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The common motion detection method cannot handle this case very well.
A laser-based flame motion detection method was used to determine the locations and frequencies of flame oscillations.
To evaluate the performance of the Kinect for motion analysis, we applied a motion detection method (Han et al. 2013) to motion capture datasets from a Kinect and a VICON, and compared the results of detection based on conventional measures of classification performances (i.e., accuracy, precision, and recall).
However, tested with the known cases described by Eisenberg et al., the well-known domain motion detection method DynDom [35] failed to identify most of those DS relationships (see Table S1 for details).
With the motion detection method (i.e., the moving standard deviation method), the head motion was identified).
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Our segmentation method is an extension of standard motion detection methods with adaptive background modeling.
In terms of intensity images, many motion detection methods are based on background modeling.
Background subtraction is one of the motion detection methods introduced to extract the foreground objects from a reference background in an image sequence.
Vision-based motion detection methods can be categorized into three major classes, i.e., temporal difference, background modeling and subtraction, and optical flow.
We compared our proposed method with the original optical flow method, the motion detection methods based on Kalman filtering [11], background modeling using Gaussian mixture model [12], difference-based spatial temporal entropy image (DSTEI) [13], and forward-backward motion history images (MHI) [14].
With the development of micro-nano manufacturing technology, nanotechnology, and nanomaterials, researchers have studied the multi-degrees-of-freedom motion parameter detection methods based on the single-chip implementation, from the perspective of miniaturization and low-cost application in the fields of nanomaterials, optical materials, and nanodevices.
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