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The problem of recovering a low-rank matrix from a set of observations corrupted with gross sparse error is known as the robust principal component analysis (RPCA) and has many applications in computer vision. In this paper, smoothing technique is used to smooth the non-smooth terms in the objective function, and we develop the fast alternating direction method for solving RPCA. Moving object detection...
Accurate and robust activity map reconstruction from emission sinogram and segmentation of region of interest geometry for dynamic PET are of great technical challenges and significant clinical values. Traditionally, activity map reconstruction and boundary or volumetric segmentation problems are treated as two sequential steps. In this paper, we present an integrated, low-rank representation based...
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