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With an appropriate two-dimensional convolution (e.g., cylindrical projection) the data can be studied with 2D image processing techniques [ 31].
Before projection, the data should be corrected to be within an ideal spherical layer of constant depth.
After flattening and projection, the data points (nuclei) will therefore have the following coordinates: ψ, φ are coordinates along orthogonal meridians in the azimuthal projection and d represents depth; all are measured in metrical units.
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If the owner interacted with the dog during the projections, the data were not used for the analysis.
It is like the microscope, but lets us understand a (larger and larger) part of the physical world through its projection on the data space.
The orthogonal projection of the data onto a finite basis is typically approached by the inversion of a Gram matrix involving the inner products of the basis functions.
A maximum-intensity projection through the data stack was calculated.
Figure 13 shows the projection of the data in samples mode to lower dimension.
These methods display a low-dimensional projection of the data highlighting the main sources of variability.
The score matrices result from projection of the data matrices X and Y on loading matrices.
We started by creating a maximum intensity projection of the data.
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