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The experimental results and the drawn conclusions are in line for all four employed datasets.
An overview of the employed datasets is shown in Figure 4. Figure 4 Dataset overview.
The first set of experiments (combination1) employed datasets (i) & (ii) while the second set of experiments (combination2) employed (iii) & (iv).
Mesh grid phase: A total of n equidistant scan lines (n=100 yields reasonable results for the employed datasets) are intersected with binary noise mask N locating 0-1 and 1-0 crossings.
In order to investigate whether the employed datasets are sufficiently large for the differences in performance observed in Table 3 to be statistically significant, a statistical significance test is used.
Most available models allow for an output of binary noise mask N indicating iris pixels; therefore, we extract P and L from N via the following scanning and pruning process (see Fig. 3). 1. Mesh grid phase: A total of n equidistant scan lines (n=100 yields reasonable results for the employed datasets) are intersected with binary noise mask N locating 0-1 and 1-0 crossings.
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Table 1 Characteristics of the employed dataset.
The employed dataset including Landsat imageries, road networks and Digital Elevation Model (DEM).
The employed dataset is one of the most important factors when building a CBIR system.
Two popular face datasets are selected to evaluate the experimental discretization performance in this section: This employed dataset is a subset of the FERET face dataset [29], in which the images were collected under varying illumination conditions and face expressions.
It would be a valid choice to use a weighted combination, but we decided against this option, because setting weights requires further tuning with regards to the employed dataset, which we tried to avoid at this stage.
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