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The quality of photogrammetric data is evaluated by comparison with a light detection and ranging scanning dataset that acts as ground truth.
Such a population was not identified in our scanning dataset, likely due to the lower stability of this ternary complex when SRP is bound in its scanning mode.
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This paper presents a fully automatic strategy to calibrate geometrically terrestrial laser scanning datasets.
Experiments with virtual and real environments perceived from 2D laser and sonar scanners are presented, including the validation of the model with a real scans dataset.
All 3 curves are computed over the entire circular B-Scan dataset.
The error correction alone took up to 12 hours for the entire circular B-Scan dataset for each reviewer.
The correlation between the MADG(r) and MOAD r) calculated over the whole circular B-Scan dataset is 0.93.
Two experts in the field (authors MM and RT) and a student individually reviewed all segmented images of the circular B-Scan dataset.
The average difference to the gold standard (MDG) over the whole circular B-Scan dataset is 2.4 µm (see Table 3).
Figure 9 shows (similar to Fig. 8) the mean absolute difference to the gold standard at A-Scan position (MADG(r)) computed over the whole circular B-Scan dataset.
Two groups of subjects formed our circular B-Scan dataset: Glaucoma patients (72 subjects, age 62.1±9.4, mean defect 5.5±6.2) and healthy normal subjects (132 subjects, age 49.8±16.3, mean defect 0.5±0.6).
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