Sentence examples for both depth and luminance from inspiring English sources

In particular, we use motion reliabilities derived from both depth and luminance as weighting factors for motion compensated temporal filtering.

The main difference compared to classical gray-scale motion estimation algorithms is that the proposed algorithm calculates similarity metrics in both depth and luminance images, which doubles the number of arithmetical operations.

This means that the motion vectors should produce small compensation errors in both depth and luminance (data term) and they should not differ much from the neighbouring motion vectors (regularization term).

One of the important novelties of the proposed method is that the motion estimation is performed on both depth and luminance sequences in order to improve the accuracy of the estimated motion.

However, in this article, we look for observations both similar in depth and luminance.

However, in the presence of noise in the depth and luminance frames, displaced frame differences for both luminance and depth are dominated by noise.

Motion estimation algorithm is performed over 7 depth and luminance frames, in a 24 × 24 pixels search window, on 8 × 8 pixel blocks.

The proposed method is depicted schematically in Figure 2. The proposed algorithm operates on a buffer which contains a given fixed number of depth and luminance frames.

where F t) and F(t - dt) are the frames containing depth and luminance values for each pixel and v t) is the motion vector between the frames F t) and F(t - dt).

We have confirmed this experimentally by calculating peak signal-to-noise ratio (PSNR) of depth and luminance measurements, using ground truth images obtained by temporal averaging of the 200 static frames.

where r l, v(l), t) is the vector that contains the displaced frame differences for the depth and luminance pixels, in the frame t, at the spatial location l.