Fast Image Stabilization and Mosaicking
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We present two fast implementations of electronic image stabilization and mosaicking systems. The rst one is based on a 2D similarity model and is targeted to process PREDATOR video data. The second system uses a 3D model and compensates for 3D rotation. Both systems have been implemented on parallel pipeline imageprocessing hardware (a Datacube MaxVideo 200) connected to a Themis 10MP. Both algorithms use a featurebased multi-resolution technique which tracks a small set of features to estimate the motion of the camera. The extended Kalman lter framework is employed by the 3D de-rotation system. The inter-frame motion estimates relative to a reference frame are used to warp the current frame in order to achieve stabilization. The estimates are also used to construct mosaics by aligning the frames. A fast mosaicking implementation is presented for the 2D system. Experimental results demonstrate the robustness of both systems at frame rates above 10 frames/second.
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