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In Section 4, we discuss the proposed methods for template representative selection and compression.
In Section 3, we describe the proposed methods for template construction, matching, and clustering.
Several wavelet- and Gabor-based methods for template matching are proposed.
In order to reduce computation and storage complexities, we also propose two methods for template selection: minimum distance template selection (MDTS) and maximum likelihood template selection (MLTS).
Uludag et al. [10] proposed two methods for template selection: DENT and MDIST, but MDIST method gets a better performance than DEND in their study.
Methods for template construction (e.g., nonlinear registration and transformation, iterative routines) that have been utilized and proven by others (Fonov et al., 2011; Sanchez et al., 2012a, b) were used to guide the current study.
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However, with increasing accuracy of prediction methods for template-based models, the focus of the assessment has shifted to the evaluation of the atomic details of a model.
Lumini and Nanni [11] presented a novel clustering method for template selection, and this method is better than MDIST in their study.
Nanosize template [9 11] is the low-cost method, but the fabrication process is more complex than nanofabrication tool method for template should be built and removed during the whole process.
The use of the heat-treatment method for template preparation provides a good alternative to the expensive and labor intensive DNA isolation methods that might not always be possible in field settings.
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.
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