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Data were corrected using background subtraction based on convolution of normal and exponential distributions [ 78].
Signal intensities for each spot were calculated by summation of the pixel intensities for each spot, followed by local background subtraction (based on the median pixel intensity of the area surrounding each spot).
The ComBat algorithm was used to minimize assay batch effects on per chip median normalized scores [ 22]. Background subtraction based on empty features was applied to all replicates in a comparison as needed.
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In this paper, we propose a pixel-based background subtraction method based on spatial similarity.
We propose a learning-based background subtraction approach based on the theory of sparse representation and dictionary learning.
Zhao et al. [34] proposed a learning-based background subtraction approach based on sparse representation and dictionary learning.
To develop a robust model with low complexity, we used a pixel-based background subtraction method based on spatial similarity computed using the structural similarity method (SSIM [36].
We will detect shadows in background subtraction results based on the properties of shadows.
To illustrate the detection of foreground objects using 3D ToF information, the background subtraction method based on the reference image model has been used.
Therefore, after performing distance and saturated pixel correction, a background subtraction method based on the reference image model is adapted to be used in a 3D point cloud.
With use of background subtraction method based on Gaussian combination, the binary image which includes moving information will use in the mean-shift algorithm.
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