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As can be noticed, these metrics can also be applied to videos by computing a frame by frame quality evaluation.
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Figure 1 Unsupervised action recognition is performed by computing a feature vector for each frame of the sequence (left), as described in Section 6.
This correlation noise model statistically describes the relationship between the SI and the source and is obtained by computing an online residual, without using the original WZ frame.
In high signal-to-noise ratio (SNR) conditions, it is a relatively simple task since we can reach a satisfying result only by computing the frame energies and setting an appropriate threshold for classification [1].
"Computing a single frame of this takes thirty hours".
We start by computing the log-spectral frames (Omega _{a} ={mathbf {u}_{ell }}_{ell =1}^{M}in mathbb {R}^{d}), where each frame u ℓ comprises d=K/2+1 frequency bins.
In [4] the constant background features are estimated for each frame by computing the sliding average over the ten preceding frames, and the average is subtracted from the frame.
The (PC^{decode}_{textit {hard}}) feature is expected to be more reliable than the P C hard feature which is computed frame by frame because it take advantage of the initial recognition results generated using triphone models as well as the language model.
Then, we also find the corresponding block in the (t) frame by computing NSAD t with the interpolated region in the (t – 1/2) frame.
In DVC [2], the source statistics are exploited at the decoder by computing the SI of the WZ frame using different techniques.
Note that the actual height was computed in a frame-by-frame manner and the height of the subject can vary with the up-and-down motion caused by walking; we therefore define the height feature as an average of the height values computed over the image sequence.
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