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These methods works on hard to get off regular nails polish too.
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Although similarity-based methods work on short reads, they explore the taxonomic content of metagenomic data according to known genomes rather than classifying reads.
Both of these methods work on color information.
The neurons of these previous deep-learning-based methods work on the whole input feature map.
Further, recognition methods working on shapes (FG silhouettes) are also present in the literature [5, 6].
We compared SHR with two landmark-based registration methods, working on high resolution facial images.
Further, all the irregular consumption detection methods work on whole load pattern data set.
Most of the sparse representation-based image magnification methods work on patches.
The group of structurally-constrained methods clearly outperforms the simple methods working on the depth image only.
Existing methods work on extracting local concepts directly on spatial domain [2, 5 7] or frequency domain [8, 9].
In addition, most of the existing methods work on pixel-level and neglect the contextual information contained in neighboring pixels.
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