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Automated recognition of spacecraft and space debris using imaging plays an important role in securing space safety and space exploration. Although deep learning is now the most successful solution for image-based object classification, it requires a myriad number of training data, which are not available for most real applications. In this paper, we investigate different single and hybrid data augmentation...
Infrared and visible image fusion is an active area in digital image processing. Many methods in spatial or transform domains have been proposed, but there are still several complex challenges. In this paper, we introduce a three-scale image transformation, which possesses multi-scale, translation-invariance and spatial-localization characteristics that are very important for image fusion. The decomposition...
This paper considers the robust filtering problem for a class of nonlinear discrete-time systems, and a conjugate unscented transform (CUT) based strong tracking H∞ filter is proposed. Firstly, an extended strong tracking H∞ filter is presented based on the fusion of the extended H∞ filter and strong tracking filter. By online estimating the time-varying noises, the fading factor in the strong tracking...
Medical image fusion technique plays an an increasingly critical role in many clinical applications by deriving the complementary information from medical images with different modalities. In this paper, a medical image fusion method based on convolutional neural networks (CNNs) is proposed. In our method, a siamese convolutional network is adopted to generate a weight map which integrates the pixel...
The square root unscented Kalman filter was introduced to provide a more numerically robust formulation of the unscented Kalman filter and to guarantee positive semi-definiteness. The filter maintains the Cholesky factor of the covariance matrix instead of the covariance itself. Efficient linear algebra techniques, including Cholesky update and downdate, are used to predict and update the Cholesky...
The aim of this article is to design a moment transformation for Student-t distributed random variables, which is able to account for the error in the numerically computed mean. We employ Student-t process quadrature, an instance of Bayesian quadrature, which allows us to treat the integral itself as a random variable whose variance provides information about the incurred integration error. Advantage...
Magnetic resonance imaging (MRI) has been extensively used in clinical practice but suffers from long data acquisition time. Following the success of compressed sensing (CS) theory, many efforts have been made to accurately reconstruct MR images from undersampled k-space measurements and therefore dramatically reduce MRI scan time. To further improve image quality, we formulate undersampled MRI reconstruction...
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