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![The cross-covariance matrices and time synchronization curves for a sample from the Hollywood dataset [16], ground truth in green, the obtained results in red. a) results for simple γ (left), b) results with auto-correlations (right). Best viewed in color.](profile/Lukas-Polok-2/publication/306000474/figure/fig1/AS:393146283380737@1470744786284/The-cross-covariance-matrices-and-time-synchronization-curves-for-a-sample-from-the.png)
The cross-covariance matrices and time synchronization curves for a sample from the Hollywood dataset [16], ground truth in green, the obtained results in red. a) results for simple γ (left), b) results with auto-correlations (right). Best viewed in color.
Source publication
In the modern digital cinema production, extremely large volumes (in order of 10s of TB) of footage data are captured every day. The process of cataloging and reviewing such footage is nowadays largely manual and time consuming process. In our work, we aim at technical quality aspects, such as correct exposure, color compatibility of adjacent shots...
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... then declare the minimum-cost sequence of frame asso- ciations, which connects the first frames q(1, 1) with the last frames q(rows(F x ), rows(F y )) of both respective shots, and is evaluated using dynamic programming. The result of this operation is depicted in Fig. 1 a). We can notice a slight discrepancy of the calculated frame association curve in the bottom right part of the image. This is likely due to self- similarity of one of the shots, which can be seen as prominent block-like patterns, spanning entire rows or columns of the γ xy matrix. This can be partially reduced by taking this ...
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