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- W2328439598 abstract "In this work we consider the problem of extraction and classification of moving targets in wide area imagery. We use the Air Force Research Laboratory's (AFRL) airborne multi-sensor dataset, MAMI-1, for testing, wherein moving targets mostly consist of people and vehicles. The movers are extracted using a novel sparse and low-rank matrix decomposition technique. We further compare the classification performance based on SIFT, Dense SIFT, and a superpixel based feature extraction. The results show the superpixel approach as the most advantageous." @default.
- W2328439598 created "2016-06-24" @default.
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- W2328439598 date "2015-06-01" @default.
- W2328439598 modified "2023-09-25" @default.
- W2328439598 title "Extraction and classification of moving targets in multi-sensory MAMI-1 data collection" @default.
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- W2328439598 doi "https://doi.org/10.1109/naecon.2015.7443102" @default.
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