Transform-based methods reduce the observed image patch to a feature vector, which is then classified; template-based methods calculate the matching scores.
In a previous work (Santos et al. 2014), we identified and classified nine templates for textual use case descriptions of Software Product Lines through a Systematic Mapping (SM) (Kitchenham and Charters 2007).
From the 154 TBM domains, 50 are classified as template-based high accuracy (TBM-HA) domains, for which at least one model with a GDT-TS score >80 was predicted.
In this part, we perform literature review on the works which are developed for mitigating security and privacy problems of biometrics and we categorize them as illustrated in Fig. 2. Jain et al. classify biometric template protection schemes into two main categories [3]: 1) Feature transformation-based schemes, and 2) Biometric cryptosystems.
Total AP-1 activity dynamics for each perturbed parameter set were then calculated by simulating AT1RGRN, and the AP-1 response was classified using the template matching procedure of Pavlidis et al [ 42].
This would suggest that statistical signal analysis may be useful to classify these templates.
The 95 targets were officially classified into 109 template-based-modeling (TBM) domains and 19 free-modeling (FM) domains, based on whether or not the structurally similar template (deposited in PDB) had been identified and used in prediction.
MinION™ reads can be classified into three types: template, complement and two-direction (2D).
Templated mesoporous carbons are reviewed by focusing on their syntheses and applications by classifying with the template used because the resultant mesoporous carbon have their own characteristics in their pore structures depending on the template used.
State-of-the-art methods can be classified into three groups: Template-based methods: these methods (Laskowski et al., 2005; Stark and Russell, 2003; Wallace et al., 1997) accept a template structure as input (e.g. catalytic triad in serine proteases) and query a given structural database for matching patterns.
The organic template techniques are classified into three, on the basis of their purposes, pore structure control in the resultant carbons (membranes and fibers), preparation of hollow carbon nanofibers by spinning, and improvement in processability, particularly spinnability for carbon nanofiber fabrication.
