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To improve invariance against rotation in the xy-plane several OPC values were calculated by iteratively shifting the reference vector counterclockwise.
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The meanshift algorithm, which iteratively shifts a datum point to the average of data points in its neighborhood, is a robust statistical method.
The mean-shift based tracker iteratively shifts a data point to the average of data points in its neighborhood, which minimizes the distance between a model histogram representing the target and candidate histograms computed on the current frame.
Moreover, the proposed algorithm iteratively shifts the kernels based on kernel histogram (i.e., spatially weighted color histogram) and histogram of oriented gradient (HOG) [31] in each video frame, and hence enables a robust and efficient human tracking solution without training required.
The reference vector, which initially points along the x-axis, was iteratively shifted by 9 degrees (the da parameter) counterclockwise until it was shifted to the next orientation class as defined by the reference vector's initial direction.
MDS analysis was performed using largely uncorrelated SNPs obtained by applying a filter of pair-wise correlation of r < 0.5 within a 50 SNP window, iteratively shifted 5 SNPs along the sequence, which revealed significant population substructure.
Once this point is reached, the algorithm iteratively shifts all upstream cut sites by -1 base until the terminal oligo length is the size of the target overlap length.
Each mask was iteratively and independently shifted over each image, and the correlation was calculated over the windowed regions of each image in order to find the optimum image position for I n x,y).
This is iteratively repeated until there is no improvement as opposed to the more widespread double justification variant with just two shifting passes.
Shifting currents.
Perspective shifting.
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