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A novel template selection strategy was employed to reduce the number of templates required for matching by up to 50%, while providing comparable performance with known approaches.
The use of an atlas with multiple template images is more effective than a single template (Heckemann et al., 2006) and depends on the quality of registration and template selection strategy.
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Uludag et al. [10] proposed two typical methods for automatic template selection: the first one, DEND, employs a hierarchical clustering strategy to choose a template set that could be best represents the intra-class variations.
This is a multi-atlas based segmulti-atlas basedtion method basegmentationE (simultaneous truth and propagation level estimethod) (Warfield et al., 2004) baseddapted tonuSTAPLEocal ranking simultaneouscording truthand similarity) for temperformancetion, thus enablevelrestimationgmentation of hippocampi Warfieldiabletmorphologies, al may occur in HS.
In this case, the three templates selected from FVC2004DB4 are all chosen by using the template selection method.
The method uses an effective consensus strategy by combining PSI-BLAST, IMPALA, and T-Coffee in both template selection and target-template alignment.
This selection strategy requires a polymerase that can synthesize GNA in a template-directed fashion.
In template selection, the memory overhead was around 6.1 MB when 20% templates were selected to be the representatives.
The proposed template selection algorithm takes advantage of the FPGA area-time measures of the enumerated patterns, which can be easily inferred from the FPGA-aware enumeration strategy.
All marketplace selection strategies outperform the random marketplace selection strategy.
But part of their selection strategy may have backfired.
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