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Color mapping for 3D models with captured images is a classical problem in computer vision. Typically, registration between 3D model and images is assumed to be provided, otherwise corresponding points need to be labeled. For many applications, 3D model and images are acquired from different devices, since registration cannot be directly obtained, manual labeling has to be adopted. In this paper,...
A novel background dictionary learning and structured sparse representation based anomaly detection method is proposed for hyperspectral imagery. First, a robust PCA spectrum dictionary is learned from the plausible background area detected by the local RX detector. With the learned dictionary, the reweighted Laplace prior based structured sparse representation model is then employed to reconstruct...
Accurate reconstruction of hyperspectral image(HSI) from a few random sampled measurements is crucial for hyperspectal compressive sensing. The underlying sparsity of HSI is one crucial factor for HSI reconstruction. However, the s-parsity is unknown in reality and varied with different noise. To address this problem, a novel nonseparable sparsity based hyperspectral compressive sensing(NSHCS) method...
In this study, we propose a globally consistent, locally sparse regularized model for fiber orientation distribution (FOD) estimation with multi-shell diffusion signal. First, a novel spherical double-lobe basis function is used to form an over-complete dictionary that guarantees the local sparsity of FOD. Furthermore, a global consistency spatial model which incorporated a spatial priori information...
To reduce the huge resource consumption in the hyperspec-tral imaging and transmission, this paper proposes a highperformance compression method. Specially, a novel 3D total variation prior is imposed on abundance fractions of end-members. In this method, compressed data is obtained by a random observation matrix in a compressive sensing way. Based on the hyperspectral linear mixed model and known...
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