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Two transformation schemes are designed for the partially adaptive linear filters.
Two transformation schemes based on subspace techniques are designed in the partially adaptive linear filters.
Two transformation markers, lin-15 and rol-6, were used.
Two transformation events targeting BdCAD1 (amiR-cad1-1 and amiR-cad1-8) and three transformation events targeting BdCOMT4 (amiR-comt4-3, amiR-comt4-5, and amiR-comt4-7) were selected for further characterization in the T2 generation.
Comparison between the two transformation methods is also reported.
Since the wave transformation is applied on the image in two directions, there are two transformation values for each pixel, namely, two transformation matrices for the image.
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In order to explore the strength of feature integration strategies, we investigate four learning-based (SVM, Gradient Boosting, NNLS, Random Forest) and two transformation-based (Mean, Max) fusion methods, resulting in six new dynamic saliency models.
We perform a detailed formal comparison of these two transformations.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com