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Most methods consider the user or item independently.
Most methods consider sampling only from a homogeneous population in which each animal has the same probability of becoming infected.
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Not all methods provided a quantitative estimate of validity and most methods considered only the positive predictive value of a set of diagnostic codes in a highly selected group of cases.
Currently, this problem is approached rather one-dimensionally since most registration methods consider the problem as either mono- or multi-modal.
While most previous methods consider only a single pixel at a time from each LDR exposure, Tocci et al. [31] presented an algorithm that incorporates a neighborhood of LDR samples in the reconstruction.
For the first time, we extend the differential signal processing approach to a more general framework, which can cope with multiple nuisance parameters, whereas most existing methods consider a single nuisance parameter.
However, most of these methods consider a single criterion that can merely be applied to detect the particular structure/distribution of data.
Most of the normalization methods consider the data as a function of the matrix column.
However, most of the existing methods consider all genes in the same set equally without noticing that some genes are less important than others.
However, most of the existed methods consider the target as a whole, resulting that it is difficult to investigate the latent binding mechanism between target and ligand.
Most haze removal methods considering noise, such as [19 21], require multiple images.
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