In this work, μ-Raman spectroscopy was used to quantitatively measure the transformation profiles in depth as a function of ageing time at 160 °C, 140 °C, 134 °C and 110 °C.
The transformation between profile face image (90° angle pose) and frontal face image (0° angle pose) is known to be nonlinear, and this nonlinearity is taken into account by introducing kernel functions.
After this transformation, each profile b r sums to one, allowing them to be interpreted as a discrete probability distribution over transcripts.
To proceed with the LOOLP, 199 individuals are used to learn the CCA transformation frontal-profile (estimate w x, w y ) and the remaining individual of the profiles set together with the frontal faces of the test set are projected onto the canonical space using w y and w x, respectively, for comparison.
Evaluation of our approach according to the leave one out-like protocol (LOOLP), 199 individuals are used to learn the CCA transformation frontal-profile (estimate w x, w y ) and the remaining individual of the profiles set together with the frontal faces of the test set are projected onto the canonical space using w y and w x, respectively, for comparison.
In each iteration, m−1 faces (from both datasets) are used to learn the CCA transformation frontal-profile.
Since we have proved that the abstract form of our CKE models is invariant by arbitrary multiple affine transformations of profiles data, we have made sure to constrain the distance of two expression levels profiles to be invariant by these types of affine transformations.
Namely, each transformation has a profile page, where users can see whether the transformation is based on experimental data, predicted data or both.
Insets in a and b are low, mid, and high excitation intensity examples of PL spectra to show the profile transformation with the increasing excitation intensity.
