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Object detection in Very High Resolution (VHR) optical remote sensing images is a challenged work for objects are usually dense and tiny. With random orientation, various backgrounds as well as unpredictable noise make traditional image processing methods perform badly. In this paper, we propose using state-of-art Region-based fully convolutional networks to solve object detection tasks in aerial...
We study the problem of linear filter optimization with finite sample size, which has wide applications such as beamformer design in wireless communications and portfolio optimization in finance. Traditional methods in both fields are not robust against the imprecise channel vector and the noise covariance matrix (or the mean return and the covariance of assets in finance) due to finite sample size...
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