Figure 6, extracted from [22], illustrates the cropping process applied to the transformed frame.
Figure 5 presents a transformed frame, where it is possible to observe the loss of information at the borders.
The two transforms were used in a unique way that scatters the watermark bits throughout the transformed frame in order to achieve high degrees of imperceptibility and robustness.
A matrix is then formed by arranging the wavelet coefficients of all detail sub-bands in a unique distributed pattern which scatters the watermark bits throughout the transformed frame to provide a high degree of robustness.
From the time-domain transformed frame, the delay τ l,j (k) is computed by finding the argument κ that maximizes the cross correlation function (s_{e_{l},e_{j}} kappa,k)): tau_{l,j}(k) = underset{-tau_{max} < kappa < tau_{max}}{text{argmax}} left{ s_{e_{l},e_{j}} kappa,k) right}.
Mezirow's transformative learning theory described lifelong learning in terms of learners' transforming frames of reference, in which reflection is the driving force [ 21].
Recent separation schemes applied non-negative matrix factorization (NMF), where the magnitude of the Fourier transformed frames is factorized as the product of two non-negative matrices.
Classical video prediction methods exploit directly and shallowly the intra-frame, inter-frame and multi-view similarities within the video sequences; the proposed video prediction methods indirectly and intensively transform the frame correlations into nonlinear mappings by using a general deep neural network (DNN) with single output node.
The stereo data was needed to calculate the inverse filter coefficients, which then was used to transform N frames of reverberant complex-valued FFT coefficient vectors to N frames of estimated clean FFT coefficient vectors.
In all cases, we first transform reference frames for each individual observation, based on the parameters appropriate for that time, and then calculate the median distribution.
