Edges: saliency measures and automatic thresholding
Machine Vision and Applications
Robust Parameter Estimation in Computer Vision
SIAM Review
Robust computer vision: an interdisciplinary challenge
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
MODEEP: a motion-based object detection and pose estimation method for airborne FLIR sequences
Machine Vision and Applications
On Advances in Statistical Modeling of Natural Images
Journal of Mathematical Imaging and Vision
Detecting Moving Objects in Airborne Forward Looking Infra-Red Sequences
CVBVS '99 Proceedings of the IEEE Workshop on Computer Vision Beyond the Visible Spectrum: Methods and Applications
Motion Estimation Using Statistical Learning Theory
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust tracking in aerial imagery based on an ego-motion Bayesian model
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
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An efficient automatic detection strategy for aerial moving targets in airborne forward-looking infrared (FLIR) imagery is presented in this paper. Airborne cameras induce a global motion over all objects in the image, that invalidates motion-based segmentation techniques for static cameras. To overcome this drawback, previous works compensate the camera ego-motion. However, this approach is too much dependent on the quality of the ego-motion compensation, tending towards an over-detection. In this work, the proposed strategy estimates a robust motion vector field, free of erroneous vectors. Motion vectors are classified into different independent moving objects, corresponding to background objects and aerial targets. The aerial targets are directly segmented using their associated motion vectors. This detection strategy has a low computational cost, since no compensation process or motion-based technique needs to be applied. Excellent results have been obtained over real FLIR sequences.