposium on Image Analysis and Interpretation
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Journal of Field Robotics - Special Issue on Space Robotics, Part III
Automatic mapping of valley networks on Mars
Computers & Geosciences
Automatic detection of dust devils and clouds on Mars
Machine Vision and Applications
Development of a Methodology for Automated Crater Detection on Planetary Images
IbPRIA '07 Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part I
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Bipartite graph matching for computing the edit distance of graphs
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Image change detection algorithms: a systematic survey
IEEE Transactions on Image Processing
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Given the large volume of images being sent back from remote spacecraft, there is a need for automated analysis techniques that can quickly identify interesting features in those images. Feature identification in individual images and automated change detection in multiple images of the same target are valuable for scientific studies and can inform subsequent target selection. We introduce a new approach to orbital image analysis called dynamic landmarking. It focuses on the identification and comparison of visually salient features in images. We have evaluated this approach on images collected by five Mars orbiters. These evaluations were motivated by three scientific goals: to study fresh impact craters, dust devil tracks, and dark slope streaks on Mars. In the process we also detected a different kind of surface change that may indicate seasonally exposed bedforms. These experiences also point the way to how this approach could be used in an onboard setting to analyze and prioritize data as it is collected.