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These alignments were checked by eye for alignment errors, poorly aligned regions were discarded.
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However, we still used manually annotated eye coordinates for alignment and estimation of facial fiducial points for local feature extraction.
Like in experiment 1, the manually annotated eye coordinates were used for alignment.
The according equal error rates can be seen in Table 4. Figure 11 Results of experiment 3. ROC curves of the proposed identification system for alignment using manually annotated coordinates of both eyes and mouth (blue solid line), automatically detected eyes (red dashed line), and without applied alignment (green dash-dotted line).
Moreover, the image-based approach obviates the need to determine the achromatic axis for each eye to achieve alignment, an especially onerous task for animal eye research.
Sequences of each gene were aligned using the Muscle algorithm under default parameters [ 23] and the alignments inspected by eye for any obvious misalignments that were subsequently corrected.
Sequences of each gene were independently aligned using the Muscle algorithm under default parameters [ 36] and the alignments inspected by eye for any obvious misalignments that were then corrected.
We checked these 6 alignments by eye and discovered alignment errors in 3 of them.
First, our general alignment was screened by eye for the presence of specific sequence signatures, as described above.
Alignments were inspected by eye and for each alignment only protein sequences showing no misalignment and having a minimum ORF length (CYPs: 450 nt, CCEs: 450 nt, GSTs: 300 nt, UGTs: 450 nt) were retained for the final alignment.
A is for alignment.
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