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In this paper, a simultaneous sparsity representation-based binary hypothesis (S-SRBBH) model for target detection in hyperspectral image (HSI) is proposed. The S-SRBBH exploits the interpixel correlation within neighboring pixels in HSI, and then, each test pixel is represented by only the background dictionary (Ab) under null hypothesis or from the union of Ab and target dictionary (At) under alternative...
Recently sparse representation has been applied to visual tracking by modeling the target appearance using a sparse approximation over the template set. However, this approach is limited by the high computational cost of the ℓ1-norm minimization involved, which also impacts on the amount of particle samples that we can have. This paper introduces a basic constraint on the self-representation of the...
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