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This paper presents a novel longitudinal framework for clinical score prediction in Alzheimer's disease (AD) diagnosis. In contrast to the previous approaches that use the data collected at a single time point only for the clinical score prediction, we propose to exploit the imaging data of multiple time points. Furthermore, a spatial-temporal group sparse method is proposed for robust feature selection...
Visual saliency detection has recently become a highly active topic in image processing, due to its wide range of applications. This paper proposes a novel saliency detection model based on discrete cosine transform (DCT). The dissimilarity between image patches is evaluated using DCT low-frequency coefficients and is inversely weighted by the spatial distance between patches. An additional weighting...
This paper addresses the problem of co-saliency detection, which aims to identify the common salient objects in a set of images and is important for many applications such as object co-segmentation and co-recognition. First, the segmentation driven low-rank matrix recovery model is used for intra saliency detection in each individual image of the image set, to highlight the regions whose features...
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