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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.
The group of structurally-constrained methods clearly outperforms the simple methods working on the depth image only.
Computer simulations are an important implementation to experimental methods working on polymer nanocomposites (PNCs), which have advanced properties because of their unique hierarchy microstructures.
We also note that the algorithm can be expanded for range search and k-NN search, and can be adjusted to process numerous patterns at every sliding step over T. Since Rakthanmanon et al. [11] reckoned that the speedup techniques used in UCR-DTW dwarf the improvements gained by multi-threading, we want to check the claim and so propose our methods working on multicores.
These bins mimic natural break-points in this metric, with the exception of Q being in Bin 3. The generally high performance of coevolution methods working on alignments encoded in A1_4 suggests that it may compliment the use of the more canonical reduced-state alphabets, and that other Atchley-factor alphabets defined based on natural breaks in the data may also yield good results.
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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, 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.
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