Sentence examples for scanning datasets from inspiring English sources

This paper presents a fully automatic strategy to calibrate geometrically terrestrial laser scanning datasets.

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.

We demonstrate its robustness and efficiency on several challenging scan datasets with varying degree of noise, outliers, extent of overlap, acquired from indoor and outdoor scenarios.

The robustness, accuracy and efficiency of our method were overall evaluated on several challenging scan datasets acquired from indoor and outdoor scenes as described in Section 7. Given two point sets P and Q, we assume ℬ p = { p i | p i ∈ P } i = 1 N and ℬ q = { q i | q i ∈ Q } i = 1 N to be the two corresponding N-points bases from P and Q, respectively.

We also set up an assay to evaluate the performance of our model to handle large datasets of protein sequences, by scanning three negative test sets of protein sequences yielding recovery values of almost 90% of the true positives with precision values above 80%, and an evident independence of the results from the number of negative instances in the scanned datasets [ 27].

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.