Time-of-flight cameras are used for diverse applications ranging from human-machine interfaces and gaming to robotics and earth topography. This paper aims at evaluating the capability of the Mesa Imaging SR4000 and the Microsoft Kinect 2.0 time-of-flight cameras for accurately imaging the top surface of a concrete beam subjected to fatigue loading in laboratory conditions. Whereas previous work has demonstrated the success of such sensors for measuring the response at point locations, the aim here is to measure the entire beam surface in support of the overall objective of evaluating the effectiveness of concrete beam reinforcement with steel fibre reinforced polymer sheets. After applying corrections for lens distortions to the data and differencing images over time to remove systematic errors due to internal scattering, the periodic deflections experienced by the beam have been estimated for the entire top surface of the beam and at witness plates attached. The results have been assessed by comparison with measurements from highly-accurate laser displacement transducers. This study concludes that both the Microsoft Kinect 2.0 and the Mesa Imaging SR4000s are capable of sensing a moving surface with sub-millimeter accuracy once the image distortions have been modeled and removed.
Most of the methods described in the literature for automatic hand gesture recognition make use of classification
techniques with a variety of features and classifiers. This research focuses on the frequently-used ones by performing a
comparative analysis using datasets collected with a range camera. Eight different gestures were considered in this
research. The features include Hu-moments, orientation histograms and hand shape associated with its distance
transformation image. As classifiers, the k-nearest neighbor algorithm and the chamfer distance have been chosen. For
an extensive comparison, four different databases have been collected with variation in translation, orientation and scale.
The evaluation has been performed by measuring the separability of classes, and by analyzing the overall recognition
rates as well as the processing times. The best result is obtained from the combination of the chamfer distance classifier
and hand shape and distance transformation image, but the time analysis reveals that the corresponding processing time
is not adequate for a real-time recognition.
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